GOVERNMENT-LED DEVELOPMENT OF INDIA’S MARINE …
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GOVERNMENT-LED DEVELOPMENT OF INDIA’S MARINE FISHERIES SINCE 1950: CATCH AND EFFORT TRENDS, AND BIOECONOMIC MODELS FOR EXPLORING ALTERNATIVE POLICIES. by BRAJGEET BHATHAL B.Sc., The Punjab University, 1998 M.Sc., The University of British Columbia, 2004 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Zoology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) March 2014 © Brajgeet Bhathal, 2014
Transcript of GOVERNMENT-LED DEVELOPMENT OF INDIA’S MARINE …
GOVERNMENT-LED DEVELOPMENT OF INDIA’S MARINE FISHERIES SINCE 1950:
CATCH AND EFFORT TRENDS, AND BIOECONOMIC MODELS FOR EXPLORING
ALTERNATIVE POLICIES.
by
BRAJGEET BHATHAL
B.Sc., The Punjab University, 1998
M.Sc., The University of British Columbia, 2004
A THESIS SUBMITTED IN PARTIAL FULFILMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
in
THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES
(Zoology)
THE UNIVERSITY OF BRITISH COLUMBIA
(Vancouver)
March 2014
© Brajgeet Bhathal, 2014
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Abstract
At present, more than 50% of global marine fisheries catches are made in developing
countries, and an increasingly large fraction of these catches are entering the world market.
Thus, fisheries-related issues in developing countries must be addressed as part of any
discussion of global fisheries issues.
This thesis analyses the status of marine fisheries in India by reconstructing essential
data and constructing biological and economic models. First, effort data were reconstructed
over the period of 1950-2005 at the state level. This showed a continuous increase. Then, the
catch data were updated and assembled from 1950-2005 at the species level for all states,
which showed a gradual increase over time but began to level off toward the end of the
period in question. CPUE, an index of relative abundance, was estimated per study area
using the final time-series of catches and effective fishing effort from 1950-2005. This
measure illustrated a continuous decline.
Using the above-compiled data, i.e., time series of catch and CPUE, surplus
production models (Fox-linear and Schaefer-non-linear) were created for India and its east
and west coasts. Both types of model used in this study indicated that at present, fisheries
yields in India are near MSY, but this is achieved at excessive levels of effort and is based on
a spatial expansion that is unsustainable.
Economic performance was evaluated by bioeconomic models for India in which
three scenarios were generated for fishing cost based on the inclusion of different levels of
subsidies. The results illustrate that economic overfishing is occurring in the Indian fisheries
and the current level of fishing effort is almost twice that corresponding to fMEY, i.e., far
beyond the level that maximizes economic rent.
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Overall, the analysis indicates that fisheries are operating unsustainably, pointing
toward a serious problem. Thus, India should not continue on its present course of expanding
its fisheries through massive subsidization, given the depletion of stocks and poor economic
efficiency of this sector. India needs to curb its existing overcapacity and could effectively
start with the phasing out of trawlers, which would increase the income of other sectors and
their catch per effort.
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Preface
This dissertation is original, unpublished and independent work of the author, Brajgeet
Bhathal. Dr. Daniel Pauly provided guidance and editorial oversight on all the Chapters.
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Table of contents
Abstract .................................................................................................................................... ii
Preface ...................................................................................................................................... iv
Table of contents ....................................................................................................................... v
List of tables ............................................................................................................................. ix
List of figures ......................................................................................................................... xii
List of acronyms ................................................................................................................... xvii
Acknowledgements ................................................................................................................ xix
Dedication ............................................................................................................................ xxii
Chapter 1 The evolving marine fisheries of India ..................................................................... 1
1.1 Introduction: the global context ....................................................................................... 1
1.2 Study areas ....................................................................................................................... 4
1.3 Oceanographic features ................................................................................................... 6
1.4 Fishery resources ............................................................................................................. 8
1.5 Fisheries management: policies and legal framework ..................................................... 9
1.5.1 Central government policies ................................................................................... 10
1.5.1.1 Five year plans ................................................................................................ 11
1.5.1.2 Comprehensive marine fishing policy (CMFP) - 2004 ................................... 12
1.5.1.3 Other policies: deep sea fishing policy (DSFP) .............................................. 15
1.5.1.4 Other policies: trade policy ............................................................................. 16
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1.5.2 State government policies ....................................................................................... 17
1.5.3 Legal framework ..................................................................................................... 18
1.6 History of fishing in India ............................................................................................. 22
1.7 Development of different sectors of fisheries ............................................................... 24
1.8 Existing and emerging problems ................................................................................... 34
1.8.1 Ecosystem health: fishing down marine food web in Indian waters ...................... 35
1.8.2 Unreported catches: discards .................................................................................. 36
1.8.3 Overcapitalization ................................................................................................... 37
1.8.4 Malthusian overfishing and sectoral conflicts ........................................................ 39
1.8.5 Overexploitation ..................................................................................................... 42
1.8.6 Poor socio-economic condition of fishers .............................................................. 44
1.9 Thesis goal and objectives ............................................................................................. 45
1.10 Thesis outline ............................................................................................................... 45
Chapter 2 Effort and catch per effort reconstruction, 1950 to 2005 ....................................... 48
2.1 Introduction ................................................................................................................... 48
2.2 Materials and methods ................................................................................................... 51
2.2.1 Effort reconstruction ............................................................................................... 51
2.2.1.1 Vessels without engines .................................................................................. 53
2.2.1.2 Vessels with engines ....................................................................................... 55
2.2.2 Catch reconstruction ............................................................................................... 63
2.2.3 Estimation of catch per unit effort .......................................................................... 70
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2.3 Results and discussion ................................................................................................... 70
2.3.1 India ........................................................................................................................ 71
2.3.2 Gujarat .................................................................................................................... 74
2.3.3 Daman and Diu ....................................................................................................... 78
2.3.4 Maharashtra ............................................................................................................ 80
2.3.5 Goa .......................................................................................................................... 83
2.3.6 Karnataka ................................................................................................................ 86
2.3.7 Kerala ...................................................................................................................... 90
2.3.8 Lakshadweep Islands .............................................................................................. 93
2.3.9 Tamil Nadu ............................................................................................................. 97
2.3.10 Puducherry .......................................................................................................... 100
2.3.11 Andhra Pradesh ................................................................................................... 103
2.3.12 Orissa .................................................................................................................. 106
2.3.13 West Bengal ........................................................................................................ 109
2.3.14 Andaman and Nicobar Islands ............................................................................ 112
2.4 Conclusion ................................................................................................................... 114
Chapter 3 Assessing the status of India’s marine fisheries using surplus-production models
............................................................................................................................................... 116
3.1 Introduction ................................................................................................................. 116
3.2 Materials and methods ................................................................................................. 124
3.2.1 Fox model ............................................................................................................. 124
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3.2.2 Non-linear Schaefer model ................................................................................... 125
3.3 Results ......................................................................................................................... 129
3.3.1 India ...................................................................................................................... 130
3.3.2 West and east coasts of India ................................................................................ 135
3.4 Discussion .................................................................................................................... 142
3.5 Conclusion ................................................................................................................... 144
Chapter 4 Bioeconomic analysis of India’s marine fisheries ................................................ 146
4.1 Introduction ................................................................................................................. 146
4.2 Materials and methods ................................................................................................. 148
4.3 Results ......................................................................................................................... 154
4.4 Discussion .................................................................................................................... 156
4.5 Conclusion ................................................................................................................... 160
Chapter 5 Conclusion ............................................................................................................ 161
Bibliography .......................................................................................................................... 169
Appendices ............................................................................................................................ 244
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List of tables
1.1 - Five Year Plans of India developed by the Planning Commission of India…………...13
1.2 - List and details of main acts (federal level) concerning fisheries along with their
associated ministries……………………………………………………………………. 19
1.3 - Marine fisheries related acts and regulations specific to the coastal states and the UTs of
India…………………………………………………………………………………….. 20
1.4 - Marine Fishing Regulation Act of the maritime states and the Union Territories……. 25
2.1 - Details of information used to assign starting year to gear-specific vessel categories.. 59
2.2 - Catch and effort of industrial trawlers, 1951-1963, which operated in the Bombay-
Saurashtra waters.............................................................................................................. 64
2.3 - Estimated total effort and catches of deep sea vessels, 1972-2005................................ 65
3.1 - Regression results of Fox models for different regions and time periods.................... 132
3.2 - Estimated values of intrinsic rate of growth (r), carrying capacity (K), catchability
coefficients (q1 and q2) in Schaefer (non-linear) model for period 1965-2005.............. 135
4.1 - The ex-vessel price of fishes landed in India (1999-2000).......................................... 150
4.2 - Estimates of fisheries subsidies categorized into different types for the year 2005 in
USD................................................................................................................................ 153
4.3 - Details of slope of cost curve, calculated MEY, total subsidies, the value of landings at
MEY and economic rent for three different cost curves................................................ 156
5.1 - Details of CPUE (kg/kW days) for India and its states and Union Territories……….162
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Appendix A - List of selected places and institutes visited during field trips made to different
coastal states of India ………………………………………………..................………….244
Appendix B – List of main sources used to compile effort data from 1950 to 2005……....246
Appendix C.1 - Marine fisheries catch (t) for India, 1950-2005...........................................248
Appendix C.2 - Marine fisheries catch (t) for Gujarat, 1950-2005...................................... 253
Appendix C.3 - Marine fisheries catch (t) for Daman and Diu, 1970-2005......................... 258
Appendix C.4 - Marine fisheries catch (t) for Goa, 1970-2005........................................... 261
Appendix C.5 - Marine fisheries catch (t) for Maharashtra, 1950-2005.............................. 264
Appendix C.6 - Marine fisheries catch (t) for Karnataka, 1970-2005.................................. 269
Appendix C.7 - Marine fisheries catch (t) for Kerala, 1970-2005....................................... 272
Appendix C.8 - Marine fisheries catch (t) for Lakshadweep Islands, 1970-2005................ 275
Appendix C.9 - Marine fisheries catch (t) for Tamil Nadu, 1970-2005............................... 278
Appendix C.10 - Marine fisheries catch (t) for Puducherry, 1970-2005.............................. 281
Appendix C.11 - Marine fisheries catch (t) for Andhra Pradesh, 1970-2005...................... 284
Appendix C.12 - Marine fisheries catch (t) for Orissa, 1950-2005...................................... 287
Appendix C.13 - Marine fisheries catch (t) for West Bengal, 1950-2005............................ 292
Appendix C.14 - Marine fisheries catch (t) for Andaman and Nicobar Islands, 1950-2005....
.............................................................................................................................................. 297
Appendix D.1 Marine fishing effort (hp days) for Gujarat, 1950-2005............................... 302
Appendix D.2 - Marine fishing effort (hp days) for Daman and Diu, 1950-2005............... 304
Appendix D.3 - Marine fishing effort (hp days) for Goa, 1950-2005.................................. 306
Appendix D.4 - Marine fishing effort (hp days) for Maharashtra, 1950-2005.................... 308
Appendix D.5 - Marine fishing effort (hp days) for Karnataka, 1950-2005........................ 311
Appendix D.6 - Marine fishing effort (hp days) for Kerala, 1950-2005.............................. 314
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Appendix D.7 - Marine fishing effort (hp days) for Lakshadweep Islands, 1950-2005.......317
Appendix D.8 - Marine fishing effort (hp days) for Tamil Nadu, 1950-2005..................... 318
Appendix D.9 - Marine fishing effort (hp days) for Puducherry, 1950-2005...................... 320
Appendix D.10 - Marine fishing effort (hp days) for Andhra Pradesh, 1950-2005............. 321
Appendix D.11 - Marine fishing effort (hp days) for Orissa, 1950-2005............................ 323
Appendix D.12 - Marine fishing effort (hp days) for West Bengal, 1950-2005.................. 325
Appendix D.13 - Marine fishing effort (hp days) for Andaman and Nicobar Islands, 1950-
2005...................................................................................................................................... 328
Appendix E - Estimated intrinsic rate of growth (r) for taxonomic categories and species.....
.............................................................................................................................................. 329
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List of figures
1.1 - Map of India, showing all maritime states and Union Territories……………………... 5
1.2 - Types of fisheries, showing subtypes of both small scale and large scale fisheries
…………………………………………………………………………………..……… 33
1.3 - Catch trends of marine fisheries of India, 1950-2010 for industrial (mechanized) and
artisanal fisheries ………………...……………….……………………………………. 41
2.1 - Total catch of India for all species, excluding tuna and billfish, 1950-2005................. 72
2.2 - Cumulative effective fishing effort by vessels of various types in India, 1950-2005.... 73
2.3 - Trend of catch-per-unit-effort in India, 1950-2005........................................................ 74
2.4 - Total catch of Gujarat for all species, excluding tuna and billfish, 1950-2005............. 75
2.5 - Cumulative effective fishing effort by vessels of various types in Gujarat, 1950-2005.....
.................................................................................................................................................76
2.6 - Trend of catch-per-unit-effort in Gujarat, 1950-2005.................................................... 78
2.7 - Total catch of Daman and Diu for all species, excluding tuna and billfish, 1950-2005.....
................................................................................................................................................ 79
2.8 - Cumulative effective fishing effort by vessels of various types in Daman and Diu, 1950-
2005.................................................................................................................................. 80
2.9 - Trend of catch-per-unit-effort in Daman and Diu, 1950-2005....................................... 81
2.10 - Total catch of Maharashtra for all species, excluding tuna and billfish, 1950-2005... 82
2.11 - Cumulative effective fishing effort by vessels of various types in Maharashtra, 1950-
2005.................................................................................................................................. 83
2.12 - Trend of catch-per-unit-effort in Maharashtra from 1950-2005.................................. 84
2.13 - Total catch of Goa for all species, excluding tuna and billfish, 1950-2005................. 85
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2.14 - Cumulative effective fishing effort by vessels of various types in Goa, 1950-2005... 86
2.15 - Trend of catch-per-unit-effort in Goa from 1950-2005............................................... 87
2.16 - Total catch of Karnataka for all species, excluding tuna and billfish, 1950-2005....... 88
2.17 - Cumulative effective fishing effort by vessels of various types in Karnataka, 1950-
2005.................................................................................................................................. 89
2.18 - Trend of catch-per-unit-effort in Karnataka from 1950-2005...................................... 91
2.19 - Total catch of Kerala for all species, excluding tuna and billfish, 1950-2005............. 92
2.20 - Cumulative effective fishing effort by vessels of various types in Kerala, 1950-2005....
................................................................................................................................................ 93
2.21 - Trend of catch-per-unit-effort in Kerala from 1950-2005........................................... 94
2.22 - Total catch of Lakshadweep Islands for all species, excludes tuna & billfish, 1950-
2005.................................................................................................................................. 95
2.23 - Cumulative effective fishing effort by vessels of various types in Lakshadweep
Islands, 1950-2005............................................................................................................ 96
2.24 - Trend of catch-per-unit-effort in Lakshadweep Islands from 1950-2005.................... 97
2.25 - Total catch of Tamil Nadu for all species, excluding tuna and billfish, 1950-2005.....98
2.26 - Cumulative effective fishing effort by vessels of various types in Tamil Nadu, 1950-
2005.................................................................................................................................. 99
2.27 - Trend of catch-per-unit-effort in Tamil Nadu from 1950-2005................................. 100
2.28 - Total catch of Puducherry for all species, excluding tuna and billfish, 1950-2005... 101
2.29 - Cumulative effective fishing effort by vessels of various types in Puducherry, 1950-
2005................................................................................................................................ 102
2.30 - Trend of catch-per-unit-effort in Puducherry from 1950-2005.................................. 103
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2.31 - Total catch of Andhra Pradesh for all species, excluding tuna and billfish, 1950-2005...
...............................................................................................................................................104
2.32 - Cumulative effective fishing effort by vessels of various types in Andhra Pradesh,
1950-2005....................................................................................................................... 105
2.33 - Trend of catch-per-unit-effort in Andhra Pradesh from 1950-2005.......................... 106
2.34 - Total catch of Orissa for all species, excluding tuna and billfish, 1950-2005........... 107
2.35 - Cumulative effective fishing effort by vessels of various types in Orissa, 1950-2005.....
...............................................................................................................................................108
2.36 - Trend of catch-per-unit-effort in Orissa from 1950-2005.......................................... 109
2.37 - Total catch of West Bengal for all species, excluding tuna and billfish, 1950-
2005...................................................................................................................................... 110
2.38 - Cumulative effective fishing effort by vessels of various types in West Bengal, 1950-
2005................................................................................................................................ 111
2.39 - Trend of catch-per-unit-effort in West Bengal from 1950-2005................................ 112
2.40 - Total catch of Andaman and Nicobar Islands, excludes tuna and billfish, 1950-2005.....
...............................................................................................................................................113
2.41 - Cumulative effective fishing effort by vessels of various types in Andaman and
Nicobar Islands, 1950-2005............................................................................................ 114
2.42 - Catch -per-unit-effort in Andaman and Nicobar Islands from 1950-2005................. 115
3.1 - The natural logarithm of CPUE against annual effective fishing effort in India, 1980-
2005................................................................................................................................ 130
3.2 - Total catch versus effective effort in India and the fitted Fox yield curve, 1980-
2005................................................................................................................................ 131
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3.3 - The natural logarithm of CPUE against annual effective fishing effort in India, 1965-
2005................................................................................................................................ 131
3.4 - Total catch versus effective effort in India and the fitted Fox yield curve, 1965 - 2005
........................................................................................................................................ 132
3.5 - Time series of observed and predicted CPUE fitted in Schaefer (non-linear) model for
India, 1965-2005............................................................................................................. 133
3.6 - The trend of catchability coefficient (q) in India, 1965-2005...................................... 134
3.7 - Plot of residuals against fishing area in natural log for India’s shelf fisheries............ 134
3.8 - The natural logarithm of CPUE against annual effective fishing effort for the west coast
of India, 1980-2005........................................................................................................ 135
3.9 - The natural logarithm of CPUE against annual effective fishing effort for the east coast
of India, 1980-2005........................................................................................................ 136
3.10 - Total catch versus effective effort and the fitted Fox yield curve for the west coast of
India, 1980-2005............................................................................................................. 136
3.11 - Total catch versus effective effort and the fitted Fox yield curve for the east coast of
India, 1980-2005............................................................................................................. 137
3.12 - The natural logarithm of CPUE against annual effective fishing effort for the west
coast of India, 1965-2005............................................................................................... 137
3.13 - The natural logarithm of CPUE against annual effective fishing effort for the east
coast of India, 1965-2005............................................................................................... 138
3.14 - Total catch versus effective effort and the fitted Fox yield curve for the west coast of
India, 1965-2005............................................................................................................. 138
3.15 - Total catch versus effective effort and the fitted Fox yield curve for the east coast of
India, 1965-2005............................................................................................................. 139
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3.16 - The time series of observed and predicted CPUE fitted in Schaefer (non-linear) model
for the west coast of India, 1965-2005........................................................................... 139
3.17 - The time series of observed and predicted CPUE fitted in Schaefer (non-linear) model
for the east coast of India, 1965-2005............................................................................ 140
3.18 - Trend of catchability coefficient (q) on the west coast of India, 1965-2005............. 141
3.19 - Trend of catchability coefficient (q) on the east coast of India, 1965-2005.............. 141
4.1 - Model- I - Total catch versus effective effort and the fitted Fox yield curve along with
total revenue and fishing costs, 1980-2005................................................................... 154
4.2 - Model- II - Total catch versus effective effort and the fitted Fox yield curve along with
total revenue and fishing costs, 1980-2005.................................................................... 155
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List of acronyms
BOBP: Bay of Bengal Programme
CCRF: Code of Conduct for Responsible Fishing
CIFNET: Central Institute of Fisheries Nautical and Engineering Training
CIFT: Central Institute of Fisheries Technology
CMFP: Comprehensive Marine Fishing Policy
CMFRI: Central Marine Fisheries Research Institute
CPI: Consumer Price Index
CPUE: Catch per Unit Effort
DAHD: Department of Animal Husbandry and Dairying
DAHD&F: Department of Animal Husbandry and Dairying and Fisheries
DSFP: Deep Sea Fishing Policy
ECC: Equatorial Counter Current
EEZ: Exclusive Economic Zone
FiB: Fishing in Balance Index
FRP: Fibreglass-Reinforced Plastic
GRT: Gross Register Tonnage
HP: Horse Power
IBM: Inboard Motor
ICAR: Indian Council of Agricultural Research
IMF: International Monetary Fund
INP: Indo Norwegian Project
KW: Kilowatt
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LOPs: Letters of Permission
MER: Maximum Economic Rent
MEY: Maximum Economic Yield
MFRA: Marine Fishing Regulation Act
MPEDA: Marine Products Export Development Authority
MSY: Maximum Economic Yield
MTI: Marine Trophic Index
NEC: North Equatorial Current
NFF: National Fishworkers Forum
OAL: Overall Length
OBM: Outboard Motor
OMZ: Oxygen Minimum Zone
QR: Quantitative Restrictions
TC: Total Cost
TL: Trophic Level
TR: Total Revenue
UT: Union Territories
WTO: World Trade Organisation
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Acknowledgements
First, I would like to thank my supervisor (guru) Dr. Daniel Pauly, who helped and
motivated me persistently throughout this study, through his innovative ideas, guidance,
never-ending encouragement, understanding and trust. Particularly, his turn-around time of
my long e-mails and revisions of thesis chapters was incredibly rapid. His approach of
“Thinking Big” and suggesting that “No” is not an option, there is “always a way”, helped
me not only in this study, but during several tests of endurance on this journey. Once again, I
thank him from the bottom of my heart, for being my pillar of strength and leaving an
everlasting imprint, which I will treasure for the rest of my life.
I am equally thankful to my committee members: I thank Dr. Dirk Zeller, for
answering reconstruction-related queries and his generosity of helping even at times when he
was not on my committee. I thank Dr. Rashid Sumaila, for answering all economics-related
questions with great patience and always encouraging me in his modest ways. I thank Dr.
Steve Martell, for helping me immensely in Chapter 3 by introducing me to models in a
simple way and for his approachable attitude, which made asking questions much easier. I
thank Dr. Villy Christensen for his insightful questions and suggestions, which prompted me
to think in new ways. I thank Dr. Jacqueline Alder (ex-committee member) for her
knowledge, expertise, and helpful advice in formative stages of this thesis and also for being
a great support and friend who always encouraged and motivated. I thank Dr. Jonathan
Shurin (ex-committee member) for his invaluable input and advice. I also thank my external
examiner Dr. Derek Johnson for his insightful comments which have improved final version
of this thesis. As well as, I thank the Chair, Dr. Vadim Marmer and University examiners,
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Drs. Peter N. Nemetz and Evgeny Pakhomov, who asked some great questions at my
defense.
I also thank Anand P. Gupta, Bhagvan Parwani, C. R. Rajan, Cheryl Verghese,
Edwin Joseph, G. R. Desh Bandhu, Harcharan Singh Josh, K. P. Raghuram, Kamlesh
Fofandi, Lokesh Kapoor, Madhusudhan Kurup, Nithyanandan Manickam, Naveen
Rajashekhar, Rajeev Raghavan, Rajendra Badonia, Rema Devi, R. Korkandy, Satinder
Singh, Sunil Kumar, V. Dixit, Vikra Lohia, Yugraj Singh Yadava and many others for their
assistance in locating and making the required publications and statistics accessible.
I extend my thanks to the staff and students at the UBC Fisheries Centre and
Department of Zoology for their great support and valuable ideas, while making my stay
memorable. Especially, thanks to Divya Varkey, Rajeev Kumar, Louise Teh and Yajie Liu to
help with models and related queries, Vicky Lam for cost data, Jonathan Anticamara and
Ahmed Gelchu for suggestions in effort reconstruction. I thank Collete Wabnitz and Robyn
Forrest for their words of encouragement and open dialogues, Ahmed Khan, Dawit
Tesfamichael, Shawn Booth, Suzzane Mondoux, Sylvie Guénette and Wilf Swartz for great
discussions and insightful suggestions, Grace Ong for being such a great friend, listening to
all my woes and joys, and Janice Doyle and Ann Tautz for their administrative support. I
thank Alice Liou from Department of Zoology for her caring and considerate attitude and
always going an extra mile to extend her support. She helped me immensely, particularly, in
the process of submitting this thesis. I also thank Rebecca Trainor from Student Academic
Services for her cooperation, support and e-mails filled with words of encouragement.
I am thankful for the financial support made available by the Sea Around Us, a
scientific cooperation between UBC and the Pew Charitable Trusts, Fisheries Economics
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Research Unit (FERU), a tuition waiver UBC scholarship and university teaching
assistantships.
I am deeply thankful to my family, Harminder K. Bhathal, Palminder S. Bhatahl,
Amanpreet K. Bhathal, Mandip S. Bhathal, Sharanjeet K. Malhi, Joga S. Malhi, Varinder K.
Malhi and Aseem S. Malhi for their support, love and encouragement. I also extend my
deepest appreciation to my friends and relatives for being there and understanding, at times
when I didn’t show up at social gatherings. I thank Helen Reddy for her beautiful song, “I am
Woman”, which gave me the full dosage of energy boosts in the times when I needed it the
most.
I am gratefully thankful to the love of my life, Harjeet S. Bhathal, who was always
beside me through his indescribable support. Above all, I can’t thank and hug enough my
munchkin, Adole S. Bhathal, who could never comprehend why mom needed to study.
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Dedication
I dedicate this thesis to Dr. Bikkar S. Lalli. He facilitated the initiation of this journey and
encouraged me all along assuring that it can be achieved.
1
Chapter 1 The evolving marine fisheries of India
1.1 Introduction: the global context
Humans have been consuming seafood since at least 165,000 years ago (Marean et al.
2007); however, the exploitation rate has increased dramatically in the last century, and
specifically in the last few decades. This is due to rapid human population growth and increased
seafood consumption rates coupled with the globalization of high value food chains due to their
increasing profitability (Delgado et al. 2003; Alder and Pauly 2010; FAO 2012). All this is
driven by an enormous increase of our technical prowess, which enables us to essentially fish
anything, anywhere, anytime (Pauly et al. 2002). Although the erroneous belief in the
inexhaustibility of the sea has now been largely superseded, except perhaps in some industry
circles, several other factors such as changes in land use (leading to habitat modification, coastal
degradation and pollution), developments in technology, subsidization and the open access
nature of most fisheries, among other things, have proved disastrous to fish stocks (Hardin 1968;
Hilborn and Walters 1992c; Pauly 1995; Burger et al. 2001; Watson and Pauly 2001; Kay and
Alder 2005; Sumaila and Pauly 2006b; King 2007a; Pauly 2007a; Agnew et al. 2008; Pelletier
and Tyedmers 2008; Sumaila et al. 2008; Pauly 2010b; Ganapathiraju 2012). Fisheries
sustainability indicators confirm this; for instance, the Marine Trophic Index (MTI; mean trophic
level of fisheries catches) is experiencing a decline at a rate of 0.05 - 0.1 trophic level (TL) per
decade as estimated based on a global data set (Pauly et al. 1998a), and verified through national
studies, i.e., for India (see Section 1.8.1), Brazil, Argentina, Thailand, Greece, Canada, Iceland,
Chile and many others (Christensen 1998; Stergiou and Koulouris 2000; Pauly et al. 2001;
Furness 2002; Valtysson and Pauly 2003; Pauly and Watson 2005; Bhathal and Pauly 2008;
Jaureguizar and Milessi 2008; Freire and Pauly 2010; Kleisner and Pauly 2010; Kleisner and
Pauly 2011; Pauly 2011a; Stergiou and Tsikliras 2011). Furthermore, within the last 100 years,
2
the biomass of predatory fishes has declined (Christensen et al. 2003; Myers and Worm 2003) by
two thirds in the global oceans, and this decline is still accelerating, as 54% of this decline has
occurred within the last 40 years (Christensen et al. 2011a).
Humans have played a role in changing global climate and ocean temperature, which
impacts species, their distribution, and community interactions (Perry et al. 2005; Cahill et al.
2012; Cheung et al. 2013a). Cheung et al. (2013b) have recently shown that under a high-
emissions scenario, the assemblage maximum body weight of marine fishes is expected to shrink
globally by 14 to 24% for the period of 2000 to 2050. The greatest impact will be within tropical
and intermediate latitudinal areas with an average reduction of more than 20%.
Global marine ecosystems, which are exposed to these negative anthropogenic effects
(direct and indirect), are impacted by ecological disruptions, e.g., jellyfish blooms around the
world (Purcell et al. 2007; Brotz et al. 2012; Pauly 2012), depletion and even extirpation of
marine populations (Pauly et al. 2002; Myers and Worm 2003; Dulvy et al. 2004). This may
portend a bleak future for fisheries.
These trends may still be turned around, and rebuilding ecosystems is possible (Worm et
al. 2009). However, for this to be achieved, overfishing, along with its environmental and
ecological impacts, must be addressed. These impacts pose a threat to the social and economic
wellbeing of countries, particularly for developing countries (Silvestre and Pauly 1997; Zeller et
al. 2007), where fish is still a cheap source of animal protein for local populations (Pauly et al.
2005; Assan and Kumar 2009; Coulthard et al. 2011; Manach et al. 2012). India, for example,
with a population of approximately 1.2 billion (Funge-Smith et al. 2005), continually struggles
with the problem of malnutrition, with nearly one in four of the world's malnourished children
(North 2012). The loss or even further decline of fish from the diet of its coastal inhabitants
would make matters far worse.
3
The rapid growth of populations in the developing world, and in many cases, of incomes
as well, has led, in conjunction with the increased demand for fish in developed countries, to a
soaring increase in global fish consumption (Delgado et al. 2003; Alder and Pauly 2010). The
development of fisheries has led to enormous increases in the effort that is exerted to catch a
dwindling resource. Globally, fishing effort is estimated to exceed the optimum by a factor of
three to four (Watson et al. 2012). Such extreme overcapacity, coupled with declining fish stocks
and high fisheries subsidies, has led to significant economic losses, estimated at 50 billion US
dollars annually (WorldBank 2009; Anticamara et al. 2011). Even in a developing country as
India, current fishing vessel capacity is estimated to be approximately three times higher than
optimum [(Somvanshi 2003); also see Section 1.8.3].
The increasing demand for seafood in the developed world is largely met by imports from
developing countries, as their own fishing grounds have long been overexploited. Thus, more
than 50% of global marine fisheries catches are made in developing countries, and an
increasingly large fraction of these catches are entering the world market (Pauly and Zeller 2003;
Zeller et al. 2009). Thus, fisheries-related issues in developing countries must be addressed as
part of any discussion of global fisheries issues.
Although FAO has compiled and published annual worldwide fishery statistics since
1950, based on member country reports, the datasets in question are assembled by large,
arbitrary statistical areas (rather than by ecosystems) and are not verified against local data sets
(Pauly and Zeller 2003; Pauly 2013). On the other hand, there is a growing need for catch-and-
effort data sets with fine spatial resolution for use in ecosystem models and other estimations,
e.g., stock abundance, fishing mortality and fishing cost (Watson et al. 2004a; Christensen et al.
2009) to explore policy options for effective fisheries management. Indeed, local data sets and
better local knowledge are preconditions to better policy in the field of marine resource
4
management (Watson et al. 2004a), especially for developing countries, such as India, where
millions of people depend on fishing for their very existence. Given that fisheries play such an
important role in food security, employment, and income, it becomes essential to closely
examine and evaluate the impacts of fishing on marine resources.
This chapter sets the general background for this study, including details on study areas,
existing policies and history of Indian fisheries, the development of their different sectors, and
my thesis goal and objectives.
1.2 Study areas
India is located between latitudes 8° 4' and 37° 6' N and longitudes 68° 7' and 97° 25' E
(Figure 1.1), has 28 states (9 maritime) and 7 Union Territories (UTs are under the direct control
of the central government of India) (4 maritime; Daman and Diu1, Lakshadweep Islands on the
west, and Puducherry and Andaman and Nicobar Islands on the east), and covers a total land area
of about 3.3 million km2 (Arora and Grover 1996d). India’s exclusive economic zone (EEZ)
covers a total area of 1.63 million km2 (including the Lakshadweep Islands on the west coast),
and in the Bay of Bengal, the EEZ of the Andaman and Nicobar Islands alone covers a total area
of 660,000 km2 (www.seaaroundus.org).
Based on geomorphological characteristics, India’s mainland coast can be divided into
the west and east coasts, each with northern and southern regions, i.e., northwest, southwest,
northeast, and southeast (excluding three island groups, i.e., Lakshadweep and Andaman and
Nicobar Islands, which are treated separately). The northwest coast (15°–23° N latitude),
comprising of Daman and Diu and the maritime states of Gujarat and Maharashtra, has extensive
fishing grounds with the broadest continental shelf [i.e., about 340 km wide, tapering to nearly
1 Daman and Diu were part of Goa until 1987 and become a separate Union Territory after statehood was conferred
on Goa in May 1987 (GOI, 2004)
5
60 km in the south (Marale and Mishra 2011)] and the sea bottom is generally muddy (Pillai and
Katiha 2004c). The southwest coast (8°–15° N latitude), covering Goa, Karnataka, and Kerala,
has a narrow continental shelf, but it is one of the major upwelling regions in the Indian Ocean
(Chiranjivi et al. 2013). The continental shelf of the eastern coast is narrower i.e., about one-third
the western shelf (Ahmad 2012).
The southeast coast (10°–15° N latitude), comprising Tamil Nadu and Puducherry, is
characterized by coral and rocky grounds, while the northeast coast (15°–21° N latitude),
covering Andhra Pradesh, Orissa, and West Bengal, has predominantly muddy sea bottoms
(Pillai and Katiha 2004c).
Figure 1.1 The maritime States and Union Territories of India with the Arabian Sea on the west
(including the Lakshadweep Islands) and the Bay of Bengal in the east (including Andaman and
Nicobar Islands). The shelf (dark grey) and its 200 m depth limits are also shown, along with the rest
of the Indian EEZ (light grey). Adapted from the Global Maritime Database, www.gd-ais.com.
6
1.3 Oceanographic features
The country tapers off near the Tropic of Cancer into the north Indian Ocean, between the
Arabian Sea on the west and the Bay of Bengal on the east.
Although both the Arabian Sea and the Bay of Bengal occur within the same latitude
range and are under the direct influence of monsoons, including seasonal reversal of surface
circulation, they are very different from each other. The Arabian Sea is characterised by a
relatively lower temperature, higher salinity2 and abundant nutrients, due to upwelling discussed
below (Pannikar and Jayaram 1966; Jhingran 1975) leading to higher plankton production. Thus,
it also has a richer fish fauna, both in terms of diversity and abundance (Ramamirtham and Rao
1973). In contrast, the Bay of Bengal has a higher temperature (resulting in a strongly stratified
surface layer), lower salinity [as major rivers, i.e., Irrawady, Brahmaputra, Ganges, Godavari,
Krishna and Cauvery drain into this sea; (Gaunsa et al. 2005)], and relatively lower primary
productivity (Dwivedi 1993; Chauhan et al. 2001). Thus, its depth integrated primary production
is almost one order of magnitude less than in the Arabian Sea (Kumar et al. 2002).
The surface circulation of waters in these two seas occurs due to the north equatorial
current (NEC), the equatorial counter current (ECC), and the monsoon current (Bowditch 2002;
Shankar et al. 2002). There are two monsoons in India: the Northeast monsoon (winter:
November to February) and the Southwest monsoon (summer: June to September); the latter
causes the reversal of surface currents, i.e., to an easterly flow, and replaces the NEC and ECC.
During the summer monsoon, coastal upwelling occurs at varying intensities along the two
coasts with stronger upwelling on the west (Pillai and Katiha 2004c).
2 Only two major rivers, Tapti and Narmada, discharge into this sea; in addition, excessive evaporation over
precipitation and saline water intrusion from the Persian Gulf and the Red Sea leads to the formation of higher
surface salinities (Kumar et al 2001).
7
In the Arabian Sea, biological productivity is maintained all year long because it benefits
from both monsoons. Strong south-westerly winds during the southwest monsoon result in
coastal and open-ocean upwelling (Gaunsa et al. 2005). During the north-east monsoon, the
prevailing northeast trade winds intensify evaporation, resulting in surface cooling which,
combined with reduced incoming solar radiation and high ambient salinity, results in weak
stratification, thus driving convective mixing and leading to upward transport of nutrients (Dey
and Ramesh 2003). Given the high primary production, the oxygen levels are very low in deeper
layers (between 150 to 1000 m), resulting in a well-developed oxygen minimum zone (OMZ),
where only a specialist community of organisms can thrive (Banse 1959; Gerson 2005; Larkin
2005).
In contrast, in the Bay of Bengal, there is no evidence of strong upwelling except for very
localized ones, such as close to the south-western boundary during summer (Shetye et al. 1991).
The scarcity of upwelling is due to the strong upper layer stratification and low-speed winds,
which are unable to break the stratification, and restrict vertical mixing to depth of < 20 m. This
curtails the upward flow of nutrients, keeping the region unproductive (Gaunsa et al. 2005).
These different geomorphology and hydrographic conditions have important
consequences for fisheries. Thus, the west coast (the southern portion of which is also known as
the Malabar Coast), which has a broader continental shelf, accounts for over 72% of Indian fish
catches (based on average catches for the time period 1950–2005). In comparison, the east coast
(also known as Coromandel Coast), contributes less than 28% to the total catch. Also, the
diversity of the pelagic resources is higher off the west than the east coast and vice versa for the
demersal resources (Bensam 2000; Pillai and Pillai 2000). This theme, i.e., the difference in the
fish resources of the two areas and their relative contributions to India’s catches, is the topic of
the next section.
8
1.4 Fishery resources
The waters along the Indian coast and offshore to the limits of the Indian EEZ are home
to a huge biodiversity. Thus, as many as 500 pelagic and demersal fish species contribute to the
multi-species, multi-sector fisheries (Pillai and Pillai 2000), and the average contribution of these
two groups to the overall catches for 1950–2005 are 53% and 47%, respectively. There also exist
species specific fisheries, for example, for Indian oil sardine (Sardinella longiceps), Indian
mackerel (Rastrelliger kanagurta) and Bombay duck (Harpadon nehereus) and yet the bycatch
of these fisheries can be very significant. Few groups dominate the total catch and based on the
average for 1950–2005, the clupeoids (29%; including the Indian oil sardine), crustaceans (16%;
including prawns) and mackerel (6%; including Indian mackerel Rastrelliger kanagurta) jointly
contributed 51% of total Indian catches. While Indian oil sardine has contributed significantly to
the catches over the years, the population and hence catch of this fish fluctuates strongly, and so
does its contribution to the total catch of India, which varied between 1% and 34% (Bhathal
2004, 2005).
Major pelagic groups reported in Indian catch statistics are clupeoids such as wolf herring
Chirocentrus dorab, Indian oil sardine Sardinella longiceps, hilsa shad Tenualosa ilisha,
anchovies Anchoviella spp. and Thrissocles spp., Bombay duck Harpadon nehereus, ribbon
fishes, carangids, i.e., jacks and their relatives; including horse mackerel, scads, pomfrets, i.e.,
black Apolectus niger silver Pampus argenteus and Chinese pomfret Pampus chinensis, Indian
mackerel Rastrelliger kanagurta, seer fishes Scomberomorus commersoni, S. guttatus and S.
lineolatus, and various tuna species (Bensam 2000; Pillai and Pillai 2000; Harper et al. 2011).
Some pelagic species enjoy wide geographical distribution, while others, such as shads and
Bombay duck have restricted distributions.
9
Major demersal resources contributing to Indian catches are elasmobranchs (i.e., sharks,
skates, and rays), eels, catfishes, lizard fishes, perches (i.e., rock cods, snappers, and breams),
goatfishes, threadfins, croakers, silverbellies (Leiognathus spp., Gazza spp.), big jawed jumper
(Lactarius lactarius), flatfishes (halibut, flounders, soles), crustaceans (penaeid and other
prawns, lobsters, crabs, stomatopods), molluscs, and cephalopods (Bensam 2000; Pillai and Pillai
2000).
A detailed listing of the taxa included in Indian fisheries statistics is given in Bhathal
(2005), while FishBase (www.fishbase.org) may be consulted for information on the 1,763
marine fish species currently3 reported as occurring in India (including the taxa mentioned
above), based on extensive sources from Indian and other authors. A similar database,
SeaLifeBase (www.sealifebase.org) can be used to obtain information on, currently, 87 species
of non-fish vertebrates (mainly marine mammals, seabirds and sea turtles) and a much larger and
rapidly growing number of invertebrate species in Indian seas.
1.5 Fisheries management: policies and legal framework
The Indian Constitution has provisions to guide the policy-making process and to define
the functions of different levels of the government, i.e., between the states, UTs and the Union
(federal). The Seventh Schedule of the Constitution of India specifies subjects that are on the
Union List, the State List and the Concurrent List (Salim and Narayanakumar 2012). Fisheries
within the territorial waters (12 nautical miles from shore) are subjected to state jurisdiction,
while the waters beyond this limit but within the EEZ fall under the purview of the central
government. Both the central and state governments play a vital role in the development,
management, conservation, and monitoring of India’s fisheries (Somvanshi 2001a; Yadav 2001).
The central government, however, is also responsible for surveying and assessing fisheries
3 As of August 2013
10
resources, research, and training (Mathew 2003) and for providing financial assistance to the
states and UTs for implementation of Central Sector and Centrally Sponsored Schemes (GOI
2004b). In addition, it also has an advisory role, for example, the Marine Fishing Regulation Act
(MFRA) was adapted by all the maritime states from a bill that the union government had
prepared and circulated in 1979 (Salim and Narayanakumar 2012).
There is no distinct fisheries ministry or department at the national level, and the
responsibility for the administration of fisheries lies with the Ministry of Agriculture, i.e., with
the Department of Animal Husbandry and Dairying, or DAHD, which was recently renamed
Department of Animal Husbandry and Dairying and Fisheries, or -DAHD&F under the Indian
Council for Agricultural Research, or ICAR. Furthermore, the responsibility for fisheries and the
marine habitat is spread over several agencies and ministries at the Central (e.g., Ministry of
Agriculture, Commerce and Industry, Environment and Forests, Food Processing Industries, and
Defence) and State (e.g., Department of Fisheries, Forests, and Ports) levels (Salim and
Narayanakumar 2012), which is discussed within the regulations Section 1.5.3.
1.5.1 Central government policies
There are two key policy documents which reveal central policy on fisheries: the Five
Year Plans developed by the Planning Commission, and the Central Marine Fishing Policy
(2004) developed by the Ministry of Agriculture. Over time, several other policies and guidelines
(e.g., trade policy, deep sea policy) were announced in response to changing circumstances in the
fisheries sector, as briefly discussed in this section.
The policy shift in 1990s affected the agriculture sector, including fisheries indirectly
through the devaluation of exchange rates, the liberalisation of external trade, and less protection
to industry. This shift happened because India nearly defaulted on a loan from the International
Monetary Fund (IMF) in 1991 due to the severity of the balance-of-payments deficit that it faced
11
since the mid-1970s. Its only escape was to agree to a liberalization strategy drawn up by the
IMF, which included the phased reduction of import duties, reduced government participation,
and reduced reliance on subsidies (Byers 1998; Johnson 2002).
Moreover, by the end of 1991, it was belatedly realized that the marine fisheries were
reaching their maximum catches in the inshore areas and had, indeed, overexploited various
fishing grounds, and that therefore, no substantial catch increase could be expected. Thus, the
emphasis of fisheries development shifted towards the expansion of the inland sector and
aquaculture, as well as to offshore and deep-sea fisheries (ICAR 1998; Johnson 2002). This led
to the announcement of a Deep Sea Fishing Policy in 1991 (see 1.5.1.3), as a part of the
economic reforms programme.
The liberalisation of the Indian economy in the 1990s coincided with the establishment of
the World Trade Organisation (WTO), through which India, one of the WTO’s influential
members, became an important signatory to the various trade agreements. Thus, the policies’
focus was to stabilize India’s economic performance by guiding the domestic economy, and at
the same time, ensuring that all the processes were in line with global trade agreements
(Salagrama 2004).
1.5.1.1 Five year plans
Until 2004, in the absence of a unified comprehensive policy, the Planning Commission
via the successive Five Year Plans of India formulated broad policies, which set out strategies
and objectives for the fisheries sector. An overview of these Five Year Plans reveals that, with
time, the priorities of the central government gradually shifted from providing fish as a protein
supply to the poor (First Five Year Plan) towards increasing foreign exchange reserves (Ninth
Five Year Plan), and recently, the need for conservation and management is explicitly
recognized (Tenth Five Year Plan–Twelfth Five Year Plan). The main objectives of fishery
12
development policies through these different plans have been to: (1) increase fish supply and
promote self-sufficiency; (2) accelerate economic growth and generate employment; (3) increase
fleet modernization and foster transition from inshore towards offshore (deep sea) fisheries; (4)
improve the socioeconomic conditions of fishers; (5) expand the export of marine products; (6)
strengthen the network of research and educational institutions meant to support fisheries; (7)
improve infrastructure and post-harvest operations; (8) increase the per capita availability and
consumption of fish; (9) adopt an integrated approach to fisheries and aquaculture; and (10)
ensure sustainability and maintain ecological integrity and biodiversity (GOI 1951, 1956, 1961,
1969, 1974, 1980, 1985b, 1992, 1997, 2002b, 2006, 2011; Bensam 1999c; Johnson 2002). It will
be noted that several of these goals (e.g., increasing local fish supply and exports are, to a large
extent, mutually incompatible).
Until recently, fish had been treated as an agricultural commodity in India and was
guided by the same goals as agriculture: increasing supply, product diversification, employment,
and foreign exchange generation. However, this started to change as the central government
promulgated a Comprehensive Marine Fishery Policy in 2004 (GOI 2004b), in which, along with
other elements, ecological sustainability was emphasized.
1.5.1.2 Comprehensive marine fishing policy (CMFP) - 2004
The Comprehensive Marine Fishing Policy (CMFP) was originally drawn up in 2000 and
adopted in 2004. The 2004 CMFP consists of the following components: (1) ensuring social and
economic equity; (2) optimal use of fishery resources; (3) environmental protection; (4)
preservation and sustainability; (5) infrastructure development; (6) strict fisheries management
system; (7) an improved regulatory and Monitoring, Control, Surveillance (MCS) systems; and
(8) fisheries development in the UTs of Lakshadweep and Andaman and Nicobar Islands
(DAHD 2004a).
13
Table 1.1 Five Year Plans of India developed and executed by the Planning Commission of India. It
details the primary objectives and major developments during a Plan period. Sources: (GOI 1951-2011;
Bensam 1999c; Johnson 2002).
Plan period Duration Main objectives and developments
I 1951-1956
Increase fish catch by introduction of mechanized/motorized boats;
Improve fisheries statistics, ground and training facilities;
Initiate the charting for deep sea fishing grounds.
II 1956-1961
Introduction of new vessels and gears (expansion of existing initiatives);
Improve infrastructure for preservation, processing, storage, transportation;
Set up multipurpose co-operative societies for well being of fishers.
III 1961-1966
Improve equipment and designs of mechanized fishing vessels;
Adequate equipments and facilities for preserving fish and their marketing;
Development of fisheries education and research institutes;
Improve condition of fishers and focus on export trade.
Annual Plan 1966-1968 Encourage export trade.
IV 1969-1974
Expansion of export trade;
Initiation of deep sea fishing (import of trawlers) and their local construction;
Construction of fishing harbours at major and minor ports;
Intensification of exploratory fishery surveys.
V 1974-1979
Declaration of EEZ (1976; came into force in 1977);
Scheme of chartering of foreign fishing vessels (1977);
Development of fishing harbours.
Annual Plan 1979 Diversification of fishery products.
VI 1980-1985
Motorisation of artisanal crafts and exploratory surveys in offshore grounds;
Maritime Zone of India Act, 1981 to regulate foreign fishing vessels;
Encouraged deep-sea fishing via licensing, chartering/ joint venture schemes.
VII 1985-1989
Revised chartering (Deep Sea) policy (1986; announced in June 1987);
Development of post-harvest technologies.
Annual Plan 1990 Further development of deep-sea fishing.
Annual Plan 1991
Newer Deep Sea Policy, 1991;
Substantial growth in motorized artisanal fleet of ring seiners.
VIII 1992-1996
Development of coastal aquaculture;
Substantial growth in motorized artisanal fleet of ring seiners;
Export trade changes from a resource-based to food engineering industry.
IX 1997-2002
Increase fish production (aquaculture and offshore fisheries) and further
diversify fisheries and fishery products;
Onset of multiday fishing and strengthen research activities.
X 2002-2007
Enhance fish production from aquaculture, marine and inland fisheries;
Practise responsible and sustainable fisheries and aquaculture;
Encourage equitable participation; improve socioeconomic status of fishers;
Central Marine Fishing Policy (2004);
Expansion of oceanic fisheries, conversion of shrimp trawlers to tuna long
liners.
XI 2007-2012
Increase fish production, focus on unexplored potentials (island fisheries);
Maintain ecosystem health, conserve aquatic resources and genetic diversity;
Improve facilities for fish landing, handling and marketing;
Conversion of shrimp trawlers to tuna long liners.
XII 2012-2017
Enhance fish production with focus on sustainable development;
Improve management of fisheries and encourage optimal exploitation;
Maximize net economic returns and expand export trade.
14
It also emphasizes that the principle of the Code of Conduct for Responsible Fishing
(CCRF) should be incorporated into every policy formulation. CCRF provides ample guidelines
for the management of fisheries and has been useful for the central government to emphasize and
include sustainability in this policy. Earlier, the code was translated into different languages and
was widespread in the maritime states of India, as a self-help guide, but implementation was not
easy given the involvement of multiple stakeholders with differing viewpoints.
In this policy, the need to reform the legal framework was identified, and regional
consultations were held to discuss how to make this policy operational. Three key objectives of
this policy are (1) “to augment marine fish production of the country up to the sustainable level
in a responsible manner so as to boost export of sea food from the country and also to increase
per capita fish protein intake of the masses; (2) to ensure socio-economic security of the artisanal
fishermen whose livelihood solely depends on this vocation; and (3) to ensure sustainable
development of marine fisheries with due concern for ecological integrity and biodiversity”
(DAHD 2004a).
Further, this policy advocates protection, consideration and encouragement of
subsistence-level fishers and technology transfer to small-scale sectors, for instance, the
motorisation of about 50% of traditional craft and infrastructure support to the industrial sector.
The government has brought traditional and coastal fishers, those existing on subsistence and
small-scale fishing, together with stakeholders in the deep-sea sector for harmonized
development of marine fisheries. This current focus on collective and harmonized expansion is a
new development, as there had been a disconnect between different sectors. Mostly, the
industrial sector received special attention (often based on political connections) for its further
development and expansion,
15
1.5.1.3 Other policies: deep sea fishing policy (DSFP)
The Union Government made several attempts to encourage joint ventures in order to
promote deep-sea fishing after declaration of its EEZ in 1976; these, unfortunately, were futile.
The first deep-sea policy was announced in 1977, providing for chartering arrangements with
foreign operators. Subsequently, a newer Deep Sea Fishing Policy (DSFP) was developed in
1986, and a revised version was announced on March 1991, as a part of the economic reforms
programme (Rao 2009a). The policy involved three schemes, i.e., (1) leasing of foreign fishing
vessels to operate in the Indian EEZ beyond 12 nautical miles; (2) engaging foreign fishing
vessels for test fishing; and (3) forming joint ventures between Indian and foreign companies on
a 49:51 equity basis in deep-sea fishing, processing and marketing (Atookaren 1991; Das 1993).
The provision of transhipment at sea of catch was included in the DSFP of 1991 because of
requests by purse seine owners (mainly targeting tuna) for a waiver from the requirement to
report back to base ports before export. They argued that it would involve avoidable fuel
expenditures and would make such projects more economically viable. As a result, to encourage
tuna purse seining operations in India, the transfer of catch at sea was permitted, but only after an
issuance of certificate by reputed surveyors (Das 1993). However, as a result of protests from
local fishers and immense pressure from the National Fishworkers Forum (NFF), this policy was
rescinded in 1997. Nevertheless, there is still continued interest in strengthening the deep-sea
fishing policy in order to encourage the exploitation of deep-water resources. However, opinions
on this are diverse, with few supporting, especially government and many opposing the
involvement of foreign companies claiming to support traditional fishers (Shajahan, 1996;
Sathiadas 2000; DAHD 2004a; Salagrama, 2004; DAHD 2011).
In 2002, similar to the 1991 provision of DSFP, the new set of guidelines came with an
order issued by DAHD for fishing operations in the EEZ. Its main focus was the registration
16
status of foreign fishing vessels; now, those fishing companies with 100% foreign owned-capital
may register as Indian companies and fly the Indian flag (Morgan 2006). The Letters of
Permission (LOPs) issued under these guidelines to 15 Indian companies (DAHD&F 2005) were
condemned countrywide as promoting IUU fishing (Ganapathiraju 2012; Hamid 2012).
Furthermore, these guidelines have been criticized as favouring foreign deep-sea fishing vessel
operators registered as Indian companies given its two major provisions: (1) deep-sea fishing
vessel above 20 m can take a transhipment at sea of fish and can leave the Indian EEZ for
foreign ports; and (2) there is no obligation for the vessels to return to the base port in India
within a stipulated period (Mathew 2003).
1.5.1.4 Other policies: trade policy
In the early 1990s, India faced a serious balance of payments crisis and thus, embarked
upon a massive programme of liberalisation (Byers 1998). In order to make trade policies
consistent with the new economic policies, substantial changes were made (Bhat 2011). Some of
the changes introduced in 1991, which had an effect on agricultural trade and the fisheries sector
were that, first, the canalization of exports and imports was significantly reduced in that the
government could no longer determine the value or nature of exports or imports. Second,
Quantitative Restrictions (QRs), i.e., measures other than tariffs or duties taken to restrict
imports or exports within agricultural trade flows, were completely dismantled in April, 2001
(Maya et al. 2001). Out of 715 items, 60 were fishery products, which included both high-value
(e.g., tuna, seerfish, pomfret) and low-value (sardines and mackerel) fish. Third, tariffs were
reduced, and fish products were allowed to be exported under the open general license (OGL),
i.e., they could be exported without a license (Anjani Kumar 2003; Salagrama 2004).
Different stakeholders had conflicting opinions on the removal of QRs and the relaxation
in tariffs from 60% (1988-89) to 35% (2002-03) (Maya et al. 2001). Seafood exporters and fish
17
processing industries welcomed the policy encouraging international trade. First, the processing
factories could fully utilize their capacity, while they otherwise worked at 14% capacity in 2000
and 2001, due to shortage of raw material during the monsoon season (Maya et al. 2001). Also,
they could import tuna as tuna prices in Indian markets were higher. Consumers saw it, by and
large, as a benefit due to competitive pricing leading to cheaper prices. Others in the fisheries
sector, including fishers, feared that this would destabilize prices, and perhaps even crash the
market due to large-scale imports, and expose Indian markets to the violent price fluctuations of
international markets, thus jeopardizing their means of livelihood. However, this was seen as a
farfetched scenario because of (1) the competitive price of fish from India in international
markets; (2) imported frozen sea food was more expensive than local fish; and (3) the absence of
a market for new imported varieties among Indian consumers who did not appreciate cold water
species (Anjani Kumar 2003; Salagrama 2004).
1.5.2 State government policies
State-level fisheries policies and policy statements are also guided by the Five Year Plans
and Comprehensive Marine Fisheries Policy, 2004. In many states, there is no exclusive fishery
policy, and the subject of fisheries is discussed as part of agricultural policy and sometimes as
part of states’ industrial policy (GOG 2009; Babu et al. 2012). However, leasing policy for
fishing in inland water bodies does exist in most of the states.
Even in states where fishery policies exist (e.g., Maharashtra, Kerala, Tamil Nadu,
Andhra Pradesh, Orissa), either on a year-to-year basis (e.g., annual policy notes in Tamil Nadu)
or as part of a Five Year Plan, they are usually incomplete. The overall focus has usually been on
the further development of fisheries to increase fish catches, and improve the socioeconomic
condition of fishers. However, the whole gamut of fisheries (development, management and
conservation) is not taken into consideration (BOBP 1990; Fernandez 2004; Tietze et al. 2007;
18
GOTN 2010; Sampath and Srinivasan 2010; GOK 2011; GOTN 2011; GOK 2012; GOTN
2012). Lately, a comprehensive draft of fisheries policies was formulated in three maritime
states, Karnataka, Kerala and Orissa, which is still awaiting approval by respective state’s
legislatures due to the lack of political will and leadership to take these initiatives forward
(Sampath and Srinivasan 2010). Many state governments are also taking steps to gradually
develop a vision, policy and strategic action plan for future development and management of
fisheries. The Department of Andhra Pradesh, for example, prepared an approach paper called
“Vision 2020” with the main objectives of reducing fishing capacity, while improving fisher
welfare and coordination of research activities (GOAP 2012).
Recently, ‘sustainable fisheries’ is emerging as a key term in several government
documents and websites. However, there are no details as to how this will be achieved, and
existing regulations do not do justice to the intention of sustainability.
1.5.3 Legal framework
A legal framework is essential for the protection and conservation of fisheries resources,
which is key component of fisheries management (Cochrane 2002). The British Government had
enacted the Indian Fisheries Act of 1897, which restrained certain injurious fishing activities in
seas and inland waters. This Act banned and penalized the usage of explosives and poisons to
catch fish, and it also empowered the provincial governments to frame rules under it (BOBP
1982a; Srivastava et al. 1991; Bensam 1999b). It is still in force, and various states and UTs have
introduced fishery legislations under its enabling provision.
Following Independence in 1947, various Acts and regulations were promulgated. The
details of some at national level related to fisheries (directly or indirectly) are listed
chronologically in Table 1.2, while state and UT-specific regulations of marine fisheries are
listed in Table 1.3.
19
Table 1.2 List and details of main acts (federal level) concerning fisheries along with their associated
ministries under which they were formulated (GOI 1972; Nawaz 1981a; Choudhury 1987; Muralidharan
2009; Jayasankar 2012; Salim and Narayanakumar 2012); nm: nautical mile; MPEDA: Marine Products
Export Development Authority.
Year Acts Details Associated ministry
1897 Indian Fisheries Act Restrains use of explosives or poisons to catch
fish.
1972
The Marine Products
Export Development
Authority Act
Provide for the establishment of an authority
(MPEDA) for the development of the marine
products industry under the control of union.
Ministry of Commerce
1972 The Indian Wildlife
Protection Act
Offers protection to marine biota; amended in
1991 and then in 2001 to include several
species of fish, corals, sea cucumbers and sea
shells requiring protection.
Ministry of Environment
and Forest
1974
Water (Prevention
and Control of
Pollution) Act
Control of pollution from land-based sources. Ministry of Environment
and Forest
1976 Maritime Zones Act
Describes various zones, the Territorial Waters
(12 nautical miles), the Contiguous Zone (24
nautical miles), the Continental Shelf (200
nautical miles), and the EEZ.
Rules framed in 1982, forbid fishing by
foreign vessels in coastal areas. Chartered
vessels shall fish beyond 24 nm from the shore
on the west and beyond 12 nm miles from the
shore on the east coast as a general rule.
Ministry of Defence
1978 Coast Guard Act
Protects and enforces maritime law with its
jurisdiction over national, international waters.
In 1993, Coast Guard was made directly
responsible for combating marine pollution.
In 1996, National Oil Spill Disaster
Contingency Plan was promulgated explaining
course of action in the event of oil spills.
Ministry of Defence
1978 Marine Fishing
Regulation Acts
Provides guidelines to the maritime states to
enact laws to regulate fishing vessels in the 12
nm territorial sea, and minimize the disputes
among different sectors of the industry
Ministry of Agriculture
1980 Forest Conservation
Act Provides protection to marine biodiversity. Ministry of Environment
and Forest
1986 Environment
Protection Act
The Coastal Regulation Zone notification,
1991, outlines a zoning scheme to regulate
development in a defined coastal strip.
It also includes standards to protect and
improve environmental quality, control and
reduce pollution from all sources.
Ministry of Environment
and Forest
2002 The Biological
Diversity Act Aims at the conservation of biological
diversity & sustainable use of its components.
Ministry of Environment
and Forest
20
Table 1.3 Marine fisheries related acts and regulations specific to the coastal states and the UTs of India
(Davidar 1968; Nawaz 1981b; Atookaren 1991; James 1992; GOAP 1994; Rajguru 1994; Arora and
Grover 1996b, a, c; JICA 1999; GOL 2000; Yadav 2001; Somvanshi 2001a; MOEF 2002; GOG 2003;
GOGD&D 2003; Vivekanandan 2003; Islands 2004; GOP 2008).
States and UTs Acts and regulations Year of
enactment
Gujarat The Indian Fisheries Act 1897
The Gujarat Fisheries Act 2003
Maharashtra The Maharashtra Fisheries Act 1961
The Maharashtra Marine Fishing Regulation Act 1981
Goa Indian Fisheries (Goa, Daman, Diu Amendment Act) 1968
The Goa, Daman and Diu Marine Fishing Regulation Act 1980
Karnataka The Mysore Game and Fish Preservation Act 2 1901
The Karnataka Marine Fishing Regulation Act 1986
Kerala
The Game and Fish Protection Regulation Act 12 1914
Cochin Fisheries Act 3 1917
The United Provinces Fisheries Act 45 1948
Government of Travancore-Cochin Fisheries Act 34 1950
The Kerala Marine Fishing Regulation Act and Rules 1980
Daman and Diu Indian Fisheries (Goa, Daman, Diu Amendment Act) 1968
The Goa, Daman and Diu Marine Fishing Regulation Act 1980
Lakshadweep Islands The Lakshadweep Marine Fishing Regulation Act 2000
Tamil Nadu
Nilgiris Game and Fish Preservation Act II 1879
Government of Bengal and Madras Amendment Act 11 1929
The Tamil Nadu Marine Fishing Regulation Rules 1983
Andhra Pradesh
Executive Order of the Government of Andhra Pradesh 1983
Indian fisheries (Andhra Pradesh Extension and Amendment Act) 1961
The Andhra Pradesh Marine Fishing Regulation Act 1994
Orissa
The Orissa Marine Fishing Regulation Act 10 1981
The Orissa Marine Fishing Regulation Rules 1983
Judgement by the Orissa High court making mandatory the use of
Turtle Exclusion Devices (TED) by fishing trawlers 1998
West Bengal
Bengal Private Fisheries Protection Act 2 1889
Government of Bengal and Madras Amendment Act 11 1929
Fisheries (Requisition and Acquisition) Act 1965
The West Bengal Marine Fishing Regulation Act 1993
Puducherry
The Indian Fisheries (Pondicherry Amendment), Act 18 1965
The Pondicherry Marine Fishing Regulation Act 2008
The Pondicherry Marine Fishing Regulation Rules 2009
Andaman and Nicobar
Islands
Andaman and Nicobar Islands Fisheries Regulation 1 1938
Andaman and Nicobar Islands Marine Fishing Regulation 2003
21
The Marine Fishing Regulation Acts (Table 1.4) were enacted in response to local issues
and attempt to manage fishery resources within territorial waters through following (1) zone
regulations based on vessel types; (2) registration and licensing of fishing vessels; and (3) control
and restriction of certain gears and mesh size restrictions. For example, in Gujarat, the use of
wounding gears, such as spears, arrows and harpoons is prohibited, and in the territorial waters
of Tamil Nadu, pair trawling and purse seining are banned (Varkey et al. 2006).
Furthermore, the states of Orissa, West Bengal, Kerala and Andhra Pradesh have
included Turtle Excluder Device (TED) regulations for trawlers to protect endangered species of
sea turtles (Rao 2011).
In addition, MFRAs also include seasonal fishing closures (mainly during monsoon
season to restrict the capture of juvenile fishes) and fishing restrictions on specific days or times
of the day. Night trawling, for example, is banned in certain parts of Tamil Nadu and Andhra
Pradesh (Soumya and Shah 2004). Fleet capacity is also regulated; for example, the MFRA of
Orissa has clearly specified that allowable fleet size for vessel below 15 m or 25 gross register
tonnage (GRT) and operating beyond 5 km from shore is 1000 vessels, and, as per the Kerala’s
MFRA, the registration of new mechanized vessels and motorized boats has stopped since
October 2008 (James 1992; Muralidharan 2009).
The registration and licensing is essential for all crafts including unmotorized traditional
vessels (except in Kerala where vessels without engines were exempted), but they can fish
anywhere in the sea, while limits exists for other categories of vessels. Moreover, it is mandatory
in all states that the registered number to be written legibly and displayed on either side of the
fishing craft (Fernandez 2004).
22
India is also a signatory to several international instruments related to marine fisheries
and habitat. Detailed information on these is available in Bhathal (2004) and the websites of
FishBase (www.fishbase.org) and the Sea Around Us (www.seaaroundus.org).
1.6 History of fishing in India
Fish and fisheries have a significant place in Indian history dating back to 3,000 B.C.
(Jhingran 1975). In Indian mythology, ‘Matsyavathara’ is an incarnations of God in the form of a
fish (Silas 1977). Numerous fish drawings and figurine remnants from the Indus valley
civilization, which thrived from 2500 to 1500 B.C. (Prashad 1936; Pushkarna 1998) are also
available. Indian ichthyology has its origin in the 18th century, at the time of foreign domination
in India, when several contributions were made to the systematic distribution and bionomics of
freshwater and marine fishes, notably by Francis Day (1888). FishBase (www.fishbase.org) may
be consulted for a comprehensive bibliography on Indian ichthyology.
Although fishing traditionally provided livelihoods to the segment of the population
living in coastal regions and along river banks, lakes and canals, nothing was done officially to
promote fisheries development. The Indian Fisheries Act of 1897 was the first formal step
towards marine fisheries development and management which delegated various erstwhile
provinces with the responsibility of fisheries administration (BOBP 1982a; Chidambram 1982;
Bensam 1999a, b). However, in pre-Independence times, regulations regarding the fisheries were
essentially revenue-oriented and expressed little interest in its development (Devanesen and
Chidambaram 1953; Bensam 1999c). The first fisheries department explicitly aiming for
advancement of this sector was the Madras Presidency, organized in 1907 by Sir F. Nicholson,
also called the “Father of Indian Fisheries Development”. Several reports were published after
World War I by committees and specialists, aiming to encourage the expansion of fisheries such
as the reports of the “Bengal Famine Commission” (GOI 1945a) and “Scientific Research in
23
India” (Hill 1945), which also emphasized fisheries as an essential aid in increasing the country’s
food supply (Panikkar 1957). During World War II (1939-1945), India provided bases for
American and other Allied military personnel, which created the problem of supplying adequate
amounts of good quality fish. This scarcity of food led to an interest in the expansion of marine
fisheries.
In preparation for a post-war development phase, Dr. Beni Prasad was asked to review
the country’s fisheries and to recommend necessary measures for their development. In his
historical memorandum “Post-War Development of Indian Fisheries,” submitted in 1941, he
proposed the first definite programme to develop a research department for fisheries (Prasad
1944; Bensam 1999a). This proposal was followed by the ‘Kharegat Memorandum’ of 1944,
wherein the advisory board of the Indian Council of Agriculture Research laid down the essential
elements of fisheries development to be achieved in the country. Among these elements were (1)
the establishment of a Central Fish committee as well as of a fisheries research station; (2) a pilot
project for the mechanization of catching and for storing catches; (3) the development of pond
culture practices; and (4) the improvement of fish transport (Panikkar 1957). Another important
document published by Dr. Beni Prasad, who was the fishery development adviser to the
government of India, was a report of the Fish Subcommittee of the Policy Committee No. 5 on
Agriculture, Forestry and Fisheries, which embodied the results of country-wide surveys carried
out by the Fish Subcommittee (GOI 1945b; Samuel 1968b; BOBP 1982a).
However, it was only after independence (1947) that concerted efforts were undertaken to
develop Indian fisheries, as expressed through a succession of National Five Year Plans (from
1951 onwards). In the 1950s, it was felt that the progress of fisheries would be one of the most
promising means of improving the Indian diet. Consequently, fishery planning in India in the
1950s was officially guided by the same goals as agriculture: increasing production and equitable
24
distribution (see Section 1.5.1.1). Over time, though, the priorities of the central government
gradually shifted from providing protein to increasing export revenue (also see Section 1.5.1.1).
1.7 Development of different sectors of fisheries
The government of Bombay (now ‘Mumbai’) made the first attempt to introduce
experimental trawling in 1902, using a steam trawler. Subsequently, several similar experimental
and exploratory surveys were conducted until Independence (Mukundan and Radhalakshmy
1998; Somvanshi 2001a, b) by the state governments of Bombay, Bengal and Madras in the
Arabian Sea and the Bay of Bengal.
Once India gained its independence in 1947, fisheries started to get the attention that was
commensurate to their potential role. It was after the first All India Fisheries Conference held in
1948 in New Delhi that the Indian government decided to ask for foreign co-operation and
technology to develop its fisheries sector. In 1950s, interest was seen worldwide in the
development of small boats, and the FAO World Fishing Boat Congress of 1953 in Paris and
Miami further reinforced this sentiment (Chidambram 1982). During that time, as India was also
embracing the change and participating in the ‘Grow More Food’ campaign, fisheries were seen
as a potential sector to meet growing demands and accomplish self-reliance.
As a result, in 1952, a tripartite technical co-operation agreement was accorded between
India, the USA and the United Nations for fisheries development. A year later, in 1953, the Indo-
Norwegian Project (INP) was started in the state of Kerala following a tripartite agreement
signed by the governments of Norway, India and the United Nations. The main objectives of this
project were to study the operational efficiency and commercial feasibility of different crafts and
gears, propagate various fishing methods, train personnel and provide technical consultancy
services (Sandven 1959; Sathiarajan 1987; Johnson 2002).
25
Table 1.4 Marine Fishing Regulation Act of the maritime states and the UTs, which have demarcated fishing areas for mechanized and unmotorized vessels
(see Section 1.7), imposed fishing closures and mesh size restrictions. OAL: overall length, GRT: gross register tonnage, nm: nautical miles (GOK 1980;
GOO 1982; GOTN 1983; GOK 1986; GOWB 1993; GOAP 1994; Devaraj and Vivekanandan 1999; Somvanshi 2001a; GOG 2003; GOGD&D 2003;
Islands 2004; GOP 2008; Muralidharan 2009; Jayasankar 2012).
States and UTs Marine Fishing
Regulation Act
Area reserved
for traditional
vessels
Area available to mechanized
vessels
Fishing seasonal closures Gear regulations (mesh size
restrictions- must not be less than)
Gujarat Gujarat Fisheries
Act – 2003
Up to 5 nm (9.3
km)
Beyond 9 km 10 June - 15 August
(67 days)
40 mm cod end of trawl net
Maharashtra MFRA 1981 Up to 5 - 10
fathoms depth
Beyond 10 fathoms depth 10 June - 15 August
(67 days)
35 mm cod end of trawl net
Goa MFRA 1980 Up to 5 km Beyond 5 km 10 June - 15 August
(67 days)
24 mm any net for catching fish;
20 mm for catching prawns.
Karnataka MFRA 1986 Up to 6 km Less than 15m OAL: 6 - 20 km;
Greater than 15m OAL: beyond 20
km.
15 June - 10 August
(57 days)
30 mm cod end of trawl net
Kerala MFRA 1980 12 - 25 fathoms
depth
Less than 25 GRT: 20 - 35 fathoms
depth
15 June - 19 July
(45 days)
35 mm cod end of trawl net;
20 mm ring seines and dip net.
Tamil Nadu MFRA 1983 Up to 3.4 nm (6.3
km)
Beyond 3.4 nm (6.3 km) East coast 15 April - 29
May (45 days); West coast
15 June - 29 July (45 days).
25 mm for gillnet;
37 mm cod end of fish trawl net;
40 mm cod end of prawn trawl net.
Andhra Pradesh MFRA 1994 Up to 8 km Less than 15m OAL: 23 km;
Greater than 15m OAL or 25 GRT:
beyond 23 km.
15 April - 31 May
(45 days)
12.5 mm cod end of trawl net
Orissa MFRA 1981 Up to 5 km Less than 15m OAL: 5-10 km;
Greater than 15m OAL: beyond 20
km
15 April - 15 June
(60 days)
West Bengal MFRA 1993 Vessels less than
9 m - up to 8 km
Vessels greater than 9 m - up to 20
km but beyond 8 km;
Vessels above 15 m - beyond 50 km
15 April - 31 May
(45 days)
Daman and Diu MFRA 1980 Up to 5 km Beyond 5 km 10 June - 15 August 24 mm any net for catching fish;
26
States and UTs Marine Fishing
Regulation Act
Area reserved
for traditional
vessels
Area available to mechanized
vessels
Fishing seasonal closures Gear regulations (mesh size
restrictions- must not be less than)
(67 days) 20 mm for catching prawns.
Lakshadweep
Islands
MFRA 2000 20 mm for seines and trawl net;
50 mm for gill net.
Puducherry MFRA 2008 3 miles (4.8 km) Beyond 3 miles (4.8 km)
Andaman and
Nicobar Islands
MFRA 2003 Up to 6 nm (11.1
km)
Up to 6 nm (11.1 km) for vessels
less than 30 hp;
Beyond 6 nm (11.1 km) for vessels
greater than 30 hp.
25 mm for gill net, shore seine and
drag net;
standard mesh size, i.e., 35 mm for
trawl net.
27
In the same year (1953), an agreement was signed between the government of India and
FAO regarding technical assistance in small craft mechanization or motorization and technology
(Pillai and Katiha 2004a). The artisanal sector, which was using vessels without engines and
traditional gears, was a mainstay during early 1950s. Thus, in the absence of the technical ability
to design and build small advanced boats, new ideas and plans proposed through these initiatives
were accepted without too much resistance. As a preliminary plan, it was suggested that the
motorization of fishing boats in India be split into a base stage and four subsequent
developmental stages, (1) the motorization of existing crafts; (2) the introduction of simple and
small mechanized boats; (3) the introduction of bigger, more specialized boats; and (4) the
broadening of the fishing fleet. Given that fishers were scattered all along the coast and the
existing vessels in operation were adapted to beach landing, evolving a suitable beach landing
craft was seen as essential, and it was decided that it should be handled separately. Moreover, the
development of ports required huge capital outlay and relocation of fishers to areas where
anchorage facilities were available was not feasible, so this initial plan seemed reasonable
(Chidambram 1982). In the initial development stages, surveys and observations revealed that
several (approximately 35) traditional vessels in the states of Gujarat (Lodhia and Machwa),
Maharashtra (Satpati and Versova), Madras (now Tamil Nadu; Tuticorin), Andhra Pradesh
(Navas of Kakinada and Masulipattinam), Orissa and West Bengal (Batchari, Chot, Diamond
Harbour) were suitable for motorization (FAO 1958). FAO personnel assigned to India under the
Expanded Technical Assistance Program (ETAP), however, soon realized that the motorization
of the Indian fishing fleet as a nationwide scheme offered little scope for coordinated efforts and
standardisation because of unique boat problems and varied local conditions in each state. Thus,
the experimental motorization did not make much headway except for local vessels in
Maharashtra (i.e., Satpati) and Gujarat (i.e., Lodhia and Machwa). As a result, the scheme for
28
motorizing traditional vessels was discontinued due to technical, economic (e.g., high initial
costs) and social (e.g., lack of acceptance by fishers) reasons (Chidambram 1982).
In the state of Kerala, the Indo-Norwegian Project (INP), now called the Integrated
Fisheries Project (IFP), also attempted to motorize the existing traditional vessels, though,
resulting in failure. Therefore, in 1954, these projects and programmes started to concentrate on
developing new designs and prototypes for mechanized boats. The central and state governments
also encouraged these endeavours, as they were receiving technical and financial assistance
(Pillai and Katiha 2004b). In the late 1950s, the maritime states of Saurashtra, Travancore-
Cochin and Madras tried shrimp trawling, purse seining and other methods of fishing using the
fishing vessels provided under the Indo-American Aid Programmes. As an initial attempt in
designing a beach landing vessel (also called surf boat), FAO naval architects developed several
prototypes and extensive trials were undertaken in Saurashtra (Gujarat), Quilon (Kerala),
Tuticorin, Madras (Tamil Nadu) and Puri (Orissa). These trials resulted in the creation of a
standard design but were only partially successful, and the project encountered great difficulties
in finding personnel to handle these boats (Pillai and Katiha 2004a). Thus, in 1963, the Central
Institute of Fisheries Nautical and Engineering Training (CIFNET) was founded at Kochi
(Kerala) to provide technical training for crew of sea-worthy fishing vessels (Swaminath 1987).
Subsequently, under the INP and FAO, various designs and sizes of mechanized harbour vessels
were introduced, and in 1963, the Central Institute of Fisheries Technology (CIFT) was tasked
with research on designing new craft types, and the activities of INP were directed to exploratory
and experimental fishing (Nair 1987).
The vessel design popularly known as Pablo was the base of mechanization programme
and was mainly used for gillnetting. Trawling with these small boats was attempted in later
years. However, in 1962, the INP introduced a new 25-foot boat with a 16 hp diesel engine
29
capable of being used as a small shrimp trawler, which was readily accepted as rich shrimp
grounds occurred nearby (Pillai and Katiha 2004a). The development of the shrimp industry and
its export-oriented expansion changed experimental trawling into a commercial venture, which
soon spread to the entire country (Mukundan and Radhalakshmy 1998). As a result of the
developing interest in the Indian seafood industry, especially exports, the Marine Products
Export Promotion Council was also set up in 1961. In 1972, it was renamed the Marine Products
Export Development Authority (MPEDA) and put under the jurisdiction of the Ministry of
Commerce (MPEDA 1987).
Other methods of fishing, such as tuna lining [introduced in 1963; (Dixitulu 2002)] and
purse seining, were also attempted by the FAO and INP. Extensive trials of purse seiners were
reported as successful; however, they were not widely accepted by the fishers because catches
consisted mainly of low-priced small pelagic fishes (Indian oil sardine and mackerel) as
compared to exportable shrimp, which fetched a high price and was caught in abundance by
trawlers. Purse seining on an experimental basis was carried out first in Goa in 1957, but it was
only successful in commercial operations in 1964 (Sadanandan et al. 1975; Verghese 1976).
Gear design was also given greater emphasis. Notably, synthetic twine for making fish nets was
introduced, which, by the 1980s, had almost totally replaced cotton twine (BOBP 1983; Thomas
2000).
During the first two of the Five Year Plans, special emphasis, besides mechanization, was
given to remove the ‘middlemen’ involved in fish marketing through the establishment of co-
operative societies. However, by 1961, it was realized that co-operatives set up mainly to avoid
the perceived exploitation of fishers by ‘middlemen’ were not very successful (BOBP 1982;
Johnson 2002). This period from 1947 to 1965 is considered as phase 1 or a pre-development
stage, where fishing was largely dominated by the artisanal sector (mainly vessels without
30
engines) and, in the later years, underwent rapid metamorphosis through newly introduced
mechanization programs (Muralidharan 2009).
With the introduction of larger boats, new techniques of equipment handling and
improved facilities for fish detection, an urgent need was felt for developing ports. Thus, the
Central Institute of Coastal Engineering for Fishery (CICEF) was established in 1968 at
Bangalore in collaboration with the FAO and the United Nations Development Program (UNDP)
with the main objective of conducting techno-economic feasibility studies regarding the
development of fishing ports (DAHD 2004b; NIO 2004).
In early 1970s, fibreglass-reinforced plastic (FRP) boats were introduced in India. They
were initially unpopular, due to high cost, lack of maintenance facilities and other problems.
However, during the late 1970s and 1980s, these FRP boats become very popular, and largely
replaced the traditional wooden canoes (Sheshappa 1998).
It was not feasible for a developing country to replace large number of indigenous fishing
boats with new mechanized boats, featuring inboard engines. Hence, it was decided to motorize
the existing small-scale vessels with outboard engines (Chandy 1970c). Motorization began in
1980s as a program of the Seventh Five Year Plan (GOI 1985a) and the support of financing
schemes operated through the co-operative sector. Although efforts to motorize traditional crafts
began as early as 1953 in Jaleshwar village, Gujarat, they were initially unsuccessful (Kuriyan
1982; Srivastava et al. 1991). Overall, though, the introduction of outboard motors brought about
a revolution in fishing, effectively reducing the search duration, increasing the sea endurance and
made accessible areas of high fish concentration, which acted as a temporary reprieve for the
artisanal sector. Several other major technological transformations were witnessed in the Indian
fisheries, all resulting from successive Five Year Plans (see Table 1.1).
31
Simultaneously, India initiated deep-sea fishing in 1972 with the import of two Gulf of
Mexico trawlers from the USA to encourage the development of offshore fisheries (Devaraj
1995). Efforts for deep-sea fishing had been made earlier, in 1946, when the West Coast
Fisheries Corporation was established by the government of Travancore. They brought three
vessels (19 m) from the UK, but these experienced many operational problems. Around the same
time, the Tata Company bought a couple of shrimp trawlers with Mexican crews, but their
operations also failed. Then, in 1954, the New Indian Fisheries Company registered as a joint
venture with the Japanese Taiyo Fishing Company, which found success and continued fishing
for several years (Pusalkar and Mammen 1985). A majority of foreign vessels (stern trawlers,
pair trawlers and tuna longliners) started to operate in Indian waters under a chartered fishing
scheme. Commercial tuna fishing in India commenced in 1985 and tuna longliners operated in
Indian waters under Indian-owned, joint-venture and leased foreign vessels scheme targeting
scombroids, i.e., tunas, seerfishes and billfishes (Somvanshi and John 1996). By the late 1980s,
over 100 chartered and joint-venture deep-sea fishing vessels, mainly trawlers, were operating,
mostly in the inshore grounds up to 50 m (Devaraj 1995). Many different countries entered into
joint ventures with India over time, including Japan, Taiwan, Mexico, Poland, Denmark,
Bulgaria, France, the USA, Germany, Thailand and Italy. Some stayed for long period and others
left after operating for short durations either due to difficult government procedures, operational
problems or lack of economic viability (Pusalkar and Mammen 1985).
However, increasing numbers of deep-sea vessels were clearly competitors of the
artisanal sector, as they operated in same waters and targeted the same resources. Due to
widespread unrest, several limitations were imposed on offshore fishing operations, and various
regulations were enacted by the states (see Table 1.4). The issue of industrial trawlers came into
the spotlight when a revised DSFP was announced in March 1991, reflecting the liberalization of
32
the Indian economy by encouraging foreign investments (detailed in Section 1.5.1.3). The Indian
fishery organizations (e.g., NFF) protested vociferously against this, and highlighted serious
conflicts between the domestic small-scale and industrial joint-venture fleets. They claimed that
Indian boats could reach those areas themselves and there were no guarantees that the joint-
ventures boats would not poach fish further inshore. These protests were so strong that the
Central Government shelved the issuing of licenses to foreign fishing vessels and launched a
commission of inquiry in 1994, to review this joint venture; the policy was rescinded in 1997
(Kocherry 1999; Johnson 2002).
Over time, various advanced designs and sizes of mechanized vessels were launched,
resulting in multiday fishing in the late 1990s. Specialized and multipurpose fishing vessels, such
as, trawler-cum-purse seiners, trawler-cum-gillnetters, trawler- cum-fish carriers, long-liners and
trolling boats were also introduced (Sreekrishna and Shenoy 2001). A new phase emerged in the
2000s, characterized by stagnating or even declining fish catches, depleted fish stocks and
increasing conflict over fish resources. As a result, the focus of the Indian government has
shifted once again towards oceanic and deep-sea fisheries, to diversify the fishing operations,
with a focus on increasing tuna catches for export. A new set of guidelines was issued by
DAHD&F in 2002 for foreign fishing operations in EEZ.
Further, under the Tenth Five Year Plan, a scheme was introduced for the conversion of
existing trawlers into resource-specific fishing, and the responsibility for its implementation was
assigned to Fishery Survey of India (FSI) and CIFT (GOI 2002a; Rao 2009b). Thus, a pilot
project was initiated in 2002 to equip the existing fleet of shrimp trawlers (23-27 m OAL) off the
upper east coast for undertaking tuna longlining (Ganga and Pillai 2006). As an initial step, the
MPEDA provided financing for converting two trawlers into use for tuna longlining, which
failed. However, eventually, 30 shrimp trawlers were converted for tuna longlining, and then, in
33
December 2006, the DAHD&F passed a notification on joint ventures, stating that the operation
of deep-sea fishing vessels would be permitted in Indian EEZ under joint ventures for only tuna
longlining, squid jigging, pole and line fishing and purse seining (Rao 2009b, c).
These ongoing efforts to encourage diversification and resource-specific effort expansion
were reflected in the Eleventh Five Year Plan, covering 2007 to 2012 (GOI 2006). During this
period, schemes were implemented for the conversion of fishing vessels (mainly trawlers), both
below and above 20 meter length overall (LOA) by the DAHD&F and MPEDA. Over 1500
vessels (a majority of which are from the state of Tamil Nadu) below 20 meters LOA
participated in the conversion programme. However, the purpose of vessel conversion (tuna
fishing) was defeated, as most of these vessels engaged in shark fishing (Ganga and Pillai 2006;
GOI 2006).
In general, fisheries can be subdivided into small scale and large scale fisheries with
further subtypes, i.e., recreational4, subsistence
5, artisanal
6 and industrial
7 (Figure 1.2).
In the present study, the marine fishing sector of India is divided into artisanal and
industrial sectors and then , based on the engine power, sectors are further subdivided into four
4 Recreational fishery is a form of small scale fisheries, in which fishing is done for pleasure or sports.
5 Subsistence fishery is an another form of small scale fisheries where fish is caught mainly for human consumption
or for bartering. 6 Artisanal fishery is a form of small scale fisheries in which the bulk of the catch is sold, i.e., not primarily obtained
for the fishers’ own consumption or their families. 7 Industrial fishery is a form of large scale fisheries in which the landings are sold.
Figure 1.2 Types of fisheries, showing subtypes of both small and large scale fisheries
Small scale Large scale
Fisheries
Recreational Subsistence Artisanal Industrial
34
distinct groups, each of which uses a combination of several gears (e.g., trawl, bagnets, gillnets,
seines and hooks and lines) (CMFRI 1981a; Sathiadas et al. 1995):
(1) Unmotorized (artisanal) sector using traditional vessels;
(2) Motorized (artisanal) sector using traditional vessels with outboard motors (OBM) of less
than 50 hp (usually 7-9 hp);
(3) Mechanized sector (industrial) using inboard motors (IBM) of 50 hp and above, e.g., small
trawlers, pair trawlers, purse seiners, gillnetters and longliners;
(4) Deep-sea fishing (industrial) sector using engines of 120 hp and above, e.g., deep-sea trawler,
deep-sea tuna longliner and deep-sea multipurpose vessels.
Throughout this thesis, the Indian terminology is used, i.e., ‘motorized’ boats are boats
fitted with outboard motors, while ‘mechanized’ boats have inboard engines. The term
‘mechanization’ is used for the introduction of inboard engines. The term ‘motorization’,
however, can mean the introduction of outboard motors or the introduction of boats with inboard
engines when specific details are absent
1.8 Existing and emerging problems
For a long time, Indian marine catches have increased steadily, but they are now reaching
a plateau. Given that the marine fisheries of India were not controlled in their initial phases and
insufficiently managed in the subsequent phases, they are currently facing challenges and
problems in achieving the kind of sustainability that will assure long-term survival. In contrast to
the view of the government, advocating the technological expansion (DAHD 2011; GOI 2006,
2011), which characterized Indian fisheries ‘development’, will impede progress toward
sustainability. Given the existing overcapitalization of the fishing fleets, any further increase of
effort in coastal waters will also increase the likelihood of collapse for many resource species
35
(Devaraj and Vivekanandan 1999). Catfish, for example, are classified as a ‘collapsed’ fish stock
in the states of Kerala and Karnataka (Mohamed et al. 2010). This is something that India cannot
afford given the acute shortage of animal protein (Raghavan 1998) and the millions of people
relying solely on marine fisheries for their livelihood.
This situation calls for an in-depth evaluation of the current state of affairs and immediate
measures in order to avoid exacerbating the problem of depleted resources. Additional problems
are besetting fisheries, including discarding, illegal fishing (including poaching), lack of
infrastructure, poor socioeconomic conditions of fishers, habitat degradation, coastal pollution,
bioaccumulation of persistent organic pollutants, and many more. Some of these are addressed
briefly in the following section.
1.8.1 Ecosystem health: fishing down marine food web in Indian waters
The evolution of fishing equipment from hand-held gear to industrial vessels has affected
the abundance and biodiversity of fish stocks. Fisheries are having an impact on ecosystems as
the fish that are removed were parts of food webs, both as consumers and as prey (Parsons
1996). Indian marine fisheries are found to be unsustainable at the ecosystem level as shown by
two indicators of fisheries’ sustainability: the Marine Trophic Index (MTI) and the ‘Fishing in
Balance’ (FiB) index. The MTI, i.e., the mean trophic level of fisheries catches has been steadily
declining in all 13 Indian maritime states and UTs at rates averaging 0.058 trophic level per
decade (Bhathal 2005; Bhathal and Pauly 2008), which is about the same as in other parts of the
world (Pauly et al. 1998b; Pauly and Palomares 2001; Pauly et al. 2009). A forward
extrapolation of the current trend in India implies the disappearance of high-trophic level, larger
and longer-lived fish from the ecosystem and the relative increase in low-TL organisms, perhaps
even jellyfish as reported in some ecosystems, or worse, good fish being replaced by jellyfish, as
for example, in the Benguela ecosystem where the ‘Myxocene’, or age of slime, has already
36
begun (Lynam et al. 2006; Pauly 2010a). Unusually high rates of jellyfish landings are being
reported from Indian waters (CMFRI 2007a), however, it is unknown if this is due to
anthropogenic causes.
The proposed explanation for this phenomenon, now widely known as “fishing down
marine food webs” is that the fishery catches are shifting from large, high TL species to the
small, low TL species in response to their relative abundance in the ecosystem. Indian shelf
fisheries have suffered from sequential depletions and have undergone a fourfold geographic
expansion through time as quantified through a spatial expansion factor (Bhathal and Pauly
2008). The decline in mean trophic level is not due to the sequential addition of newly exploited
low-TL species to the multispecies catch, as the MTI was computed after the exclusion of
species with TL lower than 3.25 (e.g., Indian oil sardine and penaeid shrimps, the catch of which
grew enormously in the 1980s).
1.8.2 Unreported catches: discards
In India, as elsewhere, fish are the major non-target species (bycatch) of shrimp trawlers,
which leads to some of the bycatch being discarded. Various reasons have been presented by
different authors worldwide to explain discarding (Clucas 1997), but saving space in order to
retain a large amount of highly priced prawns appears to be the major one. In India, discards by
industrial (mechanized and deep-sea) vessels were rarely reported (even landings go unreported
for deep-sea vessels), so these had to be estimated.
Bhathal (2004) thus, assumed that all the bycatch was retained prior to 1970, as large
industrial vessels (with engines of more than 120 hp) were only introduced in 1972 and even
low-value species had a market (George et al. 1981), resulting in negligible discarding. However,
once catches were estimated for large industrial vessels, discards were assumed to be 70% of
total fish bycatch (more details in Section 2.2.2), a conservative estimate as compared to other
37
reports (Gordon 1991; Kungsuwan 1999; Salgrama 1999). Discarding bycatch, in India, is
mainly associated with long-distance, multi-day trawlers (Gordon 1991; Zacharia et al. 2006a).
In the case of other industrial vessels, i.e., with engines of less than 120 hp, discarding was
assumed to be only 2% based on the study by Goerge et al. (1981); this was a conservative
estimate as other reports suggested higher values (Gordon 1991; Kungsuwan 1999; Salgrama
1999, Chandrapal 2007). However, some reports indicate that discards have started to decrease
as elsewhere (Zeller and Pauly 2005) in absolute terms since the 1990s, due to declining
abundances of shrimps and prawns (Kungsuwan 1999; Salgrama 1999). Shrimp trawlers are now
reported to be landing bulk of their bycatch in order to increase their revenue and thereby
compensate for the increased fishing cost (Zacharia et al. 2006).
Ganapathiraju (2012) estimated fishery extractions from India, including illegal (by
Indian and foreign vessels) and unreported catches, figures for discards by industrial trawlers,
subsistence fishing, generally missing from the catch statistics (Zeller and Pauly 2007; Pauly et
al. 2012), and underreporting by the artisanal sector, such as bait fish, dry fish landings and
mollusc collection. His findings suggested that approximately 1.5 million tonnes went
unreported in the year 2008, which had highest amount of discards (approximately 1.2 million
tonnes) from industrial trawlers and other vessels. This situation is aggravated by joint-venture
vessels targeting tuna fisheries, which report 20% of their catch, and which obviously do not
report on their discarding practices.
1.8.3 Overcapitalization
Profitable fisheries coupled with open access to resources resulted in an accelerated
growth of the fishery sector in India. The existence of caste system in India is somewhat
beneficial for fisher communities, as it allows their entry into this sector in comparison to other
castes. However, this could not limit the ownership to one specific caste or class, despite the
38
existence of traditional systems for management and allocation of resources (Tietze, 1985;
Salagrama 2006; Bavnick 2001). Over time, with increasing economic benefits and
modernisation of technology, traditional codes were ignored and ownership expanded from
fishing castes and classes to other castes, leading to rapid growth of fisher populations
(Salagrama 2006, Chalam 2007).
New fishers and vessels recruited to the fisheries led to growing competition, and the area
available per active fisher declined drastically over the years. The decline was from 956 to 170
hectares in inshore waters (0-50 m) and from 1320 to 327 hectares in offshore shelf areas for
1961-1990, excluding the Lakshadweep and Andaman and Nicobar Islands (Sathiadas et al.
1995). The number of boats continued to increase as the Indian government continued to
encourage motorization and mechanization via its subsidies (e.g., for diesel engines, use of
innovative gears and vessels; more details in Chapter 4) and loans to fishers and co-operative
organizations (Bapat and Kurian 1981; Srivastava et al. 1991; Salagrama 2004; DAHD 2005;
Aswathy and Salim 2012). The current catching capacity of the fishing fleets in Indian waters far
exceeds that required for biologically sustainable catches from most commercial stocks at depth
down to 100 m. “Too many boats chasing too few fish” applies aptly to Indian fisheries, as the
number of vessels of all sectors operating in Indian waters is estimated to be approximately three
times the optimal number (Somvanshi 2003).
Stagnating catches and declining catch per unit effort have drawn attention as seen in the
Report of the Working Group on Fisheries for the Tenth Five Year Plan of India's Planning
Commission (GOI 2002a). They reinforced the view of an earlier 1997 National-Level Review
Committee on Fishing Fleets, which attempted to address overcapacity by aiming for zero
growth in vessels between 8 and 15 m (Mathew 2003); however, no proposal or financial
provisions were made for any fishery measure to reduce the existing overcapacity. Few states
39
have announced certain measures under their MFRAs (Table 1.4). For instance, Kerala has
stopped the registration of new mechanized vessels and motorized boats since October 2008 and
the state of Orissa has fixed the optimum number of mechanized vessels (James 1992;
Muralidharan 2009); however, enforcement is reported to be weak or absent (Sampath 2005).
Overexploitation of resources (see Section 1.8.5), which results from overcapacity, is evident in
Indian waters.
1.8.4 Malthusian overfishing and sectoral conflicts
The Reverend Malthus showed that a population, other things being equal, will tend to
outstrip the food supply. This idea may appear controversial for agriculture, where increase in
grain productivity seems to have disproven Malthus. However, Mathusian limits necessarily
occur in fisheries, where the ‘production’ of fish is limited by natural process, and where
catching fish is, hence, not equivalent to harvesting a crop (Pauly 1994a, 2006b). This leads to
the notion of ‘Malthusian overfishing’ (Pauly 1990), which results from biological limits beyond
which renewable natural resources such as fisheries, cease to be sustainable and cannot continue
to absorb additional labour, and to meet increasing demand. Thus, demand of excessive
population growth cannot be solved by fisheries, and in fact, fisheries can be destroyed by excess
fishing effort, with dire implications for food security.
As described by Pauly (1990), who used the example of Indian fisheries to develop and
illustrate the concept of Malthusian overfishing, fisheries ‘development’ generally occurs in
three phases:
(1) Phase 1 occurred when the entire fishery consisted of the artisanal sector only, and operating
unmotorized boats, a situation prevailing until the mid to late 1960s; see Figure 1.3);
40
(2) Phase 2 saw a transfer of catches from the artisanal (unmotorized) to the industrial sector as
newly introduced vessels characterized by hefty subsidies began operating on the same fishing
ground as the artisanal sector. (This transfer occurred in the 1970s and 1980s; see Figure 1.3);
and
(3) Phase 3, when the catches from a given area are mostly taken by the industrial sector, forcing
the marginalized artisanal sector to seek outside intervention to deal with dwindling stocks and
increasing conflicts. (These conditions were established by 1990s - see Figure 1.3 - and are still
current).
Phase 3 is obviously the reason why several regulations, e.g., MFRAs exist which are meant to
avoid conflicts and safeguard the interests of different sectors (Figure 1.3). Figure 1.3 supports
the widespread perception, in India, that industrial fishing is responsible for the pauperization of
the artisanal fishers.
As shown below, the catches of the artisanal unmotorized sector in the 1950s was nearly
0.6 million t per year, while it currently (in 2010) only 0.1 million t per year. Indian
policymakers have always emphasized the expansion of demersal fisheries into deeper waters as
a solution to overcapacity in inshore waters. However, the low oxygen levels in deeper water
layers, especially on the West coast (Banse 1959; Gerson 2005), constrain such expansion, in
addition to the fact that generally, deep tropical waters are less productive than deep temperate
waters [see Section 1.3; (Longhurst and Pauly 1987)]. Therefore, the industrial sector (mainly
trawlers) continues to compete with small-scale fishers operating close inshore. Competing for
inshore resources is thus the major reason for the pronounced conflicts between small and large-
scale fisheries in India. Mechanized vessels deploying sophisticated gears caught the bulk of the
total catch, thus reducing the artisanal share (Pauly 1990, 1994a).
41
0.0
1.0
2.0
3.0
4.0
1950 1960 1970 1980 1990 2000 2010
Ca
tch
(t
• 1
06)
Year
Mechanized
Unmotorized
Total
Figure 1.3 Catch trends of marine fisheries for whole India, 1950 – 2010, highlighting the increasing
appropriation of catches by the mechanized8 (industrial)
fishery, which did not (only) complement the
catches of artisanal (unmotorized) fisheries, but competed against them in their inshore fishing grounds.
Sources: CMFRI 1984-2010
This led to open and severe clashes between members of the two sectors, and the
mechanized sector was blamed for the pauperization of traditional fishers (Thomas 2000). These
resulting conflicts sometimes culminate into violence, killings and burning of boats, and have
become a serious social and legal problem in many coastal fishing areas (Nair and Jayaprakash
1983; Balakrishnan and Algaraja 1984; Menon 1996).
However, the magnitude and nature of these problems may vary from region to region.
Existing conflicts among different sectors can be categorized into two types: (1) Those involving
different fisheries at the same location, e.g., fishers engaged in artisanal and mechanized fishing
in a common fishing ground (Balakrishnan and Algaraja 1984; Devaraj and Vivekanandan
1999); and (2) Those involving different groups of operators of the same gear at the same
location, e.g., the frequent conflicts occurring between trawlers from south Andhra Pradesh and
Chennai over the fishing grounds off the southern coast of Andhra Pradesh (Balakrishnan and
Algaraja 1984; Devaraj and Vivekanandan 1999).
8 In Figure 1.3, in mechanized sector, motorized artisanal vessels were also included; however their contribution to
the catches was miniscule.
42
1.8.5 Overexploitation
Overexploitation in Indian waters is the result of overcapitalization and intra- and inter-
fleet competition (discussed above) within different fisheries sectors. Growth overfishing [i.e.,
fish are caught before they had a chance to grow (Pauly 1994b)] and recruitment overfishing
[i.e., the entry of young fish into the exploited stock is impaired because reproducing adults left
are few (Pauly 1994b)] are both reported in Indian waters, as can be expected in intensive, non-
selective, fishing operations.
Commercially exploitable quantities of prawns and shrimps (which fetch significantly
higher prices than fish) occur in the same habitats as those of fish, especially juveniles. Fishers,
in an attempt to maximize the catch, use smaller mesh sized nets, resulting in the catch of young
fish and the destruction of eggs. In Karnataka, bull trawlers operating during the early post-
monsoon fishing season target several commercially important fish and catch around 23% of
juveniles (Rohit et al. 1993) in weight, which is extremely large in term of numbers of
individuals. But this is unfortunately not an extreme value. In Gujarat, the trawlers catch up to
52% of juveniles (1988 to 1993) off the Veraval coast (Puthra et al. 1998), and in Vishakapatnam
(Andhra Pradesh), the low-value component of the trawl fishery contained 67% to 94% juveniles
(Sujatha 1996). The area swept by trawl nets for prawns in the coastal waters of western India
yield approximately 16% prawns, while the rest of the catch consists of finfishes or benthic
organisms, a considerable amount of which are juveniles (Menon and Pillai 1996). Along with
trawls, other gear types, such as beach seines, boat seines and dol nets used in the states of
Gujarat, Maharashtra, Kerala, Tamil Nadu Andhra Pradesh and West Bengal also fish
indiscriminately (Luther and Sastry 1993; Rohit et al. 1993; Bensam et al. 1994; Zacharia et al.
1995b; Menon 1996; Menon and Pillai 1996).
43
Mackerel fisheries of India, for example, are facing growth overfishing due to the non-
selective operation of seines heavily exploiting early juveniles during July and September, when
peak recruitment to the mackerel population occurs (Yohannan and Sivadas 2003). Even
artisanal gears perform destructive fishing; for instance, in Vizhinjam, Andhra Pradesh, a
seasonal (November to May) a ‘nonnavu’ fishery is performed which use a gear with a mesh size
of 3-4 mm, and is reported to yield a catch of 180 t of juvenile fishes in one day (Menon and
Pillai 1996).
Further, mouth-brooding marine catfishes (Family Ariidae) and sharks have suffered
heavy declines (Pillai and Parakal 2000), due to their slow growth and low fecundity. The
catfishes, according to a recent analysis, were classified as a ‘collapsed’ fish stock in the states of
Kerala and Karnataka (Mohamed et al. 2010). Purse and ring seine catches from Karnataka have
more than 50% of male catfishes with eggs in their mouth (Silas et al. 1980; Menon and Pillai
1996; Mohamed et al. 2010). Since the introduction of purse and ring seines in the late 1970s, the
bulk removal of ripe-running oil sardines and Indian mackerel have also been reported along the
west coast (Silas et al. 1980; Mohamed et al. 2010). Similarly, the gillnets of smaller mesh types
such as Podivalai (70-100 mm) along the Tuticorin coast (Tamil Nadu) and the trawlers
operating along both the coasts land exclusively small king seer, resulting in massive growth
overfishing (Muthiah et al. 2003).
Further, the use small mesh sizes generate catch containing large amounts of non-target,
low-value fish with low consumer preference. In the case of trawlers performing long trips, these
low-value fish are not even landed; rather they are discarded because of limitations of space or
ice (see Section 1.8.2).
44
1.8.6 Poor socio-economic condition of fishers
Over time, several schemes were introduced in Five Year Plans to improve the socio-
economic conditions of fishers. However, fishers are still facing poor housing, health and
sanitation conditions, and are struggling with poverty, indebtedness and illiteracy (Korakandy
2008).
Overtime, the redistribution of resources among different sectors has resulted in
substantially increasing the gap between wealthy boat owners and poor fishers, who also have
‘middlemen’ to contend with. As a result, poverty and indebtedness have become the major
problems of traditional small-scale fisheries (Sehara et al. 1986; Sathiadas et al. 1994;
Korakandy 2008).
Nutritional-deficiency diseases (e.g., xerosis due to vitamin A deficiency) are reported to
be prominent among children of fishing communities in the state of Tamil Nadu, India and up to
70% of the children of such communities suffered from malnutrition (Natarajan 2010). Many
other diseases are also prevalent in fishing villages due to poor sanitation (Bhavani 1988;
Natarajan 2010). Nearly one-third of households do not have any sanitary facility (Korakandy
2008), and particularly during the monsoon seasons with poor drainage facilities, the houses
become unliveable. Although motorization has improved the status of artisanal fishers since the
mid-1980s (Srinath 1988), fishing is still largely done by poor people with over 40% illiteracy
(Korakandy 2008). Given that the marine fisher population, about 4 million in 2005 [for all
sectors; (CMFRI 2006a)], depends on natural resources, any further decline of resources will
have serious social and economic implications, which will only further exacerbate the existing
poor conditions.
45
1.9 Thesis goal and objectives
The ultimate goal of this study is to assess the state of marine fisheries in India at a
regional (state) level by assembling scattered data into a coherent whole and make them readily
available to interested parties. Transparency of this sort should eventually increase public
understanding and participation in policy making. Specifically, the objectives of this study are to
1) reconstruct India’s marine fishing effort from 1950 to 2005 for 13 maritime regions (9 states
and 4 UTs); 2) present and analyze time series of ‘catch per effort’, an index of relative resource
abundance obtained by dividing the catch data assembled in Bhathal (2005), and further refined
here, by the effort in (1) ; (3) evaluate the existing situation of fisheries and estimate MSY and
EMSY as reference points using surplus-production models (i.e., modified Schaefer model; Fox
model); and (4) analyze the economic performance of Indian fisheries by constructing
bioeconomic models and estimate Maximum Economic Yield and fisheries ‘rent’ for India as a
whole.
1.10 Thesis outline
In total, there are five chapters in this dissertation. Chapter 1 is an introductory chapter,
providing a literature overview covering background information for understanding the rationale
for the above-stated goal and objectives. It also provides a detailed outline for this dissertation.
In Chapter 2, objectives 1 and 2 are addressed. As an initial step in effort reconstruction,
information on several variables [e.g., total number of vessels, total power (horsepower), fishing
days spent at sea and crew size] were compiled for two vessel categories: those with engines and
those without engines for all study areas from 1950–2005. ‘Vessels with engines’ are further
subdivided into different categories based on gear types: trawlers, gillnetters, purse seiners, tuna
long liners and others. The compiled effort data suffered from various inconsistencies, which
were resolved using different methods and historical information. The final time-series of
46
nominal fishing effort from 1950 to 2005 for all of 13 study areas was expressed in units of
‘horse power fishing days’ for all vessel categories and then converted to kW·days. Next, the
effort data were corrected for increase in catchability or ‘technological creep’ (Pauly et al. 2002;
Pauly and Palomares 2010) to account for technological improvements over time and calculate
effective fishing effort. Finally, reconstructed catches from Bhathal (2005) were refined and
combined with effective effort to calculate catch per unit effort, which is a measure of relative
abundance for all 13 study areas.
In Chapter 3, surplus production models (i.e., Fox and non-linear Schaefer models) were
constructed for India and its east and west coast using the reconstructed catches of neritic species
(i.e., billfishes and tunas were excluded) and effort from Chapter 2 (objective 3). Models were
not made for the Andaman and Nicobar Islands and the Lakshadweep Islands as the fisheries in
these two UTs are mainly for oceanic fish species, i.e., tunas and billfishes. MSY and EMSY were
calculated as reference points to evaluate the current situation of fisheries in Indian waters at a
finer spatial scale and for a better understanding of the underlying trends.
Chapter 4 details the economic performance of Indian fisheries (objective 4) by
presenting bioeconomic models for India as a whole. To create these models, ex-vessel price data
were compiled, weighted based on catches, and then deflated to obtain real prices and total
revenue. Total fishing costs were estimated using two different approaches, i.e., based on (1)
predicted revenue and (2) actual cost data i.e., cost per tonne in US$ for the different gear type
combinations expressed as 2005 real values (= adjusted for inflation; Lam et al. 2011). Then,
MEY, EMEY and economic rent were estimated. Subsidies were also included, to assess their role
in aggravating the current situation of fisheries.
Chapter 5 is a concluding chapter, presenting a synthesis of the main findings of this
dissertation, along with an assessment of the strengths and limitations of this research. It also
47
details how these research findings could be used to improve on the current state of the marine
fisheries of India.
48
Chapter 2 Effort and catch per effort reconstruction, 1950 to 2005
2.1 Introduction
In recent years, the quantification of fishing effort has received much attention, as many
marine fishery resources are exploited well beyond their biological limits (Gelchu and Pauly
2007; McCluskey and Lewison 2008; Watson et al. 2013). Overall, management of fisheries
demands an overview of exploited resources and their status. The regulations concerning
management directly influence fishing fleets, and thus knowledge about these fleets becomes as
important as other aspects of fisheries science (Branch et al. 2006). Both catch and effort data
provide important information about fisheries and are considered fundamental to fishery
assessment and management (Rothschild 1977; Hoggarth et al. 2006a; Zeller and Pauly 2007).
Catches provide the data for first order assessment of fisheries, as they allow evaluation
of how the species and populations (stocks) upon which the fisheries depend have changed over
time (Grainger and Garcia 1996; Pauly and Zeller 2003; Watson et al. 2011). These fishery-
dependent data are readily available because they are generated by monitoring commercial
activities (Agger et al. 1974). In the absence of any other data, catches have been used to analyze
the status of fisheries globally (Kleisner et al. 2012), although inferences drawn from such
analyses have been criticised (Branch et al. 2011). However, catches do reflect abundance (Pauly
et al. 2013) and recently, Froese et al. (2012, 2013) have illustrated that catch trends are
consistent with the trends in biomass data of fully assessed stocks.
Although catch data provide important information, they are more informative when
combined with fishing effort, which allows better interpretation of catches and a more thorough
understanding of the fishery in question (Sparre and Venema 1998b). Recently, the literature on
fisheries management has started to give due consideration to data on fishing effort (Hilborn and
49
Walters 1992c; Gelchu and Pauly 2007). Modelling the spatial distribution of the operations of
fishing fleets shows that fishing effort undergoes spatial and temporal changes in a predictable
manner based on resource availability, management regulations and accordingly shifting fisher
responses (Yew and Heaps 1996; Walters and Martell 2004). Historically, the main reason for
developing the concept of fishing effort was to find an indicator of abundance of fish stocks
(Hannesson 2002), i.e., catch per unit effort (CPUE), a relative measure of abundance. Trends in
CPUE usually reflect the status of stock abundance, i.e., if abundance decreases so does CPUE.
However, the assumption that there is a linear relationship between the two has been criticized
(Branch et al. 2006), notably because of the danger of ‘hyper-stability’ and ‘hyper-depletion’.
For example, ‘hyper-stability’ can occur, in which case CPUE declines more slowly than
abundance. As a result, abundance is overestimated and fishing mortality is underestimated,
which results in overfishing (Hilborn and Walters 1992b; Harley et al. 2001). Conversely,
‘hyper-depletion’ can occur, i.e., rapidly declining CPUEs can suggest a decline of actual
abundance which is not occurring [(Hilborn and Walters 1992b; Walters 2003); see Section 3.1
for more details].
Also, several factors (e.g., vessel characteristics, fishing areas and seasons) limit the
usefulness of CPUE time series even when a linear relationship between CPUE and resource
abundance can be assumed, mainly because of changes in technology (Pauly and Palomares
2010). Many techniques have been suggested for CPUE standardization to mitigate such effects,
e.g., General Linear Models (Padilla and Trinidad 1995; Hinton and Maunder 2004; Hoggarth et
al. 2006b). Further, the spatial distribution of the stock and fishing effort can result in over or
underestimation of the abundance, which can be, however, resolved with stratification of catch
and effort by area (King 2007e). In this study, fishing effort and CPUE are analysed at state level
50
to better understand the underlying trends per area, i.e., instead of analysing composite data for
India as a whole (see Section 2.2).
Although the most common use of effort data is deriving CPUE values, fishing effort is
also used to estimate other variables, i.e., fishing mortality, fishing cost, catch estimations of
non-target species, and, when available at fine spatial resolutions, for use in ecosystem models
(Sparre and Venema 1998a; Dauk and Schwarz 2001; Watson et al. 2004b). Knowledge of effort
distribution is also informative in designating the spatial extent of marine parks and reserves
(Lynch 2006). Moreover, in the absence of any other data, as occurs in many developing
countries (Johannes 1998), CPUE and effort data can be used to perform stock assessments
through surplus production models (see Chapter 3).
For the purpose of this study, it is important to define the term ‘fishing effort’, which is
expressed in a variety of ways. Fishing effort represents the aggregate of all expended inputs to
catch fish (the resultant output) and thus is defined as “the means by which fishers achieve a
catch during a given period” (Gelchu and Pauly 2007).
Fishing effort can be categorized into (1) nominal effort, referring to sum total of all
resources used for fishing in a given period, which may be measured in units of time, capital,
labour, gear or others (Valle et al. 2003; McCluskey and Lewison 2008); and (2) effective effort,
representing “the fishing pressure exerted by fishers on fish stocks” (Pape and Vigneau 2001). In
other words, effective effort is a standardized measure of effort, i.e., when several gears are used,
nominal effort is adjusted to a standard type in order to account for changes and differences in
fishing power and efficiency, and ensure direct proportionality with fishing mortality (FAO
1997; see also Palomares and Pauly 2010). The relationship between fishing mortality and
fishing effort is:
F = q f (2.1)
51
where, ‘F’ is the fraction of the fish population that dies from fishing; ‘f’ is a measure of fishing
effort, and ‘q’ is the catchability coefficient (Pauly and Palomares 2010).
Change in catchability does affect fishing mortality and so effort as well (both effective
and nominal). It is, therefore, important to adjust for technological improvements and differences
among various gear types. Pauly and Palomares (2010) conducted a meta-analysis and provided
an empirical equation to estimate a factor (‘technological creep’) which account for gradual
technological improvements and the resulting increases in efficiency. They emphasized that
whenever the time series of fishing effort exceeds a decade in temporal coverage, effort should
be adjusted to account for the gradual increase in gear efficiency. The effort data were thus
adjusted in this study (see below). The following section gives a detailed account of the methods
employed for compilation, adjustment, and estimation of fisheries effort, catch, and CPUE data
for the marine fisheries of India.
2.2 Materials and methods
2.2.1 Effort reconstruction
Effort data were compiled from published sources, i.e., state reports, national reports,
publications of research institutes, and several other sources (Appendix A). These publications
and reports were collected during field trips to India in 2003, 2005, 2008, and 2009. During these
field trips, different institutes, fishing harbours, local fish markets, fish processing factories, and
libraries were also visited and discussions were conducted with local scientists and fishers
(Appendix B). These meeting were primarily conducted to better understand the dynamics of
fisheries at local level and clarify any fisheries or data related queries. As mentioned above, only
published data were compiled for further analysis.
52
The national research institutes of India, such as, CMFRI have been collecting fishing
effort data along with catches since the 1950s, but unlike catches, which are collected and
reported annually, effort data were only published periodically; also access to the official
database was restricted, and gray literature was often inaccessible. Also, whatever data were
available were not standardized in terms of units; rather, they were reported as ‘man-hours’, ‘unit
operations’, ‘fishing hours’, ‘hooks fished’, ‘boat days’, etc., and were highly scattered. To
compensate for this, all information was gathered and standardized (see below) from
miscellaneous publications and encoded with the key variables for vessels with and without
engines, i.e., total number of vessels; total power (in horsepower units); fishing days (days spent
at sea); crew size; vessel length or tonnage.
The category ‘vessels with engines’ was further subdivided based on engine type, i.e.,
with inboard engines (‘mechanized’ in the Indian literature) and with outboard engines
(‘motorized’), Deep sea vessels, also called ‘industrial’ vessels, were treated as a separate
category.
The mechanized vessels (with inboard engines) were further subcategorized based on
main gear types (excluding the Union Territories, due to lack of detailed information), which
varied with study areas based on the use of prominent gears. For example, trawlers and
gillnetters were found in all study areas, but some gears were area specific, for example,
‘dolnetters’ (using dol net, a multi-fisheries gear similar to a bag net) mainly confined to the
northwest coast of India but include West Bengal) from the northeast coast, and purse seiners
and ring seiners to the states of Kerala and Karnataka. In many states there was a category,
‘others’, which appears to include vessels using gears types other then the main ones (e.g., trawl
net, gill net, purse seine, dol net and hooks and lines), but no information was given on specifics.
53
Finally, there was not enough data to further subcategorize (e.g., based on gear types) the
vessels without engines. The data collected on all above-mentioned variables for all vessel
categories and subcategories suffered from various inconsistencies and information gaps
(outlined below). These were resolved using different methods and historical information
available in the literature, presented below for vessels with and without engine.
2.2.1.1 Vessels without engines
No information was available by state for the number of ‘vessels without engines’ for the
early 1950s. However, data were found for the whole of India, excluding UTs, for the year 1951
(GOI 1951). In order to divide this among states, the ratio of the first year for which separate
data were available (i.e., 1959; except for Gujarat and Goa, 1960) was used to split the data from
1950 to 1958. It was assumed that the state-specific contribution of this sector in fisheries
remained unchanged between 1950 and 1958. For 1958 onwards, the total effort for this
category was calculated by multiplying the total number of vessels, crew size, fishing days in a
year, and average daily energy output of a south Asian male, i.e., 0.18 hp (Karim 1985; Pauly et
al. 1987).
Crew size varied by different vessel type, e.g., dugout canoes and plank built boats, so an
average was calculated. However, the data on crew size were not available for the complete time
series. As the vessels without engines have not changed much over time; it was assumed that
average crew size had stayed the same over the years.
Similarly, in the case of missing data for fishing days, values were copied forward and
backward as a flat value time series, making the assumption that these had not undergone much
change over time. This assumption was supported in the literature as the focus of the Indian
Government had always been to improve and encourage the use of vessels with engines, and
54
minimal work was done to improve the traditional vessels (see state specific examples in results
section).
Union Territories also had no information on vessels without engines for earlier years;
Gupta et al. (1984e, f) confirms this absence of data. Although their contribution to the total
effort was miniscule, different methods were used for estimations. In the case of combined
statistics, e.g., Daman and Diu, which was part of Goa until 1987 (Rubinoff 1992), and
Puducherry, whose data for earlier years were added to Tamil Nadu, the ratio of the first year
with separate data was used to split the values assuming that the area-specific contribution of this
sector has remained unchanged during this time.
Also, demography and historical information were used to estimate effort. The Andaman
and Nicobar Islands, for example, had no information on the number of vessels without engines
before 1983. Menon (1977) mentioned that these islands had no tradition of fishing, but in a few
islands, aborigines and Nicobari tribes used bows and arrows and spears for fishing (Pillai and
Abdussamad 2008). Therefore, for the year 1950, it was assumed that the cumulative fishing
effort was equivalent to one (1) motorized vessel operating in these waters. Further, based on
historical information that three fisher families were brought to the Islands and settled in 1951
(Menon 1977), it was assumed that the number of vessels operating in 1951 was 3, with an
average crew size of 3 per vessel from 1950-1954. Thereafter, an average crew size of 7 was
used, based on two states, Andhra Pradesh and Tamil Nadu, from which a majority of fisher
families were brought under a re-settlement programs (Menon 1977).
Likewise, information was unavailable for the vessels without engines that operated in
the Lakshadweep Islands prior to 1987. First, it was established that out of 10 inhabited islands,
Minicoy Island was the only one where pole and line tuna fishing occurred (Jones and Kumaran
1959), while tuna fishing was not done around other islands (Varghese et al. 1993). There,
55
fishing activities consisted mainly of harpooning and cast netting, confined to lagoon and reef
areas, and mainly for daily consumption (GOL 1976; Varghese 1991). The population of
Minicoy Island for the years 1951 and 1961 were used for estimation purposes. The population
data were also available for year 1971 but were not used, as immigration to other islands become
prevalent in the 1960s (GOL 1976). It was reported that more than three-fourths of the
population depended on the tuna fishing industry (Jones and Kumaran 1959), but no details were
given on how many were involved actively in fishing. However, Jones (1958) gave details of the
professional and part-time fishers of Minicoy Island for the year 1958, which accounted for
approximately 24% of the total population. This ratio was used to estimate an active fisher
population for the years 1951 and 1961 and missing values were filled with interpolation, with
1950 estimated by backward extrapolation from 1951. Fishing days were estimated from the
main fishing season (September/October to April/May; approximately 8 months) along with the
assumption that fishing was done for 6 days per week. Down time was calculated based on the
information that in monsoon season, mas-odi vessels were beached for maintenance and sea
fishing was suspended with occasional fishing in lagoons (Jones and Kumaran 1959). As well as,
Muslim religious holidays falling in the main season (6 days) were deducted as the majority of
inhabitants were Muslims (Jones and Kumaran 1959). This estimated value of fishing days was
used as a flat value for the time series due to the absence of any other information.
2.2.1.2 Vessels with engines
Different agencies classify vessels differently, even within the same state, e.g., based on
gear type, vessel length, vessel material, or engine type. In Gujarat, for example, the vessels with
engines were categorized in different sources as FRP (fibre glass re-enforced plastic) or fibre
glass and wooden or plank-built boats, inboard and outboard motor boats, trawlers and
gillnetters, i.e., gear specific and general as ‘others’ with no gear specifications. Thus, wherever
56
possible, available information was used to allocate vessels into designated categories as
discussed before.
Ambiguity in reported statistics on different vessel types, e.g., vessels with engines were
usually categorized as ‘mechanized’ or ‘motorized’ in published sources. However, sometimes
number of motorized vessels were either included in the ‘traditional’ or in the ‘mechanized’
sector and sometimes both. Also, a number of vessels were reported under other categories such
as boats in operation and number of boats owned by fishers. Therefore, data were analyzed
thoroughly, and if a source of error was found and, in absence of other knowledge, data were
excluded; otherwise adjustments were made to avoid double counting.
There were discrepancies in the level of detail reported from year to year. Fishing gear
information, for example, was missing for some years, or reported as “others”, or as general as
“trawl”, or specific as “stern trawl”. In the case of as broad a category as “others,” whenever
enough information was available, it was allocated to gear specific categories. In absence of
information, they were allocated the lowest horsepower, but kept as a separate category (see
Appendix D).
Data were not available for every state and year, and sometimes multiple values were
given for a year in different sources. In case of several values, the mean was calculated, and for
missing data, values were estimated by interpolation and extrapolation from the main categories
(in Appendix D, these interpolated and extrapolated values are shown in bold). However, if the
total was given for a number of vessels, it was kept unaltered, unless enough information was
available to make the required change, e.g., one of the categories was not added in the total.
In case of gear-specific subcategories, data were limited especially for earlier years.
Historical information in literature was used to find introductory year of each gear type and thus,
create a timeline. In Karnataka, for example, details were available on the mechanization
57
program, which was started in 1957, but no information was given on gear type (Pattanayak
1988). As a timeline was created for all gear types, using the method of elimination trawlers
were found to be the first to be introduced (also see Table 2.1).
In case of Gujarat, to allocate a starting year to gillnetters, two pieces of information were
used: (1) Mathai et al. (2003) mentioned that in 1956 first vessels with inboard engines were
introduced in this state, though no details were given on which type; and (2) based on Johnson
(2002), in 1951, American Technical Cooperation Mission provided assistance to float a gillnet
boat with an inboard engine; it was assumed after cross tallying with introduction of other gear
types and catches, that gillnetters started operating in Gujarat in 1956.
In the state of Maharashtra, data for the 1950s were unavailable; the data for other early
years were rendered difficult to interpret by the fact that it was part of ‘Bombay State’ (including
Gujarat) until 1960. Thus, in the absence of information, the historical account of Gujarat was
considered to allocate starting years to specific gears (Table 2.1). Further, when no information
was available, catches were used to identify the year of introduction of a particular gear type. In
Goa, for example, the species composition of the gillnet fishery was analyzed using regional
references (Alagaraja et al. 1992). In 1968, the catch of these gillnet fishery was only 27 tonnes
and grew to 1,321 tonnes in 1969. As there was no increase in ‘vessels without engines’ for this
year; therefore, it was assumed that the mechanized gillnetters were introduced in year 1969 (see
Table 2.1 for rest of the states).
The data on number of vessels were readily available as compared to horsepower and
fishing days. To complete the first set of estimates for these two variables (horsepower and
fishing days), whenever data were absent, the information from the same zone or adjoining states
was used. Further, fishing days were also calculated by deducting downtime and religious
holidays in a year. In the Andaman and Nicobar Islands, for example, the majority of fishers,
58
85%, were Hindus (Bhargava et al. 2006), so all Hindu festivals (21 days) were considered, and
it was assumed that they work 6 days per week. Then accordingly, the number of days spent at
fishing was calculated. Similar computations were performed for fishing days in the case of the
Lakshadweep Islands, using their main fishing season and considering Muslim religious holidays
as a majority of inhabitants were Muslims (Jones and Kumaran 1959).
The effort exerted by deep sea vessels was estimated for two time periods. First, the
trawlers, which operated from 1951 (Taiyo Maru No. 17) to 1963 in the waters of Maharashtra
and Gujarat, then called Bombay and Saurashtra (Table 2.2).
These trawlers operated under a joint venture agreement between the Japanese Taiyo
Fishing Company9 and the Indian New India Fisheries Company. Although, Pusalkar and
Mammen (1985) reported that operations continued until 1969, but no data were available after
1963, and this is the only publication to mention operations beyond 1963. Therefore, data were
included only up to the year 1963. Reported effort was then divided between the state of Gujarat
and Maharashtra based on the proportion of their effort values to total effort.
The second group were the deep sea vessels which operated from 1972 to 2005. These
industrial vessels were mostly based at Vishakhapatnam, Andhra Pradesh (Devaraj 1995) and did
not report their landings regularly to the designated institutes. Even, CMFRI failed to obtain data
from them (Srinath, CMFRI, pers. comm. April, 2004). While they operated on the entire east
coast, many times their effort was reported under the state of Andhra Pradesh. Thus, the total
effort, which was calculated by collecting the above mentioned variables, was divided among the
states of Tamil Nadu, Andhra Pradesh, Orissa, West Bengal and Puducherry from 1972 to 2005.
9 Initially, Taiyo Fishing Company was invited to India for a consultancy for a Bombay firm and to provide their
technical opinion on vessels. The experimental trawling as suggested by them was considered successful. Then,
Taiyo Fishing Company applied for and received a special license for a year trial with trawler named Taiyo Maru
No. 17 in 1951 (Jayaram et al. 1959; Rao et al. 1966; Pusalkar and Mammen 1985).
59
Table 2.1 Details of information used to assign starting (introductory) year to gear-specific vessel categories, in absence of specific year being mentioned.
The lines marked in inverted commas are verbatim from references, which are mentioned under 'sources'. This assigned starting year was useful in effort
estimation per subcategory (also see Appendix D).
Area Vessel category Year Information (verbatim from sources) with remarks Sources
Gujarat Gillnetters 1956 "The mechanized boats with inboard engines were introduced in 1956 first at Veraval"; "In 1951 received assistance
from American TCM-Technical Cooperation Mission to float a 33 foot gillnet boat powered with 25 hp inboard
engine".
(Johnson 2002;
Mathai et al. 2003)
Gujarat Dolnetters 1974 "In early 1970s many entrepreneurs started venturing into mechanized sector employing mechanized trawlers, gill
netters and dol netters "
(Mathai et al.
2003)
Gujarat Others 1982 "Since 1982 boats or canoes are made of Fiber Glass Re-inforced Plastics (FRP) are also used".
FRPs were added to category 'others'. Horsepower and fishing days were used from vessels with outboard engines
(i.e., with lowest horsepower).
(Shiyani 2003)
Daman and Diu Trawlers 1961 Used same year as Goa. Goa, Daman and Diu were a union territory of India until 1987. (GOI 2004a)
Daman and Diu Purse seiners 1964 Used same year as Goa. Goa, Daman and Diu were a union territory of India until 1987. (GOI 2004a)
Daman and Diu Gillnetters 1969 Used same year as Goa. Goa, Daman and Diu were a union territory of India until 1987. (GOI 2004a)
Goa Trawlers 1961 "In 1961, there were only four trawlers"- so 1961 is assumed as starting year. (D’Cruz and
Raikar 2004)
Goa Gillnetters 1969 Catch data were used (details in Section 2.2.1).
Goa Vessels with
outboard engines
1980 Motorization of traditional vessels in India began as a program of the Sixth Five Year Plan (1980-1985; GOI 1985), so
assigned 1980 as a starting year.
(GOI 1980)
Maharashtra Gillnetters 1956 Assumed same as Gujarat; the state of Maharashtra and Gujarat were part of the Bombay state until 1960.
Pillai and Dharmaraja (1986) had reported that the level of mechanization in the two states was similar in case of
gillnetters and trawlers.
(Pillai and
Dharmaraja 1986)
Maharashtra Trawlers 1962 "Although trawling in Maharashtra started in early 1960". (Deshmukh et al.
2001)
Maharashtra Dolnetters 1972 First, 1972 was the first year when number of dolnetters was reported. Second, used information for the state of
Gujarat, i.e., “In early 1970s many entrepreneurs started venturing into mechanized sector employing mechanized
trawlers, gill netters and dol netters".
(Mathai et al.
2003)
Maharashtra Liners 1972 Used the first year for which number of liners was reported.
Maharashtra Purse seiners 1989 "Purse seiners introduced in late 1980s off Ratnagiri and Bombay coasts were of 11.5 to 13 m in length" (Pillai et al. 2000)
Karnataka Trawlers 1957 "Mechanization program were started by the state from 1957-58 only".
"Starting with 2 Pablo boats in 1958, the state had a tremendous growth in mechanized fishing".
All other vessel types were assigned with starting years, so assumed this information to be for trawlers.
(Jayaraj 1978;
Pattanayak 1988)
60
Area Vessel category Year Information (verbatim from sources) with remarks Sources
Karnataka Gillnetters 1980 "Exploitation of inshore columnar resources gained importance in eighties, since both inshore demersal and pelagic
resources are already exploited. Hence, a large number of mechanized and motorized boats were introduced for
gillnetting and long lining".
(Shanbhogue 1989;
Sudarsan 1993)
Karnataka Liners 1980 "Exploitation of inshore columnar resources gained importance in eighties, since both inshore demersal and pelagic
resources are already exploited. Hence, a large number of mechanized and motorized boats were introduced for
gillnetting and long lining".
(Shanbhogue 1989;
Sudarsan 1993)
Karnataka Vessels with
outboard engines
1981 "Exploitation of inshore columnar resources gained importance in eighties, since both inshore demersal and pelagic
resources are already exploited. Hence, a large number of mechanized and motorized boats were introduced for
gillnetting and long lining".
"In Karnataka the outboard motor type of mechanized crafts are rare".
(Gupta et al.
1984b;
Shanbhogue 1989;
Sudarsan 1993)
Kerala Trawlers 1956 "The design and performance of trawl system have progressed significantly since its introduction during 1950s"
"The mechanised boats were first introduced in this area (Sakthikulangara-Neendakara) in the mid fifties under the
auspices of the Indo Norwegian Project".
(Devaraj and Smita
1988; Sathiadas
and Venkataraman
1981)
Kerala Gillnetters 1969 "The mechanized drift gillnet fishery commenced in the inshore waters of Cochin in 1969".
Indo Norwegian project was stationed at Cochin, Kerala. So, assumed that gillnetters, which were introduced in
Cochin region represent Kerala.
(Silas et al. 1984)
Kerala Vessels with
outboard engines
1981 "The mechanization of traditional crafts with outboard motors in the early eighties can be considered as one of the
milestones in the development of artisanal fisheries of Kerala state. Even though this trend was initiated in central
Kerala by 1981, it spread to northern and southern areas by about 1983 only".
(Gopakumar et al.
1995)
Kerala Liners 1982 "The Colachal fisher started the operation of hooks and lines using mechanized boats with base at Cochin in 1982".
This information is region specific; however, liners were not reported in government statistics prior to 1982, so
assumed this to be the starting year.
(Mathew and
Venugopal 1990)
Lakshadweep
Islands
Vessels with
inboard engines
1959 "The first 9.14 metres long mechanized boats were introduced in the year 1959 at Kavaratti".
"Before 1959 there was no mechanized fishing boat available in Lakshadweep".
(Koya 2008;
Varghese 1991)
Lakshadweep
Islands
Vessels with
outboard engines
1965 "The mechanized vessels were introduced on Islands other than Minicoy, e.g., Agatti, Suheli Par and Bitra in early
sixties".
Based on above information and Varghese (1991), that even in 1962 "other islands fishing continued to be the
traditional harpooning for shark and bill fishes with practically no fishing for tuna" and "The small country crafts of
other islands were supplied with outboard motors at subsidized cost". It was assumed that vessels with outboard
engines were introduced in 1965.
(James et al. 1987;
Varghese 1991)
Tamil Nadu Gillnetters 1951 "In G.O. No. 419, Food and Agriculture (Food Production), dated 15th September 1951; the Government sanctioned
the construction of another set of 5 boats. 4 of these have launched and the last one is nearing completion pending
receipt of engine". Assumed these vessels to be Gillnetters.
(GOM 1953)
61
Area Vessel category Year Information (verbatim from sources) with remarks Sources
Tamil Nadu Trawlers 1955 "The history of fishing boat mechanization in Tamil Nadu dates back to 1954-55 when designing of a suitable
mechanized fishing craft was undertaken in collaboration with FAO. Since then gradual progress has been achieved in
the mechanized sector thereby increasing the number of trawlers".
(Pillai et al. 2000)
Tamil Nadu Liners 1990 "Mechanized trawlers also operate hooks and lines along the coral reefs ". Based on year of this publication, year was
assumed to be 1990.
(Jayasankar 1990)
Puducherry Vessels with
inboard engines
1961 "Under the scheme mechanised fishing boats of 30' and 32' size with synthetic fish net twine was supplied to
fishermen". Vessels with engines were reported to be 2 in year 1961. So assumed it as a starting year.
(Gupta et al.
1984c; Xavier
1991)
Puducherry Vessels with
outboard engines
1991 "It has been observed that only a small number of outboard engine fitted units was introduced in Pondicherry
recently". Based on year of publication, assumed it to be 1991.
(Pillai et al. 1994a)
Andhra Pradesh Trawlers 1954 "Mechanization of fishing crafts commenced in 1954-55 when the state government in collaboration with FAO
introduced the first mechanized boat".
"Mechanized fishing was introduced slowly in 1960, to start with, by the Government of India fishing trawlers and
then number of small mechanized boats came into operation".
(Pillai et al. 2000;
Rao and Lakshmi
1988)
Andhra Pradesh Vessels with
outboard engines
1955 "A small boat building unit was established at Kakinada in 1955 and 5 marine diesel engines obtained under T.C.M
aid was utilized for motorization of navas and they were put into operation in the same year".
(Rao 1986)
Andhra Pradesh Gillnetters 1972 "By the end of March, 1972, a total of 178 Pablo boats were constructed at the Boat building yard suitable for gillnet
operation and distributed to fishermen".
(Rao 1986)
Orissa Gillnetters 1957 "Government of Orissa introduced Mechanized vessel for the first time in the Orissa coast during 1957-1958". "They
were used for gill-netting in the open sea".
(Ali 1996)
Orissa Liners 1990 "Operation of this gear from motorized crafts was started in 1990s in Orissa". (Annam 1997)
West Bengal Gillnetters 1958 "The mechanized boats were introduced during the Second Plan Period in the State".
As all other types were introduced in later years so assumed it to be gillnetters and as only 2 vessels were reported to
be operating in 1958, so used it as a starting year.
(GOI 1971g)
West Bengal Trawlers 1990 "Trawling is never tried here by fishermen. It would be helpful if Government train up local fishermen in it and
encourage trawling". "In recent times, bottom trawling has been observed".
(Dan 1985; Srinath
et al. 2008)
West Bengal Liners 1994 "The introduction of master-hooks for hook & line shark fishery employing mechanized craft with the inboard engine
of 72 -120 Hp has been recently reported at Kakdweep"
(Pillai et al. 2000)
Andaman and
Nicobar Islands
Vessels with
inboard engines
1968 Based on first available data on vessels with engines, which was the year 1968 (5 in number) assigned the starting
year.
(CMFRI 1969)
Andaman and
Nicobar Islands
Vessels with
outboard engines
1981 "Motorization of traditional vessels in India began as a program of the Sixth Five Year Plan (1980-1985)". No data
were available for vessels with outboard engines for year 1980, so assigned 1981 as a starting year.
(GOI 1980)
62
Some of these industrial vessels were reported fishing for lobster as far as Kerala
(Sudarsan, 1992b). Deep sea lobster fishing started in 1988 due to declining shrimp catches in
the upper Bay of Bengal (Sivaprakasam, 1992) so the states of Kerala and Karnataka were also
included from 1987 to 2005. The estimated total effort was divided among all the states based on
the proportion of their value in India’s total catches (see Table 2.3; Section 2.2.2).
As mentioned above, total fishing effort was estimated in this study as the product of the
number of vessels, total engine power (or crew size for unmotorized vessels) and annual number
of fishing days at sea for each category summed over for all vessel types. The final time-series of
nominal fishing effort from 1950-2005 for all study areas, which was expressed in horsepower-
days for all vessel categories, was converted into kilowatt-days using a conversion factor of
0.7457 (Gelchu and Pauly 2007;Anticamara et al. 2011). Inclusion of engine power in fishing
effort calculations, other than being a standardized measure, has other advantages, i.e., it provide
a means to compare different fisheries in terms of fuel consumption and the amount of energy
consumed per kilogram of fish caught (Tyedmers et al., 2005; Gelchu and Pauly 2007 ).
Indian fisheries have gone through several waves of improvements regarding gear
material and designs, introduction and improvement of vessels with engines and increase in
information technology, use of electronic gadgets, such as fish detection devices and personnel
(skipper) training (Pillai and Katiha 2004b). As discussed in the introduction section, Pauly and
Palomares (2010) conducted a meta-analysis and estimated technological creep of around 2 to
4% per year. However, to account for the slower rate of adaption of technological changes in
India, a technological creep (factor) of only 1% was applied, which was a conservative annual
increase in efficiency. This final time series of effective fishing effort was used in further
analysis.
63
2.2.2 Catch reconstruction
CMFRI as well as the state fisheries departments monitor and estimate the annual fish
catches in India. Other federal institutes, such as MPEDA10
also publish catches by state
(MPEDA 2001). However, the sampling design and methods used for the collection of marine
fish catch statistics differ among states, UTs and CMFRI (CMFRI, 1985). State and UT reports
provide taxonomically highly aggregated landings statistics (e.g., only 26 groups in Gujarat
state), with the bulk of landings grouped under the ‘miscellaneous category’, i.e., little or no
information on species caught.
Moreover, no details are provided about the methodology used for arriving at these catch
figures. On the contrary, CMFRI, which collected data for all coastal states, describe in their
reports the multistage stratified random sampling design used to collect the information required
for estimation of marine fish landings with a stratification that is both temporal (days) and spatial
(zones)11
(Srinath, 2003). CMFRI statistics also have a ‘miscellaneous’ group, as in state reports,
but its contribution is quite low. Catches are also divided better taxonomically, i.e., into 65
subcategories.
The catches (weight in tonnes) were thus assembled for this study from 1994 to 2005 for
the maritime States of India and its UTs using CMFRI as a main source. Unreported landings
were updated and estimated from 1970 to 2005 using several sources (details below; See
Appendix D showing catch data for all study areas).
10
MPEDA: Marine Products Export Development Authority 11 Under this approach, all maritime states were divided into contiguous and compact ‘fishery zones’, where each
zone was comprised of 20 to 30 landing centers with similar landings levels (Silas, 1977; Algaraja, 1998). For
example, the states of Maharashtra and Gujarat were divided into 8 and 6 zones, respectively, taking into
consideration the topography and fishing intensity along the coasts (Kumari and Dharmaraja, 1981). In order to
ensure homogeneity among landing centres, a further stratification was applied, if required, within a zone, to reduce
sampling variance (Algaraja, 1998). Also, important landing centres such as major fisheries harbours were treated as
a single zone. In total, samples were collected from 2251 fish landing centres and the frequency of observations
were up to 18 days per month (Vivekanandan, 2003).
64
Table 2.2 Catch and effort of industrial trawlers, 1951-1963, which operated in the Bombay-Saurashtra waters (i.e., present day Maharashtra and Gujarat
waters). Fishing effort was calculated as product of number of vessels, horsepower and fishing days. Effort and catch for each state was estimated based on
proportion of their values in total effort and catch for 1951 to 1963. Values in bold represent interpolated and extrapolated data (Jayaraman et al. 1959;
Rao et al. 1966).
Year Total catch
(t)
Number of
trawlers
Trawler type Vessel power
(hp)
Fishing days Total fishing
effort (hp days)
Catches:
Maharashtra
(t)
Catches:
Gujarat (t)
Fishing
effort:
Maharashtra
(hp days)
Fishing
effort:
Gujarat
(hp days)
1951 977 1 Otter trawler 550 219 120,450 664 313 58,804 61,646
1952 913 1 Otter trawler 550 230 126,500 641 271 66,808 59,692
1953 962 1 Otter trawler 550 251 138,050 679 284 77,133 60,917
1954 1,410 2 450 251 225,900 996 414 128,426 97,474
1955 1,857 3 350 251 263,550 1,310 547 155,148 108,402
1956 2,304 4 Bull trawler 250 251 251,000 1,581 723 166,641 84,359
1957 3,281 4 Bull trawler 250 251 251,000 2,105 1,175 173,610 77,390
1958 3,581 4 Bull trawler 250 251 251,000 2,415 1,166 177,198 73,802
1959 2,720 4 Bull trawler 250 251 251,000 1,894 826 180,827 70,173
1960 4,248 4 Bull trawler 250 251 251,000 2,199 2,049 177,599 73,401
1961 3,939 4 Bull trawler 250 251 251,000 2,238 1,701 163,671 87,329
1962 3,550 4 Bull trawler 250 251 251,000 2,039 1,510 167,567 83,433
1963 2,682 4 Bull trawler 250 251 251,000 1,496 1,186 154,650 96,350
65
Table 2.3 Estimated total effort (hp days) and catches (t) of deep sea vessels, 1972-2005. Total fishing
effort was calculated as product of number of vessels, vessel power and fishing days. Total shrimp catch
was calculated as average shrimp catch per vessel times number of vessels and total fish catch was
calculated as shrimp catch times 9 (1:9 shrimp with head to fish ratio; see details in Section 2.2.2).
Interpolated and extrapolated values are represented in bold (Rao 1988; Gordon 1991; Sudarsan 1992;
Rao 1993; Devaraj 1995; Unnithan et al. 1995; Verghese 1996; Chennubhotla et al. 1999).
Year Number of
deep sea
vessels
Vessel power
(hp)
Fishing days Total fishing
effort (hp
days)
Average
shrimp catch
per vessel (t)
Total shrimp
catch (t)
Total fish
catch (t)
1972 2 373 168 125,160 28 56 503
1973 8 373 168 490,210 28 219 1,970
1974 14 373 168 855,260 28 382 3,438
1975 20 373 168 1,220,310 28 545 4,905
1976 25 373 168 1,585,360 28 708 6,372
1977 31 373 168 1,950,410 28 871 7,840
1978 37 373 168 2,315,460 28 1,034 9,307
1979 50 373 160 2,970,688 28 1,397 12,577
1980 50 373 230 4,283,750 28 1,397 12,577
1981 56 373 206 4,307,393 28 1,565 14,086
1982 58 373 210 4,497,938 48 2,761 24,848
1983 58 381 147 3,256,759 32 1,890 17,010
1984 61 390 129 3,084,113 50 3,067 27,599
1985 75 398 137 4,101,788 30 2,231 20,079
1986 98 407 113 4,496,053 33 3,261 29,350
1987 117 415 89 4,284,025 11 1,223 11,009
1988 136 415 93 5,209,539 8 1,094 9,849
1989 152 417 93 5,875,915 20 3,045 27,405
1990 168 420 93 6,529,805 19 3,230 29,073
1991 168 422 93 6,565,178 18 3,095 27,857
1992 151 424 93 5,938,251 11 1,589 14,300
1993 134 426 93 5,304,225 11 1,412 12,705
1994 117 429 93 4,663,100 11 1,234 11,110
1995 101 431 93 4,014,877 11 1,057 9,515
1996 84 433 93 3,359,555 11 880 7,920
1997 67 435 93 2,697,134 11 703 6,326
1998 50 438 93 2,027,615 11 526 4,731
1999 51 440 93 2,072,112 11 534 4,809
2000 52 440 93 2,106,081 11 543 4,888
2001 53 440 93 2,140,050 11 552 4,967
2002 53 440 93 2,174,019 11 561 5,046
2003 54 440 93 2,207,988 11 569 5,125
2004 55 440 93 2,241,957 11 578 5,204
2005 55 440 93 2,241,957 11 578 5,204
66
Catches were updated for the early 1950s in the case of Gujarat, Maharashtra, Orissa,
West Bengal, and Andaman and Nicobar Islands, using the reconstructed effort data and
historical information from varied sources.
Catches were aggregated into 28 broad taxonomic categories with further sub-divisions
into subgroups at family, genus and species level. In total, 65 statistical categories were used in
all analyses through a common template applied to all study areas, which roughly corresponds to
CMFRI’s published format for landing statistics. Similar data were compiled earlier by Bhathal
(2004) from 1994 to 2000; however, they were not used in this analysis because reported data
were based on sampling methods different from those used by CMFRI and the recent data clearly
deviated, in many cases, from the trends suggested by the earlier years.
The compiled data suffered from various imperfections, including: (1) different formats,
e.g., data were sometimes reported using common names, broad taxonomic groups, or at species
level; (2) missing values for some species or data from UTs which were more problematic than
those from states; (3) catches reported in the non-informative ‘miscellaneous’ group; (4) absence
of landing estimates for important fisheries, notably on catches of deep-sea fishing (industrial)
vessels, i.e., > 120 hp; and (5) absence of information on discards by all sectors, illegal fishing
and subsistence fisheries.
Thus, the available dataset was checked for inconsistencies and missing years were
interpolated12
and occasionally extrapolated13
, keeping the given total unaltered, using the
miscellaneous group as a ‘reservoir’ (details below). Various other adjustments were also made
based on information from the literature.
12
Interpolation is a class of methods for estimating a value or values between two known data points.
13
Extrapolation is a class of methods for estimating a value or values beyond the available data, i.e., for extending
the data.
67
The total catches for the year 2005 were by state with no information on species caught.
Therefore, the 2005 taxonomic disaggregation is based on that for the year 2004. The
‘miscellaneous group’ was not excluded as it represented a considerable amount of landings.
Rather, it was reduced to zero by following a two-step approach. In the first step, this group was
treated as a ‘reservoir’ from which all interpolated and extrapolated catches were taken out, and
to which the catches of a few erroneous taxa were added. In the second step, the remaining
miscellaneous landings at state level were assigned to specific fish, crustacean, and mollusc taxa
in proportion to their value in the total. This was done as George et al. (1981) reported that the
miscellaneous group mainly contains so-called “low value fish”, which are of smaller size and
low consumer preference. Several reports confirmed that the catches of the trawl fishery contains
considerable amount of juveniles and low-value fishes; see also see Section 1.8.5 in Chapter 1
(Sivasubramaniam 1990; Gordon 1991; Sujatha 1995; Menon and Pillai 1996; Sujatha 1996;
Puthra et al. 1998).
Total catch was available for the industrial trawlers, which operated from 1951 to 1963 in
the waters of Gujarat and Maharashtra. This catch was distributed between the two states based
on their catch proportion in total catches (see Table 2.2). Then, based on details given in Rao
(1966) about the fishes caught by these industrial trawlers, the catches were assigned to
designated subcategories using their proportion in the total.
In the case of industrial (deep-sea) vessels, mainly trawlers, introduced in 1972, the
unreported fish catches (both landings and discards) were estimated for India as a whole using
data on the number of vessels (also see Section 2.2.1), reported prawn catches (Rao 1988b;
Sudarsan 1992; Devaraj 1995; Verghese 1996), and heads-on shrimp to fish ratio (i.e., the
bycatch ratio) of 1:9 [see Table 2.3 and Section 1.8.2 for more details; (Gordon 1991)].
68
Fishes are a major non-target group or bycatch of these shrimp trawlers. Bycatch, in this
study, refers to landed bycatch plus discards (the latter being the bycatch which is thrown back to
the sea). It was assumed that, from 1972 to 2005, only 30% of the fishes caught by trawlers were
retained and 70% were discarded. Although discard estimates can vary among states, it has been
found that, overall, one-third of all bycatch was discarded, and other sources suggest that the
figure was closer to 20% (Chandrapal 2007). However, the estimate used in the current study
was a conservative measure as compared to other reports (Gordon 1991; Kungsuwan 1999;
Salgrama 1999; Ganapathiraju 2012). Similar to the effort data as detailed in Section 2.2.1, the
catches of the large vessels which generally operate on the east coast (Devaraj, 1995; Srinath,
CMFRI, pers. comm. April, 2004) were divided among the states of Tamil Nadu, Puducherry,
Andhra Pradesh, Orissa, and West Bengal from 1972 to 2005. This division of catches for
respective years was done using catch proportion of each state in India’s total landings. Deep sea
lobster fishing was initiated around 1988 (Sivaprakasam, 1992), thus, from 1987 to 2005, the
states of Kerala and Karnataka were also included, and likewise, catches were divided in
proportion to their value in India’s total catch.
Further to distribute catches to species, it was assumed that the taxa, which were
commercially valuable were landed ashore, while the remainder was thrown overboard as
reported by Sivasubramaniam (1990). Then, the price data were used to rank the demersal groups
from high- to low-value. Pomfrets, for example, were one of the highly valued groups, followed
by cephalopods, eels, big-jawed jumper, elasmobranchs and mullets. The retained bycatch (30%)
was assumed to consist of the highest priced species and were distributed among species on basis
of their proportion in total landings of each state. Once the retained bycatch was distributed, the
discards (70%) were distributed among the remaining demersal groups based on their proportion
69
in the total catch (also see Section 1.8.2). This procedure was performed independently for each
year.
Unreported discards by rest of industrial fleet (i.e., vessels using inboard engines of
above 50 hp) were quantified by assuming that 2% of India’s total marine landings were
discarded from 1970 onwards; this was a conservative measure. The discard figure was based on
the study by George et al. (1981) on bycatch of shrimp fisheries, in which he reported that in
1979 discards by mechanised vessels (excluding large trawlers) were very low (i.e., 2%) and
most of the bycatch was utilised. These estimated discards were then assigned to all states and
UTs based on their proportion in India’s total. The discards in each state were then, assigned
proportionally among all taxonomic groups (except for the ‘miscellaneous fish’).
Illegal fishing, here referred to foreign vessels poaching in India’s EEZ (including both
mainland and Islands), is reported for trawlers, followed by tuna long liners. These vessels were
reported to be from Taiwan, Thailand, Myanmar, Indonesia, Sri Lanka, Pakistan, Korea, Japan,
and Russia (Dan 1982; Rajan 2003; Ganapathiraju 2012). A recent study (Ganapathiraju 2012)
has estimated and published illegal catches per decade (1970-1980, 1980-1990, 2000-2009) for
India as a whole except the Andaman and Nicobar Islands. However, state-wise estimation was
done for only domestic illegal catches, i.e., Indian trawlers infringing the reserved artisanal zone
and violating regulations and did not consider unreported catches. Thus in absence of detailed
statistics, these estimates were not further considered in the present study.
Similarly information was missing on subsistence fisheries, which go unreported in
government statistics. However, Ganapathiraju (2012) has estimated subsistence catches for
major estuaries, backwaters, mangroves, and reefs for the year 2008. In the absence of any
quantifiable information prior to 2008, statistics on subsistence fisheries could not be used in this
present study.
70
Overall, the final assembled database for all study areas from 1970-2005 (1950-2005 for
some states) included reported landings (refined and completed), unreported landings and
discards, aggregated into 28 broad taxonomic categories with further subdivisions as discussed
above.
2.2.3 Estimation of catch per unit effort
Catch per unit effort, an index of relative abundance (also see the Introduction section)
was estimated per study area using the final time-series of catches (t) and effective fishing effort
(kilowatt days) from 1950-2005. Catches from 1950 to 1969 were used from Bhathal (2004),
except Gujarat, Maharashtra, Orissa, West Bengal, and the Andaman and Nicobar Islands as data
for these states were updated with new available information, mainly based on effort data. Both
catch and effort data for India were compiled for the component states and Union Territories to
better understand the underlying trends at a finer spatial scale and then summed up to get a
complete composite dataset for the country. Catch data were compiled at the species level and
effort data to gear level, but catches could not be assigned per gear type due to the absence of
information on gear-species associations.
2.3 Results and discussion
The following sections present results, i.e., temporal and spatial trends of catch, effort
and CPUE, along with interpretive comments starting with India and progressing geographically
from the northwest to the northeast states and Union Territories. The percentage contribution per
state and species were calculated as average for 56 years (1950-2005). The catch trends are
discussed only from 1970 to 2005 except for Gujarat, Maharashtra, Orissa, West Bengal, and
Andaman and Nicobar Islands where catches were revised from 1950s. In calculating and
depicting trends of CPUE, catches without tuna and bill fishes were used. The reconstructed
71
effort (nominal) data in Appendix C includes an extra data point for year 2005, this value was
not considered in the analysis as it seems to be an outlier in case of many states.
2.3.1 India
Reconstructed Indian marine catches (Figure 2.1) indicate a gradual increase from 0.6
million t in 1950 to 1.8 million t in 1988. This was followed by a sharp increase in the 1980s and
again in the 1990s, before a plateau was reached. For 1988, both oil sardine and Indian mackerel
reported high catches, resulting in a visible surge from 1988 to 1989. Oil sardine, Indian
mackerel, sciaenids, penaeid and non-penaeid prawns, and Bombay duck jointly contributed
about half of the overall catch, on average, over the 56 years.
The time series of reconstructed effort illustrates a continuous increase from 1950 (16.80
x 106 kW days) to 2005 (1051.597 x 10
6 kW days). Effort accelerated from the 1990s onwards
(Figure 2.2).
0
500
1000
1500
2000
2500
3000
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
(t
1
03)
Year
Figure 2.1 Total catch of India for all species, excluding tuna and billfish, 1950-2005.
Vessels without engines contributed on average 70% of the effort in the first two decades,
which decreased as new vessels with engines were introduced, these new vessels accounted for
72
89% on average in last three decades. Out of all subcategories of vessels with engines, trawlers
had the major contribution, followed by gillnetters and then vessels with outboard engines (using
different gear types; Figure 2.2). On average all three jointly contribute about 86%.
0
200
400
600
800
1000
1200
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
To
tal eff
ort (k
W d
ay
s
10
6)
Year
Other vessels
with engines
Bagnetters
Liners
Purse seiners
Trawlers
Gillnetters
Commercial trawlers
Vessels with
inboard engines
Vessels with
outboard engines
Vessels without
engines
Figure 2.2 Cumulative effective fishing effort by vessels of various types in India, 1950-2005.
Catch per unit effort displays a continuous decline from 1960 (28.99 kg/kW days) to
2005 (2.24 kg/kW days; Figure 2.3). The CPUE of India was calculated as the sum of the CPUE
values across all states and UTs over 56 years.
The continuous increase in effort and thus catches could only occur due to technological
improvements over time facilitating exploitation of resources. It was after India’s independence
in 1947 that fisheries were seen as a potential sector for development, meeting growing food
73
demands, and accomplishing self-reliance. As a result, in 1952, a tripartite technical co-operation
agreement was accorded between India, the USA, and the United Nations for fisheries
development. That same year a tripartite agreement was signed by Norway, India, and the United
Nations resulting in the Indo-Norwegian Project (INP), which was started in the state of Kerala.
(Sandven 1959; Sathiarajan 1987; Johnson 2002).
0
10
20
30
40
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
per u
nit
eff
ort (k
g/k
W d
ay
s)
Year
Figure 2.3 Trend of catch-per-unit-effort in India, 1950-2005.
Another agreement was signed between the government of India and FAO regarding
technical assistance in small craft mechanization/ motorization and technology (Pillai and Katiha
2004a). These projects and programmes started to concentrate on developing new designs and
prototypes for mechanized boats. Vessels with engines using gillnets were introduced in early
1950s followed by experimental trawling. Commercial trawlers started to operate as early as
1951 in the waters of Gujarat and Maharashtra; however, they ceased their operation in 1963 (see
Section 2.2.1 for more details). Various designs and sizes of mechanized fishing vessels were
introduced over time under the guidance of these integrated programmes. The development of
the shrimp industry and its export-oriented expansion in the mid-1960s led to an increase in
trawlers countrywide (Mukundan and Radhalakshmy 1998). Other methods of fishing such as
74
tuna lining, dol netting and purse seining, were also attempted by the FAO and INP, leading to
their addition in the existing fleet (Sadanandan et al. 1975; Verghese 1976; Dixitulu 2002).
Boat material and gear design also went through consequent improvements in the
fisheries sector, e.g., introduction of synthetic twine for making fish nets, addition of Fibreglass-
Reinforced Plastic (FRP) boats, and others (BOBP 1983; Sheshappa 1998; Thomas 2000).
Several other major technological transformations were witnessed in the Indian fisheries, all
resulting from successive Five Year Plans (see Table 1.1). In 1970s, open clashes between the
traditional and mechanized sectors led to government intervention and the motorization of
traditional vessels was seen as an important program of the Seventh Five Year Plan (GOI 1985a).
This had support of financing schemes operated through the co-operative sector. However,
efforts to motorize traditional vessels began earlier in some states.
Deep sea fishing also received continuous support and encouragement through
government programmes and industrial trawlers were introduced in year 1972. Over time,
various advanced designs, high power and large size vessels with engines were launched,
resulting in multiday fishing in majority of states after the1990s. Specialized and multipurpose
fishing vessels, such as trawler cum purse seiners, trawler cum gillnetters, trawling cum fish
carrying vessels, long-liners and trollers were also introduced (Sreekrishna and Shenoy 2001).
However, a new phase of stagnating and even declining fish catches became visible after the year
2000 (Figure 2.1).
2.3.2 Gujarat
The marine catches from Gujarat state constitute on average 14% of India’s total catch,
and Gujarat ranks fourth among India’s maritime states in its contribution. The reconstructed
marine catches (Figure 2.4), although fluctuating, show a gradual increase until the year 1985,
75
i.e., from 0.05 x 106 t in 1950 to 0.3 x 10
6 t in 1985. Thereafter, it shows a sharp increase until
2000, and then a decline (i.e., from 0.7 x 106 t in 2000 to 0.4 x 10
6 t in 2005).
0
150
300
450
600
750
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
(t
1
03)
Year
Figure 2.4 Total catch of Gujarat for all species, excluding tuna and billfish, 1950-2005.
The reconstructed effort of Gujarat for all sectors combined shows an increasing trend
from 0.5 x 106 kW days in 1950 to 232 x 10
6 kW days in 2005 (Figure 2.5). The effort
contribution of vessels without engines increased gradually from 0.5 x 106 kW days in 1950 to
2.7 x 106 kW days in 2005. Their relative contribution in total, however, decreased from 100% in
1950 to a 1% in the last decade. In the case of vessels with engines, effort increased from 0.05 x
106 kW days in 1951 to 39.94 x 10
6 kW days in 1985, then surged, reaching 229.51 x 10
6 kW
days in 2003. Out of all subcategories trawlers have made the greatest contribution since their
introduction (on average 53%), followed by gillnetters, 30% (Figure 2.5).
The time series of the CPUE (Figure 2.6) does not depict a clear trend in the initial years,
but from 1956 onwards, relative abundance shows a continuous decline, i.e., 155.57 kg/kW days
in 1956 to 1.81 in 2005 kg/kW days. This trend was also discussed in local publications.
Fernandez (2004), for example, emphasized that uncontrolled increase in effort had led to
reduction in CPUE, but did not provide any detailed analysis. However, Sehara (1998), who
76
demonstrated a declining CPUE of prawn trawler landings, found no increase in catches with
escalating effort.
0
50
100
150
200
250
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
To
tal eff
ort (k
W d
ay
s
10
6)
Year
Other vessels with
engines
Bagnetters
Trawlers
Gillnetters
Commercial
trawlers
Vessels with
outboard engines
Vessels without
engines
Figure 2.5 Cumulative effective fishing effort by vessels of various types in Gujarat, 1950-2005.
This continuous increase in catches until 2000 and effort until 2003 was due to the state
government’s focus on promoting mechanization of fishing vessels, supported by technical and
financial assistance, e.g., in 1951, the Grow More Food scheme and the American Technical
Cooperation Mission [United Nations Programme (GOG 1998)] and then, operation of industrial
trawlers under the New India Fisheries Company, established with Japanese assistance
(Jayaraman et al. 1959). This was followed by FAO assistance in mid 1950s (FAO 1958) and the
77
Columbo Plan in 1958 to develop the region’s gillnet and trawl fisheries (Johnson 2002). All the
boats were added with financial assistance from the state government.
0
40
80
120
160
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
per u
nit
eff
ort (k
g/k
W d
ay
s)
Year
Figure 2.6 Trend of catch-per-unit-effort in Gujarat, 1950-2005.
To speed the adoption of trawling, in 1968, the state government of Gujarat established
additional subsidies and loans for trawler boats to match those subsidies already in place
(Johnson 2002). In the 1970s and early 1980s, World Bank funding was in place to upgrade the
harbour facilities of several ports, including Veraval, Porbandar, and Mangrol in the state of
Gujarat (Johnson 2001). As a result many fisher entrepreneurs started venturing into the
mechanized sector, deploying mechanized trawlers, gill netters, and dol netters (Mathai et al.
2003). These changes led to promoted multiday fishing trips and a sharp increase in effort from
the 1980s, as illustrated by Figure 2.5.
Further, processing firms had rapid growth since the 1970s, which peaked in the early
1990s (Johnson 2001), and since the 1990s, state consumption has also increased (Fernandez
2004). The development of the shrimp industry over time and its export-oriented expansion,
particularly in the late 1980s (Johnson 2002) due to high profits and market demand effort was
spurred even further.
78
2.3.3 Daman and Diu
Daman and Diu contributes only 0.4% in total marine catches of India. Overall, the
catches increased steadily until 1990 (6.33 x 103 t), followed by a spike towards 2000 (16.12 x
103 t) and a flat trend thereafter, an artefact due to data extrapolation (Figure 2.7). A peak in
catches for the year 1998 was due to high catches of catfish (12 fold increase), other clupeoids (4
fold increase) and silver pomfret. Similarly, seer fishes saw a 3 fold increase when the catch for
year 1998 is compared to that of 1997.
0
5
10
15
20
25
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
(t
1
03)
Year
Figure 2.7 Total catch of Daman and Diu for all species, excluding tuna and billfish, 1950-2005.
The time series of the reconstructed effort initially shows a very slow increase, i.e.,
during 1950 (0.05 x 106 kW days) to 1965 (0.30 x 10
6 kW days), followed by a gradual progress
until 1977 (1.46 x 106 kW days); thereafter, effort increased considerably, reaching 7.76 x 10
6
kW days in 2005 (data for the last 5 years was extrapolated due to absence of information;
Figure 2.8).
Vessels without engines formed the mainstay of the fishery until the mid-1960s.
However, government programs to motorize the existing traditional vessels and introduce new
mechanized ones resulted in shift of effort toward vessels with engines. As a result effort
79
contribution of these increased reaching more than 98% in 1980s and vessels with inboard
engines contributed over 95% (Figure 2.8).
0
2
4
6
8
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
To
tal eff
ort (k
W d
ay
s
10
6)
Year
Purse seiners
Trawlers
Gillnetters
Vessels with
outboard engines
Vessels without
engines
Figure 2.8 Cumulative effective fishing effort by vessels of various types in Daman and Diu, 1950-2005.
Catch per unit effort i.e., (relative abundance) fluctuated in the initial years; however,
after the 1960s, there is a continuous decline, from 57.63 in 1960 to 2.08 kg/kW days in 2005
(Figure 2.9).
During the Portuguese period, fishing in Goa, Daman and Diu used traditional methods,
operations were limited to coastal areas (Balan et al. 1987) and there was no focus on expansion
(Verlekar 2008). The federal government promoted expansion of fisheries, including deep sea
80
fisheries through a rapid mechanization process supported with subsidies and loans. This resulted
in increasing catches and rapid surge in effort (Figure 2.7 and 2.8).
0
10
20
30
40
50
60
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
per u
nit
eff
ort (k
g/k
W d
ay
s)
Year
Figure 2.9 Trend of catch-per-unit-effort in Daman and Diu, 1950-2005.
A fisheries federation was established at Panaji (Goa) to encourage this process and thus,
increase catches (Gupta et al. 1984a; IDBI 1974a, b). Vessels with outboard engines were
introduced in the 1980s, which further increased the effort generated by the newly introduced
vessels with inboard engines.
2.3.4 Maharashtra
The state of Maharashtra ranks second among India’s maritime states and contributes
19% on average to the total marine fish catches of the country. The reconstructed catches (Figure
2.10) indicate a steady increase from 0.1 x 106 t in 1950 to 0.5 x 10
6 t and 2002, followed,
however, by a decline to 0.3 x 106 t by 2005.
Reconstructed effort of Maharashtra shows a tremendous increase in effort from 0.57 x
106 kW days in 1950 to 23.42 x 10
6 kW days in 1970, and reaching 106.57 x 10
6 kW days in
1992. Then, it hits a plateau with 111.59 x 106 kW days in 2005 (Figure 2.11). As in Gujarat,
81
vessels without engines accounted for major share in effort in the early years, which shifted to
vessels with engines contributing 97% to total effort in the last four decades. Out of all vessel
types with engines, trawlers made the largest contribution, followed by gillnetters and dolnetters
(Figure 2.11).
0
100
200
300
400
500
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
(t
1
03)
Year
Figure 2.10 Total catch of Maharashtra for all species, excluding tuna and billfish, 1950-2005.
Catch per unit effort fluctuated during early 1950s, but then show a continued decline
over time, i.e., 208.24 kg/kW days in 1953 to 11.09 kg/kW days in 1965 and then 2.55 kg/kW
days by 2005 (Figure 2.12).
The increase in effort and catches resulted from government’s initiative to promote
fisheries, which started as early as 1951, when the state government introduced a scheme to
mechanize/motorize fishing vessels. In order to lure attention and popularise the scheme, the
government provided subsidies and loans, which took care of almost 100% capital cost. A
interesting aspect of the mechanization programme initiated in Maharashtra was that traditional
vessels were modified for use with engines (Rao 1982). As a result, vessels with outboard
engines started operating as early as 1951 and industrial trawlers belonging to the ‘New India
Fisheries Company’ were added to the existing fleet the same year (Jayaraman et al. 1959).
82
0
20
40
60
80
100
120
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
To
tal eff
ort (k
W d
ay
s
10
6)
Year
Bagnetters
Liners
Purse seiners
Trawlers
Gillnetters
Commercial
trawlers
Vessels with
outboard engines
Vessels without
engines
Figure 2.11 Cumulative effective fishing effort by vessels of various types in Maharashtra, 1950-2005.
The introduction of vessels with inboard engines, i.e., gillnetters in 1956, trawlers in 1962
and longliners and dolnetters 1972 led to a considerable increase in effort (Figure 2.11) and
catches (Figure 2.10). This rapid increase of effort was due to government subsides, e.g., the
50% subsidy and 50% loan for the cost of an engine in the mid-1960s (GOMH 1964). If for the
motorization/mechanization of vessels locally manufactured engines were used, then the subsidy
amount given was much higher, and loan repayments were extended from 7 to 15 years (GOMH
1964).
In the late 1980s, purse seiners were introduced off the Ratnagiri and Bombay coasts of
Maharashtra (Pillai et al. 2000), adding further to the existing effort. However, catches did not
83
increase as expected given the state of the resource base, which was reported to be over-
exploited, with decreasing abundance and even extirpation of some species. Sand lobster (Thenus
orientalis), for example, was reported to be depleted in waters of Maharashtra within less than
two decades of exploitation, from 1978 to 1994 (Deshmukh 2001).
0
50
100
150
200
250
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
per u
nit
eff
ort (k
g/k
W d
ay
s)
Year
Figure 2.12 Trend of catch-per-unit-effort in Maharashtra from 1950-2005.
However, the state government then formulated a scheme to add larger vessels to the
fleet, which could undertake fishing trips 15 to 20 days (rather than currently 4 to 10 days) and
fish at the depth of 70 fathoms, but this scheme has not received much funding from the central
government (Shah 2007).
2.3.5 Goa
Goa contributes 2% in total marine catches of India. Reconstructed catches (Figure 2.13)
show a steady increase until mid-1980s, followed by a sharp increase. However, after 1993, a
decline in catches was observed from 0.12 x 106 t in 1993 to 0.08 x 10
6 t in 2005. The catches
exhibit strong fluctuations, due to the highly variable Indian mackerel, oil sardine, and other
clupeoids catches which contribute significantly to total catches (Longhurst and Pauly 1987).
84
0
30
60
90
120
150
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
(t
1
03)
Year
Figure 2.13 Total catch of Goa for all species, excluding tuna and billfish, 1950-2005.
Reconstructed fishing effort in Goa increased slowly from 0.3 x 106 kW days in 1950 to
0.6 x 106 kW days in 1968; it then increased at a faster rate in the late 1970s and reached 14.40 x
106 kW days in 1997. Thereafter, it levelled off and reached 15.14 x 10
6 kW days in 2005
(Figure 2.14). The effort by vessels without engines did not increase much over time; instead
their contribution to total declined to 3% in the last decade. Vessels with engines make a major
contribution to the total effort, with trawlers at about 71% on average, followed by purse seiners
at about 22% on average (Figure 2.14).
Catch per unit effort fluctuated for the first 25 years, but overall shows a declining trend
(Figure 2.15).
Goa became part of India in 1961. Subsequent to incorporation into India, Goa received
incentives to mechanize its vessels. During the Portuguese period, the fisheries in Goa were left
in the hands of traditional fishers, except that some trawlers were introduced in 1960. The
Portuguese administration in Goa also attempted purse seining in 1957, but was unsuccessful
(Verghese 1976).
85
0
4
8
12
16
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
To
tal eff
ort (k
W d
ay
s
10
6)
Year
Purse seiners
Trawlers
Gillnetters
Vessels with
outboard engines
Vessels without
engines
Figure 2.14 Cumulative effective fishing effort by vessels of various types in Goa, 1950-2005.
The department of fisheries of the government of Goa was established in 1963 (D’Cruz
and Raikar 2004) and introduced several schemes to develop the infrastructure, construct new
boats, and provide financial assistance to motorize existing traditional vessels, especially for the
ramponkars, a traditional fishing community using very large beach seines, called ‘rampons’
(D’Cruz and Raikar 2004). In 1964, purse seining was successfully introduced (Panikkar et al.
1994), which was followed by the introduction of gillnetters in 1969. These development phases
were reflected in slowly increasing catches and effort until the late 1960s. Introduction of new
vessels with outboard engines in the 1980s and the motorization of existing traditional vessels
further added to existing effort (Figure 2.14). The addition of vessels reduced its pace by the
86
mid-1990s due to declining catches (Figure 2.13). There was then a huge concern over declining
catches, and in response to pressure from environmental and traditional fishing groups, in 1999,
the Goa government imposed a ‘monsoon ban’ (Sonak et al. 2006).
0
10
20
30
40
50
60
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
per u
nit
eff
ort (k
g/k
W d
ay
s)
Year
Figure 2.15 Trend of catch-per-unit-effort in Goa from 1950-2005.
Goa was always perceived as a good location for the deep sea fishing industry (IDBI
1974b), and thus, in the regional plan for Goa, 2001, further development of deep sea fishing
with medium-sized mechanized vessels was still suggested (D’Cruz and Raikar 2004). However,
hypoxic conditions develop in the deep waters off the Goa coast, and generally along the west
coast of India, which have an adverse effect on the demersal fisheries resources (Banse 1968;
Ansari et al. 2006). Thus, the addition of vessels did not increase catches, as shown in Figure
2.13.
2.3.6 Karnataka
Karnataka contributes 8% on average to India’s total catch. The reconstructed catches
show huge fluctuations, mainly due to Oil sardine and Indian mackerel, which form the bulk of
the catches. Overall, catches doubled from 0.1 x 106 t in 1970 to 0.2 x 10
6 t in 2005 (Figure
2.16).
87
0
50
100
150
200
250
300
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
(t
1
03)
Year
Figure 2.16 Total catch of Karnataka for all species, excluding tuna and billfish, 1950-2005.
Reconstructed effort shows a gradual increase in effort until 1965, i.e., from 1.68 x 106
kW days in 1950 to 3.67 x 106 kW days in 1965, followed by two waves of rapid increase.
Figure 2.17 shows that vessels without engines made a considerable contribution to total effort
until 1965, but were then supplanted by vessels with engines. Out of all subcategories, trawlers
accounted for the majority of the effort in this state, followed by gillnetters and purse seiners
(Figure 2.17).
The catch per unit effort time series fluctuates sharply for first two decades and then
declines from 1970, i.e., 11.94 kg/kW days to 2.65 kg/k W days in 2005 (Figure 2.18).
The continuous increase in effort and catches, as shown in Figures 2.16 and 2.17 over
time could occur due to support made available through government programmes and schemes,
financial institutions, and the interest of private parties. Mechanization programmes were
initiated soon after the state was reorganized in 1956 (GOI 1971c), and trawlers were introduced
in 1957 in the Dakshinda Kannada district of Karnataka (Sudarsan 1993). The same year saw the
first demonstration of mechanized fishing by FAO Expert Mr. Illugason (Gupta et al. 1984b).
88
Vessels with engines were introduced to fishers on a loan-cum-subsidy basis (Dhulkhed and
Bhatt 1985).
0
20
40
60
80
100
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
To
tal eff
ort (k
W d
ay
s
10
6)
Year
Other vessels
with engines
Liners
Purse seiners
Trawlers
Gillnetters
Commercial
trawlers
Vessels with
outboard engines
Vessels without
engines
Figure 2.17 Cumulative effective fishing effort by vessels of various types in Karnataka, 1950-2005.
Then, in the 1960s, with foreign collaboration, two new projects promoting
mechanization were implemented in the state, i.e., extension of the Indo-Norwegian Project to
Karwar in 1963 and the setting up of a marine products processing centre with Japanese
assistance at Mangalore in 1967 (GOI 1971c). To further enhance these efforts, a scheme for the
construction and distribution of trawlers was introduced in 1966 through the Dakshina Kannada
District Cooperative Fish Marketing Federation of Karnataka (Bhatta et al. 2003).
89
0
15
30
45
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
per u
nit
eff
ort (k
g/k
W d
ay
s)
Year
Figure 2.18 Trend of catch-per-unit-effort in Karnataka from 1950-2005.
More subsidies were introduced in the mid-1970s but restricted to boats of 32 feet and
above. Then, in 1975, purse seiners were introduced – again with government support – to target
pelagic fish resources, mainly mackerel and oil sardine (Jayaraj 1978). Attracted by the returns
accruing to these purse seiners, private vessels also joined the fleet (Dhulkhed and Bhatt 1985).
However, with the introduction of purse seiners, the traditional sector using rampani
(shore seine) saw their catch declining rapidly (Jacob et al. 1979; Muthiah 1982). Although the
area of operation was different for these two gears, one operating offshore and the other being
shore-based, but they exploited the same resources, and thus the purse seiners had an impact on
the traditional sector (Jacob et al. 1979). As a result of the growing conflict between the two
sectors, the government of Karnataka in the late 1970s provided rampani groups with more
subsidies, and a lower interest rate to obtain loans to purchase purse seiners. Even financial
agencies were instructed to be preferential towards rampani operating fishers (Jacob et al. 1979).
Overall, the increase in catches and effort during the late 1970s was mainly due to enlarged fleet
strength of trawlers and to the introduction of purse seiners.
90
In the 1980s unexploited mid-water resources, as opposed demersal or near-surface
pelagic resources were seen as another avenue for expansion. As a result, vessels with outboard
engines were introduced along with gillnetters and long liners (Sudarsan 1993) in 1980 and
1981, which further added to the existing effort in total. A change occurred in trawling patterns
in the early 1980s, when trawling acquired greater significance due to the increasing demand for
prawns in the export industry. A multiday trawl fishery was introduced, which expanded the
trawling grounds farther offshore and into deep waters, which resulted in increased catches of
highly-priced large prawns (mainly Metapenaeus monoceros, Penaeus indicus, and P. monodon),
squids, and finfishes (Zacharia et al. 1996). These multiday trawlers become popular from mid-
1990s (Zacharia et al. 1996).
2.3.7 Kerala
Kerala occupies the foremost position in India’s total marine catches by contributing 28%
on average over the span of 55 years. Reconstructed catches show fluctuations corresponding to
erratic catches of Indian mackerel and oil sardine (Longhurst and Pauly 1987) as in the state of
Karnataka. Overall, there is a gradual increase in catches from 0.4 x 106 t in 1970 to 0.7 x 10
6 t in
1990; thereafter, catches reach a plateau staying at 0.5 to 0.6 x 106 t (Figure 2.19). Indian oil
sardine and Indian mackerel alone contributed 39% to the total catches, as estimated based on an
average of 56 years. Note the rapid catch increase in the late 1980s, which happens to correspond
with the introduction of ring seine (discussed below), which mainly target these two species in
Kerala (Balan and Sathianandan 2007).
Reconstructed effort increased steadily from year 1950 (2.71 x 106 kW days) to 1990
(77.45 x 106
kW days), thereafter, sharply increased and reached at an effort of 236.48 x 106 kW
days in 2000 and stayed stable for rest of the years with effort increase of 3% in 2005 (243.45 x
106 kW days; Figure 2.20). Vessels without engines made a major contribution to the effort in
91
the early-1960s. This changed thereafter to the extent that effort exerted by vessels with engines,
mainly trawlers, accounted for over 90% on average for the last two decades (Figure 2.20).
0
200
400
600
800
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
(t
1
03)
Year
Figure 2.19 Total catch of Kerala for all species, excluding tuna and billfish, 1950-2005.
Catch per unit effort fluctuated during the first 15 years, but declined steadily from about
48.71 kg/kW days in the 1961 to 2.20 kg/kW days in 2005 (Figure 2.21).
As in the case of other states, vessels with engines using different gear types were
introduced over time, resulting in high cumulative effort. Under the patronage of the Indo-
Norwegian Project (INP), a mechanization programme was initiated in 1954 (GOI 1971d), and
commercial trawling was first introduced at Sakthikulangara-Neendakara in the Quilon Coast of
the state in 1956 (Sathiadas and Venkataraman 1981; Silas et al. 1984). This was followed by the
start of an inshore drift gill net fishery in 1969 (Silas et al. 1984).
As commercial trawling gained momentum and new vessel types joined the fleet, the
need for improved harbor facilities became acute. Thus, in 1978 through a Grant-in-Aid Project
of Ministry of Food and Agriculture, the Cochin Port Trust (CoPT) constructed the Cochin
Fisheries Harbour (Silas et al. 1984). This development was considered a boon for
mechanization programme and individual entrepreneurs started to invest in fishing, mainly
92
trawling for prawns in coastal areas (Jacob et al. 1987). New technological developments
enabled the offshore expansion of trawlers.
0
50
100
150
200
250
300
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
To
tal eff
ort (k
W d
ay
s
10
6)
Year
Other vessels
with engines
Liners
Purse seiners
Trawlers
Gillnetters
Commercial
trawlers
Vessels with
outboard engines
Vessels without
engines
Figure 2.20 Cumulative effective fishing effort by vessels of various types in Kerala, 1950-2005.
In 1979 commercial purse seining was initiated in the inshore waters followed by the
upgrading of traditional boats by adding outboard engines (Jacob et al. 1982; James et al. 1991).
The INP had tried to motorize traditional vessels in 1953 using outboard motors, but it failed. In
1969 the Indo-Belgian project tried the kerosene outboard motors with no success. Another
attempt was made in 1975 by the Marianad Community Development Project in the Trivandrum
district of Kerala with no success (Vivekanandan 1993). However, the motorization programme
picked up in 1980s, and it largely resulted in replacing the vessels without engines (Jacob et al.
93
1982; Gopakumar et al. 1995). As in the 1970s, this came throughout the 1980s at the cost of
severe conflicts between the operators of vessels with and without engines.
0
20
40
60
80
100
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
per u
nit
eff
ort (k
g/k
W d
ay
s)
Year
Figure 2.21 Trend of catch-per-unit-effort in Kerala from 1950-2005.
Vessels with engines also started fishing using hooks and lines in 1982 (Mathew and
Venugopal 1990) and the mini-purse seine, called the ring seine, was introduced in 1986. This
gear become very popular among the traditional fishers using vessels with outboard engines
(Edwin and Hridayanathan 1997; Ammini 1999). To increase their catches, fishers modified or
purchased mechanized boats by using or borrowing money from different agencies, which
included government subsidies, cooperative societies, banks, private money lenders, and loans
advanced from the Agricultural Refinance Development Corporation [ARDC; (Panikkar and
Alagaraja 1981)]. Fisheries export-orientated focus and steady government support and
encouragement led to rapid addition of improved vessels with engines and gear types over time
and thus, effort increased rapidly from late 1980s (Figure 2.20).
2.3.8 Lakshadweep Islands
The Lakshadweep Islands contribute only 0.2% on average to India’s total marine catch.
Their reconstructed catches in general indicate an increase from 1970 to 2000, with the flat trend
94
thereafter being an artifact due to data extrapolation (Figure 2.22). The peak in catches for the
year 1998 was due to high catches of tunas and billfishes, which are the major target groups,
contributing on average 73% over the period of 56 years.
0
1
2
3
4
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
(t
1
03)
Year
Figure 2.22 Total catch of Lakshadweep Islands for all species, excludes tuna & billfish, 1950-2005.
The time series of reconstructed effort shows a continuous increase from 0.02 x 106 kW
days in 1950 to 0.33 x 106 kW days in 1970 reaching 2.92 x 10
6 kW days in 2005 (Figure 2.23).
A shift in major effort contribution from vessels without engines to vessels with engines is
evident. Vessels with inboard engines had a major share (73% since their introduction) of vessels
with engines (Figure 2.23).
Catch per unit effort increased until the 1970s, as fishers learned to deploy the new
technology available to them. Then, predictably, it shows a slow decline, from 1.95 kg/kW days
in 1970 to 1.02 kg/kW days in 2005 (Figure 2.24).
The Lakshadweep Islands were declared a UT in 1956. This declaration was followed by
establishment of fisheries department in 1959 at Kavaratti and an appointment of fisheries officer
from the state of Kerala (Jones 1958; GOL 1976;). Until the 1960s, fishing was done from
95
traditional boats, mainly for sharks and seer fish. Pole and line tuna fishing was unknown in
many islands, except Minicoy, where row boats known as ‘mas odies’ were used by the tuna
pole and line fishery (Raghavan and Shanmughnam 1993).
0.0
0.5
1.0
1.5
2.0
2.5
3.0
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
To
tal eff
ort (k
W d
ay
s
10
6)
Year
Vessels with
inboard engines
Vessels with
outboard engines
Vessels without
engines
Figure 2.23 Cumulative effective fishing effort by vessels of various types in Lakshadweep Islands, 1950-
2005.
Government initiated motorization as early as 1958, but the existing boats at Minicoy
were found unsuitable for motorization; thus, the first 9.14 metres boat with an inboard engine
was procured from mainland and introduced at Kavaratti in 1959 (James et al. 1987). There was
tremendous opposition on the use of boats with engines. However, a successful demonstration of
pole and line fishing in year 1962 paved the way for the introduction of more vessels in other
islands including Agatti and Minicoy (GoL 1976).
96
0
1
2
3
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
per u
nit
eff
ort (k
g/k
W d
ay
s)
Year
Figure 2.24 Trend of catch-per-unit-effort in Lakshadweep Islands from 1950-2005.
The islanders were encouraged to shift to new way of fishing by a new financial scheme
for the “issue of mechanized boats to fishermen” (Varghese 1991), wherein vessels were
provided with a subsidy of 100% on engines and 25% on the cost of the hull. Monthly loan
repayments were spread out over a period of 9 nine years. Local fishers were also given training
in tuna fishing by government supported experts.
Also, to meet with the growing demands of boats, a boat building yard was established in
Kavaratti in 1963 (Varghese 1991). Further, small traditional vessels were fitted with outboard
engines provided through the Fisheries Department and an Integrated Rural Development
Programme (IRDP) at a subsidized cost. Finally, in 1969, a tuna canning plant was established
by the government at Minicoy (GOL 1976; Varghese 1991). By 1970, the mechanization
programme had taken off. This is reflected in increasing effort and corresponding catches
starting from mid 1960s. Over time, the department introduced new and improved vessels to the
main islands as well as spread out the mechanization process to new islands. In 1984, for
example, a multipurpose low-cost boat was introduced for pole and line fishing (Varghese 1991).
97
However, it was realized in the 1990s that the fishery needed sustained attention
regarding further diversification in terms of fishing methods and resources. Thus, the
government introduced other low-cost fishing methods, e.g., troll lines, long line and gill nets in
many islands, including Andrott, Amini, and Kiltan, where no scope was seen for pole and line
fishing (Varghese 1991).
The rapid increase in catches and effort after the 1990s was due to new boats, based on
more islands entering the fisheries, though this expansion was limited by an overall shortage of
skilled fishers in the Lakshadweep Islands (Raghavan and Shanmughnam 1993). Even at present,
the islanders are assisted when acquiring low-cost boats, which they prefer for functional and
financial reasons (Koya 2008).
2.3.9 Tamil Nadu
Among the various maritime states of India, Tamil Nadu ranks third with a 56 year
average of 15% to the marine catches. The reconstructed catches show a gradual increase until
1997, reaching 0.5 x 106 t; thereafter they started to level off at around 0.4 x 10
6 t (Figure 2.25).
0
100
200
300
400
500
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
(t
1
03)
Year
Figure 2.25 Total catch of Tamil Nadu for all species, excluding tuna and billfish, 1950-2005.
98
Fishing effort increased steadily from 6.33 x 106 kW days in 1950 to 59.51 x 10
6 kW
days in 1990, which was followed by a surge in effort, and in recent years, a stabilization phase,
with an effort of 151.6 x 106 kW days in 2005 (Figure 2.26). Vessels without engines had a
major contribution in first two decades (89% on average). This reversed thereafter, with the
effort contribution of vessels with engines rising to 91% on average for the last 2 decades. Out of
all subcategories, trawlers accounted for most of the effort (Figure 2.26).
There was a continuous and marked reduction in catch per unit effort from 1950 (13.26
kg/kW days) to 2005 (1.85 kg/kW days), with larger fluctuations until late 1960s (Figure 2.27).
0
20
40
60
80
100
120
140
160
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
To
tal eff
ort (k
W d
ay
s
10
6)
Year
Other vessels
with engines
Liners
Trawlers
Gillnetters
Commercial
trawlers
Vessels with
outboard engines
Vessels without
engines
Figure 2.26 Cumulative effective fishing effort by vessels of various types in Tamil Nadu, 1950-2005.
99
The state’s mechanization program was started in 1951, in collaboration with FAO and
INP (IIM 1984b; Pillai et al. 2000), enabling mechanized vessels to be used for the operation of
gill nets. Trawling was still experimental in the mid-1950s, and its growth began in the mid-
1960s (Gupta et al. 1984d); thereafter, trawling expanded statewide and in the 1970s, it became
the dominant mode of exploitation (Rao and Pillai 1992). Under the Indo-Belgium Fisheries
Project, in 1970, an attempt was made to fit traditional vessels with outboard engines in the
Kanyakumari district of Tamil Nadu (Subramani 1998), but such vessels were widely introduced
only in 1979 (Bennet and Arumugam 1993). Overall, the motorization of traditional vessels
occurred at a slower rate than in other states and gained momentum only by 1992 (Bennet and
Arumugam 1993; Subramani 1998; Pillai et al. 2000).
0
4
8
12
16
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
per u
nit
eff
ort (k
g/k
W d
ay
s)
Year
Figure 2.27 Trend of catch-per-unit-effort in Tamil Nadu from 1950-2005.
In the early 1980s, pair trawling was also introduced in the Rameswaram and Mandapam
areas of Tamil Nadu under the Bay of Bengal Programme (BOBP) (Subramani 1987). Large
trawlers (above 42 feet OAL) were added all along the east coast in the 1990s (Jayasankar 1990;
Devaraj et al. 1996). Further, the government of Tamil Nadu distributed nearly 400 outboard
100
engines during 1991 to accelerate the motorization process (Pillai et al. 1994b). All these added
to a rapid increase of effort.
Then in 2000, due to dwindling catches, many shrimp trawlers were modified for drift net
fishing, and began to target the tuna resources (Balasubramaniam 2000). The tsunami had a
major impact on this state in 2004; however, relief initiatives replaced the damaged and lost
boats very rapidly, and with newer and better equipped vessels (Pillai and Thirumilu 2005;
UNDP 2007; Bavinck 2008), which further added to the existing fishing effort.
2.3.10 Puducherry
Puducherry (formerly ‘Pondicherry’) contributes about 1% to India’s total marine catch.
Its reconstructed catches increased from the 1950s (6.84 x 103 t) to 21.19 x 10
3 t in 1997, then
levelled off (Figure 2.28). High fluctuations in the last two decades were mainly due to varying
catches of oil sardine, other sardines and Indian mackerel, which are boom-and-bust fishes
Longhurst and Pauly 1987).
0
5
10
15
20
25
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
(t
1
03)
Year
Figure 2.28 Total catch of Puducherry for all species, excluding tuna and billfish, 1950-2005.
101
The time series of reconstructed effort shows a rapid increase from 0.30 x 106 kW days in
1950 to 6.88 x 106 kW days in 1994 and then a slower increase, reaching 7.55 x 10
6 kW days in
2005 (Figure 2.29). Until the 1960s only vessels without engines were operating in the inshore
waters of Puducherry; however, with the introduction of vessels with engines, the major effort
contribution shifted in the last three decades to a situation where vessels with inboard engines
became dominant (Figure 2.29).
0
2
4
6
8
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
To
tal eff
ort (k
W d
ay
s
10
6)
Year
Commercial
trawlers
Vessels with
inboard engines
Vessels with
outboard engines
Vessels without
engines
Figure 2.29 Cumulative effective fishing effort by vessels of various types in Puducherry, 1950-2005.
Similar to the state of Tamil Nadu, Puducherry exhibits a trend of continuous decline in
catch per unit effort, from 23 kg/kW days in 1950 to 1.43 kg/kW days in 2005, with wider
fluctuations in first two decades (Figure 2.30).
102
0
5
10
15
20
25
30
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
per u
nit
eff
ort (k
g/k
W d
ay
s)
Year
Figure 2.30 Trend of catch-per-unit-effort in Puducherry from 1950-2005.
After the transfer of sovereignty from France (the erstwhile colonial master) to India in
1954, a Department of Fisheries was formed in 1955 (GOP 1965) and vessels with inboard
engines were introduced in 1961 (Gupta et al. 1984c). These vessels were given to fishers on a
loan-cum-subsidy basis. The initial subsidy was about 33% of cost of the vessel and fishing
equipment, and loan repayments were spread over a period of 6 years (Xavier 1991). Fibreglass-
reinforced plastic boats were also provided under a loan-cum-subsidy basis. However, even in
late 1970s, no detailed statistics were reported by gear type, but the majority of vessels were
reported to be trawlers and of 30 to 32 feet in length (Gupta et al. 1984c). In the late 1980s, a
scheme called “Introduction of Beach Landing Boats” was initiated to encourage the acquisition
of vessels designed by BOBP and FAO consultants (Xavier 1991). Then, in 1991, vessels fitted
with outboard engines, of 7 to 8 m in length, and mainly operating gill nets were introduced
(Dharmaraja et al. 1987). This addition occurred again only due to government assistance
channeled through a scheme, initiated in 1984, called “assistance to small scale fishermen”. This
scheme was introduced for assisting traditional fishers to purchase new vessels or outboard
engines. Under this scheme the government provided financial assistance for new vessels as 40%
103
loan, 40% subsidy, and 20% beneficiary’s contribution. To motorize the vessels, a 25% subsidy
was given. Along with this, a 20% subsidy was given through the Fishermen Co-operative
Federation and Fishermen Co-operative Marketing Union on required supplies, such as salt
(Xavier 1991). The result of these subsidy schemes on effort is illustrated in Figure 2.29
2.3.11 Andhra Pradesh
Andhra Pradesh contributes 8% to India’s total marine catch. Its reconstructed catches
fluctuate sharply, though generally increasing over the period of 55 years and peaking at 0.24 x
106 t in 1999 (Figure 2.31). Sardines, anchovies, penaeid prawns, sciaenids and ribbon fishes
contributed about 40% of total catches.
0
50
100
150
200
250
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
(t
1
03)
Year
Figure 2.31 Total catch of Andhra Pradesh for all species, excluding tuna and billfish, 1950-2005.
Andhra Pradesh’s reconstructed effort shows a steady increase from 3.82 x 106 kW days
in 1950 to 25.14 x 106 kW days in 1987, followed by a sharp increase reaching 65.81 x 10
6 kW
days in 1991 and thereafter, another surge leading to 136 x 106 kW days of effort in 1999 (Figure
2.32). Vessels without engines accounted for 81% of effort for the first two decades, but this
reversed itself in the last two decades when vessels with engines accounted for 81%. Trawlers
104
contribute the bulk of effort over the years (on average 87% since their introduction; see Figure
2.32).
0
20
40
60
80
100
120
140
160
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
To
tal eff
ort (k
W d
ay
s
10
6)
Year
Trawlers
Gillnetters
Commercial
trawlers
Vessels with
outboard engines
Vessels without
engines
Figure 2.32 Cumulative effective fishing effort by vessels of various types in Andhra Pradesh, 1950-2005.
The catch per unit effort did not depict a clear trend until the mid-1970s; thereafter, it
slowly declined from 14.98 kg/kW days in 1974 to 1.15 kg/kW days in 2005 (Figure 2.33).
The increasing catch and effort trends as depicted in the above graphs occurred due to
initiatives of the state government, which started a mechanization programme in 1954 and in
collaboration with the FAO introduced the first mechanized boat (Pillai et al. 2000). In the
following year, fishing vessels called ‘navas’ were fitted with outboard marine diesel engines
procured through the aid of a Technical Cooperation Mission (TCM) of the United Nations
105
Development Programme (UNEP). The government provided 50% subsidy on these engines to
promote motorization and opened several demonstration centers in 1956. However, these fishing
vessels were found suitable only for the Kakinada Bay of the state and not other areas. Thus, the
state fisheries department in 1959 established a boat-building yard at Kakinada to construct and
distribute Pablo (small mechanized boats of about 7-9 m length) boats. At this yard, in 1972,
Pablo boats suitable for gillnet operations were constructed and distributed to fishers (Rao 1986).
0
5
10
15
20
25
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
per u
nit
eff
ort (k
g/k
W d
ay
s)
Year
Figure 2.33 Trend of catch-per-unit-effort in Andhra Pradesh from 1950-2005.
Although experimental trawling was initiated in 1954, commercial trawling for prawns
using small trawlers (9 m in length) started only in 1964 at Kakinada and then gradually spread
out to other areas of the state (CMFRI 1981b; Rao and Lakshmi 1988). By the late 1960s, two
new kinds of vessels with engines, called ‘royya’ (8 m OAL) and ‘sorrah’ (10 m OAL) were
introduced and mainly used for trawling (Chittibabu et al. 1988; Pillai et al. 2000). Two deep-sea
trawlers (24 m) were imported from the USA to exploit the shrimp grounds of the Sand Heads
area, and they started operating in this area in 1972 (Rao 1988a). Similar large trawlers, based at
Vishakhapatnam, grew in numbers but since the mid-1980s, their catch per effort started to
strongly decline (Sehara et al. 1993). Local protests in 1990 and the rescinding of the deep-sea
106
fishing policy, added to their reduced profitability, resulted in a reduction of the strength of this
fleet (also see Table 2.3).
Motorized beach landing craft were introduced in 1986, and distributed to fishers under
the Small Farmers Development Agency (SFDA) on a 50% subsidy scheme (Rao 1987). Also, in
1985, mini-trawlers (16 m OAL) were introduced which became enthusiastically adopted by the
industry (Rao 1988a). In 1987, ‘sona’ boats (mechanized boats of 16 m OAL) were introduced
which were also used for trawling (Rao 1999). The rapid increase in effort post 1987 was due to
these rapid developments, which resulted in a wave of new vessels entering the fisheries (Figure
2.32).
2.3.12 Orissa
Orissa has contributed 2% to India’s total marine catch on average over the period of 56
years from 1950 to 2005. Overall, its reconstructed catches (Figure 2.34) indicate a gradual
increase in first two decades, followed by a sharper increase thereafter, i.e., from 5.1 x 103 t in
1950 to 13.4 x 103 t in 1970 and 103 x 10
3 t in 2005. Various sardines contribute strongly
(around 20%) to this catch.
0
20
40
60
80
100
120
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
(t
10
3)
Year
Figure 2.34 Total catch of Orissa for all species, excluding tuna and billfish, 1950-2005.
107
The time series of fishing effort shows a gradual increase from 0.45 x 106 kW days in
1950 to 2.50 x 106 kW days in 1975, followed by a strong increase in effort reaching to 22.69 x
106 kW days in 1994, after which there was a slow decline in effort, to 20.85 x 10
6 kW days in
2005 (Figure 2.35). Vessels without engines had a major contribution of 95% in the first two
decades, which declined to 20% in the last two decades. The reverse is true for vessels with
engines, introduced in 1957. Trawlers and gillnetters have been the major contributors since their
introduction. However, in the last decade, vessels with outboard engines have contributed
significantly to total effort, on average 40% (Figure 2.35).
0
4
8
12
16
20
24
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
To
tal eff
ort (k
W d
ay
s
10
6)
Year
Liners
Trawlers
Gillnetters
Commercial
trawlers
Vessels with
outboard engines
Vessels without
engines
Figure 2.35 Cumulative effective fishing effort by vessels of various types in Orissa, 1950-2005.
The catch per unit effort has exhibited an overall pattern of decline, from 11.22 kg/kW
days in 1950 to 4.94 kg/kW days in 2005 (Figure 2.36).
108
0
3
6
9
12
15
18
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
per u
nit
eff
ort (k
g/k
W d
ay
s)
Year
Figure 2.36 Trend of catch-per-unit-effort in Orissa from 1950-2005.
The development of the marine fisheries sector was overall slow and steady in this state
(BOBP 1984). A plan to introduce new vessels was started by the government of Orissa in 1957;
when it introduced 2 mechanized vessels for inland fishing, which however, ended up at sea for
gill-netting (Ali 1996). This mechanization programme in the 1950s was initiated with the
technical and financial assistance of the international Aid Programmes (GOI 1971e). A scheme
known as ‘experimental power fishing in the sea’ was introduced in 1958 (GOO 1959). This was
followed by another scheme in 1961, called ‘pilot power fishing in sea’, aiming at establishing 6
marine fishing stations along the coast of Orissa and to accelerate mechanization (GOO 1963).
Under the same plan, a sister scheme called the ‘Pilot Marine Bi-Products Scheme’ was also
introduced to better utilize catches by producing canned fish and fish meal (GOO 1967).
As the private sector did not take an interest in this mechanization process in 1962, the
Orissa State Fisheries Development Corporation was set up to encourage and popularize
mechanized fishing as part of its activities (GOI 1971e). The artisanal sector received attention in
the mid-1970s and efforts were made by BOBP to motorize the traditional fishing vessels called
‘dingis’ (Ali 1996; BOBP 1984). The financial assistance for this initiative was given by the
109
Norwegian Agency for International Development (NORAD) under the Indo-Norwegian
bilateral project (Johansen and Gulbrandsen 1986; Ali 1996). The cumulative effect of all these
vessel types resulted in increasing catches and effort after the mid-1970s (Figure 2.34 and 2.35).
Another development of interest of artisanal fishers was the introduction of beach landing
craft (BLCs) in 1986, developed by the BOBP (Ali 1996), followed by the 1990s introduction of
fibreglass-reinforced plastic boats. The notion that expansion of the fisheries is preferable is still
prevalent in the state of Orissa, where technical and financial assistance, including subsidies, are
firmly in place (GOO 2013).
2.3.13 West Bengal
West Bengal contributed 2% to the total marine catch of India on average between 1950
to 2005. Its reconstructed catches show fluctuations with a gradual increase from 1950 (0.1 x 103
t) to 1988, and a rapid increase thereafter, reaching 202.1 x 103 t in 2005 (Figure 2.37).
0
30
60
90
120
150
180
210
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
(t
1
03)
Year
Figure 2.37 Total catch of West Bengal for all species, excluding tuna and billfish, 1950-2005.
The reconstructed effort saw a gradual increase from 1950 (0.003 x 106 kW days) to 1980
(5.14 x 106 kW days); it then accelerated, reaching 32.68 x 10
6 kW days in 1999 (Figure 2.38).
110
Vessels without engines accounted for 85% on average in the first two decades with a shift
towards vessels with engines, which on average contributed 90% from 1971 to 2005. Out of all
vessel types, gillnetters have been the major contributors since the late-1950s (Figure 2.38).
0
5
10
15
20
25
30
35
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
To
tal eff
ort (k
W d
ay
s
10
6)
Year
Launches/Carriers
vessels
Other vessels with
engines
Liners
Trawlers
Gillnetters
Commercial
trawlers
Vessels with
outboard engines
Vessels without
engines
Figure 2.38 Cumulative effective fishing effort by vessels of various types in West Bengal, 1950-2005.
The time series of catch per unit effort fluctuates in the first decade, but exhibits a
declining trend thereafter, i.e., from 15.49 kg/kW days in 1968 to 6.18 kg/kW days in 2005
(Figure 2.39).
In West Bengal, vessels with engines were initially introduced to promote inland
fisheries, and only later used for marine fisheries (Pillai et al. 2000). In this state, inland
fisheries were always more developed than the marine fisheries, due to consumer preference for
111
fresh water fish. It was during the Second Five Year Plan (1956-1961) that the mechanized boats
were introduced under the guidance of FAO Naval architects. These were used for gillnetting
(GOI 1971g). Introduction of vessels with outboard engines in mid 1970s with government
support, mainly through financing schemes via cooperative societies (Srinath et al. 2008)
resulted in some increase in effort and catches. Then in the 1980s the entry of refugees from
Bangladesh somewhat increased the pace of development.
0
5
10
15
20
25
30
35
40
45
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
per u
nit
eff
ort (k
g/k
W d
ay
s)
Year
Figure 2.39 Trend of catch-per-unit-effort in West Bengal from 1950-2005.
In order to promote trawling, the state government of West Bengal provided trawlers
through co-operative societies, which ended up being used as gill netters or carrier boats (Dan
1985). In mid-1980s it was realized that a potential existed for the expansion of the small-scale
marine fish industry (Philipose et al. 1987), and thus infrastructure development received much
attention in the Seventh Five Year Plan (1985-1990), which led to the introduction of trawlers in
1990 followed by liners in mid-1990s (Srinath et al. 2008), leading to increase in effort and
catches (Figure 2.37 and 2.38).
112
2.3.14 Andaman and Nicobar Islands
The Andaman and Nicobar Islands contributed only 0.4% to the total marine catch of
India on average for 56 years. Their catches exhibit a gradual increase over three decades, i.e.,
from 1 t in 1950 to 1.9 x 103 t in 1981 and, thereafter, are followed by a sharp increase, reaching
a value of 30.2 x 103 t in 2000, extrapolated to 2005 in the absence of more recent data (Figure
2.40). The abrupt catch increase in catches after 1981 was apparently due to an expansion of the
fishery to the South Andaman Islands, which had not been exploited before (Alagaraja 1987).
0
7
14
21
28
35
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
(t
1
03)
Year
Figure 2.40 Total catch of Andaman and Nicobar Islands, excludes tuna and billfish, 1950-2005.
The effort shows (Figure 2.41) a gradual increase from 119 kW days in 1950 to 0.16 x
106 kW days to 1980, but from 1981 onwards, effort increased rapidly to 1.73 x 10
6 kW days in
2005. As for other areas, the initial effort was contributed by vessels without engines; in 1968,
vessels with inboard engines were introduced, followed by vessels with outboard engines.
The catch per unit effort (relative abundance) had no discernible pattern in the first four
decades. However, from 1993 onwards relative abundance shows a decline, i.e., 34.87 kg/kW
days in 1993 to 17.39 kg/kW days in 2005 (Figure 2.42).
113
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
To
tal eff
ort (k
W d
ay
s
10
6)
Year
Vessels with
outboard engines
Vessels with
inboard engines
Vessels without
engines
Figure 2.41 Cumulative effective fishing effort by vessels of various types in Andaman and Nicobar
Islands, 1950-2005.
The slow growth in the first two decades was due to fishing by indigenous tribes, who
fished only to meet their personal requirements (Kumaran 1973). The Indian government thus
promoted fisheries via the settlement of fishers and their families, through a scheme which
started with 3 families in 1951 (Menon 1977). Later on, between the period of 1955 and 1974,
about 76 families from Kerala, Tamil Nadu, Andhra Pradesh, and West Bengal were settled
through government schemes (Menon 1977).
The increase in 1980s in effort was mainly due to the introduction of vessels with
outboard engines (Figure 2.41), which were promoted and supported through government
114
subsidies. This was supplemented with an increasing number of fishers, who had migrated to the
islands and even permanently settled in the last two decades (Ganapathiraju 2012). The
development of fisheries on other islands also increased overall effort (Alagaraja 1987).
0
10
20
30
40
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
per u
nit
eff
ort (k
g/k
W d
ay
s)
Year
Figure 2.42 Catch -per-unit-effort in Andaman and Nicobar Islands from 1950-2005.
2.4 Conclusion
As discussed above, for India as a whole and its individual states and UTs, the
progressive addition of vessels with engines using different gear types has led to a strong
increase in effort over time, and initially, in catches as well. The strong encouragement and
promotion of mechanization programmes and financial assistance by the government in the form
of subsidies to fishers, kept this trend going. Further, financial and technical support from
international bodies made it viable for the Indian government to encourage industrial fisheries.
However, catches have started to level off in last decade as illustrated above (Figure 2.1). It
seems that existing fishing effort is far in excess of what is needed, as evidenced by the drastic
decline in CPUE over time (Figure 2.3).
Given this, it is imperative to better understand and assess fish stocks and further analyse
the current situation. Reconstructions as done in this study are necessary and helpful in
115
quantifying the total biomass extractions from the marine ecosystem for applied effort, which
otherwise are difficult and expensive to estimate, especially in developing countries (Pauly
1998a; Zeller and Pauly 2007). Several prediction models, which are in place for fisheries
management, require data. Some require detailed and elaborate biological data and others are
simple biological models, such as surplus production models, which utilize only catch and effort
data to analyse fisheries dynamics. In data-poor situations, as in the case of India, such
reconstructed data can be used in these surplus production models to arrive at a better
understanding and management of fisheries. They let us assess the fish stocks and determine the
required level of exploitation considering maximum sustainable yields (MSY) and the
corresponding effort. These area specific models are discussed in Chapter 3.
116
Chapter 3 Assessing the status of India’s marine fisheries using surplus-
production models
3.1 Introduction
Fishing methods have evolved and diversified from a subsistence to a commercial
undertaking over the millennia. John Cleghorn’s 1854 term ‘overfishing’ drew the attention of
the scientific community to an incipient problem (Smith, 2002). A famous debate on British
fisheries in 1884, in which Thomas Huxley and Ray Lankester offered contradictory perspectives
regarding fishing’s impacts on fish populations, encouraged interest in the scientific exploration
of fisheries (Hart and Reynolds 2002; Smith 2002). However, it was only during the beginning
of 20th
century when the recording of fishery statistics received serious attention due to
increasing fishing efficiency and its unexpected effects. In 1900, Garstang (1900) presented the
results of analysis of Scottish data in a paper titled “The Impoverishment of the Sea”, which
supported Lankester’s argument about complex interactions. He agreed that fishing does reduce
the abundance of fishes, and thus he rejected the notion of resource inexhaustibility (Smith
2002). Soon, there was a growing scientific consensus that extensive research was needed to
identify the effects of fishing, and over time fisheries science14
had some major breakthroughs
and prompted changes in fisheries management (not detailed here). In general, it was understood
that fish populations respond to fishing (a perturbation) and undergo many changes. In an
attempt to understand the exploited fish population dynamics, fisheries science created
mathematical and statistical representations of populations and their changes, referred to as
14
Fisheries science has been recognized as a scientific discipline since the late 1850s, when the Norwegian
government hired scientists to find out why the catches of Atlantic cod fluctuated from year to year (Smith 1994;
Jennings et al. 2001). At present, fisheries science is a multidisciplinary discipline, drawing on fisheries biology, marine ecology, oceanography, stock assessments, computer modelling, natural resource economics, fishing
technology, statistics and the social sciences (Bonfil 2005).
117
models, used in fisheries assessments leading to management with specific objectives (Quinn
and Deriso 1999a; Haddon 2011a).
These stock (= sub-population of species) assessments make quantitative predictions
about the near future of their biomass (Haddon 2011a). The concept of Maximum Sustainable
Yield (MSY), i.e., the highest yield that can be taken over the long term, was introduced in the
1930s. The mathematical models implementing this concept, however, appeared later, in the
1950s. The ‘Surplus Production Model’ of Schaefer (1954) was the first to implement
operationally the concepts behind ‘MSY’, and it illustrated how populations decline with
excessive effort (Schaefer 1954; King 2007d).
Surplus production models are also known as surplus yield models, or biomass dynamic
models. These are simple models in which all aspects of production; i.e., recruitment, growth and
mortality, are aggregated into a single production function (see equation below). The stock is
treated as a homogeneous unit of biomass; i.e., differences within a stock, for example, age and
size structure, are not considered (Ricker 1975; Hilborn and Walters 1992a; Haddon 2011b).
Hence, they are often viewed as ‘holistic’ models, and they are all based on the assumption that,
under most conditions, a fish stock increases in abundance, i.e., produces a ‘surplus’ which can
be removed without any effect on the stock (King 2007d). This model complies with the concept
of density-dependence compensation, and in fact emphasizes pure compensation; i.e., there is for
each population level a fishing pressure that this population can withstand. The Allen effect or
‘depensation’ reported in many marine species, (i.e., ‘a decrease in per capita growth at low
biomass levels’) is not considered in production models (King 2007c).
Almost all the fisheries models, including surplus production models, directly or
indirectly have a fundamental understanding of basic processes affecting fish stock as modeled
by Russell (1931) (Jennings et al. 2001b). Russell’s formulation included reproduction
118
(recruitment) and growth as the inputs to the population biomass and natural and fishing
mortality as the outputs (King 2007d). However, fluctuations due to abiotic factors and
immigration and emigration were not included. Sustainable fisheries would require a balance
between additions (inputs) and removals (outputs).
So, in general form, the surplus production models are based on the following principle
(Bonfil 2005a; Hilborn and Walters 1992a):
Next year biomass = last year biomass + recruitment + body growth-natural mortality-catch
If there is no catch, then,
Next year biomass = last year biomass + production – losses due to natural mortality (or)
Next year biomass = last year biomass + surplus production
Thus,
Next year biomass = last year biomass + surplus production – catch
Therefore, the dynamics of the population related to Russell’s mass balance formulation can be
described by the equation
Bt+1 = Bt + f(Bt) - Ct (3.1)
where, Bt+1 is the biomass at the beginning of the year t+1, Bt is the biomass in year t, f(Bt) is the
surplus production as a function of biomass at the start of year t and Ct is the catch (biomass)
caught during year t (t = 1, 2, 3...).
Production
Surplus Production
119
The three most popular variants of surplus production models with different production
functions are the Graham-Schaefer, Fox and Pella-Tomlinson models (with the first two being
the most commonly used).
(1) Schaefer model: This model originated from the logistic model of population growth, i.e., an
S-shaped curve, which can be written as a continuous form differential equation (Bonfil 2005a;
Graham 1935; Hilborn and Walters 1992a; Schaefer 1954):
dB/dt = rB(1-B/K) (3.2)
where B=biomass, K = carrying capacity, and r = intrinsic rate of population increase
And in a discrete form as
Bt+1 = Bt + rBt(1-Bt/K) (3.3)
where, Bt+1 is the biomass at the beginning of year t+1, Bt is the biomass in year t , K is the
carrying capacity, and r is the intrinsic rate of population increase.
This continuous logistic model can be expressed in a discrete version of the Schaefer
model with the catch in year t included as:
Bt+1 = Bt + rBt(1-Bt/K)- Ct (3.4)
The middle term represents the surplus production. So, in theory, if catch equals surplus
production, catch is sustainable and the fishery is assumed to be in equilibrium (Pitcher and Hart
1982). In the case of the Schaefer model, the yield curve is symmetrical and the Biomass at MSY
(BMSY), at which maximum production occurs, is at 50%, i.e., K/2 (Quinn and Deriso 1999b).
(2) Fox model: This model is based on the Gompertz (1825) model of growth, initially derived to
model individual growth in weight (Pitcher and Hart 1982; Jennings et al. 2001b). This can be
written as
120
Bt+1 = Bt + rBt[1-(lnBt/lnK)]- Ct (3.5)
where, Bt+1 is the biomass in the beginning of the year t+1, Bt is the Biomass in the year t , K =
carrying capacity, r = intrinsic rate of population increase, and ln = natural log (Fox 1970; King
2007d).
In contrast to the Schaefer model, the yield curve in the Fox model is asymmetrical and
BMSY is reached at 37% of the carrying capacity or pristine biomass (Pauly 1984b; Quinn and
Deriso 1999b). This model was often considered more reasonable because of the underlying
assumption that the population will not go extinct even at high levels of effort (Sparre et al.
1989a). However, at present this does not seem to hold true given that anthropogenic
interventions have resulted in severe depletions and extinction of populations (Bonfil 2005a).
(3) Pella-Tomlinson model: This is considered a more generalized model, and it specifies a
relationship in mathematical form similar to the Schaefer model; however, another parameter ‘p’
is added. When the exponent p = 1 in the equation below, it describes the Fox model; when p
=2, it is equivalent to the Schaefer model (Quinn and Deriso 1999b; Haddon 2011b)
Bt+1 = Bt + r/p Bt[1-(Bt/K)p]- Ct (3.6)
where, Bt+1 is the biomass in the beginning of the year t+1, Bt is the Biomass in the year t , K =
carrying capacity, r = intrinsic rate of population increase, and p = asymmetry term which allows
production curve to take different shapes (Pella and Tomlinson 1969).
While this model is more flexible than the other two, it does this at the price of an
additional parameter to describe changes in the growth rate with regard to the stock size.
However, this flexibility can also be viewed as a liability, and the two other models are more
frequently used (Cooper 2006).
121
All three models have the underlying assumption that CPUE (an index of abundance) is
related to true abundance. While exact measurements of stock biomass are unattainable, its
relationship to an index of abundance (here CPUE) can be ascertained and expressed, e.g.,
through
It = CPUEt = qBt (3.7)
where It is an index of abundance for year t, CPUEt is the catch per unit effort for year t and q is
the catchability coefficient and Bt is the biomass in year t (Jennings et al. 2001b).
However, the above-mentioned assumptions may not always be realistic. For example,
‘hyper-stability’ can occur, in which case CPUE declines more slowly than abundance. In this
case, abundance is overestimated and fishing mortality is underestimated, which results in
overfishing (Hilborn and Walters 1992b; Harley et al. 2001); this situation occurs in many
fisheries, particularly in small pelagic, schooling fish such as anchovies (Csirke 1987).
Conversely, ‘hyper-depletion’ can occur, i.e., rapidly declining CPUEs can suggest a decline of
actual abundance which is not occurring (Hilborn and Walters 1992b; Walters 2003).
Hyperstability and hyperdepletion often occurs when the CPUE used does not account for the
spatial and schooling behaviours of fishes and thus CPUE data must account for these behaviours
if they are to be used as indicator of the stock abundance (Hilborn and Walters 1992b; Walters
2003; Walters and Martell 2004).
The ultimate objective of the surplus production model, as with other fisheries models, is
to produce useful outputs for fisheries management. Therefore, after choosing the type of
production curve, the second step is to fit such curve to the available data, and estimate
management parameters, i.e., MSY, the corresponding optimum effort fMSY, and fishing
mortality at MSY (FMSY). There are two popular methods of data fitting which differ in their
122
underlying assumptions: linear (equilibrium) and non-linear (non-equilibrium) (Jennings et al.
2001b; King 2007d; Haddon 2011a).
The former model assumes an equilibrium steady state and uses regression methods
(simple linear and multiple) to estimate the parameters. Equation 3.5 will lack time subscripts in
this type, as the time series nature of data is ignored and at equilibrium Bt+1 = Bt (Haddon
2011b). However, its results are biased because the two regressed variables are not independent,
given effort on both the axes. Gulland (1961) suggested a moving average method to overcome
this problem; however, Roff and Fairbairn (1980) disagreed, suggesting that this method could
not deal with the bias as expected.
On the other hand, non-linear methods use different techniques such as process-error and
observation-error (time series fitting), which consider the dynamic non-equilibrium catches
(Hilborn and Walters 1992a; Jennings et al. 2001b; King 2007d; Haddon 2011a). The process-
error method assumes all error occurs in equation 3.1 describing changes in population and
considers catch and effort data to be measured without error. By contrast, the observation-error
method assumes the opposite, that all error is in the catch and effort data, and the population
growth relationship is accurate (Jennings et al. 2001b; King 2007d; Haddon 2011a). An attempt
has been made to model both types of residual errors using a technique called the Kalman filter,
but it was found difficult to use to obtain reliable estimates (Sullivan 1992; Reed and Simons
1996; Haddon 2011b). Of all the techniques, the model that fits the observation errors is
considered to be the best method (Polacheck et al. 1993; Quinn and Deriso 1999b; Haddon
2011b). Thus, it is the one used here.
Regardless of the different methods, uncertainty in stock assessments does exist, not least
due to questionable data. As a result, parameter estimation from the models becomes
123
problematic; e.g., CPUE data depicting one-way trip15
(i.e., lack of contrast in data) may not
produce reasonable parameter estimates (Hilborn and Walters 1992a; Bonfil 2005b). In order to
address this issue, nowadays in fisheries modeling Bayesian methods are becoming popular,
especially in such cases of relatively uninformative data. This approach allows for the inclusion
of pre-existing knowledge of parameters and assumes a probability distribution (called priors),
resulting in a better estimates (McAllister et al. 2001). In recent fisheries literature, a
management-oriented approach in stock assessments has also been discussed; i.e., management
variables, e.g., MSY and FMSY are estimated. These are then used to derive population
parameters, e.g., r, K, and q (as done in this study; see details in the methods section), instead of
vice versa, as in the case of the biological approach. This has been found to be more effective in
communicating results, i.e., key management variables to managers, and in providing statistically
sound results. In one way trip data sets, for example, it was reported to have reduced
confounding in estimated parameters (Schnute and Kronlund 1996; Schnute and Richards 1998;
Forrest et al. 2008; Martell et al. 2008).
In tropical countries, including India, the catches include a plethora of species, and single
species assessments are not feasible. Thus, multispecies stocks are treated as a single unit (i.e.,
pooling catch of all species), to which a simple surplus production model is applied, commonly
of the Schaefer or Fox type. Similarly, the effort by all fleets are added up into one overall
measure, of which a time series is required in order to calculate CPUE for these models (Pauly
1978; Pauly 1984b, 1986; Vivekanandan 2005). A similar method was followed in the present
study, and Fox models using simple linear regression for data fitting and non-linear surplus
production models using observation-error method (with prior on r) were created. These are
discussed in detail in the following methods section.
15
Fishery data with consistently increasing fishing effort and declining catch per effort (Hilborn and Walters 1992a).
124
3.2 Materials and methods
Surplus production models have two data requirements: time series of (a) catch data, and
(b) relative abundance data (e.g., CPUE from the fishery). These were compiled and
reconstructed in this study as detailed in Chapter 2. Thus, using these data as an input, two types
of surplus production models as mentioned below were created for India and its east and west
coast. The Lakshadweep Islands and Andaman & Nicobar Islands were not included, as their
major catches were comprised of tuna and billfish. The two models were:
(1) Fox models for two time periods, i.e., 1980 to 2005 and 1965 to 2005. A linear regression
method was used for data fitting;
(2) Non-linear Schaefer models for 1965-2005. Data were fitted using the observation-error
(time-series) method, and a Bayesian approach was also incorporated, i.e., a prior on intrinsic
rate of population increase ‘r’.
For earlier decades, the fisheries statistics were not very good (or insufficient), so the analysis
did not include data prior to 1965. The parameters were estimated in Excel using different
approaches of model fitting, and a detailed account of it with specifics per model is given in
Sections 3.2.1 and 3.2.2.
3.2.1 Fox model
The Fox model was created using catch and CPUE data, and the equilibrium assumption
was used to calculate the MSY and fMSY. Pseudo-equilibrium conditions were generated,
following Gulland (1961), by using a moving average of effort (current effort + effort of n
preceding years, where n=2). Under equilibrium conditions, removals by catch will be balanced
exactly by the biomass growth and Equation 3.5 becomes:
C = rB(1-lnB/lnK) (3.8)
125
where C is catch, r is the intrinsic rate of population increase, B is the biomass and K is the
carrying capacity (Pauly 1984b; Jennings et al. 2001b).
Based on Equation 3.7, biomass was related to CPUE and q, which was then, substituted
into Equation 3.8. This equation was then solved for CPUE, and the curve of the Fox model was
converted into a straight line by using natural logarithms expressed as:
ln (CPUE) = ln (C/f) = a + bf (3.9)
where CPUE is the catch per unit effort, C is the catch, f the is fishing effort, a is the intercept
and b is the slope and ln is the natural log.
The values of (a) the intercept and (b) the slope were obtained by plotting the regression
line through the plots of effort against the ln(CPUE). In the Fox model, an asymmetrical curve is
described for the relationship of the effort with catch (yield). The catch, MSY and fMSY
were
estimated as (Pauly 1984b; Sparre et al. 1989a; Jennings et al. 2001b; King 2007d):
Catch (Yield) = f ea + bf
(3.10)
MSY = (-1/b) e (a-1)
(3.11)
fMSY
= -1/b (3.12)
3.2.2 Non-linear Schaefer model
This model is based on a logistic model of growth, and the parameters were estimated
using a non-equilibrium assumption. Data fitting was done with an observation-error (time-series
fitting) method, which assumes that Equation 3.4, describing the production relationship, is
accurate and all residual errors occur in the observations, i.e., in the CPUE values used as an
index of abundance [e.g., sampling error and catchability fluctuations; (Pella and Tomlinson
1969; Jennings et al. 2001b; Haddon 2011b)].
126
The process of time series fitting for estimation of parameters was divided into three
steps (Hilborn and Walters 1992a; Hilborn and Mangel 1997; Haddon 2011b):
(1) Population model: First, using Equation 3.4, a time series of expected biomass was created
using observed catch and values of r, K and B. Generally, initial guesses for the parameters r and
K are required; however, in this study the biomass dynamic model was parameterized from a
fisheries management perspective. So, instead of estimating r and K, MSY and FMSY were
estimated and r and K were derived based on it. Initially, the maximum catch was entered as a
guess estimate of MSY and r/2 for FMSY. The r value used here was based on biological data
(discussed below). It was assumed that B0 (here the year 1965) is equal to the carrying capacity
(K); i.e., there was no depletion of the stock. This assumption seemed reasonable based on
historical information, which supports the idea that the nature of fisheries was mainly artisanal
then and mechanization picked up gradually over time (see Chapter 2 for more details)
The management parameters of importance were calculated with r and K as
MSY = r K/4 (3.13)
FMSY = r/2 (3.14)
which, can also be expressed as:
r = 2 FMSY (3.15)
K = 4 MSY/r (3.16)
(2) Observation model: The time series of expected biomass was then used to produce a
predicted series of CPUEt (catch per unit effort at year t) by multiplying Bt (biomass at year t)
with a catchability coefficient (q). This includes an error term (e), which represents log normal
residual errors, a standard assumption with catch rate data (Haddon 2011b). In this study the time
series was split into two, i.e., 1965-1980 and 1981-2005 to resolve for confounding between
127
MSY and FMSY. So, two different catchability coefficients (q1 and q2) were used in the
calculations, and the conditional maximum likelihood estimates of q1 and q2 for the respective
time period were calculated using closed-form solution of q, i.e.:
q = e 1/nLn(CPUEt/Bt)
(3.17)
(3) Statistical model: The predicted series of CPUEt were compared to the observed series of
CPUEt. The best set of parameters were obtained by minimizing the negative log likelihood
criterion [assuming that the data were log normally distributed (McAllister et al. 2001; Haddon
2011b) ]. Log normal prior distribution on r for aggregated species was added (intrinsic rate of
growth) with a mean of 1.6 to 1.7 and a standard deviation of 0.08 to 0.1. Then, the Solver
algorithm in the Excel spreadsheet was used to minimize the statistical criterion by changing
logMSY and logFMSY (log space made it easier for Solver runs).
Estimation of r values
The series of relative abundance (CPUE) in study areas has illustrated a one-way trip
trend (see results section of Chapter 2), which can be described by both high values of r and low
values of K and vice versa. However, if r is known without error, K can be estimated even from
one-way-trip data, but it is not possible to known r with any certainty. Therefore, a prior
distribution of r from typical stock assessments should help in the estimation of K (McAllister et
al. 2000).
In this study, the following steps were used to construct a prior on r
(1) A list of commercial species for each group as defined in catches were compiled using
FishBase (www.fishbase.org), SeaLifeBase (www.sealifebase.org) and miscellaneous Indian
sources (Samuel 1968a, b, c; Shanbhogue 1973; Kurian and Sebastian 1976a, b, c; Karbhari
1982; Jhingran 1991; Lazarus et al. 1992; Murty et al. 1992; Suseelan et al. 1992; James et al.
128
1996; Menon et al. 1998; Appukuttan and Ramadoss 2000; Bensam 2000; Chellam et al. 2000;
Devadoss et al. 2000; Kripa et al. 2000; Kurian 2000; Menon et al. 2000; Nair 2000; Kasim
2003; Kripa and Appukuttan 2003; Meiyappan and Mohamed 2003; Menon 2003; Mohanraj et
al. 2003; Murty et al. 2003; Muthiah et al. 2003; Nair et al. 2003; Nair and Prakasan 2003;
Nandakumar and Maheswarudu 2003; Pillai et al. 2003; Pillai and Rohit 2003; Pillai and
Gopakumar 2003; Radhakrishnan and Manisseri 2003; Raje and Joshi 2003; Sivakami and
Ramalingam 2003; Sivakami et al. 2003; Vivekanandan 2003; Vivekanandan et al. 2003a;
Vivekanandan et al. 2003b; Vivekanandan et al. 2003c; Vivekanandan et al. 2003d; GOG 2004);
(2) Weight data were compiled for the species using FishBase for fishes and SealifeBase and the
Sea Around Us database (www.searoundus.org) for invertebrates. Though data were not
available for all the species, all groups were aptly represented (see Appendix E);
(3) Then, a relationship between r and adult mean weight (w) was used for the estimation of r
r = 0.025 (w)-0.26
(3.18)
as suggested by (Pauly 1982), based on Blueweiss et al. (1978). As equation 3.18 is expressed on
a daily basis, it was multiplied by 365 to obtain annual intrinsic rate of growth;
(4) When weight data were unavailable, data on natural mortality (M) were compiled from
regional sources (Sukumaran 1987; James and Thirumilu 1993; Kagwade 1993; Thomas and
Nasser 2009; Jagadis et al. 2010). It was assumed that FMSY = M, and so, r was estimated by
2·FMSY. This estimation method was used in case of lobsters (Panulirus polyphagus), stomatpod
(Oratosquilla nepa), and molluscs (Xancus pyrum, Paphia malabarica), excepting cephalopods.
In the absence of information on weight or natural mortality, the r value given in FishBase was
used (e.g., Bregmaceros macclellendii) or the value from a similar group was used, e.g.,
estimated r value of penaeid prawns was used for non-penaied prawns;
129
(5) Weighted average of r per year was calculated using catch composition, and finally the grand
geometric mean of r values was calculated. This value was used for prior and Fmsy initial estimate
as r/2.
Estimation of total area fished for India
The data on total area fished per year was calculated using Bhathal and Pauly (2008),
who suggested that Indian fisheries had undergone a fourfold spatial expansion from 1970 to
2000. It was assumed that the entire shelf, i.e., the area down to 200 m (i.e., approximately
372,000 km2) surrounding India and adjacent islands was exploited in 2000. Thus, given a
fourfold expansion, the area fished from 1950 to 1970 was approximately 93,000 km2, which
corresponds to the inner shelf, down to 20 m isobaths. This result is reasonable because the
regional fisheries literature supports the notion that, in earlier decades, the fisheries were
confined to inshore waters (Srinath 2003). Then, in order to create a time series for the fished
area, the above-stated values were used, assuming that no change occurred in the area fished
from 1950 to 1969. This was followed by linear expansion from 1970 to 1989 and area down to
50 m; thereafter, expansion down to 200 m, with no further increase. The missing values for in
between years were interpolated.
3.3 Results
The following section presents the results of the Fox and non-linear surplus production
models. The area-specific interpretive comments are included in this section; otherwise
additional comments on general issues are detailed in the discussion section.
130
3.3.1 India
Fox (linear) model
The Fox yield curve and the data points of yield versus effective fishing effort for the
time period 1980-2005 are shown in Figure 3.2. The values of the intercept (a) and the slope (b)
for the model were obtained by plotting the regression line through the plots of natural log of
CPUE against effective effort (Figure 3.1; Table 1). The results generated from this model
indicate that for India the Maximum Sustainable Yield (MSY) is 2.54 x 106 t and the effort
required to take it, fMSY, is 859 x 106
kW days.
R² = 0.9753
0
1
2
3
4
5
6
7
0 100 200 300 400 500 600 700 800 900 1000 1100
ln C
PU
E (
t/k
W d
ay
s)
Effort (kW days106)
Figure 3.1 The natural logarithm of CPUE against annual effective fishing effort in India for the period
1980-2005. The open dots show the data points (1950-1979), which were not included in the analysis.
Another analysis was conducted for a longer time series, 1965-2005. The values of the
intercept (a) and the slope (b) for the model were obtained by plotting the regression line through
the plots of natural log of CPUE against effective effort (Figure 3.3; Table 1). These values were
then, used to construct the Fox yield curve along with the data points of yield versus effective
fishing effort for the time period 1965 to 2005 as shown in Figure 3.4.
131
0
1
2
3
0 400 800 1200
Yie
ld (
t1
06)
Effort (kW days106)
Figure 3.2 Total catch versus effective effort in India and the fitted Fox yield curve for the period 1980-
2005. The open dots show the data points prior to 1980, which were not included in the analysis.
The Fox model indicates that for India the Maximum Sustainable Yield (MSY) is 2.61 x
106 t and effort required to take it, fMSY, is 616 x 10
6 kW days.
R² = 0.8989
0
1
2
3
4
5
6
7
0 100 200 300 400 500 600 700 800 900 1000 1100
ln C
PU
E (
t/k
W d
ay
s)
Effort (kW days106)
Figure 3.3 The natural logarithm of CPUE against annual effective fishing effort in India for the period
1950-2005. The open dots show the data points (1950-1964), which were not included in the analysis.
The models for both time series illustrate that the current catches are near MSY, but this
catch could be extracted with a much lower fishing effort, i.e., current effort levels are higher
than the estimated fMSY (Table 3.1). The fMSY values as estimated for time series 1965-2005 were
reached by year 1992 and for time series, 1980-2005 by year 1996. India could maintain catches
132
at such a high level of effort only by expanding fishing in new areas. Spatial expansion is indeed
well established in India (Bhathal and Pauly 2008).
0
1
2
3
0 400 800 1200
Yie
ld (
t1
06)
Effort (kW days106)
Figure 3.4 Total catch versus effective effort in India and the fitted Fox yield curve for the period 1965-
2005. The open dots show the data points prior to 1965, which were not included in the analysis.
However, due to an absence of details on fished area, i.e., in terms of horizontal and
vertical expansion, this could not be resolved here. Similar results in terms of expansion are
discussed and illustrated in the following section on the non-linear Schaefer model (1965-2005).
Table 3.1 Regression results of Fox models for different regions and time periods, i.e., showing values of
intercept (a), slope (b) and coefficient of determination (R2), along with the ratios of current catch (C)
(average of last 6 years) to maximum sustainable yield (MSY) and current effort (E) (average of last 6
years) to fishing effort at MSY (fMSY).
Area Time period a b R2 C/MSY E/fMSY
India
1965-2005 -4.4658 -1.6E-09 0.8989 0.97 1.68
1980-2005 -4.8233 -1.2E-09 0.9753 0.99 1.21
West coast
1965-2005 -4.3484 -2.6E-09 0.8697 0.94 1.79
1980-2005 -4.7881 -1.7E-09 0.9562 0.98 1.20
East coast
1965-2005 -4.7176 -4.2E-09 0.9369 1.01 1.47
1980-2005 -4.9108 -3.5E-09 0.9544 1.02 1.22
133
Schaefer (non-linear) model
The observed and the predicted CPUE for India are shown in Figure 3.5 and the details of
the derived variables (r, K, q1 and q2), which provide the best fit are given in Table 3.2. The ratio
of current biomass (calculated as an average of last 6 years) to the Biomass at MSY is (BMSY) is
0.81, suggesting that current biomass is lower than the BMSY. The MSY for India was estimated
to be 2.43 x 106 t at a fishing mortality rate (FMSY) of 0.843.
0
2
4
6
8
10
12
14
16
18
1965 1970 1975 1980 1985 1990 1995 2000 2005
CP
UE
(t
1
03
/kW
da
ys)
Year
Observed CPUE
Predicted CPUE
Figure 3.5 Time series of observed and predicted CPUE fitted by using time series method of fitting in
Schaefer (non-linear model) for period 1965-2005
In this model, the calculated value of q1 was higher than q2 (values given in Table 2;
more details in the methods section). Using the expression that catchability coefficient (q) equals
to catch/(effort x biomass), a time series of q was created which had its highest values in initial
periods followed by a declining trend, but with an increase that occurred in the last 5 years
(Figure 3.7).
Such a declining trend appears to be due to spatial expansion of fisheries as demonstrated
by Bhathal and Pauly (2008; more details in the methods section) and the increase in later years
– if it is more than an artefact due to questionable data – presumably indicates that there is no
room for further expansion.
134
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
ab
ilit
y c
oeff
icie
nt /
10
9
Year
Figure 3.6 The trend of catchability coefficient (q) in India for 1965-2005
In absence of data on area fished, some assumptions were made using an earlier inference
of a fourfold expansion in Indian fisheries (Bhathal and Pauly 2008), and a time series was
created (more details in the methods section). To visualize this time series of total area fished
each year, its natural log was plotted against the CPUE residuals. This yielded a negative
relationship (Figure 3.7).
R² = 0.134
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
11.2 11.4 11.6 11.8 12.0 12.2 12.4 12.6 12.8 13.0
Resi
du
als
ln Fishing area (km2)
Figure 3.7 Plot of residuals against fishing area in natural log for India’s shelf fisheries
Figure 3.7 suggests that, indeed, fishing area is a missing variable and 13.4% of the
residual variation can be explained by including it. The fit gets even better if the trend line is
135
fitted after splitting the times series into two time periods similar to those used for q1 (1965-
1980) and q2 (1981-2005), giving R2 values of 0.768 and 0.351 respectively.
Table 3.2 Estimated values of intrinsic rate of growth (r), carrying capacity (K), catchability coefficients
(q1 and q2) from the time series method of fitting in Schaefer (non-linear) model for period 1965-2005,
along with the ratios of current catch (C) (average of last 6 years) to maximum sustainable yield (MSY)
and the current biomass (B) (average of last 6 years) to biomass at maximum sustainable yield (BMSY).
Area r K q1 q2 C/MSY B/BMSY
India 1.69 5,754,856 2E-09 1E-09 1.04 0.81
West coast 1.67 4,055,287 3E-09 2E-09 1.02 0.78
East coast 1.58 1,849,114 5E-09 3E-09 1.08 0.88
3.3.2 West and east coasts of India
The time series 1980 to 2005 of the natural log of effort and the natural log of CPUE for
the west and east coasts of India are shown in Figures 3.8 and 3.9. The values of the intercept (a)
and the slope, (b) which were obtained by plotting a regression line in the above mentioned
graphs, are given in Table 3.1.
R² = 0.9562
0
1
2
3
4
5
6
7
0 200 400 600 800
ln C
PU
E (
t/k
W d
ay
s)
Effort (kW days106)
Figure 3.8 The natural logarithm of CPUE against annual effective fishing effort for the west coast of
India during the period 1980-2005. The open dots show the data points (1950-1979), which were not
included in the analysis.
136
R² = 0.9544
0
1
2
3
4
5
6
7
0 100 200 300 400
ln C
PU
E (
t/k
W d
ay
s)
Effort (kW days106)
Figure 3.9 The natural logarithm of CPUE against annual effective fishing effort for the east coast of
India during the period 1980-2005. The open dots show the data points (1950-1979), which were not
included in the analysis.
The estimated Fox yield curve as shown in Figure 3.10 and Figure 3.11 estimated a MSY
of 1.76 x 106 t for the west and 0.78 x 10
6 t for the east coast. The corresponding fMSY values
were estimated to be 575 x 106
kW days for the west and 287 x 106
kW days for the east coast of
India.
0
1
2
0 200 400 600 800
Yie
ld (
t1
06)
Effort (kW days106)
Figure 3.10 Total catch versus effective effort and the fitted Fox yield curve for the west coast of India
from 1980-2005. The open dots show the data points prior to 1980, which were not included in the
analysis.
137
0
200
400
600
800
1000
0 100 200 300 400
Yie
ld (
t1
03)
Effort (kW days106)
Figure 3.11 Total catch versus effective effort and the fitted Fox yield curve for the east coast of India
from 1980-2005. The open dots show the data points prior to 1980, which were not included in the
analysis.
Then, fisheries for both coasts were also analyzed by fitting the Fox model for a longer
time series, 1965-2005. The Fox yield curve was fitted (Figure 3.14 and Figure 3.15) as
mentioned above using the parameters (a and b) estimated from the regression of natural log of
CPUE against the effective effort (Figure 3.12 and Figure 3.13).
R² = 0.8697
0
1
2
3
4
5
6
7
0 200 400 600 800
ln C
PU
E (
t/k
W d
ay
s)
Effort (kW days106)
Figure 3.12 The natural logarithm of CPUE against annual effective fishing effort for the west coast of
India during the period 1965-2005. The open dots show the data points (1950-1964), which were not
included in the analysis.
138
R² = 0.9369
0
1
2
3
4
5
6
7
0 100 200 300 400
ln C
PU
E (
t/k
W d
ay
s)
Effort (kW days106)
Figure 3.13 The natural logarithm of CPUE against annual effective fishing effort for the east coast of
India during the period 1965-2005. The open dots show the data points (1950-1964), which were not
included in the analysis.
In this case, MSY was estimated to be 1.83 x 106 t and 0.78 x 10
6 t and the fMSY as 384 x
106
kW days and 238 x 106
kW days for the west and the east coast respectively.
0
1
2
0 200 400 600 800
Yie
ld (
t1
06)
Effort (kW days106) Figure 3.14 Total catch versus effective effort and the fitted Fox yield curve for the west coast of India
from 1965-2005. The open dots show the data points prior to 1965, which were not included in the
analysis.
139
0
200
400
600
800
1000
0 100 200 300 400
Yie
ld (
t1
03)
Effort (kW days106)
Figure 3.15 Total catch versus effective effort and the fitted Fox yield curve for the east coast of India
from 1965-2005. The open dots show the data points prior to 1965, which were not included in the
analysis.
Schaefer (non-linear) model
The observed and the predicted CPUE for the west and east coasts of India are shown in
Figure 3.16 and Figure 3.17. The details of the derived variables (r, K, q1 and q2), which
provided the best fit, are given in Table 3.2.
0
5
10
15
20
25
1965 1970 1975 1980 1985 1990 1995 2000 2005
CP
UE
(t
1
03
/kW
da
ys)
Year
Observed CPUE
Predicted CPUE
Figure 3.16 The time series of observed and predicted CPUE fitted by using time series method of fitting
in Schaefer (non-linear model) for the west coast of India from 1965-2005
140
0
2
4
6
8
10
12
1965 1970 1975 1980 1985 1990 1995 2000 2005
CP
UE
(t
1
03
/kW
da
ys)
Year
Observed CPUE
Predicted CPUE
Figure 3.17 The time series of observed and predicted CPUE fitted by using time series method of fitting
in Schaefer (non-linear model) for the east coast of India from 1965-2005.
The ratio of current biomass (calculated as an average of last 6 years) to the biomass at
MSY is (BMSY) is 0.78 for the west coast and 0.88 for the east coast; i.e., the current biomass is
lower than the BMSY. The MSY was estimated to be 1.69 x 106 t for the west coast and 0.73 x 10
6
t for the east coast at a fishing mortality rate (FMSY) of 0.843 and 0.791 respectively.
Similar to India as a whole, the models for its east and west coasts had higher q1 values
than q2 (Figure 3.18 and 3.19; see also Table 3.2). As discussed for India (Section 3.3.1; Figure
3.6), this is due to spatial expansion, which is indeed well established in all of its states (Bhathal
and Pauly 2008), and the increase in the last 5 years perhaps suggests the end of an expansion
phase. However, this would require further analysis, and given the absence of spatialized
fisheries data, such an analysis cannot be done.
Both the coasts show similar results, but the MSY and fMSY values are higher on the west
coast. This is because the Arabian Sea has high biological productivity, which has consequences
for fisheries (more details in Section 1.3), i.e., resulting in higher catches and thus a
correspondingly higher effort.
141
0.0
1.0
2.0
3.0
4.0
5.0
6.0
1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
ab
ilit
y c
oeff
icie
nt /
10
9
Year
Figure 3.18 Trend of catchability coefficient (q) on the west coast of India from 1965-2005.
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
1965 1970 1975 1980 1985 1990 1995 2000 2005
Ca
tch
ab
ilit
y c
oeff
icie
nt /
10
9
Year
Figure 3.19 Trend of catchability coefficient (q) on the east coast of India from 1965-2005.
Overall, the Fox models for both the time series and the Schaefer (non-linear) model have
illustrated that the current catches are near MSY in both the regions (Table 3.1 and 3.2).
However, the current level of effort is higher than the estimated fMSY in the Fox models (Table
3.1), which is disconcerting. Catches can only be maintained due to spatial expansion, which as
discussed above is well established in all of its states (Bhathal and Pauly 2008) and is supported
by the declining trend of q as shown in Figures 3.18 and 3.19.
142
3.4 Discussion
Both types of models used here indicate that at present, fisheries yields in India are near
MSY, but that this is achieved at excessive levels of effort, and based on a spatial expansion that
is not sustainable.
Although similar results were obtained by both types of models, one could question these
results, for example, because of the underlying assumption of equilibrium implied in the Fox
model (Paine 1984; Jennings et al. 2001b; Haddon 2011b). However, despite widespread
perception, complicated models, which have higher data requirements, are not necessarily better
(Ludwig and Walters 1985, 1989). Sometimes, models as simple as surplus production models
have performed as well or even better in terms of management advice. These models very clearly
demonstrate that for every fishery, there is a certain level of effort, which should not be exceeded
(King 2007d; Haddon 2011b).
Some concerns related to parameter estimations are addressed by non-equilibrium models
and use of the Bayesian approach as applied here. However, the estimates of MSY and FMSY may
be biased due to expansion of fisheries, which is well established in the case of India and its
states (Bhathal and Pauly 2008), where the regional literature documents the expansion and the
introduction of larger boats (Zacharia et al. 1995a; Zacharia et al. 1996; Rao 1999; Rao et al.
2008).
Hyperstability in CPUE can be a point of concern in this study as it is known that both
fish and fisher behaviour can lead to this phenomenon (Harley et al. 2001). However,
hyperstability is more likely to be of concern for single species, schooling fish stocks, and not
discussed in the literature on multispecies fisheries. Also, fishing effort, in India is distributed all
along the coast, so it is unlikely that hyperstability is a major factor, if at all.
143
As the fishing fleet increased in numbers and fishing power, operations extended beyond
coastal areas and into deeper waters. In the early 1990s, fishing fleets shifted from single day to
multiday fishing operations and so the fisheries expanded to new areas. This happened at a fast
pace due to government support at all levels (more details in Chapter 2). Until the late 1980s,
fishing was mainly confined to coastal waters up to a depth range of 50 m. As a result of
accumulating high levels of effort and indiscriminate fishing practices, the area from 0-50 m was
reported to be overexploited with many species at the verge of collapse (Devaraj and
Vivekanandan 1999; Fernandez 2004; Mohamed et al. 2010).
As in most tropical fisheries (mainly demersal), a collapse in yield may not be visible due
to the multispecies nature of fisheries. Other signs, however, may appear with increasing effort
pointing towards an alteration in the ecosystem (Pauly 1984a; Sparre et al. 1989b). Pauly
(1984b) details the changes in population with increasing effort as a decrease in large-sized
fishes, a reduction in fish size, an increase in the relative contribution of low-value and small-
sized fishes, and an increase of previously unseen components of the system (e.g., squids or
jellyfish), which occurs due to the removal of predators. These changes are all visible in India’s
coastal fisheries; i.e., the fish caught are reported to be of smaller size and high-trophic level
fishes are declining (Bhathal 2005; Bhathal and Pauly 2008). Even unusual landings of jellyfish
are now reported in Indian waters (CMFRI 2007a); however, whether this is due to changing
system still needs to be explored.
The multispecies nature of fisheries and the ongoing expansion of fisheries seem to be
masking the current state of fisheries in India. The Government of India proposes still further
expansion based on a revalidation study done by the Government of India (Silas 2000), which
144
found that India’s fisheries yield could be increased to an assumed ‘potential yield’ of 3.39
million tonnes16
.
However, hypoxic conditions are reported in Indian waters (Banse 1959; Gerson 2005;
Larkin 2005). The situation becomes more worrisome because the majority of yield (58%) is
estimated to be within the depth range of 0-50 m (DAHD 2001), which is already reported to be
overexploited (Devaraj and Vivekanandan 1999; Fernandez 2004; Ansari et al. 2006; Mohamed
et al. 2010). Potential yield is estimated to be about 34.9% of total in the depth range from 50-
200 m (DAHD 2001), to which Indian fisheries have already expanded (Bhathal and Pauly 2008)
and only 0.7% within the depth range of 200-500 m (DAHD 2001). Thus, the current situation
calls for immediate attention, as the Government’s focus is mainly on deeper waters to further
increase the catches, which may not augment as expected.
3.5 Conclusion
In conclusion, the results suggest that India’s current catch is near MSY, but that the
effort deployed to generate that catch is excessive. Moreover, the current catch is maintained
through a spatial expansion that cannot be continued, and hence is not likely to be sustainable.
This could not be addressed here due to limited provisions and the unavailability of data.
MSY alone as a management target or goal is not considered appropriate, as it changes
due to variable natural systems (this variability is reflected as noise in the data), and it is difficult
to be sure that the real MSY has been estimated until a fishery has past the overexploitation
phase. At present, it forms the basis for deriving targets and limits or ‘biological reference
points’ (Sissenwine 1978; Hilborn and Walters 1992a; Bonfil 2005b; Pauly 2012).
16
Earlier estimates are also available on potential yield from India’ EEZ, but their values varied considerably
(Fernandez 2004). This latest estimate is no less fanciful than the earlier values, even if it was derived by a working
group installed by the Government of India.
145
Therefore, instead of focusing on controlling the output of fisheries or catch management
(e.g., total allowable catches or bycatch limiting), the focus needs to be on inputs to curb
mounting fishing effort, even if fisheries management aiming to reduce fishing pressure is
considered difficult to achieve (Hilborn and Walters 1992a). A management target considering
the economic aspects of fishery would be appropriate for such undertaking, e.g., maximum
economic yield (MEY), which is achieved at lower levels of effort and is a less risky target
compared to MSY (King 2007). This is the topic of Chapter 4, where bioeconomic models of
India are presented, and national MEY and resource rent are estimated.
146
Chapter 4 Bioeconomic analysis of India’s marine fisheries
4.1 Introduction
In fisheries management, economic analysis of fisheries is just as important as biological
evaluation because most fisheries are actually commercial ventures. Thus, commercial fishers
will continue to fish as long as this venture is profitable, as for any commercial undertaking
(Robinson and Pascoe 1996; Sumaila 2010, 2012a). Also, new entrants are attracted when there
is competitive exploitation of common resources, such as fish when access to fishing is free and
open to all (Hanneson 1993; Conrad 2010; Sumaila 2012b). This often leads to over-
capitalization and thus overfishing, which is now occurring in many fisheries worldwide (see
Chapter 1). All different types of overfishing, e.g., biological (growth and recruitment),
ecosystem, Malthusian and economic overfishing, occur in temperate and tropical fisheries
(Pauly 1979; Silvestre et al. 1987; Pauly 1988; Pauly 1994a, b, 1997; Israel and Banzon 1998;
Murawski 2000; Jackson et al. 2001; Stergiou 2002).
Economic overfishing is defined as “fishing at a level of effort higher than the level
which maximizes economic rent” (Pauly et al. 1989; see also Gordon 1954; Clark 1990; Sumaila
et al. 2012). When it comes to managing fisheries, fishers’ behaviour plays a crucial role, and
this is significantly influenced by economic factors (Robinson and Pascoe 1996; Gaertner et al.
1999; Pascoe 2006). Thus, the inclusion of economic parameters in fisheries models makes them
more versatile.
In general, bioeconomic models help us understand the development process of fisheries
with respect to both population biology and human behaviour, with the latter of these being
largely driven by economic factors (Padilla and Charles 1994; Jennings et al. 2001a). Gordon
147
(1954), made an initial attempt at using a Schaefer surplus-production model to perform an
economic analysis of fisheries. He used the relationships between revenue, fishing cost and
fishing effort to illustrate how an open access fishery results in overexploitation, both in terms of
biology and economics (Jennings et al. 2001a; King 2007b).
At present, there are numerous simple and complex bioeconomic models, ranging from
static to dynamic, which have been created to evaluate and quantify the outcome of various
management strategies (Gordon 1954; Seijo et al. 1998; Jennings et al. 2001a; Smith 2008;
Christensen et al. 2011b). Despite their differences, all of these models have some representation
of biological dynamics of the underlying fisheries as their basis (Seijo et al. 1998). In such
models, when government subsidies are included, their direct or indirect contribution to
overcapacity and the resulting overfishing can be highlighted.
The literature about how subsidies motivate overcapacity and overfishing is abundant
(Munro and Sumaila 2002; Clark et al. 2005; Khan et al. 2006; Pauly 2006a; Sumaila et al. 2008;
Sumaila et al. 2010a; Sumaila and Pauly 2011). It demonstrates that subsidies contribute to
excess fishing capacity by reducing the cost of fisheries operation, by acting as a revenue-
enhancing factor, thus making fishing a more profitable business at the expense of resource
conservation and sustainability (FAO 1998; Milazzo 1998; Khan 2006; Sumaila et al. 2011).
However, some subsidies are considered essential and constructive, such as subsidies for
fisheries management, research and conservation programmes, e.g., marine protected areas
(Milazzo, 1998; Sumaila and Pauly 2006a; Sumaila et al. 2013).
Surplus-production models, mainly of the Schaefer and Fox types, are widely used in
tropical fisheries with maximum economic yield (MEY) as a policy target or limit (Silvestre and
Pauly 1985; Sparre et al. 1989a; Piumsombun 1992; Berachi 2003; Ahmed et al. 2007). The
preference of MEY over MSY is considered more reasonable because MEY occurs at lower
148
effort levels than MSY and thus having MEY as a target helps in avoiding other forms of
biological overfishing, i.e., growth and recruitment overfishing.
As discussed in Chapter 3, due to the large number of species in tropical coastal fisheries
countries, single-species assessments are, in addition to being difficult to perform, essentially
unhelpful to management (Pauly and Martosubroto 1980). Thus, multispecies stocks are treated
as a single stock to which a surplus-production model is applied, commonly of the Schaefer or
Fox type. Similarly, the effort by all fleets is added up into one overall measure of effort. Then,
the value of landings and the cost of fishing are estimated using the average fish price and, cost
per tonne and assuming bioeconomic equilibrium, along with subsidies, to evaluate if fisheries
are sustainably profitable. Specific details are discussed in the following methods section.
4.2 Materials and methods
The models of fisheries economics used here is based on the Fox surplus-production
model (Fox 1970), which has an underlying assumption of equilibrium. The biological attributes
and data requirements of these models are discussed in detail in Chapter 3. Two bioeconomic
models of India were constructed for 1980 to 2005, without discounting and with different costs
of fishing (details below). For earlier decades (i.e., the 1950s and 1960s), the fisheries statistics
were poor or insufficient and the mechanization and motorization of vessels gained momentum
only in the 1980s. Thus, this analysis covers the period from 1980 to 2005 only. The
Lakshadweep Islands and Andaman and Nicobar Islands were not included, as the bulk of their
catches were comprised of tuna and billfish, which are not included in this analysis.
The yield curve as shown for India in Chapter 3 (Fig. 3.2) was used to express the gross
(ex-vessel) value of India’s marine fish catches by converting weight (tonnes) in Equation 3.10
to monetary units (Silvestre and Pauly 1985) using 2005 real values (details below). The total
revenue was calculated as
149
TRt = ct p (4.1)
where, TRt is the total revenue for the year t (t = 1, 2, 3...), ct is the catch for the year t (t = 1, 2,
3...) and p is the assumed constant price (weighted average) per tonne in US$ for year 2005.
Ex-vessel price data for year 1999 were used, excluding tuna and billfishes (see Table
4.1). The price was calculated as a weighted average of catches and then converted from Indian
rupees to US dollars and adjusted for inflation to the reference year 2005. Inflation was adjusted
using the consumer price index (CPI) from the World Bank, while currency exchange rates were
provided by the Reserve Bank of India.17
Total cost of fishing was estimated using two different approaches. In bio-economic
Model I, the costs were estimated by assuming that bio-economic equilibrium was reached in the
early to mid-2000s. A straight line was drawn from the origin to the predicted revenue at the
average level of effort for the years 2003-2005, here assumed to correspond to the level of effort
at the bio-economic equilibrium.
In bio-economic Model II, the costs were estimated based on actual cost data documented
in Lam et al. (2011), i.e., cost per tonne in US$ for the different gear type combinations
expressed as 2005 real values (= adjusted for inflation; Lam et al. 2011). An average was
calculated of the variable cost18
of all gear types excluding pole line tuna and long line tuna.
Only variable costs were used as they are proportional to the fishing effort, in contrast to the
fixed costs, which do not vary with the fishing activity and are therefore considered sunk
(Jennings et al. 2001a).
17
The Reserve Bank of India is owned by the Government of India. 18
Costs are commonly divided into three main types: variable (short-term) cost, fixed (long-term) cost and
opportunity cost of labour and capital. Variable cost depends on whether fishing is occurring or not, whereas fixed
cost is independent; i.e., it does not change with fishing operations, e.g., loan repayment, interest, licence fees,
depreciation or insurance on boats. As fixed cost does not vary with the level of fishing activity, they were not
included here (Seijo et al. 1998; Jennings et al. 2001a; Lam et al. 2011).
150
Table 4.1 The ex-vessel price of fishes landed in India (1999-2000) as reported by Sathiadhas and Hassan
(2002). Price per kg in Indian Rupees (INR) was converted to US$ using a conversion rate of 1 US$ =
43.1 INR as reported by the Reserve Bank of India.
# Species Price per kg
(INR)
Price per tonne
(US$)
1 Elasmobranchs 35 812
2 Eels 43 998
3 Catfishes 18 418
4 Clupeids 15 348
5 Bombay duck 11 255
6 Lizard fishes 14 325
7 Half beaks and Full beaks 22 510
8 Flying fishes 17 394
9 Perches 32 742
10 Goatfishes 13 302
11 Threadfins 23 534
12 Croakers 23 534
13 Ribbon fishes 25 580
14 Carangids 25 580
15 Silverbellies 12 278
16 Big jawed jumper 40 928
17 Pomfrets 80 1,856
18 Mackerel 23 534
19 Seer fishes 81 1,879
20 Tunnies 22 510
21 Bill fishes 16 371
22 Barracudas 29 673
23 Mullets 36 835
24 Unicorn cod 5 116
25 Flatfishes 20 464
26 Crustaceans 104 2,413
28 Cephalopods 70 1,624
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The variable costs included fuel, running cost, repair and maintenance costs of vessels
and gear, and labour cost19
.
The fishery will have maximum profit at the point where the slope of total revenue (TR)
curve is equal to the slope of total cost (TC) curve, i.e., where the tangent to the TR curve is
parallel to the TC curve. It is at this point that the level of fishing effort is efficient (in economic
terms), i.e., fMEY, which corresponds to MEY; the distance between the two curves is the
maximum sustainable amount of profit or maximum economic rent [MER; (Dalzell et al. 1987;
Flaaten 2010)]. The effort at MEY (fMEY
), MEY and MER were calculated as detailed below.
The value of fMER was estimated by taking the first derivative of the revenue curve. Then,
Equation 4.2 was solved for fMER by the Solver algorithm in an Excel spreadsheet, i.e., for the
value of effort where the slope of the cost curve and tangent of the revenue curve are equal. The
fishing effort at MER, i.e., fMER, is equal to fMEY (Silvestre and Pauly 1985).
exp (a+b fMER) (1+b fMER) – S = 0 4.2
where a and b are the intercept and slope20
, respectively; fMER is the fishing effort at MER or
MEY, S is the slope of the cost curve
MEY = fMER exp (a+b fMER)] / p 4.3
where, a and b are the intercept and slope, fMER is the fishing effort at MER, p is the constant
price (weighted average) per tonne in US$ for year 2005. The numerator represents the yield in
value.
MER = MEY- S fMER 4.4
19
Amortization costs were not considered in this analysis. 20
The values of the intercept (a) and the slope (b) were obtained by plotting the regression line through the plots of
effort against ln(CPUE). However, these values (a and b) are different from biological models because catches are
expressed in this model as value of landings (i.e., catch times average fish price per t), which changed the values of
these parameters.
152
where MER is the maximum economic rent, MEY is the maximum economic yield , S is the
slope of the cost curve, fMER is the fishing effort at MER or MEY.
Subsidies for the year 2005 were also included in this analysis, which were compiled
from reported national statistics (described below) and refined for aggregated, missing or
underrepresented data. Subsidies for marine protected areas, for example, were missing, and thus
estimates from Cullis-Suzuki and Pauly (2008) were used. Fuel subsidies reported in national
statistics seem to be underrepresented given the amount of existing effort, so they were replaced
by the estimates from Sumaila et al. (2006); (2008).
The national statistics report outlay and expenditure for different schemes under which
the subsidies are made available to the fisheries sector (DAHD 2005). The data were reported for
five years as the Five Year Plans and had all the sectors included, i.e., marine, inland, brackish
water, agro-processing and aquaculture. Here, only schemes pertaining to the marine sector were
used. The segregation of sub-sectors was clear in the case of national schemes, but not in the
state schemes; therefore, the total outlay for only coastal states was added (excluding Andaman
and Nicobar Islands and Lakshadweep Islands) and the same percentage as in the allocation of
national schemes for the marine sector was used, i.e., about 68%. The outlay figures were used
because expenditures were not reported completely for all schemes and the complete Five Year
Plan (i.e., 2002-2007). As the reported expenditure for three years was close to about 60% of the
outlay, i.e., about 20% per year, the use of outlay figures was reasonable. International projects
were not detailed separately in the national statistics but seem to be included in the values
because there was an explicit mention of World Bank assistance (for a shrimp and fish culture
project) in the report of the Planning Commission of India (GOI 2001). As all the values were
reported for five years and in Indian Rupees, the total subsidies for one year were calculated and
153
converted into US$ using the conversion rate of 1US$ = INR 48.42 from the Reserve Bank of
India.
Then, based on Khan et al. (2006) and Sumaila et al. (2010b), subsidies were categorized
into Beneficial subsidies, Capacity-Enhancing subsidies and Ambiguous subsidies using
information from regional sources (Table 4.2). In this analysis, only the total of Capacity-
enhancing and Ambiguous subsidies was used.
Table 4.2 Estimates of fisheries subsidies categorized into different types for the year 2005 in USD. A1 to
A3 are Beneficial subsidies, B1 to B7 are Capacity-Enhancing subsidies and C1 to C3 are Ambiguous
subsidies. This does not include the state subsidies (US$ 22 x 106).
Categories Amount (US$) Source
A1-Fisheries Management and services 1,073,994 DAHD, 2005
A2-Research and Development 371,767 DAHD, 2005
A3-Marine Protected Areas 1,198,000 Cullis-Suzuki and Pauly, 2008
Subtotal 2,643,761
B1-Boat Construction and Modernisation 2,581,716 DAHD, 2005
B2-Development Projects & Support 2,455,729 DAHD, 2005
B3-Port Construction and Renovation 2,604,436 DAHD, 2005
B4-Marketing and Storage support 2,581,716 DAHD, 2005
B5-Tax Exemption
B6-Foreign Access Agreements
B7-Fuel Subsidies 283,192,744 Sumaila et al. 2006; 2008
Subtotal 293,416,341
C1-Fisher Assistance Programs 2,478,448 DAHD, 2005
C2-Vessel Buyback
C3-Rural Development Programs 2,478,448 DAHD, 2005
Subtotal 4,956,896
TOTAL 301,016,998
154
Two more scenarios were generated and cost curves were adjusted accordingly, i.e., with
50% reduction and 50% increase in existing subsidies. For each scenario, the subsidies were
deducted from the estimated maximum economic rent, because they were made available by the
state, and thus should go back to the real owner of the resources. These government subsidies
were a contribution of the Indian public and should not be included in the maximum economic
rent (Pauly and Thia-eng 1988; Sumaila et al. 2014).
4.3 Results
The results of Fox bioeconomic Model I and Model II depicting yield, revenue and cost
curves versus effective fishing effort for the time period 1980-2005 are shown in Figure 4.1 and
4.2.
0
5
10
15
20
25
30
35
0
5
10
15
20
25
30
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140
Va
lue o
f la
nd
ing
s a
nd
tota
l co
st (
US
$
10
8)
Ca
tch
es
(t
10
5)
Effort (kW days 107)
fMEY fMSY fE
MEY
MSYA+
AA-
Figure 4.1 Bio-economic Model I: Annual catches (Y-axis on the left) versus total effort for the period
1980-2005 (closed dots); open dots represent data (not used) for the period 1950-1979. fE is fishing effort
at bio-economic equilibrium. Annual revenues (Y-axis on the right) were calculated based on ex-vessel
prices, and costs were estimated by assuming that bio-economic equilibrium (E) was reached in the early
to mid-2000s, i.e., by drawing a straight line from the mean (predicted) revenue for 2003 - 2005 to the
origin. (A) Cost with current subsidies. (A+) Cost with 50% reduction in subsidies. (A-) Cost with 50%
increase in subsidies.
155
The results from Model I suggest that for India, the Maximum Economic Yield (MEY) is
2.11 million t at an effort (fMEY), of 437 x 106·kW days; also fMEY is about 51% of the fishing
effort generating MSY and 42% of the present effort level. The fMEY value as estimated for time
was reached by the year 1990. The corresponding maximum economic rent is US$ 919 million
(i.e., after deducting subsidies).
In scenario (1), with a 50% reduction in subsidies (i.e., cost curve A+), MEY changes to
2.07 million t at effort of 421 x 106 kW days. Conversely, in (2) when subsidies are increased by
50% (i.e., cost curve A-), MEY changes to 2.15 million t and effort required to take it to 453 x
106 kW days.
0
5
10
15
20
25
30
35
0
5
10
15
20
25
30
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140
Va
lue o
f la
nd
ing
s a
nd
tota
l co
st (
US
$
10
8)
Ca
tch
es
(t
10
5)
Effort (kW days 107)
fE
A+A
A-
fMSYfMEY
MEY
MSY
Figure 4.2 Bio-economic Model II: Annual catches (Y-axis on the left) versus total effort for the period
1980-2005 (closed dots); open dots represent data (not used) for the period 1950-1979. fE is fishing effort
at bio-economic equilibrium. Annual revenues (Y-axis on the right) were calculated based on ex-vessel
prices, and costs were estimated based on data in Lam et al. (2011). (A) Cost with current subsidies. (A+)
Cost with 50% reduction in subsidies. (A-) Cost with 50% increase in subsidies.
The results from Model II suggest that for India, the Maximum Economic Yield (MEY)
is 2.22 million t at an effort (fMEY) of 487 x 106·kW days. The fMEY is about 57% of the fishing
effort generating MSY and 46% of the present effort level. The fMEY values as estimated for time
156
series 1980-2005 were reached by the year 1991. The corresponding maximum economic rent is
US$ 1.13 billion (i.e., after deducting subsidies).
In scenario (1), with a 50% reduction in subsidies (i.e., cost curve A+), MEY changes to
2.18 million t at effort of 469 x 106·kW days. Conversely, in (2) when subsidies are increased by
50% (i.e., cost curve A-), MEY changes to 2.25 million t and effort required to take it to 504 x
106·kW days.
Table 4.3 give details about slope of cost curve, MEY, the value of landings at MEY,
economic rents and total subsidies as calculated for three different cost curves in case of both
models.
Table 4.3 Details of slope of cost curve, calculated maximum economic yield (MEY), total subsidies, the
value of landings at MEY and economic rent (i.e., maximum economic rent with deducted subsidies) for
three different cost curves, i.e., with 50% reduced subsidies (A+), with current level of subsidies (A) and
with 50% increased subsidies (A-). Note that the key results from Models I and II are very similar (bold
values) in spite of the radically different methods to estimate cost of fishing (see text)
Cost curve Slope MEY
(t · 106)
Total subsidies
(US$ · 106)
Value of landings at MEY
(US$ · 106 )
Economic rent
(US$ · 106)
Model-I: A+ 2.8940 2.07 160 2,393 1,013
Model-I: A 2.7408 2.11 321 2,436 919
Model-I: A- 2.5877 2.15 481 2,479 826
Model-II: A+ 2.4339 2.18 160 2,521 1,218
Model-II: A 2.2808 2.22 321 2,561 1,131
Model-II: A- 2.1277 2.25 481 2,600 1,046
4.4 Discussion
The bioeconomic models above illustrate that currently economic overfishing is indeed
occurring in Indian fisheries. Although Model I and II differed radically in the methods used to
estimate cost of fishing (which differed by only 17%), they produced similar results. Overall,
suggesting notably that India’s fishing effort is excessive and generates economic overfishing.
157
The current level of fishing effort is almost twice that corresponding to fMEY, i.e., far beyond the
level that maximizes economic rent. A recent study by Aswathy et al. (2011) in the state of
Kerala has presented similar findings of economic overfishing and reduced economic efficiency
in that State.
The resource rent is maximized at a lower level of effort than MSY, corresponding to
MEY; thus, when compared to MSY, MEY is considered a better management choice. However,
MEY depends on price and cost, which vary with time, and thus the equilibrium assumption
inherent in this model, raises concerns about its reliability. Nevertheless, this model has clearly
demonstrated that the present fishing effort in India causes economic wastage, by dissipating an
economic rent that could have been utilized in other productive ways.
Further, it is obvious from Figure 4.1 and 4.2 that as the cost curve swings downwards
when fishing cost decreases (e.g., through the granting of subsidies), the total catch of India also
decreases. On the contrary, in the scenario where subsidies are reduced by 50%, the cost curve
swings upwards, and economic rent as well as the catches increase. If the fisheries is at or about
to reach open access equilibrium, reducing existing subsidies will help improve food security by
making more fish available for consumption. However, in general, benefits to society at large are
not seen, and the benefits are only accruing to fishers, or more precisely, to boat owners (Pauly
and Thia-eng 1988; Sumaila et al. 2014).
In India, the majority of subsidies fall under the category of ‘capacity-enhancing
subsidies’, also known as ‘bad’ subsidies, which reduce the cost of fishing. They provide an
incentive for fishers to continue adding more effort and fishing beyond sustainable levels. There
were two decades ago a number of cost and earnings studies which depicted a profitable fisheries
sector in India (Panikkar et al. 1990; Panikkar et al. 1993; Sathiadhas et al. 1991; Sathiadhas and
Panikkar 1989; Sehara and Kanakkan 1993). Entrepreneurs were lured into the fisheries sector
158
by the profitable returns of prawn fisheries and the government’s cost reducing subsidies [e.g.,
100% subsidy on the cost of engine (Varghese 1991)].
However, reports of declining profitability and the uneconomic nature of fisheries started
to emerge in regional fisheries literature in the mid-1990s (Sathiadas et al. 1995; Sathiadas 1996;
Narayanakumar et al. 2000; Narayanakumar et al. 2009; Aswathy et al. 2011). Both the artisanal
and industrial vessels (mainly, deep sea trawlers) reported declining economic viability and
losses (Sathiadas et al. 1995; Narayanakumar et al. 2000). The multispecies nature of fisheries
proved beneficial for the trawlers because as the prawn catches declined, they shifted towards
targeting other fish species, whose prices increased over time. This diversification in operations
thus compensated for increasing costs due to overfishing (Panikkar et al. 1990; Vivekenandan
2013). However, the shrimp trawlers, which discarded most of their bycatch which was
generated by the small mesh sizes they used (Rao et al. 1980; Mahaeswarudu et al. 2013), are
now reported to be landing the bulk of their bycatch in order to increase their revenue and
thereby compensate for their increased fishing costs (Zacharia et al. 2006b).
Along with depleting coastal resources, the substantial increase in fuel cost was
considered as a main reason for financial losses for vessels with engines (Unnithan et al. 2005),
although from the very beginning, the government has provided tax exemptions to fishers for
high-speed diesel (HSD) and kerosene (Shiyani 2003; Aswathy and Salim 2012). Further, under
the Tenth (2002-2007) and Eleventh Five Year Plan (2007-2012), a scheme was introduced for
the conversion of existing trawlers into resource-specific fishing vessels, i.e., mainly for tuna
longlining. Under this scheme, 50% of the cost of conversion, with a ceiling of Rs 15 lakh21
per
vessel (equivalent to US$24,45422
), was covered through government subsidies (GOI 2002a;
Ganga and Pillai 2006; Rao 2009b; DAHD 2013). More than 1,500 vessels (a majority from the
21
INR 1 lakh = INR 100,000. 22
Indian Rupees are converted into US dollars based on the conversion rate of Rs. 61.34 = 1 US$ of October 2013.
159
state of Tamil Nadu) below 20 meters LOA participated in the conversion programme. However,
the purpose of vessel conversion (tuna fishing) was defeated, as most of these vessels engaged in
shark fishing (Ganga and Pillai 2006; GOI 2006).
Moreover, trawlers are reported to compete with small-scale fishers operating close
inshore. This has resulted in pronounced conflicts between small and large-scale fisheries in
India, which have become a serious social and legal problem in many coastal fishing areas (Nair
and Jayaprakash 1983; Balakrishnan and Algaraja 1984; Menon 1996). Nevertheless, the
government, as well as community cooperation can minimize such conflicts and make
implementation of regulations feasible. In the state of Tamil Nadu, for example, the closed
season for the mechanized fishing is widely accepted along the coast (Bavinck et al. 2008;
Johnson and Bavinck 2010).
As the current state of fisheries requires a cutback in effort, and in light of the above
discussion, the focus should thus shift toward reduction in the effort of trawlers, although this
goes against the plans of successive governments to “diversify” fishing operations and tap into
“new” areas and resources. Ideally, for maximum rent, the effort should be at the fMEY, effort
level, which, based on the results of this model, requires a 50% reduction in total effort. Trawlers
alone contributed about 53% to total effort in year 2005. Thus, the phasing-out of trawlers would
single-handedly double the catch per effort (i.e., from 2.2 kg/kW days to 4.5 kg/kW days) or
other sectors, and massively increase income of other sectors. Current levels of fishing effort are
clearly excessive and wasteful, and profits are largely limited to the industrial sector (Rao,
2009a), which also gets most subsidies. The suggested massive reduction of trawling may not be
easy to implement given political constraints. However, a large economic rent, which is now
dissipated, would be generated and could be used, for example, to boost education levels in
fisher communities, which currently suffer from illiteracy rates exceeding 40% (Korakandy
160
2008), or to create alternative employment opportunities in these communities (Smith 1981;
Dalzell et al. 1987; Sumaila et al. 2013).
4.5 Conclusion
India should not continue on its present course of expanding its fisheries through massive
subsidization, given the poor economic efficiency of this sector. The challenge, rather, is to
combat the “tragedy of the commons” and find ways to balance ecological sustainability, social
equity and economic efficiency. At this stage, a reduction in effort is certainly required, which
may start with the phasing out of trawlers. They are already struggling to make profits at present,
compete with other sectors (Sathiadas et al. 1995; Sathiadas 1996) and can continue only
because of government support. Overall, their removal would increase the income of other
fisheries sectors and their catch per effort, as well as perhaps stabilize the catches or even
slightly increase them (Dalzell et al. 1987; Heymans et al. 2011). Furthermore, it would also
reduce tensions and conflicts with coastal, artisanal fisheries, and thus reduce social and legal
problems associated with this conflict. However, before any scheme of effort stabilization and
reduction is implemented, more studies at the level of the states may be able to fine-tune the
interventions.
161
Chapter 5 Conclusion
This research aimed to evaluate the current state of marine fisheries in India. A detailed
literature review of India’s marine fisheries was provided in Chapter 1, which included
information on the oceanographic features and fisheries resources of study areas. This chapter
also included a historical account of fisheries along with a narrative of the development of the
different sectors, and the underlying policies at the national and state level. The background
material in this chapter was provided to enable readers to understand the rationale for the goals
and specific objectives of the research as addressed in Chapters 2, 3 and 4. The approach and
findings detailed in these chapters are summarized below, along with limitations and suggestions
for future research.
In Chapter 2, India’s marine fishing effort from 1950 to 2005 was reconstructed for 13
maritime regions (9 states and 4 Union Territories) by compiling information about several
variables for different vessel categories. The data suffered from various inconsistencies, which
were resolved using various methods and historical information. The results showed a continued
increase in effort (kW days) from 1950 to 2005, which accelerated from the 1990s onward
(Figure 2.2), with trawlers as the major contributors, followed by gillnetters. Similarly, catches
(weight in tonnes), which were updated, estimated and assembled for the maritime states of India
and its UTs, indicated an increase from 1950 to 2000 but thereafter have started to level off. The
time series of ‘catch per effort’, an index of relative resource abundance, revealed a continuous
decline from 1960 to 2005 (Figure 2.3; Table 5.1), indicating that existing fishing effort is far in
excess of what is needed to achieve the catch.
162
Hyperstability in CPUE is a usual point of concern, but since fishing effort in India is
distributed all along the coast, it is unlikely to be a major factor, if at all one (more details are
provided in Sections 2.1, 3.1 and 3.4). While survey-based estimates of abundance are
considered superior over fishery dependent data, commercial fisheries data are also essential as
they are readily available, usually cheaper and have larger sample sizes than research vessel
survey data (Hoggarth et al. 2006). Nevertheless, when factors of concern such as changes in
catchability or spatial distribution of fishing effort are addressed, as was done in this study by
applying a factor expressing ‘technological creep’ (Pauly and Palomares 2010) and stratification
of catch and effort (King 2007e) by area (state), catch per unit of effort (or CPUE) becomes
exceedingly useful.
Table 5.1 Details of catch per unit effort (kg/kW days) for India and its states and Union Territories.
CPUE values are an average of specified years for respective areas.
Region
Year 1950-1959 1960-1969 1970-1979 1980-1989 1990-1999 2000-2005
India 30.22 15.91 9.21 5.43 3.42 2.45
Gujarat 100.82 21.18 8.93 5.62 3.69 2.15
Daman and Diu 33.32 17.48 3.23 1.61 1.71 2.08
Maharashtra 131.79 13.67 7.96 4.36 3.32 3.41
Goa 0.67 23.99 13.35 7.16 5.37 4.67
Karnataka 27.46 17.83 7.51 4.55 3.00 2.38
Kerala 56.35 34.35 15.51 8.16 4.22 2.38
Lakshadweep Islands 0.66 1.35 1.82 1.55 1.34 1.15
Tamil Nadu 11.87 9.41 6.53 5.14 3.61 2.39
Puducherry 20.35 15.22 5.95 3.60 2.16 1.92
Andhra Pradesh 10.84 9.03 9.51 5.96 1.95 1.30
Orissa 11.79 6.70 7.11 7.26 2.85 3.86
West Bengal 26.15 9.64 6.56 3.20 3.51 4.76
Andaman and Nicobar
Islands 15.40 9.86 17.94 16.30 30.00 22.30
Moreover, reconstructions as done in this study are important and necessary because fish
stocks can be assessed with these two data sets (Pauly 1998a; Zeller and Pauly 2007). Although
163
the National Research Institutes of India have been collecting catch and effort data since the
1950s, effort data were only published intermittently, unlike catches, which are collected and
reported annually. Further, access to official databases was restricted and gray literature was
often inaccessible. Therefore, this study is unique in the extent of spatial and temporal coverage
and its compilation of scattered data into a coherent whole with standardization of units of effort
for all sectors along with emphasis on transparency. Transparency of this sort should eventually
increase public understanding and participation at all levels in policy making (Zeller and Pauly
2004). In the field of marine resource management, the local data sets and better local knowledge
are considered preconditions to a better policy (Watson et al. 2004b).
In Chapter 3, the reconstructed data on effort and CPUE were used to create surplus
production models of the Fox and Schaefer (non-linear) types for India and its east and west
coasts. These models indicated that at present fisheries yields in India are near MSY. However,
this is achieved by spatial expansion and excessive levels of effort, i.e., effort levels beyond fMSY,
which is not sustainable. These models have clearly demonstrated that for every fishery, there is
a certain level of effort that should not be exceeded (King 2007d; Haddon 2011b), otherwise the
fish population may be not be able to withstand the fishing pressure and show signs of
overfishing, as is currently the case (more details in Section 3.4). Sometimes, the results of
surplus production models are questioned because of their underlying assumption of equilibrium,
homogeneity and simplicity. However, despite widespread perception, complicated models with
their higher data requirements are not necessarily better (Ludwig and Walters 1985, 1989).
Instead, models as simple as surplus production models have performed as well or even better in
terms of management advice. Moreover, in this study, non-equilibrium models with the
incorporated priors on population growth rate (‘r’) were created, which addressed some of these
concerns. The use of a management approach for parameter estimations is more effective in
164
communicating and providing statistically sound results. In one-way trip data sets, for example,
it was reported to have reduced confounding in estimated parameters (Schnute and Kronlund
1996; Schnute and Richards 1998; Forrest et al. 2008; Martell et al. 2008). The estimates of
MSY and fMSY, however, may be biased due to expansion of fisheries. Given, the current level of
effort, and the spatial expansion of fisheries, catches cannot be maintained around MSY,
However, the role of spatial expansion could not be explored further in the absence of spatialized
data.
In Chapter 4, Fox bioeconomic models were created for India, and national MEY and
resource rent were estimated for three scenarios altering the costs of fishing with the inclusion of
subsidies, i.e., at current level, 50% reduction and 50% increase. The results from this model
indicated that currently economic overfishing is indeed occurring in the Indian fisheries in all
scenarios. The current level of fishing effort is almost twice that corresponding to fMEY, i.e., far
beyond the level which maximizes economic rent. The resource rent is maximized at a lower
level of effort corresponding to the MEY; thus, when compared to MSY, it is considered a better
management choice because it helps avoid other forms of biological overfishing. However, MEY
depends on price and cost, which vary with time, and thus the equilibrium assumption of this
model is of concern. Despite this shortfall, it is a useful means of evaluating fisheries as it
models and considers the economic characteristics of the fishery. This study has clearly
demonstrated that the present fishing effort has caused economic wastage by dissipating a rent
that could have been utilized in other productive ways.
Evaluation of Indian fisheries, in both biological and economic terms raises deep
concerns as they are not sustainably profitable. This could only occur due to an incessant
addition of vessels with engines, resulting in continuous increase in effort and thus catches.
Despite a continuous increase in effort, catches have, however, started to level off in the last
165
decade. Technological improvements happened over time with government support, because the
government advocated motorization and mechanization programmes and financially supported
fishers through subsidies to maintain this expansion. Financial and technical support from
international bodies made it viable for the Indian government to encourage these endeavours.
Until recently, fish have been treated as an agricultural commodity in India, guided by the same
goals as agriculture: increasing supply, product diversification, employment, and foreign
exchange generation. This, however, seems to have started to change when the central
government promulgated a Comprehensive Marine Fishery Policy in 2004 (GOI 2004b), in
which, along with other elements, ecological sustainability was emphasized. The emerging key
term ‘sustainable fisheries’ in several government documents and websites is a new
development, but there are no details about how this will be achieved and existing regulations do
not do justice to the intention of sustainability. Further, expansion into “new areas” and
“untapped resources” is still the focus of the Government’s Five Year Plans.
Indian policymakers have always emphasized the expansion of demersal fisheries into
deeper waters as a solution to overcapacity in inshore waters. However, the low oxygen levels in
deeper water layers, especially on the west coast (Banse 1959; Gerson 2005), constrain such
expansion, in addition to the fact that, generally, deep tropical waters are less productive than
deep temperate waters (Longhurst and Pauly 1987). The situation becomes more worrisome
because a majority of yield (58%) is estimated to be within the depth range of 0-50 m (DAHD
2001), which is already reported to be overexploited (Ansari et al. 2006; Devaraj and
Vivekanandan 1999; Fernandez 2004; Mohamed et al. 2010), and about 34.9% of the total in the
depth range from 50-200 m (DAHD 2001), into which Indian fisheries have already expanded
(Bhathal and Pauly 2008). The high hopes of the Government to further increase catches from
deeper waters needs re-evaluation, as this is unlikely to provide the desired results.
166
Indian fisheries have suffered from sequential depletion of coastal stocks and would have
shown the signs of this depletion many years ago were it not masked by the expansion into new
areas and the multispecies nature of fisheries (see Section 3.4).
The question remains: Can the resulting situation of mismanagement be rectified?
Although given the complexity of the problem there is no straightforward answer, in the light of
this study I suggest curtailing the existing effort. This may start with the phasing out of trawlers,
which account for more than 50% of current effort and at present are already struggling to turn a
profit and can continue only because of government subsidies (Sathiadas et al. 1995; Sathiadas
1996). Overall, their removal would increase the income of other sectors and their catch per
effort, as well as perhaps stabilize total catches or even slightly increase them over time (Dalzell
et al. 1987; Heymans et al. 2011).
Government support to further expand the sector through subsidies also needs to be
constrained. Although the negative side of subsidies has received attention in India, a general
assumption prevails that subsidies in the long run will help the poor. Overlooked is the fact that
beyond a certain point, a decrease in catches and reduced or diminished profitability will occur
as illustrated in this study, which may have serious implications, especially for the poor. It was
expected that there would be some reduction in subsidies after the 1990s due to a liberalization
strategy drawn up by the IMF, which included the phased reduction of import duties, reduced
government participation and reduced reliance on subsidies (Byers 1998; Johnson 2002).
However, this was not visible. Rather, the flow of subsidies shifted, as the focus of government
changed from overexploited to new unexploited resources.
The suggested massive reduction of trawling in this study will not be easy to implement
given political constraints. In general, politicians refrain from taking concrete actions given the
complexity of the issues involved and the number of stakeholders, including a large number of
167
poor fishers, which represent a large vote ‘bank’ to well-off entrepreneurs with political
connections. Therefore, because of the political power of their owners, it may be extremely
difficult to ban trawlers out right. However, if capacity-enhancing subsidies can be reduced and
the funds be used in the public interest instead (for example to provide employment), then allies
may be found among other powerful members of society to support such reduction in subsidies.
Ecosystem models could be an important next step to evaluate in detail all scenarios and trade-
offs among social, economic and ecological objectives and search for other policy options using
Ecopath with Ecosim (EwE) approach (Pitcher and Cochrane 2002; Walters et al. 2002;
Christensen and Walters 2004; Alder et al. 2007). For example, the Bay of Bengal Large Marine
Ecosystem Programme is currently initiating such studies.
Overall, change requires political will and a shift to sustainable fisheries, e.g., towards a
focus on small-scale fisheries (Johnson 2006; Pauly 2010b, 2011b), and which would also
increase consumer awareness and ecological understanding and educate the public about the
advantages of sustainable use, e.g., partake for future generations or through economic valuation
of ecosystem (Kumar and Chen 2011; Groot et al. 2012; Sumaila et al. 2012). Fisheries policies
in India have mainly followed a top-down approach directed by national development priorities.
Thus, a shift from purely hierarchical governance to the inclusion of participatory governance
and community management (Symes 2006; Armitage et al. 2009; Jentoft et al. 2009; Bavinck et
al. 2013) may make policy implementation somewhat easier. In some districts of Tamil Nadu,
for example, a Peace Council was formed to resolve emerging conflicts among sectors with the
local Regional District Officer (commonly referred as ‘RDO’) as chairman and representatives
from the state fisheries department and mechanized and traditional vessels owners. They
allocated fishing nights (4 for vessels without engines and 3 for vessels with engines) and issued
tokens after collecting a nominal fee from vessels with engines. This money was added to the
168
association’s general fund, which was then used to pay compensation toward damage of any
traditional gears during the nights. Interestingly, fishers from all sectors honoured this system of
regulated fishing (Balakrishnan and Alagaraja 1984).
In conclusion, India should not continue on its present course of expanding its fisheries
through massive subsidization, given the current state of declining resource abundance and poor
economic efficiency. I hope that this research will bring some focus to a problem that has been
overlooked and can safeguard the interest of millions of people who rely on fisheries as a source
of protein and livelihood.
169
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Appendices
Appendix A. List of important places and institutes visited during field trips made to different coastal
states of India
Places of visit State Year Degree
Institute of Economic Growth New Delhi 2003, 2005 MSc., PhD.
Economic Management Institute New Delhi 2008 PhD.
Indian Agriculture Research Institute New Delhi 2008 PhD.
Ministry of Minorities and Affairs New Delhi 2008, 2009 PhD.
Indian Council of Agriculture Research New Delhi 2008, 2009 PhD.
Department of Animal Husbandry and Dairying New Delhi 2008, 2009 PhD.
Central Institute of Fisheries Technology Gujarat 2008 PhD.
Marine Products Export Development Authority Gujarat 2008 PhD.
The Best Fish Service Gujarat 2008 PhD.
Veraval Fishing Harbour Gujarat 2008 PhD.
Fish Market-Veraval Gujarat 2008 PhD.
Department of Fisheries-Veraval Gujarat 2008 PhD.
Mumbai University Maharashtra 2003 MSc.
Department of Fisheries Maharashtra 2008 PhD.
Versova Fisheries Harbour Maharashtra 2008 PhD.
Goa University Goa 2003 MSc.
National Institute of Oceanography Goa 2003 MSc.
Goa Public Library Goa 2003 MSc.
Fisheries Department of Goa Goa 2008 PhD.
Fish Market-Panjim Goa 2008 PhD.
Integrated Fisheries Project Kerala 2003 MSc.
Changampuzha Smaraka Grandhasala (library) Kerala 2003 MSc.
Chavara Library Kerala 2003 MSc.
St. Albert College Kerala 2003 MSc.
ABAD Overseas Private Limited Kerala 2003 MSc.
Matsyafed Kerala 2003 MSc.
Central Institute of Fisheries Technology Kerala 2003, 2005 MSc., PhD.
Fisheries Department of Kerala Kerala 2003, 2005 MSc., PhD.
Central Marine Fisheries Research Institute Kerala 2003, 2005, 2008 MSc., PhD.
Cochin University of Science and Technology Kerala 2003, 2005, 2008 MSc., PhD.
Ernakulum Public Library Kerala 2005 PhD.
Eloor Lending Library Kerala 2005 PhD.
Marine Products Export Development Authority Kerala 2005 PhD.
Fish Market-Kochi Kerala 2005 PhD.
Central Institute of Fisheries Nautical and Engineering
Training Kerala 2005 PhD.
Fishery Survey of India Kerala 2005 PhD.
245
Places of visit State Year Degree
Thoppumpady Harbour Kerala 2008 PhD.
Connemara Public Library Tamil Nadu 2003 MSc.
Department of Fisheries Tamil Nadu 2003, 2005 MSc., PhD.
Zoological Survey of India Tamil Nadu 2003, 2005, 2008 MSc., PhD.
Bay of Bengal Programme Tamil Nadu 2008 PhD.
National Biodiversity Authority Tamil Nadu 2008 PhD.
Kesimedu Fishing Harbour Tamil Nadu 2008 PhD.
Chindrapet Fish Market Tamil Nadu 2008 PhD.
National Library West Bengal 2005, 2008 PhD.
Department of Fisheries West Bengal 2008 PhD.
246
Appendix B. List of important sources used to compile effort data from 1950-2005 for all maritime states and Union Territories of India. All these sources
are included in the Bibliography of this thesis.
Data Type Sources Remarks
Number of vessels Balan 1998; Bhargava et al. 2006; Bhat and Bhatta 2006; Bhatta et al. 2003; Bhatta and Shetty 2006; BOBP
1982; CMFRI 1969b, a, 1978, 1981, 2006; D'Cruz 2006; D’Cruz and Raikar 2004; DAHD 1982, 1992, 1994,
2001, 2004, 2005; Dan 1985; Desai and Baichwal 1960; Gendy 1992; GOG 1998, 2001, 2002, 2003, 2004;
GOI 1961, 1971a, b, c, 2002; GOK 1968, 1970, 1973, 1974, 2000, 2001a, b, 2004; GoL 1966; GoM 1953,
1955, 1957, 1958, 1959, 1960; GoMH 1962, 1963, 1964; GoP 1965, 1966, 1967, 1969, 1970, 1971, 1972,
1973, 1974, 1976; Gupta et al. 1984a, b, c, d, e, f; Jacob et al. 1982; Jacob et al. 1979; James et al. 1987;
James et al. 1991; James and Pillai 1993; James et al. 1989; Jayaraj 1978; Khan 1998; Korakandy 1994; Koya
2008; Kurup 2006; Kurup et al. 1987; Lery et al. 1998; Mathew and Venugopal 1990; Menon 1977; MOA
1993; Monteiro 2006; NCAER 1962; Panikkar et al. 1994; Patil et al. 2006; Pattanayak 1988; Philipose et al.
1987; Pillai et al. 2000; Raghavan and Shanmugham 1993; Rao 1993; Rao et al. 2008; Rao 1986; Samuel
1968; Sathiadas et al. 1995; Sathiadhas et al. 2006; Scariah et al. 2000; Shiyani 2003; Silas and Pillai 1993;
Somvanshi 2001; Srinath et al. 2008a, b; Srinath et al. 1987; Thomas et al. 2006; Varghese 1991; Varghese et
al. 1993; Venkatesh 1987; Verlekar 2008
This includes all vessels
types, i.e., with and
without engines.
Horsepower Ali 1996; Chakraborty et al. 1983; Chittibabu et al. 1988; CMFRI 1981; DAHD 2005; Dan 1985; Devaraj and
Smita 1988; Dineshbabu et al. 2002; DoF 1970; G Luther et al. 1997; GOI 1961, 1971a, b, c, d; GoO 1959,
1969; GoP 1967, 1969, 1973; Gupta et al. 1984a, b, c; James and Pillai 1993; Jayabalan and Devadoss 1980;
Jayasankar and Pillai 1994; Joel and Ebenezer 1996; Johnson 2002; Kalavathy and Tietze 1984; Kemparaju
1994; Korakandy 1994; Kuriyan and Krishnamurthy 1959; Kuthalingam 1970; Kuthalingam et al. 1978;
Mathai et al. 2003; Mathew and Venugopal 1990; Mohamed et al. 2001; Mohan and Nandakumaran 1988;
Muthu et al. 1977; Naik and Neelakantan 1988; Narasimham et al. 1979; Pajot and Mohapatra 1986; Pandya
2003; Panikkar 1998; Panikkar et al. 1993; Panikkar et al. 1994; Philipose et al. 1987; Pillai and Sathiadhas
1982; Pillai et al. 2002; Pillai et al. 2000; Pillai et al. 1983; Pillai et al. 1994; Raghavan and Shanmugham
1993; Rajasree and Kurup 2005; Rane 2001; Rao 1987; Rao 1988; Rao 1993; Rao 1999; Rao et al. 2008; Rao
and Pillai 1992; Rohit et al. 1993; Sathiadas 1982; Sathiadhas 1997a, b, c; Sathiadhas and Panikkar 1989;
Scariah et al. 2000; Sehara 1998; Sehara et al. 1993; Sehara et al. 1988; Sehara et al. 2000; Shiyani 2003;
Silas and Pillai 1993; Silas et al. 1984; Somvanshi et al. 2003; Sreekrishna and Shenoy 2001; Subramani
1987; Sukumaran et al. 1988; Sukumaran et al. 1982; Unnithan et al. 1995; Verghese 1976; Varghese 1991;
Varghese et al. 1993; Verlekar 2008; Yohannan and Sivadas 1993; Zacharia et al. 1996
This includes all vessels
types except vessels
without engines
Fishing days Ali 1996; Balan et al. 1987; Balasubramanian et al. 1993; BOBP 1984; Chakraborty et al. 1983; DAHD 2005;
Gendy 1992; GOI 1961, 1971a, b, c, d, e, f; Gupta et al. 1984; Jacob et al. 1979; James et al. 1987; Jayaraj
1978; Jayasankar 1990; Johnson 2002; Kemparaju 1994; Koya 2008; Kuthalingam 1970; Kuthalingam et al.
1978; Mathew and Venugopal 1990; Mohan and Nandakumaran 1988; Muthu et al. 1977; Naik and
This includes all vessels
types, i.e., with and
without engines.
247
Data Type Sources Remarks
Neelakantan 1988; Narasimham et al. 1979; Noble and Kutty 1978; Panikkar et al. 1993; Panikkar et al. 1994;
Pillai et al. 1991; Raje and Ramamurthy 1990; Rao 1993; Rao 1986; Sathiadhas 1997a, b, c; Sathiadhas and
Panikkar 1989; Sathiadhas et al. 2000; Sehara 1998; Sehara and Kanakkan 1993; Sehara and Karbhari 1989;
Sehara et al. 1991; Sehara et al. 1992; Sehara et al. 1993; Sehara et al. 1988a; Sehara et al. 1988b; Sehara et
al. 2000; Silas et al. 1984; Sukumaran et al. 1982; Unnithan et al. 1995; Verghese 1976; Vivekanandan 1993;
Zacharia et al. 1991; Zacharia et al. 1996
Crew size Balan et al. 1987; George 1972; GOI 1971; Gupta et al. 1984a, b, c; Jacob et al. 1979; James et al. 1987;
Kemparaju 1994; Kurup 1985; Naik and Neelakantan 1988; Nair and Jayaprakash 1986; Noble and Kutty
1978; Rao 1986; Rohit et al. 1993; Sarada 1998; Sehara et al. 1992; Sehara et al. 2000; Sukumaran et al. 1988;
Ummerkutty 1972; Zacharia et al. 1996
This includes only
vessels without engines
248
Appendix C.1. Marine fisheries catch (t) for India, 1950-2005 (contd.)
S pe c ie s 19 5 0 19 5 1 19 5 2 19 5 3 19 5 4 19 5 5 19 5 6 19 5 7 19 5 8 19 5 9 19 6 0 19 6 1
1 Ela s m o bra nc hs
a S ha rks 9851 16069 18015 6726 7869 11914 13624 14350 14434 13725 19925 17725
b S ka te s 509 820 832 400 337 482 551 618 586 558 1211 1084
c R a ys 8150 12592 18184 8225 7518 8572 9178 8585 9225 9270 14154 14990
2 Ee ls 8515 8561 9182 12063 11458 12578 3226 6188 9408 6243 6559 12277
3 C a tf is he s 13697 17356 19113 22795 21084 18904 25763 28099 30387 20770 25615 11129
4 C lupe ids
a Wo lf he rring 8494 3291 2203 2262 5156 4497 7054 5969 5475 5342 5271 6792
b Oil s a rdine 36048 18028 15251 50960 33414 31568 9654 193056 125360 70033 193088 169392
c Othe r s a rdine s 61780 52067 46383 20920 19490 42925 39558 46751 39813 41578 32111 19726
d Hils a s ha d 2434 2347 2264 2111 1968 2349 938 1159 972 789 3342 1110
e Othe r s ha ds 1241 1244 1230 1123 1089 1146 1154 1615 2640 2740 8328 6425
f A nc ho v ie s
i A nc ho v ie lla 21121 42316 29675 18907 21370 19373 21215 12258 29023 24375 35870 22325
ii T hris s o c le s 6847 17949 12242 10924 9427 6895 8052 4456 3875 8132 7449 4983
g Othe r c lupe ids 23647 13218 21491 19537 24897 19765 33051 30491 15121 21083 20841 15208
5 B o m ba y duc k 13571 7042 24124 47616 34127 102931 125892 118658 67579 53880 108083 94437
6 Liza rd f is he s 600 189 489 872 889 1364 1148 389 370 719 603 1002
7 Ha lf a nd F ull 649 594 622 635 606 640 628 449 397 423 247 521
8 F lying f is he s 2564 2583 1781 936 2270 4897 4033 2069 1251 2401 7159 1632
9 P e rc he s 2 3 6 8 10 13 16 23 19 24 32 32
a R o c k c o ds 1924 325 641 402 314 294 508 365 651 579 554 1013
b S na ppe rs 1925 320 673 476 349 405 448 415 549 570 553 1066
c P ig fa c e bre a m s 2491 188 540 399 386 385 513 408 624 663 608 1151
d Thre a df in 9040 4791 7144 3205 1766 2003 4423 2783 6181 3855 4218 6638
e Othe r pe rc he s 10849 2271 4150 2381 1977 3058 3025 2704 3858 3485 3824 5937
10 Go a tf is he s 1686 1875 1106 1186 1301 2281 11388 4027 2177 1724 2716 2404
11 Thre a df ins 15780 15282 14870 15768 14568 14508 8939 14965 6058 7624 6657 6101
12 C ro a ke rs 32037 32529 24533 36509 72245 51909 59374 29718 25370 20294 24992 31135
13 R ibbo n f is he s 19484 18655 34971 57398 29825 28438 24888 37997 41958 31855 17523 19735
14 C a ra ng ids
a Ho rs e m a c ke re l 7176 10047 9760 7247 11623 11900 20029 10956 18143 9906 21847 23232
b S c a ds 4456 3483 3365 3407 3263 3745 3376 3175 3035 3137 3279 3299
c Le a the r- ja c ke ts 9909 9537 9474 9236 9353 9794 10735 3291 3000 2663 4213 3566T ra c hyno t us 83 81 82 79 81 84 83 84 81 23 24 23
d Othe r C a ra ng ids 314 284 290 275 273 291 283 276 271 271 280 295C o ryp ha e na 347 337 331 325 330 346 375 145 253 206 253 145Ela c a t e 427 410 435 399 400 413 459 297 578 280 259 217
15 S ilv e rbe llie s 151 433 283 135 29 24 18 16 13 9 8 6Le io g na t hus 8738 10988 7864 4753 11783 6551 18437 17039 12986 13379 15920 16208G a z z a 447 385 449 347 643 512 1082 936 324 291 608 218
16 B ig ja we d jum pe r 7173 8461 6034 4261 7585 5982 10958 8888 17728 15683 16407 10267
17 P o m fre ts
a B la c k po m fre t 3027 4599 2612 6760 5807 4097 5247 5010 5341 6590 6467 5232
b S ilv e r po m fre t 2330 4537 3257 14074 12045 9676 8113 11588 11275 16727 15385 11713
c C hine s e 41 153 32 140 134 38 98 41 66 55 52 45
18 M a c ke re l
a India n m a c ke re l 90564 102744 83696 69813 27415 22652 18493 89748 124023 62967 134152 35139
b Othe r m a c ke re ls 5 5 5 6 6 6 6 8 7 8 7 6
19 S e e r f is he s
a S . c o m m e rs o ni 3025 2632 2365 2439 3742 2995 7571 5682 5060 4100 4785 6730
b S . g ut t a t us 5784 4304 3739 1925 2271 3194 4906 3413 2623 2586 3696 4702
c S . line o la t us 35 80 43 18 64 55 242 85 116 71 119 53
d A c a nt ho c yb ium 5 5 5 5 5 5 5 5 5 5 5 5
2 0 Tunnie s
a E. a f f in is 3710 625 638 656 1744 2631 3056 2217 2154 2153 3649 5125
b A uxis s p p . 592 110 121 115 273 369 507 302 388 499 873 1009
c K. p e la m is 392 383 382 382 384 386 390 392 389 381 386 661
d T . t o ng g o l 44 35 36 35 38 39 42 40 40 42 50 68
e Othe r tunnie s 635 193 170 194 340 508 604 718 813 339 720 1027
2 1 B ill f is he s 284 228 221 223 215 243 222 212 202 208 217 216
2 2 B a rra c uda s 242 239 1485 336 4013 506 3858 675 985 1131 2099 1442
2 3 M ulle ts 112 115 377 259 165 226 284 1088 747 592 965 843
2 4 Unic o rn c o d 2180 4733 3191 15559 9388 3196 1325 1152 3912 3387 6114 3945
2 5 F la t f is he s
a Ha libut 23 20 19 19 19 20 19 18 18 18 20 19
b F lo unde rs 347 272 263 266 255 292 264 249 239 247 258 262
c S o le s 31205 1981 6440 4564 1636 5610 11048 3749 13115 12206 14654 7993
2 6 C rus ta c e a ns 0
a P e na e id pra wns 33533 33440 31097 33464 48577 40330 68768 75776 29603 34974 32571 39816
b N o n-P e na e id 43930 43029 45750 62971 100237 66830 94794 61960 57222 37325 38722 25409
c Lo bs te rs 23 23 19 17 14 16 19 44 102 143 192 111
d C ra bs 168 221 156 106 108 149 167 653 1134 1597 1983 1812
e S to m a to po ds 234 457 269 105 104 202 103 92 180 283 348 179
2 7 M o llus c s 7 7 7 6 6 6 7 6 6 6 7 6
2 8 C e pha lo po ds 343 335 323 314 323 337 413 346 347 392 502 109
2 9 M is c e lla ne o us
To ta l 576977 542452 536800 589981 590398 598355 714650 878935 759887 587613 883628 687125
249
S pe c ie s 19 6 2 19 6 3 19 6 4 19 6 5 19 6 6 19 6 7 19 6 8 19 6 9 19 7 0 19 7 1 19 7 2 19 7 3
1 Ela s m o bra nc hs
a S ha rks 22233 25635 19464 15620 19259 16815 17141 18679 29268 30550 36069 32063
b S ka te s 1631 1448 1218 952 1064 858 1109 951 1564 1503 1808 1771
c R a ys 17361 17865 15050 15550 16893 11875 13442 16678 27040 25900 23671 24105
2 Ee ls 9689 9157 2286 2550 2637 2595 3466 3127 8422 4787 5272 4571
3 C a tf is he s 19572 18329 23005 17977 22293 24322 24244 27717 72091 67868 56245 59897
4 C lupe ids
a Wo lf he rring 9189 8082 7781 7556 8016 9244 10853 9586 7997 8181 8307 9938
b Oil s a rdine 112299 66114 281534 265038 251141 261486 308307 182495 200328 183002 116154 128313
c Othe r s a rdine s 19613 28098 41518 43467 59409 32794 33833 53498 47346 53036 39320 96040
d Hils a s ha d 1683 2832 3539 1415 1077 890 851 833 933 1504 585 394
e Othe r s ha ds 9047 5562 6692 7875 8745 7075 7999 8040 8194 8902 10502 10578
f A nc ho v ie s
i A nc ho v ie lla 19376 29759 25729 24507 27130 29564 18681 32512 21188 17145 16663 22696
ii T hris s o c le s 5993 5857 6791 4990 9048 9511 8119 9325 12491 9704 10079 11865
g Othe r c lupe ids 12355 15063 17261 19256 23821 22754 25992 26947 23720 21075 24842 31984
5 B o m ba y duc k 86019 94324 82379 75636 79109 76088 83793 77204 67385 61343 44613 57046
6 Liza rd f is he s 1448 760 1641 599 2269 1801 2276 3589 4772 6833 6269 5084
7 Ha lf a nd F ull 165 1219 1626 1182 1465 1295 1113 1026 1577 890 615 1009
8 F lying f is he s 4559 1124 1043 515 3910 3331 4073 4302 2559 8239 1356 5848
9 P e rc he s 30 41 44 53 126 129 139 120 165 157 214 304
a R o c k c o ds 488 543 635 463 770 531 598 862 1006 968 1066 1274
b S na ppe rs 550 611 775 541 859 561 638 861 909 917 1069 1168
c P ig fa c e bre a m s 426 576 543 513 844 626 700 983 933 973 1082 977
d Thre a df in 4860 4201 6733 3692 5552 3517 3809 5795 9827 9018 9770 15936
e Othe r pe rc he s 3062 3337 4154 3497 4300 3308 3382 4643 5464 6337 6838 7756
10 Go a tf is he s 1779 2527 5237 2153 4768 1422 1874 3583 2724 5609 8053 5004
11 Thre a df ins 3030 4631 2215 1716 4674 2666 2491 3547 8793 9613 8677 11339
12 C ro a ke rs 33524 24037 25821 24044 26467 25920 26715 35851 55091 49843 52077 107987
13 R ibbo n f is he s 21200 17369 26471 41898 45494 29604 26170 32151 23478 39093 32005 46950
14 C a ra ng ids
a Ho rs e m a c ke re l 7590 18541 27556 18195 20039 24911 16549 22057 16129 18312 24504 23113
b S c a ds 3385 3398 3499 3409 3478 3469 3518 3464 2913 2940 2947 3172
c Le a the r- ja c ke ts 3566 3354 2543 3190 3569 2975 3231 2506 1855 1954 2645 2530T ra c hyno t us 25 25 72 41 30 72 130 303 291 209 247 283
d Othe r C a ra ng ids 729 180 206 191 201 301 221 624 955 283 301 271C o ryp ha e na 176 30 92 99 207 341 228 252 225 58 200 204Ela c a t e 262 175 231 243 152 168 194 335 467 403 375 321
15 S ilv e rbe llie s 3 4 4 5 6 6 7 8 11 109 73 34Le io g na t hus 18886 18531 29264 26689 38057 44367 37198 45409 66089 47022 42392 63630G a z z a 229 140 121 111 91 109 146 143 234 212 321 345
16 B ig ja we d jum pe r 8493 9092 6726 6011 6019 8929 5550 4879 6978 8944 10723 18890
17 P o m fre ts
a B la c k po m fre t 10839 5897 6440 5266 5050 8033 8604 7087 6963 9051 7993 9704
b S ilv e r po m fre t 15327 11712 13337 12756 13015 19792 19657 17424 15670 18631 15520 17842
c C hine s e 254 70 73 44 43 75 155 65 78 179 140 158
18 M a c ke re l
a India n m a c ke re l 33726 88130 36628 56743 45779 38642 32926 93357 122546 180472 100116 74165
b Othe r m a c ke re ls 8 7 7 8 9 9 8 9 8 8 12 21
19 S e e r f is he s
a S . c o m m e rs o ni 6185 5018 6317 4967 5699 5614 6703 5833 5646 8287 9556 8389
b S . g ut t a t us 4756 4298 5007 4596 4490 4436 6204 5798 5767 7449 8905 9127
c S . line o la t us 50 63 64 40 41 54 97 94 145 226 248 149
d A c a nt ho c yb ium 5 5 5 5 5 5 5 5 4 4 5 5
2 0 Tunnie s
a E. a f f in is 1473 2804 3626 2440 1855 2007 2378 2126 1452 3358 3274 2651
b A uxis s p p . 188 541 381 431 291 342 441 280 269 607 715 550
c K. p e la m is 123 475 410 267 191 277 422 591 437 592 422 789
d T . t o ng g o l 20 48 40 33 25 32 42 49 39 55 47 67
e Othe r tunnie s 648 740 1143 613 776 1186 635 579 510 758 775 1051
2 1 B ill f is he s 220 222 228 222 227 227 229 229 193 194 196 208
2 2 B a rra c uda s 1182 1356 1727 1999 1097 4517 1609 2086 1407 1188 2113 3025
2 3 M ulle ts 1034 1647 3086 1490 1563 3222 3230 2784 3224 5217 2073 3953
2 4 Unic o rn c o d 3312 5517 3822 5731 2771 2203 1937 1797 1972 3805 4830 3782
2 5 F la t f is he s
a Ha libut 19 19 21 22 24 21 22 24 30 31 36 28
b F lo unde rs 269 265 273 266 271 270 274 270 362 352 337 365
c S o le s 18186 9170 6427 9086 6980 5868 11444 12272 20039 19223 14613 18949
2 6 C rus ta c e a ns
a P e na e id pra wns 49505 42770 64934 38412 56430 63497 70636 74352 126733 108583 108041 180210
b N o n-P e na e id 38653 41296 32291 43010 35791 31730 32525 34705 37614 86598 95710 78230
c Lo bs te rs 58 134 218 136 193 183 138 192 298 397 483 574
d C ra bs 941 1805 4403 2377 3494 4194 3509 5690 12722 10686 13915 14655
e S to m a to po ds 93 228 184 57 149 142 112 182 241 3037 813 1694
2 7 M o llus c s 6 6 6 6 7 6 6 6 10 15 10 9
2 8 C e pha lo po ds 109 289 494 307 985 560 1561 799 1537 2167 1354 1785
2 9 M is c e lla ne o us
To ta l 651715 667102 873089 832772 890239 863378 904290 913570 1107331 1184608 1000202 1246824
250
S pe c ie s 19 7 4 19 7 5 19 7 6 19 7 7 19 7 8 19 7 9 19 8 0 19 8 1 19 8 2 19 8 3 19 8 4 19 8 5
1 Ela s m o bra nc hs
a S ha rks 44176 44748 38714 45124 47693 41923 42294 44157 50844 49749 43176 42615
b S ka te s 2853 2705 2216 2559 2605 1931 2300 2299 4130 5003 3820 4261
c R a ys 37609 35996 32124 32977 31563 26325 29508 26480 26880 33727 27976 20628
2 Ee ls 5326 7124 10264 15566 11010 9157 14877 6050 8755 9733 8468 9894
3 C a tf is he s 82692 85643 62081 71243 52693 68778 55374 79910 87939 76962 73201 55318
4 C lupe ids
a Wo lf he rring 8383 10569 9351 10741 9986 9394 11477 10245 13364 14720 14995 15507
b Oil s a rdine 114319 146686 154666 139913 154975 136357 99308 196346 185002 159878 163678 104402
c Othe r s a rdine s 75270 101105 90765 59412 47597 60957 59241 54659 48872 68509 59307 53153
d Hils a s ha d 3442 8079 7194 3747 8751 10955 6118 4773 2810 3405 5171 7653
e Othe r s ha ds 7159 6702 7565 13186 11812 8153 8294 15563 11761 14146 18196 10441
f A nc ho v ie s
i A nc ho v ie lla 37174 28724 28303 32347 37646 26936 33216 49660 53817 92302 87178 71752
ii T hris s o c le s 10493 8801 15432 8947 12658 14656 16959 12118 17220 17586 18441 24702
g Othe r c lupe ids 40402 47621 51898 37617 34284 30902 33890 24078 23477 30088 34263 31199
5 B o m ba y duc k 58152 89753 79909 76617 115624 118776 85501 118959 75392 88712 103737 97980
6 Liza rd f is he s 15858 18950 6615 11827 14412 14928 14366 15249 16517 19358 19768 18206
7 Ha lf a nd F ull 4062 1792 1046 2131 1476 1398 1458 1670 2342 2313 1516 1867
8 F lying f is he s 1024 1845 1482 727 1659 2430 1226 2980 1887 1447 2526 1190
9 P e rc he s 366 433 405 486 493 554 762 607 830 1112 1223 1157
a R o c k c o ds 2163 2014 1101 2025 3040 2159 2047 2239 2957 3228 4421 4320
b S na ppe rs 1664 1963 1290 1942 2347 1554 2033 1822 3105 4580 5632 5439
c P ig fa c e bre a m s 1588 1591 1082 1535 2035 1322 1442 1363 3265 3377 2627 3160
d Thre a df in 28062 25684 11934 25529 41286 30702 28489 22014 31560 35611 50466 51840
e Othe r pe rc he s 12607 14371 8735 12276 16840 12855 14638 14372 18328 24323 30706 29587
10 Go a tf is he s 8750 3855 7801 3228 4081 4186 3468 4557 7277 8185 5827 7662
11 Thre a df ins 14129 17177 18126 5037 6910 7460 7747 5238 8435 7237 9615 11207
12 C ro a ke rs 103314 144692 115257 126586 127838 124572 114125 102331 107930 122952 136856 124148
13 R ibbo n f is he s 56296 51988 57905 37893 70689 63520 55496 36120 42792 34085 46604 73693
14 C a ra ng ids
a Ho rs e m a c ke re l 17394 18512 20044 24768 14417 20195 9007 2500 3070 2746 3302 3179
b S c a ds 3238 3075 3101 3012 3065 3005 3074 5862 6261 9594 11555 6840
c Le a the r- ja c ke ts 3427 3079 2962 3738 3010 2988 3760 4315 5001 8853 8013 7647T ra c hyno t us 135 65 50 39 35 29 225 483 723 851 1047 1240
d Othe r C a ra ng ids 412 6803 8436 6930 3888 11454 12683 20258 20894 24905 28570 29769C o ryp ha e na 259 394 249 257 142 156 139 159 183 222 250 216Ela c a t e 214 419 419 426 368 530 1118 1848 2564 3187 3869 4200
15 S ilv e rbe llie s 3 1 7 12 20 28 30 32 38 44 548 424Le io g na t hus 64111 52230 57919 46582 58008 73833 71378 90743 94933 119329 77964 72380G a z z a 413 426 367 322 335 610 427 353 297 907 917 599
16 B ig ja we d jum pe r 11916 15093 15268 13534 10734 6216 9597 10544 17147 24193 18086 30772
17 P o m fre ts
a B la c k po m fre t 8998 10830 13104 11884 12830 12737 12119 12570 16735 14792 20408 13057
b S ilv e r po m fre t 19703 20085 33858 31219 40765 38985 35177 45829 44657 50093 41474 27477
c C hine s e 111 127 112 158 165 103 91 773 462 362 677 177
18 M a c ke re l
a India n m a c ke re l 34335 41147 59612 57716 80892 64267 47236 43598 25006 27214 36999 53019
b Othe r m a c ke re ls 13 25 18 23 21 18 26 29 39 42 53 58
19 S e e r f is he s
a S . c o m m e rs o ni 9341 8163 8854 10600 9032 12209 11492 11045 17556 15799 16159 14397
b S . g ut t a t us 8405 8688 9191 8324 9718 13926 11330 12799 12024 15141 15371 15162
c S . line o la t us 184 105 165 197 186 236 210 257 252 410 417 435
d A c a nt ho c yb ium 5 4 4 5 5 5 4 4 4 57 68 39
2 0 Tunnie s
a E. a f f in is 6271 6312 12036 8120 6842 15007 11492 10207 10423 8289 10335 14418
b A uxis s p p . 1174 1269 2529 1402 1350 3032 2112 1177 1804 1617 1765 2855
c K. p e la m is 975 1412 1023 931 1392 2037 1324 1611 2153 2504 3154 2915
d T . t o ng g o l 91 119 120 96 139 245 151 141 178 170 374 1207
e Othe r tunnie s 1279 1330 1889 1461 2786 3162 2913 2628 3780 1611 2190 6085
2 1 B ill f is he s 212 202 205 199 201 198 1113 522 1808 926 1092 1038
2 2 B a rra c uda s 4301 1943 2125 2415 3345 2296 1529 2210 3198 3340 3312 2921
2 3 M ulle ts 6096 4507 3339 2892 3478 1950 2201 2656 3044 4101 4229 6601
2 4 Unic o rn c o d 1709 1065 527 221 250 684 930 486 485 485 2610 892
2 5 F la t f is he s
a Ha libut 31 28 30 33 34 47 56 636 1786 2138 1679 2261
b F lo unde rs 383 354 376 345 356 347 391 1522 121 430 562 964
c S o le s 24426 15747 14205 15123 19395 16681 16769 19192 30655 33414 51835 36096
2 6 C rus ta c e a ns
a P e na e id pra wns 147927 184761 159153 128493 172600 146880 139897 111211 141558 149708 164471 151585
b N o n-P e na e id 65112 94984 94330 88315 61011 74806 70853 73506 59372 57062 73067 78504
c Lo bs te rs 699 3636 3132 1498 2109 1441 851 1918 2129 1630 3952 4895
d C ra bs 18020 22971 25125 25451 17490 23269 22097 35772 31625 24675 37088 28600
e S to m a to po ds 2688 4272 1785 1667 2218 6031 22734 23842 37278 27992 37833 41218
2 7 M o llus c s 9 21 30 28 23 30 32 18 28 56 57 44
2 8 C e pha lo po ds 4613 10382 15159 14034 21802 20428 14295 12562 19991 22643 26087 39566
2 9 M is c e lla ne o us
To ta l 1245962 1455199 1394121 1293684 1436459 1412943 1286517 1421678 1476752 1593860 1690837 1586731
251
S pe c ie s 19 8 6 19 8 7 19 8 8 19 8 9 19 9 0 19 9 1 19 9 2 19 9 3 19 9 4 19 9 5 19 9 6 19 9 7
1 Ela s m o bra nc hs
a S ha rks 43336 37636 45401 37992 31621 40254 57019 57896 41894 51198 42455 53836
b S ka te s 4234 5047 2967 2898 2108 1558 2510 1905 2257 2585 3005 4509
c R a ys 20904 28441 23270 26852 31941 23691 21263 24326 20750 23715 25309 31064
2 Ee ls 9516 7359 5104 6149 6250 8268 7329 8104 6387 6290 7455 8313
3 C a tf is he s 66819 51581 77941 67131 48721 49276 45359 52631 48658 41407 38702 47719
4 C lupe ids
a Wo lf he rring 12880 14459 11554 13593 11819 14202 15606 15381 17949 14895 15319 19669
b Oil s a rdine 68669 86117 111899 225913 226211 158556 93296 85786 52497 61503 118159 238183
c Othe r s a rdine s 61282 84058 68262 72638 67865 77710 85908 77966 93807 136925 114667 128623
d Hils a s ha d 3383 4129 2213 13177 13427 25410 22490 28368 28248 22639 28725 31982
e Othe r s ha ds 13350 15403 9817 6655 5777 11624 9387 8108 23699 12767 6892 10808
f A nc ho v ie s 137262 147041 137387 143570
i A nc ho v ie lla 86830 64538 107492 89125 83404 113586 99435 91604 1156 1007 1029 936
ii T hris s o c le s 23836 27950 31624 20447 20205 30716 44272 28743 308 308 304 292
g Othe r c lupe ids 33982 35693 39678 49470 44625 49321 56010 40572 60689 51278 66540 60836
5 B o m ba y duc k 80220 65589 59079 106816 114978 119641 107298 85738 116235 94586 97102 111346
6 Liza rd f is he s 19024 20911 32831 29510 33736 37280 37953 32836 34994 36412 32252 22041
7 Ha lf a nd F ull 1715 1788 3400 2250 2406 2503 2316 2206 3126 6342 4287 4476
8 F lying f is he s 1482 1160 3772 12247 1488 5789 4961 1871 289 4603 1091 254
9 P e rc he s 1605 2196 2464 2674 3196 3492 3837 3567 3225 2818 2748 2923
a R o c k c o ds 3443 5853 6346 7303 6037 8206 10938 13694 10271 13406 15635 16481
b S na ppe rs 3553 4899 5354 5208 3901 3134 3827 4216 4108 4283 4920 6303
c P ig fa c e bre a m s 3844 2717 4102 2735 5365 7292 5972 8972 11073 11329 13287 15655
d Thre a df in 77763 59586 68025 97909 115700 86878 89470 114111 87852 74687 96327 76795
e Othe r pe rc he s 32335 39224 32562 31195 34787 33951 39491 49834 42798 44036 47159 51267
10 Go a tf is he s 10619 14308 36845 30464 30256 47030 23559 18164 14597 11518 12066 12810
11 Thre a df ins 8878 10366 5909 10152 9996 10150 11105 7463 8998 11772 9567 11815
12 C ro a ke rs 132309 131284 120347 132382 155314 181331 204285 191028 208357 183492 170294 168833
13 R ibbo n f is he s 84175 71688 59629 57932 65224 84012 101713 79965 119683 79003 133807 185150
14 C a ra ng ids
a Ho rs e m a c ke re l 8737 6456 18234 13395 19082 16749 9807 19962 26204 23142 18719 20985
b S c a ds 36561 14383 33883 54078 58845 75971 86852 52418 53014 108091 69856 40918
c Le a the r- ja c ke ts 3329 4933 4391 3040 2873 3842 4736 3619 5734 6547 5715 7856T ra c hyno t us 1527 1660 1847 2048 2216 2390 2575 2727 87 163 209 180
d Othe r C a ra ng ids 60550 43268 51430 39877 41907 38006 41831 42407 64209 70774 60728 78453C o ryp ha e na 260 200 211 346 239 223 221 241Ela c a t e 5103 5262 5858 7014 7113 7621 8228 8750 57 98 123 106
15 S ilv e rbe llie s 69 186 1320 1618 1566 3359 3490 1454 77761 70479 66376 71043Le io g na t hus 99405 85615 81378 66691 72800 68908 66351 79139 2423 2298 2117 2062G a z z a 292 372 345 137 116 241 180 172
16 B ig ja we d jum pe r 19418 15646 15283 9285 11986 12413 8341 5335 6189 7588 6909 7841
17 P o m fre ts
a B la c k po m fre t 13390 17198 18150 17679 17912 18216 12936 15394 15405 21889 13505 14529
b S ilv e r po m fre t 24471 26471 32979 37850 32873 34385 28720 34643 27325 27653 25195 34438
c C hine s e 424 423 444 670 578 181 338 1223 1225 577 553 896
18 M a c ke re l
a India n m a c ke re l 71417 68612 89811 251588 157273 99247 120617 214602 218886 186559 288083 234450
b Othe r m a c ke re ls 65 70 73 76 99 92 80 83 131 177 184 177
19 S e e r f is he s 114 165 357 380
a S . c o m m e rs o ni 20501 15995 18149 19015 14189 15203 21875 18074 26949 30977 26569 28468
b S . g ut t a t us 8484 11129 13283 16265 11637 17279 15096 18390 16938 17934 12906 15098
c S . line o la t us 710 814 1389 1209 1326 1640 1898 1841 53 95 101 975
d A c a nt ho c yb ium 86 135 184 231 154 80 10 11 7 10 28 28
2 0 Tunnie s
a E. a f f in is 15946 12401 13690 23202 28861 16052 21057 15263 14761 17372 16555 21351
b A uxis s p p . 7619 4222 5527 6812 6401 4223 5861 4592 11534 5884 12387 10026
c K. p e la m is 3634 4897 4310 5119 4929 5369 6207 12735 5866 6535 7003 6827
d T . t o ng g o l 421 666 1339 1031 998 3384 2511 3651 4613 6521 4801 5809
e Othe r tunnie s 2263 4625 2490 3759 5220 3658 2564 7141 4227 5850 4309 7188
2 1 B ill f is he s 998 792 804 1024 1095 851 1337 1658 1629 1305 3894 3493
2 2 B a rra c uda s 3935 4947 6753 7967 10052 12051 11227 10279 11190 15607 15285 14172
2 3 M ulle ts 4431 6885 9910 11680 5772 9257 7016 6835 11275 8319 8008 7492
2 4 Unic o rn c o d 712 1042 990 726 401 1459 1199 970 454 151 315 516
2 5 F la t f is he s
a Ha libut 1496 2182 2104 2159 3742 3213 2472 2201 2986 3269 3778 2667
b F lo unde rs 1265 1556 1505 2150 2706 2804 3138 2389 402 432 129 447
c S o le s 34830 34038 31795 45501 36511 45303 76890 56744 44405 37418 40008 52322
2 6 C rus ta c e a ns
a P e na e id pra wns 171404 192852 189932 198513 211714 242243 235655 212636 235622 186960 194365 216530
b N o n-P e na e id 82555 35908 56570 94387 95382 118985 106895 81871 76257 77124 107394 159046
c Lo bs te rs 3651 3121 1920 2029 2293 2816 2615 2051 3217 2385 3038 3196
d C ra bs 27626 29192 22373 22189 32227 37766 35073 34757 32708 33605 31553 48763
e S to m a to po ds 60171 122442 87912 102077 89528 83588 97770 109687 102967 69727 75733 97212
2 7 M o llus c s 169 64 59 92 30 139 226 948 1783 1431 3625 2110
2 8 C e pha lo po ds 52802 39189 47538 75015 76962 84645 113102 118858 123663 125210 114451 121741
2 9 M is c e lla ne o us
To ta l 1760714 1713831 1854050 2308359 2245966 2328709 2373608 2340807 2433434 2332166 2491351 2806274
252
S pe c ie s 19 9 8 19 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5
1 Ela s m o bra nc hs
a S ha rks 53801 46016 53275 39681 43103 34930 42274 37681
b S ka te s 4439 3071 3161 2787 3258 3229 4193 3749
c R a ys 27945 24026 24562 22550 21731 27425 23210 18501
2 Ee ls 10576 12188 9845 8984 10173 11183 8732 7987
3 C a tf is he s 58544 51621 62899 55487 65257 60592 59392 57609
4 C lupe ids
a Wo lf he rring 21028 15108 13632 15066 18449 16191 16197 14888
b Oil s a rdine 217138 254002 385614 279106 355471 416032 398499 352600
c Othe r s a rdine s 106559 135250 87038 81712 108761 107243 97067 80424
d Hils a s ha d 19987 11656 12758 18880 26802 41155 67028 68077
e Othe r s ha ds 14352 10152 10982 3682 4847 5201 4856 4279
f A nc ho v ie s 175476 117866 119627 114421 127554 116847 118453 106478
i A nc ho v ie lla 845 1249 1206 1206 1206 1206 1206 1206
ii T hris s o c le s 343 288 644 644 644 644 644 644
g Othe r c lupe ids 60321 53721 51844 49370 49320 43050 42008 38199
5 B o m ba y duc k 139550 100407 102527 94048 135707 142617 122129 118151
6 Liza rd f is he s 25745 18759 28334 25929 28441 31735 38589 35018
7 Ha lf a nd F ull 6743 6289 8708 5736 7292 6913 5184 4424
8 F lying f is he s 3639 2070 4119 6401 6821 5275 3104 2266
9 P e rc he s 2659 4439 6953 6953 6953 6953 6953 6953
a R o c k c o ds 19850 16048 26277 27408 26745 17755 19511 17235
b S na ppe rs 6998 7603 7202 4716 5706 6818 8287 6487
c P ig fa c e bre a m s 12795 12341 12401 14602 12317 11290 13118 9707
d Thre a df in 86644 78013 122392 121008 117684 111297 125387 115579
e Othe r pe rc he s 37705 38552 58862 47183 51303 40234 41249 35788
10 Go a tf is he s 18425 23260 18210 12419 13955 14251 18958 16097
11 Thre a df ins 10276 8586 9527 7862 10938 11324 10117 9493
12 C ro a ke rs 216090 176781 182504 131946 134290 133055 131092 124961
13 R ibbo n f is he s 123394 130856 191878 187241 209893 159821 141887 136116
14 C a ra ng ids
a Ho rs e m a c ke re l 27238 34287 23400 20310 22554 32712 27298 27349
b S c a ds 57039 34517 26872 42677 40376 29785 43753 39812
c Le a the r- ja c ke ts 7865 6292 8180 7890 7123 11682 13085 12041T ra c hyno t us 293 228 245 245 245 245 245 245
d Othe r C a ra ng ids 64589 58696 58541 53426 62648 61137 59997 50302C o ryp ha e na
Ela c a t e 172 134 143 143 143 143 143 143
15 S ilv e rbe llie s 59863 56557 53086 54919 63673 49832 57811 45647Le io g na t hus 1912 1513 2008 2008 2008 2008 2008 2008G a z z a
16 B ig ja we d jum pe r 9668 5203 6510 5189 4212 3306 3462 3308
17 P o m fre ts
a B la c k po m fre t 19865 11423 14432 14535 14135 17413 19499 19192
b S ilv e r po m fre t 36651 26222 29390 30010 30563 26735 24726 23648
c C hine s e 626 965 916 1251 2237 2145 2170 2137
18 M a c ke re l
a India n m a c ke re l 185996 218495 141685 94546 100290 117794 150850 134267
b Othe r m a c ke re ls 286 141 153 155 171 158 154 153
19 S e e r f is he s 1169 178 181 181 181 181 181 181
a S . c o m m e rs o ni 34580 33835 39568 29834 38821 36379 35893 31780
b S . g ut t a t us 24120 14001 14430 15994 17615 16614 16210 15384
c S . line o la t us 122 140 63 47 20 6 9 8
d A c a nt ho c yb ium 28 35 86 158 31 29 206 195
2 0 Tunnie s
a E. a f f in is 18046 22238 23071 20493 23416 20865 20004 17285
b A uxis s p p . 9256 8430 9116 10761 11474 13540 7697 7076
c K. p e la m is 8810 7954 8718 6988 7378 6869 7633 7419
d T . t o ng g o l 5604 8724 9486 8719 6424 4160 6844 6029
e Othe r tunnie s 8433 7500 7460 4372 4889 9907 7302 6360
2 1 B ill f is he s 3389 2967 3791 4675 4348 4878 7512 6114
2 2 B a rra c uda s 23179 15199 19341 19373 18443 15365 15371 13041
2 3 M ulle ts 8837 7494 7290 6849 7684 6170 8344 7803
2 4 Unic o rn c o d 417 333 517 508 933 955 855 712
2 5 F la t f is he s
a Ha libut 2449 1266 1901 1103 1328 1309 1203 985
b F lo unde rs 857 497 576 660 550 445 519 498
c S o le s 47655 47913 52706 39774 42223 46515 36595 33982
2 6 C rus ta c e a ns
a P e na e id pra wns 224596 179681 210822 182663 210332 221803 178626 159949
b N o n-P e na e id 183581 152410 155898 153345 146305 144283 122810 114263
c Lo bs te rs 3287 2295 2668 1596 1526 1431 1585 1350
d C ra bs 37838 30217 52535 33146 39517 45781 45967 39741
e S to m a to po ds 76375 52159 48130 36461 50449 38826 33449 32842
2 7 M o llus c s 2133 1346 1077 2400 5408 2134 2586 2098
2 8 C e pha lo po ds 114562 98181 118067 108265 111372 125340 121347 109858
2 9 M is c e lla ne o us
To ta l 2793248 2509886 2770038 2402697 2705665 2703152 2653269 2405802
253
Appendix C.2. Marine fisheries catch (t) for Gujarat, 1950-2005 (contd.)
S pe c ie s 19 5 0 19 5 1 19 5 2 19 5 3 19 5 4 19 5 5 19 5 6 19 5 7 19 5 8 19 5 9 19 6 0 19 6 1
1 Ela s m o bra nc hs
a S ha rks 26 59 170 104 696 739 1225 1954 1070 729 5164 1717
b S ka te s 3 6 14 7 57 61 100 161 88 60 425 142
c R a ys 8 18 55 33 226 239 397 633 347 236 1674 555
2 Ee ls 5239 5590 5244 4443 4041 4100 819 2074 3083 1104 2649 402
3 C a tf is he s 147 101 137 658 751 1192 1436 1366 2084 560 3486 1293
4 C lupe ids
a Wo lf he rring 83 89 83 27 89 231 817 504 228 377 230 116
b Oil s a rdine 1 1 1 1 1 1 1 1 1 1 1 1
c Othe r s a rdine s 59 62 58 43 33 29 23 3 2 1 2 3
d Hils a s ha d 1258 1333 1251 1058 958 1099 279 422 476 372 2702 469
e Othe r s ha ds 540 583 546 464 425 434 449 671 1367 1347 4197 2394
f A nc ho v ie s
i A nc ho v ie lla 60 64 42 334 50 44 58 48 37 23 18 8
ii T hris s o c le s 138 146 95 774 114 102 133 109 76 33 34 17
g Othe r c lupe ids 5806 2971 5367 5108 3010 3013 6234 7758 2337 2579 1818 1087
5 B o m ba y duc k 4444 2499 7916 22107 17990 34912 50569 57860 26339 23607 76102 65147
6 Liza rd f is he s 46 26 81 227 184 358 185 167 147 110 116 102
7 Ha lf a nd F ull 9 9 9 8 7 7 7 6 5 3 3 2
8 F lying f is he s 41 43 41 34 31 31 32 17 1 7 17 25
9 P e rc he s
a R o c k c o ds 10 3 52 27 9 5 3 5 15 2 4 6
b S na ppe rs 13 4 69 36 11 7 4 7 20 2 5 8
c P ig fa c e bre a m s 10 3 52 27 9 5 4 5 15 2 4 6
d Thre a df in 162 38 810 3 132 80 55 85 227 37 56 91
e Othe r pe rc he s 129 58 669 26 141 108 104 167 281 100 218 216
10 Go a tf is he s 94 131 151 60 147 208 273 227 180 134 144 127
11 Thre a df ins 92 116 88 85 229 3136 2085 5367 1698 2049 4533 3252
12 C ro a ke rs 4067 4854 2133 7052 13993 7136 8013 5276 3394 1656 2774 1764
13 R ibbo n f is he s 1970 875 962 8022 3539 1826 2259 1905 1881 408 198 541
14 C a ra ng ids
a Ho rs e m a c ke re l 9 10 9 22 52 48 210 28 33 10 14 77
b S c a ds 1 1 1 1 1 1 1 1 1 1 1 1
c Le a the r- ja c ke ts 386 415 385 325 295 298 304 302 95 84 454 252T ra c hyno t us 1 1 1 1 1 1 1 1 1 1 1 1
d Othe r C a ra ng ids 20 22 20 17 16 16 16 16 16 13 16 16C o ryp ha e na 1 1 1 1 1 1 1 1 1 1 1 1Ela c a t e 1 1 1 1 1 1 1 1 1 1 1 1
15 S ilv e rbe llie s 151 433 283 135 29 24 18 16 13 9 8 6Le io g na t hus
G a z z a
16 B ig ja we d jum pe r 38 110 71 34 7 7 6 731 1432 1788 1620 1094
17 P o m fre ts
a B la c k po m fre t 54 7 109 481 426 537 254 658 528 835 1488 751
b S ilv e r po m fre t 286 44 589 2614 2311 2916 1382 3575 2868 4536 8084 4078
c C hine s e 1 1 1 1 1 1 1 1 1 1 1 1
18 M a c ke re l
a India n m a c ke re l 79 84 79 67 60 61 62 60 56 45 48 43
b Othe r m a c ke re ls 1 1 1 1 1 1 1 1 1 1 1 1
19 S e e r f is he s
a S . c o m m e rs o ni 1 1 1 1 7 9 19 11 11 13 20 15
b S . g ut t a t us 22 23 46 33 211 255 554 309 289 366 593 433
c S . line o la t us 0 0 0 0 0 0 0 0 0 0 0
d A c a nt ho c yb ium 1 1 1 1 1 1 1 1 1 1 1 1
2 0 Tunnie s
a E. a f f in is 5 5 5 6 8 11 13 5 9 11 19 24
b A uxis s p p . 1 1 1 1 1 1 1 1 1 1 1 1
c K. p e la m is 1 1 1 1 1 1 1 1 1 1 1 1
d T . t o ng g o l 1 1 1 1 1 1 1 1 1 1 1 1
e Othe r tunnie s 1 1 1 2 3 4 5 2 4 5 8 9
2 1 B ill f is he s 1 1 1 1 1 1 1 1 1 1 1 1
2 2 B a rra c uda s 8 8 8 28 45 66 86 106 124 120 73 1
2 3 M ulle ts 51 54 51 43 39 177 194 526 424 518 636 529
2 4 Unic o rn c o d 738 1716 1068 4106 2489 823 346 55 699 12 18 21
2 5 F la t f is he s
a Ha libut 1 1 1 1 1 1 1 1 1 1 1 1
b F lo unde rs
c S o le s 69 74 454 32 21 58 106 100 38 1409 6 4
2 6 C rus ta c e a ns
a P e na e id pra wns 5831 6149 6149 7278 10881 6986 10243 16919 5037 8436 4817 2990
b N o n-P e na e id 13682 14425 14426 17075 25526 16382 24056 18461 16851 7183 358 189
c Lo bs te rs 4 4 4 3 3 3 3 3 1 8 19 10
d C ra bs 1 1 1 1 1 1 1 1 1 1 1 1
e S to m a to po ds 1 1 1 1 1 1 1 1 1 1 1 1
2 7 M o llus c s 1 1 1 1 1 1 1 1 1 1 1 1
2 8 C e pha lo po ds 1 1 1 1 1 1 1 1 1 1 1 1
To ta l 45908 43288 49875 83088 89313 87795 113459 128696 73942 60956 124888 90049
254
S pe c ie s 19 6 2 19 6 3 19 6 4 19 6 5 19 6 6 19 6 7 19 6 8 19 6 9 19 7 0 19 7 1 19 7 2 19 7 3
1 Ela s m o bra nc hs
a S ha rks 9378 4767 5372 2311 2629 1938 2321 2553 5502 5764 6038 3248
b S ka te s 771 392 441 190 216 160 191 210 453 473 497 266
c R a ys 3037 1544 1740 748 851 628 752 827 1782 1867 1956 1052
2 Ee ls 530 806 554 80 51 47 400 153 1319 1055 1484 1632
3 C a tf is he s 8190 4833 4971 2325 2641 1835 2233 2426 5426 4664 5080 2384
4 C lupe ids
a Wo lf he rring 216 369 309 597 632 440 423 565 732 1902 1991 1090
b Oil s a rdine 1 1 1 1 1 1 1 1 1 1 1 1
c Othe r s a rdine s 10 2 7 5 2 3 1 5 8 9 82 64
d Hils a s ha d 1113 2040 1199 662 226 469 451 433 524 493 132 40
e Othe r s ha ds 3354 1492 2138 1228 2123 2608 2689 3004 3673 4347 4524 5468
f A nc ho v ie s
i A nc ho v ie lla 8 1 2 3 4 5 5 6 6 7 8 43
ii T hris s o c le s 27 63 147 229 244 50 91 133 521 182 96 152
g Othe r c lupe ids 873 1962 1323 5412 6513 5243 5594 5037 3973 2802 3365 1734
5 B o m ba y duc k 54061 66537 59205 49390 45850 44548 53085 48867 36543 29715 24552 24101
6 Liza rd f is he s 85 71 52 37 19 2 236 471 961 1241 1544 658
7 Ha lf a nd F ull 1 1 1 1 1 1 1 1 1 1 1 1
8 F lying f is he s 33 41 48 57 65 73 80 88 82 87 95 105
9 P e rc he s
a R o c k c o ds 4 2 2 2 5 5 8 8 22 16 58 88
b S na ppe rs 5 3 3 2 7 7 11 11 29 21 77 117
c P ig fa c e bre a m s 4 2 2 2 5 5 8 8 22 16 58 89
d Thre a df in 58 28 33 24 86 86 132 131 336 249 907 1385
e Othe r pe rc he s 175 123 26 19 67 68 105 104 267 198 720 1096
10 Go a tf is he s 109 90 70 51 32 29 18 6 7 5 3 1
11 Thre a df ins 807 296 400 136 399 234 128 157 1363 3108 2105 3402
12 C ro a ke rs 4776 2820 2461 1635 1566 1421 1498 2211 5443 4535 5008 46958
13 R ibbo n f is he s 280 476 336 647 1183 1449 1515 1419 1370 942 1209 807
14 C a ra ng ids
a Ho rs e m a c ke re l 73 125 112 931 922 876 880 730 312 645 194 212
b S c a ds 1 1 1 1 1 1 1 1 1 1 1 1
c Le a the r- ja c ke ts 138 206 158 37 29 22 15 7 174 89 5 181T ra c hyno t us 1 1 1 1 1 1 1 1 1 1 1 1
d Othe r C a ra ng ids 16 16 13 27 44 58 71 86 86 98 167 118C o ryp ha e na 1 1 1 1 1 1 1 1 1 1 1 1Ela c a t e 1 1 1 1 1 1 1 1 1 1 1 1
15 S ilv e rbe llie s 3 4 4 5 6 6 7 8 11 109 73 34Le io g na t hus
G a z z a
16 B ig ja we d jum pe r 554 3 2 12 71 130 188 247 416 481 556 894
17 P o m fre ts
a B la c k po m fre t 684 914 888 834 760 703 714 673 1341 1103 687 609
b S ilv e r po m fre t 3717 4967 4820 4529 4130 3819 3873 3658 7283 5994 3727 3311
c C hine s e 1 1 1 1 1 1 1 1 1 1 1 1
18 M a c ke re l
a India n m a c ke re l 37 32 25 19 13 7 7 4 2 1 1 3
b Othe r m a c ke re ls 1 1 1 1 1 1 1 1 1 1 1 1
19 S e e r f is he s
a S . c o m m e rs o ni 8 19 9 21 29 30 27 34 30 70 81 120
b S . g ut t a t us 237 572 270 624 842 866 809 987 847 2038 2339 3473
c S . line o la t us 0 0
d A c a nt ho c yb ium 1 1 1 1 1 1 1 1 1 1 1 1
2 0 Tunnie s
a E. a f f in is 8 32 13 3 2 2 2 1 1 1 1 118
b A uxis s p p . 1 1 1 1 1 1 1 1 1 1 1 1
c K. p e la m is 1 1 1 1 1 1 1 1 1 1 1 1
d T . t o ng g o l 1 1 1 1 1 1 1 1 1 1 1 1
e Othe r tunnie s 3 12 5 1 1 1 1 1 1 15 30 46
2 1 B ill f is he s 1 1 1 1 1 1 1 1 1 1 1 1
2 2 B a rra c uda s 3 8 3 3 3 1 7 13 16 21 26 22
2 3 M ulle ts 484 459 302 390 663 1024 1468 1007 1524 2213 1115 1301
2 4 Unic o rn c o d 2 7 9 1 8 15 23 30 32 37 45 52
2 5 F la t f is he s
a Ha libut 1 1 1 1 1 1 1 1 1 1 1 1
b F lo unde rs
c S o le s 8 8 24 42 86 98 96 81 105 68 489 1681
2 6 C rus ta c e a ns
a P e na e id pra wns 1504 1729 1331 3983 4138 3662 3211 2610 3605 3779 2637 12232
b N o n-P e na e id 851 2005 838 515 455 419 501 651 1293 187 287 82
c Lo bs te rs 3 5 22 40 57 11 1 5 15 20 26 29
d C ra bs 1 1 1 1 1 1 1 1 3 4 519 875
e S to m a to po ds 1 1 1 1 1 1 1 1 1 1 1 1
2 7 M o llus c s 1 1 1 1 1 1 1 1 1 1 1 1
2 8 C e pha lo po ds 1 1 1 1 1 1 1 1 3 3 4 1
To ta l 96257 99905 89711 77827 77693 73121 83895 79684 87476 80690 74611 121391
255
S pe c ie s 19 7 4 19 7 5 19 7 6 19 7 7 19 7 8 19 7 9 19 8 0 19 8 1 19 8 2 19 8 3 19 8 4 19 8 5
1 Ela s m o bra nc hs
a S ha rks 9797 10004 6565 14453 10723 5011 12695 10836 12466 7981 8046 12348
b S ka te s 806 823 540 1189 881 412 1044 892 1025 651 515 954
c R a ys 3174 3241 2127 4682 3474 1624 4112 3509 4037 4781 4432 2660
2 Ee ls 3367 3009 3923 10007 2524 3834 10531 2526 3235 5258 3664 4198
3 C a tf is he s 7527 2986 2520 10440 5488 7666 6467 11848 14472 11883 11030 12731
4 C lupe ids
a Wo lf he rring 1671 2001 1815 2814 1695 1892 3372 2432 2939 2811 2442 6154
b Oil s a rdine 1 1 1 2 2 2 2 2 2 3 3 3
c Othe r s a rdine s 50 23 2 4 6 7 8 9 11 13 64 109
d Hils a s ha d 246 2869 909 273 44 218 49 14 125 687 60 309
e Othe r s ha ds 3193 3306 2833 4690 5169 3682 3524 8371 4143 3095 4137 4559
f A nc ho v ie s
i A nc ho v ie lla 92 118 758 1407 2201 3064 3518 3843 4554 5304 5933 9652
ii T hris s o c le s 241 105 236 93 573 685 845 1079 4142 3280 2198 7140
g Othe r c lupe ids 11585 11722 9653 8263 12441 5506 7844 5946 3348 2444 4312 6543
5 B o m ba y duc k 29805 39669 30502 28267 50559 63588 33756 45618 32616 43395 49423 35380
6 Liza rd f is he s 2129 1528 3344 50 110 9 107 128 936 743 1947 1501
7 Ha lf a nd F ull 1 1 9 9 13 18 1 1 1 1 2 3
8 F lying f is he s 130 109 101 95 96 95 82 69 65 60 55 48
9 P e rc he s
a R o c k c o ds 106 123 173 45 175 44 97 175 129 394 814 482
b S na ppe rs 142 164 232 60 234 60 129 234 172 836 753 918
c P ig fa c e bre a m s 107 124 176 45 177 45 98 177 161 149 135 55
d Thre a df in 1672 1939 2735 706 2755 702 1517 2761 2029 1408 5104 5323
e Othe r pe rc he s 1323 1535 2166 561 2183 556 1201 2188 1608 3158 3475 5393
10 Go a tf is he s 1 326 643 730 3 9 150 270 371 482 303 467
11 Thre a df ins 7198 10441 7303 311 445 1056 820 1802 4064 4121 4533 5893
12 C ro a ke rs 33305 54990 34180 47104 45322 41139 39503 40718 31161 35009 38179 42371
13 R ibbo n f is he s 1834 965 10622 12260 6436 4408 9870 6932 8045 5420 6962 15978
14 C a ra ng ids
a Ho rs e m a c ke re l 1077 905 1437 881 255 509 426 641 285 398 872 535
b S c a ds 1 1 1 1 1 1 1 1 2 65 33 1
c Le a the r- ja c ke ts 79 41 10 289 248 382 776 1921 1856 906 1926 1393T ra c hyno t us 1 1 1 1 1 1 1 1 1 1 1 1
d Othe r C a ra ng ids 75 12 19 27 37 47 52 55 134 363 527 762C o ryp ha e na 1 1 1 1 1 1 1 1 1 1 1 1Ela c a t e 1 1 2 2 2 2 2 2 2 2 2 2
15 S ilv e rbe llie s 3 1 7 12 20 28 30 32 38 44 548 424Le io g na t hus
G a z z a
16 B ig ja we d jum pe r 1359 6485 9283 8693 4500 1145 3159 4371 7443 13704 8796 21618
17 P o m fre ts
a B la c k po m fre t 1358 831 311 1332 2490 1673 1937 2729 1884 2600 2915 2038
b S ilv e r po m fre t 7370 4510 1687 7235 13517 9086 10520 14818 10229 8652 7600 9526
c C hine s e 1 1 1 1 3 1 1 2 1 68 135 64
18 M a c ke re l
a India n m a c ke re l 4 5 5 7 9 10 30 24 21 17 13 11
b Othe r m a c ke re ls 1 1 1 1 1 1 1 1 1 1 1 1
19 S e e r f is he s
a S . c o m m e rs o ni 23 56 48 59 117 89 129 156 176 194 114 1110
b S . g ut t a t us 668 1622 1381 1717 3398 2585 3731 4525 2300 3308 3306 4546
c S . line o la t us
d A c a nt ho c yb ium 1 1 1 1 1 1 1 1 1 1 1 1
2 0 Tunnie s
a E. a f f in is 293 245 322 147 214 221 129 679 149 227 789 2353
b A uxis s p p . 1 1 1 1 1 1 1 1 1 1 1 2
c K. p e la m is 1 1 1 1 1 1 1 1 1 1 1 1
d T . t o ng g o l 1 1 1 1 1 1 1 1 1 1 1 44
e Othe r tunnie s 113 95 125 56 82 85 50 263 69 11 503 4010
2 1 B ill f is he s 1 1 1 1 1 1 1 1 555 74 12 21
2 2 B a rra c uda s 47 20 7 116 175 240 273 296 350 347 49 71
2 3 M ulle ts 1772 721 1308 1061 1687 1096 1293 1293 1427 2702 2858 3863
2 4 Unic o rn c o d 67 81 102 124 155 366 708 386 333 274 216 150
2 5 F la t f is he s
a Ha libut 1 1 1 1 1 1 1 1 438 436 496 962
b F lo unde rs
c S o le s 1891 3400 3347 813 339 568 2907 4248 2854 1317 7182 3309
2 6 C rus ta c e a ns
a P e na e id pra wns 8155 16020 13634 10398 10545 12487 18010 12637 14082 11709 12936 14893
b N o n-P e na e id 205 2869 9275 1487 4137 4884 5139 5486 4800 10236 10241 8454
c Lo bs te rs 41 1934 1358 382 346 235 194 695 427 435 1469 874
d C ra bs 7 12 268 2922 782 1145 6225 19183 8857 3293 17364 11225
e S to m a to po ds 1 1 1 1 1 1 1 1 5964 2606 4941 4478
2 7 M o llus c s 1 1 1 1 1 1 1 1 1 1 1 1
2 8 C e pha lo po ds 10 737 2733 1702 2624 7825 4351 3181 3507 4707 2779 5406
To ta l 144136 192735 170750 188031 199425 190055 201428 230015 204044 212069 247151 283322
256
S pe c ie s 19 8 6 19 8 7 19 8 8 19 8 9 19 9 0 19 9 1 19 9 2 19 9 3 19 9 4 19 9 5 19 9 6 19 9 7
1 Ela s m o bra nc hs
a S ha rks 8344 8522 10968 8957 8584 15574 18581 25377 16081 24443 15480 19136
b S ka te s 1017 1010 481 356 352 101 191 364 1055 218 553 638
c R a ys 2276 2644 1962 2774 7954 3661 3295 2403 2778 4155 3330 3896
2 Ee ls 4677 2014 1631 2715 2536 3963 3215 3103 3049 3081 3106 3812
3 C a tf is he s 13661 11685 11291 11538 14364 12817 13732 15143 15329 14488 16136 21382
4 C lupe ids
a Wo lf he rring 4034 5400 3237 3956 3110 4544 4867 4440 5959 5256 4528 6982
b Oil s a rdine 3 3 2 1 1 1 1 1
c Othe r s a rdine s 17 126 16 9 2 4 6 4 1 17 372 503
d Hils a s ha d 210 361 573 636 98 1144 909 148 436 547 571 504
e Othe r s ha ds 4539 2853 1813 2573 2754 3054 3317 3157 4269 2229 1551 4261
f A nc ho v ie s 27243 26766 27230 27810
i A nc ho v ie lla 10659 8296 8543 13346 16845 22388 13903 13632
ii T hris s o c le s 2877 4975 4736 4342 4284 6867 5802 5138
g Othe r c lupe ids 3775 3696 3318 4202 4007 4088 3894 4332 8415 7909 7173 13340
5 B o m ba y duc k 41278 37241 30927 78008 68344 67240 71497 53147 91082 63977 71695 85905
6 Liza rd f is he s 283 386 837 780 2983 3813 1434 1745 2054 2419 2544 3378
7 Ha lf a nd F ull 4 4 4 4 3 2 2 4 33 19 52 22
8 F lying f is he s 42 1 1 1 11 4 2 2 1 1 1 1
9 P e rc he s
a R o c k c o ds 280 1125 192 813 442 726 777 1139 1396 1867 2669 2636
b S na ppe rs 296 335 311 572 436 102 159 386 1268 1081 1325 1439
c P ig fa c e bre a m s 56 49 71 60 94 51 314 341 701 802 1144 1353
d Thre a df in 7147 2495 3317 3054 4239 5964 4876 6944 7590 8836 9546 19046
e Othe r pe rc he s 3812 3099 2492 3997 3022 4205 4647 5649 7439 6174 5604 7848
10 Go a tf is he s 103 376 532 1833 556 316 1042 1825 1956 2487 2768 2956
11 Thre a df ins 3843 2873 2558 3247 3221 3748 4569 3164 4444 7679 4899 7849
12 C ro a ke rs 35837 33573 28858 43072 48659 83953 100346 83316 110458 91795 76681 88715
13 R ibbo n f is he s 24935 16714 13259 20986 21670 25443 37001 33914 47317 36219 57295 88733
14 C a ra ng ids
a Ho rs e m a c ke re l 1416 878 1138 2002 1860 3366 1991 2793 7058 5821 4607 7833
b S c a ds 1 3 1 21 5 16 9 1 77 557 3351 2205
c Le a the r- ja c ke ts 1147 1095 940 696 543 1218 639 677 1379 2319 1666 2224T ra c hyno t us 1 1 1 1 1 1 1 1
d Othe r C a ra ng ids 354 927 711 830 764 689 1098 1086 2789 3268 3838 4818C o ryp ha e na 1 1 1 1 1 1 1 1Ela c a t e 2 2 2 2 2 2 2 2
15 S ilv e rbe llie s 69 186 1320 1618 1566 3359 3490 1454 1086 1955 2309 2399Le io g na t hus
G a z z a
16 B ig ja we d jum pe r 7952 8945 5460 3836 3337 4286 3364 1576 1489 2551 1812 1708
17 P o m fre ts
a B la c k po m fre t 3129 2491 2248 1017 716 1895 1530 1531 2798 2269 2391 4504
b S ilv e r po m fre t 6818 6208 7839 8291 5767 7470 7224 8708 6950 7354 8349 13085
c C hine s e 153 43 8 10 10 10 9 10 79 264 80 230
18 M a c ke re l
a India n m a c ke re l 11 4 21 34 6 42 101 71 430 562 2775 4625
b Othe r m a c ke re ls 1 1 1 1 1 1 1 1 1 1 1 1
19 S e e r f is he s
a S . c o m m e rs o ni 3264 1978 1255 2262 1760 2227 2290 2906 6595 9510 4311 6011
b S . g ut t a t us 1593 2841 1795 2922 1553 3604 4444 7310 6377 9498 4296 7981
c S . line o la t us
d A c a nt ho c yb ium 1 1 1 1 1 1 1 1 1 3 1 1
2 0 Tunnie s
a E. a f f in is 492 467 352 241 144 925 241 519 1056 1258 1585 2372
b A uxis s p p . 185 4 60 182 220 141 129 107 1211 1009 882 849
c K. p e la m is 1 1 1 1 1 1 1 1 19 7 1 1
d T . t o ng g o l 31 18 2 1 15 1540 1726 2485 3211 4861 3533 4800
e Othe r tunnie s 375 2446 545 1533 2220 1540 367 4331 1560 3260 835 2485
2 1 B ill f is he s 14 124 4 17 63 52 152 131 398 378 2020 1385
2 2 B a rra c uda s 19 29 168 474 364 908 477 317 996 853 1308 1474
2 3 M ulle ts 2588 3709 6456 9404 3332 3570 2725 2302 2423 3088 2667 2643
2 4 Unic o rn c o d 87 26 49 12 4 2 3 1 46 12 24 1
2 5 F la t f is he s
a Ha libut 262 232 333 421 299 619 379 615 999 1363 2024 1667
b F lo unde rs
c S o le s 1379 3274 1761 2047 2645 5816 4574 3240 4979 6663 4522 6360
2 6 C rus ta c e a ns
a P e na e id pra wns 17308 19687 18922 18150 23992 31107 35019 23747 40209 35779 28598 44347
b N o n-P e na e id 12055 8382 9558 40556 48762 61529 63301 43293 51697 55173 69032 89772
c Lo bs te rs 565 988 770 902 500 836 827 868 1334 1301 1186 1392
d C ra bs 5530 5464 4792 4303 9687 8877 6397 2830 7461 10904 10077 14227
e S to m a to po ds 4107 5220 2965 2603 2309 4684 5838 2471 3025 3671 4669 6819
2 7 M o llus c s 1 1 1 1 1 1 1 1 1 1 1 1
2 8 C e pha lo po ds 8282 8373 3451 9173 6632 15775 15784 17259 26282 18388 23201 28872
To ta l 253196 233904 204860 325394 337652 439883 462514 401467 544371 506365 512205 679239
257
S pe c ie s 19 9 8 19 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5
1 Ela s m o bra nc hs
a S ha rks 28196 21622 24708 12363 12618 10433 11328 11685
b S ka te s 1416 747 1291 707 831 1132 1303 1344
c R a ys 4142 2537 2797 1763 1442 1541 1803 1860
2 Ee ls 6064 6921 4262 3354 3659 3504 1936 1997
3 C a tf is he s 28761 25614 24307 18936 25806 23881 23677 24423
4 C lupe ids
a Wo lf he rring 8116 5542 5103 4945 5877 4928 4535 4678
b Oil s a rdine
c Othe r s a rdine s 186 195 277 211 207 285 196 202
d Hils a s ha d 75 138 188 21 1772 7 66 68
e Othe r s ha ds 3770 1930 5369 1780 1822 1856 1489 1536
f A nc ho v ie s 35740 24483 23337 20842 23453 22591 17048 17585
i A nc ho v ie lla
ii T hris s o c le s
g Othe r c lupe ids 11995 8719 7490 6117 5368 5341 4529 4672
5 B o m ba y duc k 89089 73995 72377 53856 73631 65003 45557 46993
6 Liza rd f is he s 6125 4217 9738 6039 7533 6860 9157 9446
7 Ha lf a nd F ull 86 38 209 261 391 72 181 187
8 F lying f is he s 1 118 47 1 1 10 89 92
9 P e rc he s
a R o c k c o ds 6129 3965 10579 3886 1877 1056 2419 2495
b S na ppe rs 1392 1117 729 220 380 405 514 530
c P ig fa c e bre a m s 1051 1410 1007 225 256 582 1024 1056
d Thre a df in 25832 18871 47838 27373 33432 25130 18038 18607
e Othe r pe rc he s 9953 8287 26487 8208 5982 3405 4095 4224
10 Go a tf is he s 5940 3308 3335 1069 593 415 937 967
11 Thre a df ins 6772 4609 5063 4081 6439 7041 4212 4345
12 C ro a ke rs 122731 99967 92976 53002 45589 43240 43760 45139
13 R ibbo n f is he s 51532 47209 59390 63923 57895 62593 59473 61347
14 C a ra ng ids
a Ho rs e m a c ke re l 12292 7743 7111 4790 2673 5321 5501 5675
b S c a ds 1277 1777 5201 2301 2318 1681 3783 3902
c Le a the r- ja c ke ts 2882 1398 2977 2823 1980 2745 4047 4174T ra c hyno t us
d Othe r C a ra ng ids 5095 3939 6927 2484 1756 2103 2816 2905C o ryp ha e na
Ela c a t e
15 S ilv e rbe llie s 1889 1025 2197 2052 658 677 534 551Le io g na t hus
G a z z a
16 B ig ja we d jum pe r 834 569 560 266 270 610 256 264
17 P o m fre ts
a B la c k po m fre t 4026 2292 2877 1523 2067 1534 2271 2342
b S ilv e r po m fre t 13772 8776 9321 6904 6984 7269 5859 6043
c C hine s e 122 365 236 482 453 569 336 347
18 M a c ke re l
a India n m a c ke re l 2136 2362 2577 1305 1175 1031 3261 3364
b Othe r m a c ke re ls 1 1 1 2 1 1 1 1
19 S e e r f is he s
a S . c o m m e rs o ni 6278 5139 3620 2455 4082 3419 2249 2320
b S . g ut t a t us 10744 4539 6861 5349 3909 4242 6207 6403
c S . line o la t us 2 2
d A c a nt ho c yb ium 1 1 21 2 1 1 1 1
2 0 Tunnie s
a E. a f f in is 1316 1536 1899 1451 923 592 1171 1208
b A uxis s p p . 428 209 776 162 605 75 545 562
c K. p e la m is 1 1 48 18 30 6 133 137
d T . t o ng g o l 3521 5215 7392 4006 3925 1138 2153 2220
e Othe r tunnie s 1479 188 167 577 408 631 1433 1478
2 1 B ill f is he s 817 518 423 171 108 403 350 361
2 2 B a rra c uda s 2935 1691 3768 1953 847 741 1226 1265
2 3 M ulle ts 4160 2969 2323 2351 2857 1807 2699 2784
2 4 Unic o rn c o d 15 1 117 1 42 145 106 109
2 5 F la t f is he s
a Ha libut 1578 710 844 190 134 101 142 146
b F lo unde rs
c S o le s 10809 5433 6178 2791 1316 1607 1392 1435
2 6 C rus ta c e a ns
a P e na e id pra wns 51912 35304 36589 24310 23923 34852 25976 26794
b N o n-P e na e id 100479 92175 85140 74659 64541 48484 49025 50570
c Lo bs te rs 1155 1027 1132 446 244 206 208 215
d C ra bs 8816 5013 21930 7454 4872 8444 9292 9585
e S to m a to po ds 7708 3069 5965 2615 1350 1589 1547 1596
2 7 M o llus c s 1 1 1 1 5 1 1 1
2 8 C e pha lo po ds 33045 25825 43934 27385 26310 29654 25274 26071
To ta l 746617 586374 698015 476466 477619 452987 417162 430310
258
Appendix C.3. Marine fisheries catch (t) for Daman & Diu, 1970-2005 (contd.)
S pe c ie s 19 7 0 19 7 1 19 7 2 19 7 3 19 7 4 19 7 5 19 7 6 19 7 7 19 7 8 19 7 9 19 8 0 19 8 1
1 Ela s m o bra nc hs
a S ha rks 57 62 70 42 101 120 85 176 113 51 144 133
b S ka te s 42 47 53 31 75 89 63 131 85 38 107 99
c R a ys 44 48 54 32 77 92 65 134 87 39 110 103
2 Ee ls 1 1 1 1 1 1 1 3 1 1 3 1
3 C a tf is he s 67 61 71 37 93 43 39 152 69 95 88 174
4 C lupe ids
a Wo lf he rring 38 98 102 54 72 97 90 139 78 82 159 125
b Oil s a rdine 53 87 121 155 189 223 257 291 325 359 393 427
c Othe r s a rdine s 20 27 231 175 118 62 5 9 14 19 23 27
d Hils a s ha d 36 35 9 3 14 189 61 19 3 13 3 1
e Othe r s ha ds 192 228 234 275 139 163 143 234 241 162 168 435
f A nc ho v ie s
i A nc ho v ie lla 1 1 1 1 1 1 3 5 7 9 11 14
ii T hris s o c le s 6 2 1 2 3 1 3 1 6 7 8 13
g Othe r c lupe ids 72 52 60 31 176 201 169 144 202 85 130 108
5 B o m ba y duc k 592 484 393 376 403 605 476 439 732 870 499 736
6 Liza rd f is he s
7 Ha lf a nd F ull 2 1 4 2 4 3 77 80 111 142 4 3
8 F lying f is he s
9 P e rc he s
a R o c k c o ds
b S na ppe rs 3 2 11 18 17 23 35 8 28 7 17 33
c P ig fa c e bre a m s
d Thre a df in 1 1 2 4 3 5 7 2 7 1 3 8
e Othe r pe rc he s 2 2 7 13 12 17 26 6 22 5 13 25
10 Go a tf is he s 1 1 1 1 1 82 175 186 1 2 35 69
11 Thre a df ins 23 54 39 69 118 198 149 6 8 17 15 35
12 C ro a ke rs 17 15 17 178 102 194 131 170 143 124 133 148
13 R ibbo n f is he s 40 27 35 22 44 26 296 340 167 108 261 200
14 C a ra ng ids
a Ho rs e m a c ke re l 4 8 3 3 11 10 17 10 3 5 5 8
b S c a ds 1 1 1 1 1 1 1 1 1 1 1 1
c Le a the r- ja c ke ts 41 21 1 40 15 8 3 65 51 75 164 443T ra c hyno t us 4 4 3 3 3 3 3 3 3 3 3 3
d Othe r C a ra ng ids 9 10 17 12 7 1 2 3 3 4 5 6C o ryp ha e na
Ela c a t e 1 1 1 1 1 1 10 10 10 10 10 10
15 S ilv e rbe llie sLe io g na t hus
G a z z a
16 B ig ja we d jum pe r
17 P o m fre ts
a B la c k po m fre t 387 332 222 218 389 276 111 450 732 476 612 933
b S ilv e r po m fre t 1520 1306 871 852 1530 1083 438 1769 2877 1872 2402 3666
c C hine s e
18 M a c ke re l
a India n m a c ke re l 5 1 3 6 8 11 14 16 19 21 68 59
b Othe r m a c ke re ls 1 1 1 1 1 1 1 1 1 1 1 1
19 S e e r f is he s
a S . c o m m e rs o ni 3 9 10 15 3 7 6 8 14 10 15 20
b S . g ut t a t us 8 19 21 30 5 14 12 14 27 20 31 41
c S . line o la t us
d A c a nt ho c yb ium
2 0 Tunnie s
a E. a f f in is 1 1 1 69 149 141 189 86 116 115 71 414
b A uxis s p p . 1 1 1 1 1 1 1 1 1 1 1 1
c K. p e la m is 1 1 1 1 1 1 1 1 1 1 1 1
d T . t o ng g o l 1 1 1 1 1 1 1 1 1 1 1 1
e Othe r tunnie s 1 1 1 2 4 3 5 3 3 3 2 11
2 1 B ill f is he s 1 1 1 1 1 1 1 1 1 1 1 1
2 2 B a rra c uda s 3 3 5 3 7 3 1 20 29 37 45 54
2 3 M ulle ts 3 6 4 4 5 2 4 3 5 2 3 3
2 4 Unic o rn c o d
2 5 F la t f is he s
a Ha libut 1 1 1 1 1 1 1 1 1 1 1 1
b F lo unde rs
c S o le s 6 3 27 104 94 197 209 48 17 28 159 253
2 6 C rus ta c e a ns
a P e na e id pra wns 24 26 20 102 54 123 114 82 72 83 132 100
b N o n-P e na e id 2 1 1 1 1 7 23 3 8 10 11 13
c Lo bs te rs 3 6 7 10 10 576 438 116 92 60 56 213
d C ra bs 1 1 1 1 1 1 1 1 1 1 2 8
e S to m a to po ds
2 7 M o llus c s 1 1 1 1 1 1 1 1 1 1 1 1
2 8 C e pha lo po ds 1 1 1 1 1 1 1 1 1 1 1 1
2 9 M is c e lla ne o us
To ta l 3346 3104 2746 3003 4072 4910 3965 5396 6540 5081 6133 9183
259
S pe c ie s 19 8 2 19 8 3 19 8 4 19 8 5 19 8 6 19 8 7 19 8 8 19 8 9 19 9 0 19 9 1 19 9 2 19 9 3
1 Ela s m o bra nc hs
a S ha rks 154 99 100 163 108 107 134 112 110 205 244 325
b S ka te s 115 73 58 114 119 116 53 40 41 13 22 43
c R a ys 119 142 130 83 70 80 56 82 242 114 103 73
2 Ee ls 1 2 1 1 1 1 1 1 1 1 1 1
3 C a tf is he s 215 178 164 202 212 177 164 172 220 203 217 233
4 C lupe ids
a Wo lf he rring 148 140 120 308 202 264 160 196 155 225 243 221
b Oil s a rdine 461 494 529 564 598 631 422 212 2 2 2 2
c Othe r s a rdine s 31 36 173 303 46 343 45 25 4 11 18 12
d Hils a s ha d 8 47 4 21 14 24 38 42 7 77 61 10
e Othe r s ha ds 212 155 205 230 231 142 90 129 139 153 167 159
f A nc ho v ie s
i A nc ho v ie lla 16 18 20 33 37 28 29 46 58 76 48 47
ii T hris s o c le s 47 37 24 80 32 54 52 48 48 76 65 57
g Othe r c lupe ids 59 43 75 116 67 64 58 74 71 71 69 77
5 B o m ba y duc k 516 678 760 555 651 573 479 1218 1066 1047 1120 832
6 Liza rd f is he s
7 Ha lf a nd F ull 1 7 12 24 28 35 29 30 26 20 19 28
8 F lying f is he s
9 P e rc he s
a R o c k c o ds
b S na ppe rs 25 122 108 142 44 50 43 83 65 16 25 57
c P ig fa c e bre a m s
d Thre a df in 6 4 14 15 20 7 9 9 12 18 14 19
e Othe r pe rc he s 18 37 39 66 45 36 28 46 36 51 57 67
10 Go a tf is he s 97 127 80 130 29 100 136 481 151 88 289 492
11 Thre a df ins 81 82 89 123 79 57 49 65 65 79 96 65
12 C ro a ke rs 115 130 141 165 138 126 104 159 184 328 392 317
13 R ibbo n f is he s 227 151 191 448 703 460 366 586 604 708 1035 949
14 C a ra ng ids
a Ho rs e m a c ke re l 3 5 10 7 17 10 14 24 22 40 24 33
b S c a ds 20 714 360 6 12 31 6 235 54 175 93 8
c Le a the r- ja c ke ts 420 202 423 313 258 241 208 156 122 272 144 151T ra c hyno t us 3 3 3 3 3 3 3 3 3 3 3 3
d Othe r C a ra ng ids 14 37 53 77 36 92 71 83 77 70 111 110C o ryp ha e na
Ela c a t e 10 10 10 10 10 10 10 10 10 10 10 10
15 S ilv e rbe llie sLe io g na t hus
G a z z a
16 B ig ja we d jum pe r
17 P o m fre ts
a B la c k po m fre t 651 904 1006 748 1124 878 761 354 255 694 561 547
b S ilv e r po m fre t 2559 2176 1897 2530 1773 1583 1919 2083 1486 1981 1917 2247
c C hine s e
18 M a c ke re l
a India n m a c ke re l 49 41 31 25 26 10 49 80 14 98 241 168
b Othe r m a c ke re ls 1 1 1 1 1 1 1 1 1 1 1 3
19 S e e r f is he s
a S . c o m m e rs o ni 22 25 14 139 412 243 156 282 219 277 287 364
b S . g ut t a t us 20 29 28 40 14 25 15 25 14 31 39 64
c S . line o la t us
d A c a nt ho c yb ium
2 0 Tunnie s
a E. a f f in is 89 134 459 1395 293 271 206 142 84 545 143 307
b A uxis s p p . 1 1 1 3 207 5 65 202 243 157 144 120
c K. p e la m is 1 1 1 1 1 1 1 1 1 1 1 1
d T . t o ng g o l 1 1 1 5 4 3 1 1 2 192 216 311
e Othe r tunnie s 3 1 20 165 15 99 22 63 90 63 15 177
2 1 B ill f is he s 216 28 4 9 6 48 1 6 24 20 59 50
2 2 B a rra c uda s 62 61 8 13 3 5 29 83 65 160 84 56
2 3 M ulle ts 5 8 8 13 8 11 19 29 10 11 9 7
2 4 Unic o rn c o d
2 5 F la t f is he s
a Ha libut 45 45 51 105 28 24 34 44 31 68 41 65
b F lo unde rs
c S o le s 172 79 431 211 86 201 103 123 163 370 292 201
2 6 C rus ta c e a ns
a P e na e id pra wns 113 95 104 127 144 161 149 147 198 265 299 197
b N o n-P e na e id 12 25 24 21 30 20 22 97 120 155 159 106
c Lo bs te rs 132 136 455 288 183 312 233 280 159 275 272 278
d C ra bs 3 1 7 5 3 2 2 1 4 4 2 1
e S to m a to po ds
2 7 M o llus c s 1 1 1 1 1 1 1 1 1 1 1 1
2 8 C e pha lo po ds 1 1 1 1 1 1 1 1 1 3 2 2
2 9 M is c e lla ne o us
To ta l 7302 7566 8452 10147 8174 7768 6648 8414 6778 9523 9476 9673
260
S pe c ie s 19 9 4 19 9 5 19 9 6 19 9 7 19 9 8 19 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5
1 Ela s m o bra nc hs
a S ha rks 255 536 4530 4982 1616 1624 1601 1601 1601 1601 1601 1601
b S ka te s 40 87 728 801 844 201 204 204 204 204 204 204
c R a ys 71 151 1268 1395 901 350 355 355 355 355 355 355
2 Ee ls 1 1 23 2 191 170 171 171 171 171 171 171
3 C a tf is he s 267 205 334 173 2099 2104 2063 2063 2063 2063 2063 2063
4 C lupe ids
a Wo lf he rring 229 228 204 170 267 107 341 341 341 341 341 341
b Oil s a rdine 148 283 364 232 245 88 14 14 14 14 14 14
c Othe r s a rdine s 19 24 27 28 53 48 51 51 51 51 51 51
d Hils a s ha d 67 499 322 319 712 564 718 718 718 718 718 718
e Othe r s ha ds 164 162 144 128 214 172 184 184 184 184 184 184
f A nc ho v ie s
i A nc ho v ie lla 58 40 57 24 160 238 244 244 244 244 244 244
ii T hris s o c le s 57 56 49 43 71 56 60 60 60 60 60 60
g Othe r c lupe ids 107 130 308 414 1621 316 337 337 337 337 337 337
5 B o m ba y duc k 1185 1303 1043 758 1846 1288 810 810 810 810 810 810
6 Liza rd f is he s
7 Ha lf a nd F ull 115 193 237 171 224 132 142 142 142 142 142 142
8 F lying f is he s
9 P e rc he s
a R o c k c o ds
b S na ppe rs 70 84 74 71 144 125 127 127 127 127 127 127
c P ig fa c e bre a m s
d Thre a df in 22 26 22 22 43 38 38 38 38 38 38 38
e Othe r pe rc he s 77 89 76 73 146 126 128 128 128 128 128 128
10 Go a tf is he s 539 597 491 410 730 564 573 573 573 573 573 573
11 Thre a df ins 67 55 130 118 150 71 25 25 25 25 25 25
12 C ro a ke rs 476 522 111 535 655 276 1305 1305 1305 1305 1305 1305
13 R ibbo n f is he s 973 961 851 755 1264 412 441 441 441 441 441 441
14 C a ra ng ids
a Ho rs e m a c ke re l 65 892 20 179 179 53 57 57 57 57 57 57
b S c a ds 37 64 79 88 177 165 177 177 177 177 177 177
c Le a the r- ja c ke ts 191 223 225 223 409 320 443 443 443 443 443 443T ra c hyno t us 87 163 209 180 293 228 245 245 245 245 245 245
d Othe r C a ra ng ids 130 144 141 136 244 209 224 224 224 224 224 224C o ryp ha e na
Ela c a t e 57 98 123 106 172 134 143 143 143 143 143 143
15 S ilv e rbe llie sLe io g na t hus 358 289 97 180 304 221 211 211 211 211 211 211G a z z a
16 B ig ja we d jum pe r
17 P o m fre ts
a B la c k po m fre t 671 544 237 239 695 7 343 343 343 343 343 343
b S ilv e r po m fre t 2387 1936 843 851 2473 508 1398 1398 1398 1398 1398 1398
c C hine s e
18 M a c ke re l
a India n m a c ke re l 303 420 473 229 783 267 286 286 286 286 286 286
b Othe r m a c ke re ls 38 69 87 99 201 71 79 79 79 79 79 79
19 S e e r f is he s 114 165 357 380 1169 178 181 181 181 181 181 181
a S . c o m m e rs o ni 370 363 319 281 467 373 399 399 399 399 399 399
b S . g ut t a t us 64 61 53 46 75 58 62 62 62 62 62 62
c S . line o la t us
d A c a nt ho c yb ium
2 0 Tunnie s
a E. a f f in is 319 318 285 305 593 538 577 577 577 577 577 577
b A uxis s p p . 129 133 123 113 196 162 173 173 173 173 173 173
c K. p e la m is 55 103 133 99 136 86 92 92 92 92 92 92
d T . t o ng g o l 293 265 215 174 282 220 236 236 236 236 236 236
e Othe r tunnie s 160 138 106 91 148 116 124 124 124 124 124 124
2 1 B ill f is he s 57 60 58 50 80 63 67 67 67 67 67 67
2 2 B a rra c uda s 57 56 50 44 74 59 63 63 63 63 63 63
2 3 M ulle ts 9 5 12 26 74 79 81 81 81 81 81 81
2 4 Unic o rn c o d
2 5 F la t f is he s
a Ha libut 66 69 54 46 81 64 66 66 66 66 66 66
b F lo unde rs 48 167 17 278 495 383 389 389 389 389 389 389
c S o le s 204 212 164 139 250 108 22 22 22 22 22 22
2 6 C rus ta c e a ns
a P e na e id pra wns 387 409 99 151 450 113 264 264 264 264 264 264
b N o n-P e na e id 147 219 180 159 303 88 638 638 638 638 638 638
c Lo bs te rs 286 300 235 200 365 53 85 85 85 85 85 85
d C ra bs 4 1 5 3 3 2 42 42 42 42 42 42
e S to m a to po ds
2 7 M o llus c s 66 133 153 147 298 257 289 289 289 289 289 289
2 8 C e pha lo po ds 4 4 4 3 6 4 4 4 4 4 4 4
2 9 M is c e lla ne o us
To ta l 12170 14257 16550 16869 25670 14253 17391 17391 17391 17391 17391 17391
261
Appendix C.4. Marine fisheries catch (tonnes) for Goa, 1970-2005 (contd.)
S pe c ie s 19 7 0 19 7 1 19 7 2 19 7 3 19 7 4 19 7 5 19 7 6 19 7 7 19 7 8 19 7 9 19 8 0 19 8 1
1 Ela s m o bra nc hs
a S ha rks 430 1373 794 371 486 677 1376 688 922 1399 885 1101
b S ka te s 10 29 16 7 9 13 27 13 18 26 17 21
c R a ys 83 261 151 70 93 129 261 130 176 267 169 210
2 Ee ls 3 7 3 2 2 92 98 77 71 46 7 24
3 C a tf is he s 271 610 903 520 528 1957 1338 1222 1754 1119 1378 2759
4 C lupe ids
a Wo lf he rring 9 11 18 29 70 27 55 28 68 105 106 159
b Oil s a rdine 979 1720 3315 2957 1895 6408 1417 696 1220 2618 2018 6696
c Othe r s a rdine s 2326 62 1892 1098 1055 3333 11354 3507 4493 2135 1533 2020
d Hils a s ha d 3 3 3 3 3 3 3 3 3 2 7 2
e Othe r s ha ds 1 1 1 1 1 1 1 1 2 12 12 23
f A nc ho v ie s
i A nc ho v ie lla 29 38 107 126 80 26 20 8 4 110 212 95
ii T hris s o c le s 72 138 105 54 73 105 297 253 1141 787 664 522
g Othe r c lupe ids 31 224 166 104 248 357 682 448 563 327 257 300
5 B o m ba y duc k 2 2 3 5 7 9 47 17 24 8 10 1
6 Liza rd f is he s 3 7 3 2 2 216 40 318 361 138 238 882
7 Ha lf a nd F ull 1 1 1 1 1 1 8 11 49 11 5 2
8 F lying f is he s 1 1 1 1 1 1 1 1 2 1 2 2
9 P e rc he s
a R o c k c o ds 3 7 3 2 2 1 2 1 3 1 1 4
b S na ppe rs 3 7 3 2 2 1 2 1 3 1 1 4
c P ig fa c e bre a m s 3 7 3 2 2 1 10 13 19 5 6 11
d Thre a df in bre a m s 16 65 39 5 6 30 231 313 471 126 150 701
e Othe r pe rc he s 19 73 42 5 8 33 253 343 515 138 164 768
10 Go a tf is he s 3 22 13 14 52 110 104 69 52 36 18 75
11 Thre a df ins 13 109 42 29 67 90 51 1 1 8 12 17
12 C ro a ke rs 309 770 900 387 1340 4363 4234 3700 4211 1973 1832 2009
13 R ibbo n f is he s 52 20 10 41 100 302 1149 387 440 474 928 602
14 C a ra ng ids
a Ho rs e m a c ke re l 166 329 500 658 857 918 812 991 1573 1161 621 99
b S c a ds 1 1 1 1 1 1 1 1 1 1 1 1
c Le a the r- ja c ke ts 1 2 3 3 4 46 105 38 49 121 61 39T ra c hyno t us
d Othe r C a ra ng ids 1 1 1 1 1 1 1 1 45 164 916 497C o ryp ha e na
Ela c a t e
15 S ilv e rbe llie sLe io g na t hus 1057 1133 922 344 2155 865 1437 610 910 1165 2060 2580G a z z a 3 7 3 2 2 1 5 4 4 4 7 10
16 B ig ja we d jum pe r 45 36 363 384 566 271 542 499 573 385 735 1023
17 P o m fre ts
a B la c k po m fre t 38 138 80 43 117 117 128 316 380 145 247 130
b S ilv e r po m fre t 10 36 19 11 29 29 32 79 94 37 61 24
c C hine s e po m fre ts 3 7 3 2 2 1 2 1 1 1 1 1
18 M a c ke re l
a India n m a c ke re l 12595 30404 17476 6574 7112 5772 6596 6607 2941 3795 2085 3466
b Othe r m a c ke re ls
19 S e e r f is he s
a S . c o m m e rs o ni 22 65 41 40 171 131 356 128 419 661 436 393
b S . g ut t a t us 8 24 15 15 63 49 132 47 154 245 161 145
c S . line o la t us 2 4 3 3 12 9 25 9 29 46 30 27
d A c a nt ho c yb ium
2 0 Tunnie s
a E. a f f in is 2 2 2 2 2 2 17 69 196 481 228 124
b A uxis s p p . 1 1 1 1 1 1 4 15 42 103 49 26
c K. p e la m is 1 1 1 1 1 1 1 1 1 1 1 1
d T . t o ng g o l 1 1 1 1 1 1 2 9 24 58 27 15
e Othe r tunnie s 1 1 1 1 1 1 1 1 1 1 1 1
2 1 B ill f is he s 1 1 1 1 1 1 1 1 1 1 1 4
2 2 B a rra c uda s 2 2 3 3 4 4 6 5 6 6 146 33
2 3 M ulle ts 29 94 58 27 8 178 6 61 41 19 13 257
2 4 Unic o rn c o d 5 9 7 3 4 7 9 6 6 6 6 8
2 5 F la t f is he s
a Ha libut 6 7 13 5 6 4 5 9 10 24 31 19
b F lo unde rs 3 7 3 2 2 1 2 1 1 1 1 1
c S o le s 379 465 710 215 297 23 220 446 539 1181 1570 907
2 6 C rus ta c e a ns
a P e na e id pra wns 1997 2026 1803 1774 2197 2522 7446 1912 2130 2108 2218 2792
b N o n-P e na e id 64 283 93 34 29 40 55 32 34 44 49 60
c Lo bs te rs 3 7 3 2 2 9 5 9 17 8 22 11
d C ra bs 6 22 6 14 39 97 467 254 206 547 728 656
e S to m a to po ds 13 58 16 32 91 228 1091 594 481 1276 1586 2708
2 7 M o llus c s 3 7 3 2 2 14 22 21 16 22 25 11
2 8 C e pha lo po ds 10 29 13 18 20 123 205 197 145 206 226 106
To ta l 21151 40780 30706 16055 19925 29753 42801 25226 27653 25896 24980 35188
262
S pe c ie s 19 8 2 19 8 3 19 8 4 19 8 5 19 8 6 19 8 7 19 8 8 19 8 9 19 9 0 19 9 1 19 9 2 19 9 3
1 Ela s m o bra nc hs
a S ha rks 555 762 620 257 786 164 354 74 200 173 229 473
b S ka te s 21 21 21 21 20 21 33 42 43 43 44 29
c R a ys 205 50 250 156 77 36 41 56 19 11 60 1
2 Ee ls 18 5 34 5 6 14 11 9 4 4 4 3
3 C a tf is he s 2372 1847 1552 2076 454 1663 5224 9312 2509 1171 245 1062
4 C lupe ids
a Wo lf he rring 66 68 138 388 291 168 285 86 89 201 434 196
b Oil s a rdine 4914 5177 1556 3662 469 8389 14171 11488 10657 23444 4019 751
c Othe r s a rdine s 858 613 2268 837 2030 5971 3125 1115 4002 7069 12987 1782
d Hils a s ha d 2 2 2 2 2 2 2 2 2 2 2 2
e Othe r s ha ds 14 5 1 2 1 4 3 4 3 4 5 3
f A nc ho v ie s
i A nc ho v ie lla 20 405 320 140 705 58 20 363 26 203 42 63
ii T hris s o c le s 1904 952 1351 2567 3722 810 6450 328 275 3254 13838 188
g Othe r c lupe ids 241 342 465 682 857 310 1215 1327 162 682 1343 278
5 B o m ba y duc k 6 11 10 8 7 6 5 4 3 3 5 3
6 Liza rd f is he s 492 655 898 553 285 1148 547 1098 251 363 708 202
7 Ha lf a nd F ull 9 27 4 110 100 19 30 8 27 63 41 19
8 F lying f is he s 2 1 1 1 1 1 1 1 1 1 1 1
9 P e rc he s
a R o c k c o ds 6 5 508 252 2 1 3 5 7 4 4 3
b S na ppe rs 6 2 1 96 48 6 15 30 43 54 70 56
c P ig fa c e bre a m s 15 19 23 28 50 76 56 39 2 2 2 1
d Thre a df in bre a m s 534 1529 1296 885 1495 2292 52 33 6 22 16 7
e Othe r pe rc he s 563 278 196 386 270 634 724 1275 344 519 1128 941
10 Go a tf is he s 65 55 46 38 27 19 28 44 36 24 14 1
11 Thre a df ins 12 6 1 6 46 45 47 56 73 86 104 81
12 C ro a ke rs 2808 3273 2046 2812 4424 3020 2643 2271 1096 2047 1696 886
13 R ibbo n f is he s 922 989 445 1262 1313 1949 3177 358 310 527 3566 1216
14 C a ra ng ids
a Ho rs e m a c ke re l 84 70 345 131 147 357 3388 2803 1899 1178 844 242
b S c a ds 2 66 129 1089 246 123 4278 3002 1388 1163 672 11
c Le a the r- ja c ke ts 53 15 32 47 34 31 58 34 6 256 296 6T ra c hyno t us
d Othe r C a ra ng ids 1218 880 1376 1361 2675 633 1982 1733 1286 3709 5735 1294C o ryp ha e na
Ela c a t e
15 S ilv e rbe llie sLe io g na t hus 1231 1621 2027 933 1659 1032 2200 706 370 1156 805 59G a z z a 5 7 10 5 9 6 15 5 4 11 10 1
16 B ig ja we d jum pe r 1181 1933 959 2635 3406 798 1386 447 436 719 606 162
17 P o m fre ts
a B la c k po m fre t 352 181 138 639 197 816 1367 1681 1318 366 297 211
b S ilv e r po m fre t 82 189 251 268 51 109 206 133 148 82 124 100
c C hine s e po m fre ts 1 1 33 15 13 11 9 9 6 2 8 10
18 M a c ke re l
a India n m a c ke re l 2321 205 2493 5063 514 5261 10423 56753 25689 3371 9333 61804
b Othe r m a c ke re ls
19 S e e r f is he s
a S . c o m m e rs o ni 101 240 37 14 123 326 201 103 511 905 1894 94
b S . g ut t a t us 465 433 299 826 600 431 251 139 97 135 126 231
c S . line o la t us 85 151 211 263 320 373 415 498 522 723 904 698
d A c a nt ho c yb ium
2 0 Tunnie s
a E. a f f in is 4 18 105 170 65 53 25 40 105 213 339 308
b A uxis s p p . 1 4 22 37 42 301 409 36 38 69 104 91
c K. p e la m is 1 1 1 1 1 1 1 1 1 1 1 1
d T . t o ng g o l 1 2 13 20 20 19 31 29 24 13 17 69
e Othe r tunnie s 1 1 1 1 1 1 15 31 45 74 105 75
2 1 B ill f is he s 67 134 199 100 6 6 6 6 6 7 8 6
2 2 B a rra c uda s 25 6 22 24 79 1 13 26 35 59 52 36
2 3 M ulle ts 78 21 113 132 55 42 20 37 28 11 32 4
2 4 Unic o rn c o d 8 9 9 12 13 16 21 29 15 21 31 28
2 5 F la t f is he s
a Ha libut 15 21 62 105 24 2 3 4 4 4 4 3
b F lo unde rs 2 2 2 2 2 2 3 4 4 4 4 3
c S o le s 754 1028 2822 3646 2988 2014 1609 1168 768 1432 1776 4291
2 6 C rus ta c e a ns
a P e na e id pra wns 4266 9397 5922 4299 5321 6996 5275 8088 3526 6007 5973 3030
b N o n-P e na e id 68 76 85 96 98 103 113 149 159 158 169 117
c Lo bs te rs 29 10 7 28 5 93 11 11 9 7 2 6
d C ra bs 1105 894 1348 2200 3315 2807 662 573 1064 809 662 1225
e S to m a to po ds 4282 3230 5648 8059 14347 16817 19816 13727 7908 13178 24535 32564
2 7 M o llus c s 21 50 50 37 163 58 52 84 22 132 219 941
2 8 C e pha lo po ds 182 448 448 337 1464 525 467 750 208 1186 1965 8464
To ta l 34722 38442 39275 49827 55489 66991 92991 122263 67835 77135 98260 124438
263
S pe c ie s 19 9 4 19 9 5 19 9 6 19 9 7 19 9 8 19 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5
1 Ela s m o bra nc hs
a S ha rks 198 465 51 141 232 76 23 328 496 437 452 444
b S ka te s 3 1 1 1 1 5 3 1 1 1 1 1
c R a ys 5 30 8 2 106 4 4 11 192 641 929 911
2 Ee ls 2 1 6 1 92 1 1 1 1 55 9 9
3 C a tf is he s 84 2001 20 67 2218 1612 9 361 327 241 458 450
4 C lupe ids
a Wo lf he rring 320 142 181 102 86 251 13 14 298 260 280 274
b Oil s a rdine 2 516 1117 8057 5524 6986 12984 9251 29080 44059 43338 42532
c Othe r s a rdine s 732 2604 5309 5430 1908 2153 1357 287 719 767 1257 1234
d Hils a s ha d 1 1 1 1 1 1 1 1 33 137 1 1
e Othe r s ha ds 1 1 1 1 1 1 1 1 1 36 140 137
f A nc ho v ie s 2258 819 183 514 384 369 273 175 1344 1819 1106 1086
i A nc ho v ie lla
ii T hris s o c le s
g Othe r c lupe ids 1074 596 378 198 298 490 166 362 1768 2080 933 915
5 B o m ba y duc k 1 1 1 1 3 1 1 1 1 1 1 1
6 Liza rd f is he s 365 144 243 30 534 26 50 20 282 419 493 484
7 Ha lf a nd F ull 56 5 21 57 29 6 1 86 110 38 9 9
8 F lying f is he s 1 1 1 1 1 1 1 1 1 1 1 1
9 P e rc he s
a R o c k c o ds 1 1 1 232 332 398 104 590 1313 800 753 739
b S na ppe rs 1 1 1 1 1 1 1 1 1 1 1 1
c P ig fa c e bre a m s 1 1 1 1 1 1 1 1 1 1 1 1
d Thre a df in bre a m s 1 1 3 1036 1448 531 720 918 895 1749 1056 1037
e Othe r pe rc he s 2480 332 1202 5227 175 1268 3063 65 1059 1669 3068 3011
10 Go a tf is he s 29 1 1 1 1 1 1 1 1 2 1 1
11 Thre a df ins 1 1 1 1 5 1 1 1 1 1 1 1
12 C ro a ke rs 963 825 1105 1222 1497 930 1577 634 1334 1163 897 880
13 R ibbo n f is he s 2443 913 426 1435 546 461 2264 337 2633 1178 2580 2532
14 C a ra ng ids
a Ho rs e m a c ke re l 598 320 1236 198 970 6323 4108 1698 962 4107 1285 1261
b S c a ds 1 664 50 282 1 459 263 518 1050 263 4000 3925
c Le a the r- ja c ke ts 199 986 155 211 1 1 1 86 9 58 60 59T ra c hyno t us
d Othe r C a ra ng ids 1627 2300 2027 5007 1564 2344 2198 424 1300 2809 1866 1831C o ryp ha e na
Ela c a t e
15 S ilv e rbe llie s 1024 499 236 698 99 200 207 424 1542 3336 1461 1434Le io g na t hus
G a z z a
16 B ig ja we d jum pe r 274 261 135 334 432 138 139 234 267 172 218 214
17 P o m fre ts
a B la c k po m fre t 1536 118 385 109 1213 235 128 60 194 1542 171 168
b S ilv e r po m fre t 101 114 76 101 1881 121 8 47 181 127 115 113
c C hine s e po m fre ts 18 1 1 1 1 1 1 1 4 1 1 1
18 M a c ke re l
a India n m a c ke re l 8205 6291 23212 15759 13934 11981 21308 8835 4706 8440 3427 3363
b Othe r m a c ke re ls
19 S e e r f is he s
a S . c o m m e rs o ni 2316 1869 1141 2233 2465 2836 153 1152 1167 1112 642 630
b S . g ut t a t us 367 29 264 58 565 69 4 12 2 1 1 1
c S . line o la t us 1 1 1 1 1 1 1 1 1 1 1 1
d A c a nt ho c yb ium 1 1 1 1 1 1 1 1 1 1 171 168
2 0 Tunnie s
a E. a f f in is 1 968 145 1 1 1 1 1 316 478 226 221
b A uxis s p p . 487 6 35 223 814 1685 1179 775 1099 1515 1175 1153
c K. p e la m is 1 1 1 179 1 1 1 1 1 1 1 1
d T . t o ng g o l 1 1 1 1 1 1 1 1 30 9 1 1
e Othe r tunnie s 1 1 1 1 1 1 1 1 1 2003 5 5
2 1 B ill f is he s 1 1 1 1 1 1 1 1 1 1 26 25
2 2 B a rra c uda s 31 79 45 59 42 2 1 50 221 202 211 207
2 3 M ulle ts 9 9 1 14 9 7 16 4 9 20 15 15
2 4 Unic o rn c o d 1 1 1 1 1 2 1 1 1 1 1 1
2 5 F la t f is he s
a Ha libut 1 1 1 1 1 1 1 1 1 1 1 1
b F lo unde rs 1 1 1 1 1 1 1 1 1 1 1 1
c S o le s 1049 758 914 3530 915 536 4807 2087 1418 3044 4460 4377
2 6 C rus ta c e a ns
a P e na e id pra wns 2641 1879 3230 2941 1734 990 1676 2180 2640 3040 1896 1861
b N o n-P e na e id 1 1 1 1 1 1 1 1 1 1 2 2
c Lo bs te rs 4 1 1 1 1 1 1 1 1 1 1 1
d C ra bs 465 222 238 723 205 161 466 330 484 568 386 379
e S to m a to po ds 28773 4497 6676 8561 5834 1466 2648 2593 6278 7025 3718 3649
2 7 M o llus c s 1 1 1 1 1 1 1 1 1 1 1 1
2 8 C e pha lo po ds 2236 661 1516 2484 3423 2247 1158 2706 502 370 1496 1468
To ta l 62996 31947 51991 67477 51541 47392 63104 37677 66286 97808 84810 83233
264
Appendix C.5. Marine fisheries catch (t) for Maharashtra, 1950-2005 (contd.)
S pe c ie s 19 5 0 19 5 1 19 5 2 19 5 3 19 5 4 19 5 5 19 5 6 19 5 7 19 5 8 19 5 9 19 6 0 19 6 1
1 Ela s m o bra nc hs
a S ha rks 37 443 582 238 592 2013 2512 5620 3609 4040 2452 2322
b S ka te s 1 12 16 6 15 52 65 146 94 105 64 60
c R a ys 10 121 159 64 162 549 685 1533 984 1102 668 633
2 Ee ls 3045 2763 3730 7417 7215 8263 2283 3958 6073 4834 3587 10817
3 C a tf is he s 486 1317 1267 2602 2052 4100 3876 10554 9376 4798 4314 2287
4 C lupe ids
a Wo lf he rring 18 17 187 79 140 310 2404 1352 1366 1267 687 926
b Oil s a rdine 4068 91 1049 1333 75 850 417 9188 4196 2961 1627 464
c Othe r s a rdine s 1115 2018 1191 125 34 566 590 4486 3723 10380 663 269
d Hils a s ha d 216 206 218 260 234 279 154 187 222 119 229 176
e Othe r s ha ds 40 60 62 71 76 89 98 155 166 196 1053 365
f A nc ho v ie s
i A nc ho v ie lla 10 9 7 64 10 9 12 12 167 55 138 225
ii T hris s o c le s 456 436 348 3154 484 433 603 654 509 690 570 937
g Othe r c lupe ids 11499 5377 10920 6677 8466 8206 17280 14084 4746 8133 7250 6762
5 B o m ba y duc k 8788 4461 15960 24975 15686 67266 74320 59452 40373 29215 29957 27529
6 Liza rd f is he s 91 46 163 254 158 678 540 4 2 23 5 16
7 Ha lf a nd F ull 201 192 203 242 217 228 229 266 56 32 37 141
8 F lying f is he s 173 166 157 164 126 112 91 63 16 5 2 26
9 P e rc he s
a R o c k c o ds 30 86 149 102 26 12 14 21 18 16 27 141
b S na ppe rs 35 101 174 119 30 14 16 24 20 18 30 165
c P ig fa c e bre a m s 2 6 10 6 2 1 1 1 1 1 2 10
d Thre a df in 545 1578 2731 1878 466 228 264 374 323 275 481 2580
e Othe r pe rc he s 50 204 308 231 128 132 159 213 236 188 233 433
10 Go a tf is he s 240 188 382 214 515 757 987 703 419 224 268 367
11 Thre a df ins 10204 9824 9437 10283 8422 8009 5950 8988 3467 4778 898 1620
12 C ro a ke rs 9039 8582 8621 18409 39181 20485 23349 11544 8818 5621 5528 11435
13 R ibbo n f is he s 4055 2907 1971 23054 9628 5241 5374 6520 5289 1896 1793 2492
14 C a ra ng ids
a Ho rs e m a c ke re l 143 94 52 69 259 219 278 296 324 256 438 541
b S c a ds 1 1 1 1 1 1 1 1 1 1 1 1
c Le a the r- ja c ke ts 568 551 579 687 621 649 651 644 626 423 265 420T ra c hyno t us 1 1 1 1 1 1 1 1 1 1 1 1
d Othe r C a ra ng ids 1 1 1 1 1 1 1 1 1 1 1 1C o ryp ha e na 1 1 1 1 1 1 1 1 1 1 1 1Ela c a t e 1 1 1 1 1 1 35 115 174 65 21 25
15 S ilv e rbe llie sLe io g na t hus 226 584 128 124 93 19 125 151 793 245 47 223G a z z a 1 1 1 1 1 1 1 1 1 1 1 1
16 B ig ja we d 245 642 139 135 102 20 137 13 141 10 97 150
17 P o m fre ts
a B la c k po m fre t 173 32 361 1734 1340 1210 872 1370 1366 2256 833 1260
b S ilv e r po m fre t 692 128 1441 6934 5360 4837 3486 5482 5465 9023 3332 5039
c C hine s e 1 1 1 1 1 1 1 1 1 1 1 1
18 M a c ke re l
a India n m a c ke re l 1023 7125 10132 13470 4076 3544 1647 4608 1728 7067 12175 358
b Othe r 0 0 0 0 0 0 0 0 0 0 0
19 S e e r f is he s 0 0 0 0 0 0 0 0 0 0 0
a S . c o m m e rs o ni 127 122 88 60 102 201 1238 435 351 401 233 296
b S . g ut t a t us 85 81 58 40 67 134 824 290 235 268 155 198
c S . line o la t us
d A c a nt ho c yb iu 1 1 1 1 1 1 1 1 1 1 1 1
2 0 Tunnie s
a E. a f f in is 2 1 1 1 1 7 7 21 39 4 26 23
b A uxis s p p . 1 1 1 1 1 1 1 1 1 1 1 1
c K. p e la m is 1 1 1 1 1 1 1 3 6 1 4 3
d T . t o ng g o l 1 1 1 1 1 1 1 1 1 1 1 1
e Othe r tunnie s 25 3 15 15 10 97 87 272 512 60 338 310
2 1 B ill f is he s 1 1 1 1 1 1 1 1 1 1 1 1
2 2 B a rra c uda s 1 1 28 17 1 7 54 5 9 5 22 21
2 3 M ulle ts 1 1 24 19 11 3 3 2 125 4 142 54
2 4 Unic o rn c o d 1435 3011 2116 11447 6892 2366 973 1091 3207 3369 6089 3916
2 5 F la t f is he s
a Ha libut 1 1 1 1 1 1 1 1 1 1 1 1
b F lo unde rs 1 1 1 1 1 1 1 1 1 1 1 1
c S o le s 88 241 1084 168 69 198 548 303 34 264 130 67
2 6 C rus ta c e a ns
a P e na e id pra wns 10799 10396 11419 16910 27813 18557 25972 33141 6701 6387 9961 8813
b N o n-P e na e id 28659 27580 30294 44865 73795 49226 68901 42056 37082 28625 37097 23427
c Lo bs te rs 1 1 1 1 1 1 1 1 1 1 1 1
d C ra bs 12 11 13 18 31 20 33 163 23 18 51 50
e S to m a to po ds 1 1 1 1 1 1 1 1 1 1 1 1
2 7 M o llus c s
2 8 C e pha lo po ds 1 1 1 1 1 1 1 1 1 47 13 57
2 9 M is c e lla ne o us
To ta l 98773 91834 117989 198754 214801 210214 248159 230578 153228 139788 134047 118463
265
S pe c ie s 19 6 2 19 6 3 19 6 4 19 6 5 19 6 6 19 6 7 19 6 8 19 6 9 19 7 0 19 7 1 19 7 2 19 7 3
1 Ela s m o bra nc hs
a S ha rks 2195 3323 2841 1957 3260 4490 3505 5382 3920 3822 5070 6794
b S ka te s 57 86 74 51 84 117 92 140 101 99 132 176
c R a ys 599 906 775 534 889 1225 956 1467 1070 1043 1383 1853
2 Ee ls 8197 7255 1488 2038 2169 1975 2525 2392 6281 3181 3538 2345
3 C a tf is he s 2447 4665 2235 2531 5122 5142 4651 6929 12591 20047 14220 10678
4 C lupe ids
a Wo lf he rring 789 786 1003 807 1049 1733 1196 1693 622 1033 1094 1877
b Oil s a rdine 5211 1688 880 67 65 361 227 408 259 348 3099 2166
c Othe r s a rdine s 720 234 490 1123 961 931 1160 1251 2943 1595 2481 2684
d Hils a s ha d 185 160 16 148 97 69 58 35 24 19 11 78
e Othe r s ha ds 254 361 757 1727 391 298 448 1035 1333 959 1207 741
f A nc ho v ie s
i A nc ho v ie lla 350 148 79 310 535 174 108 70 418 847 180 500
ii T hris s o c le s 638 951 1435 723 750 647 573 715 644 1347 1066 2624
g Othe r c lupe ids 4100 5342 7832 7360 6563 8737 8259 8531 5614 7513 8180 13101
5 B o m ba y duc k 30446 25053 21489 24798 31790 28797 26444 25740 29162 29086 18324 29817
6 Liza rd f is he s 27 24 503 28 1453 56 120 289 192 164 640 1142
7 Ha lf a nd F ull 13 120 9 56 65 154 84 257 373 109 93 102
8 F lying f is he s 2 7 11 17 22 26 31 16 1 42 11 1
9 P e rc he s
a R o c k c o ds 147 96 208 46 112 18 32 78 109 42 69 167
b S na ppe rs 171 112 240 52 131 22 37 90 126 49 80 192
c P ig fa c e bre a m s 10 6 14 3 7 1 2 5 7 3 4 12
d Thre a df in 2687 1758 3805 828 2063 341 580 1428 1999 781 1260 3047
e Othe r pe rc he s 425 291 351 77 190 32 53 132 184 72 116 281
10 Go a tf is he s 368 229 4273 1222 1492 198 444 690 312 571 1257 1142
11 Thre a df ins 841 2127 369 208 2838 1093 395 625 3826 1096 2411 2700
12 C ro a ke rs 9763 8477 9073 8345 8083 7763 7347 10976 15022 14813 12532 16573
13 R ibbo n f is he s 1875 2445 8962 7044 5130 5270 5810 4902 4295 5615 7369 11658
14 C a ra ng ids
a Ho rs e m a c ke re l 119 420 1876 734 417 743 629 3987 3789 1951 1182 701
b S c a ds 1 1 1 1 1 1 1 1 1 1 1 1
c Le a the r- ja c ke ts 151 134 88 189 270 120 92 896 239 74 147 388T ra c hyno t us 1 1 2 4 6 33 60 86 96 117 141 165
d Othe r C a ra ng ids 1 1 1 1 1 46 9 449 750 115 25 73C o ryp ha e na 1 1 1 1 1 1 2 2 3 3 3 3Ela c a t e 22 2 2 7 11 12 2 1 172 88 5 110
15 S ilv e rbe llie sLe io g na t hus 144 225 271 830 480 3243 120 926 282 257 297 1105G a z z a 1 1 1 1 1 1 1 1 86 140 202 274
16 B ig ja we d 94 63 125 115 71 191 221 541 681 320 357 880
17 P o m fre ts
a B la c k po m fre t 892 796 1224 1365 1608 3077 2158 2613 980 1285 1522 1900
b S ilv e r po m fre t 3569 3183 4893 5459 6433 12306 8636 10452 3920 5142 6085 7601
c C hine s e 1 1 1 1 1 1 1 1 1 1 1 1
18 M a c ke re l
a India n m a c ke re l 2056 4735 2109 792 180 327 476 21153 14869 3979 4750 2066
b Othe r 0 0
19 S e e r f is he s 0 0
a S . c o m m e rs o ni 287 382 1218 1018 738 626 657 1577 776 774 1081 926
b S . g ut t a t us 191 255 812 679 492 417 438 1051 517 517 721 618
c S . line o la t us
d A c a nt ho c yb iu 1 1 1 1 1 1 1 1 1 1 1 1
2 0 Tunnie s
a E. a f f in is 11 9 13 7 24 62 11 9 16 17 18 45
b A uxis s p p . 1 1 1 1 1 1 1 1 1 1 1 1
c K. p e la m is 2 1 2 1 3 9 2 1 3 3 3 6
d T . t o ng g o l 1 1 1 1 1 1 1 1 1 1 1 1
e Othe r tunnie s 145 115 177 92 310 822 151 116 221 230 233 597
2 1 B ill f is he s 1 1 1 1 1 1 1 1 1 1 1 1
2 2 B a rra c uda s 16 8 5 12 119 1740 10 28 31 39 47 18
2 3 M ulle ts 115 25 109 499 188 231 227 292 86 80 61 75
2 4 Unic o rn c o d 3298 5496 3795 5708 2736 2158 1878 1722 1899 3718 4733 3675
2 5 F la t f is he s
a Ha libut 1 1 1 1 1 1 1 1 1 1 1 1
b F lo unde rs 1 1 1 1 1 1 1 1 1 1 1 1
c S o le s 28 19 18 87 345 32 63 419 1676 1004 613 3168
2 6 C rus ta c e a ns
a P e na e id pra wns 8950 5298 14620 10170 10148 8331 11621 14874 33662 21071 22374 19553
b N o n-P e na e id 37313 38207 29978 41956 34273 29057 31184 31941 33086 82887 93112 73444
c Lo bs te rs 1 1 1 1 1 1 1 1 1 1 1 1
d C ra bs 2 14 18 60 139 36 14 147 6547 1087 540 795
e S to m a to po ds 1 1 1 1 1 1 1 1 1 1 1 1
2 7 M o llus c s
2 8 C e pha lo po ds 59 28 22 7 27 2 104 150 379 409 313 580
2 9 M is c e lla ne o us
To ta l 129995 126083 130603 131907 134339 133302 123916 168721 196208 219611 224402 231230
266
S pe c ie s 19 7 4 19 7 5 19 7 6 19 7 7 19 7 8 19 7 9 19 8 0 19 8 1 19 8 2 19 8 3 19 8 4 19 8 5
1 Ela s m o bra nc hs
a S ha rks 5825 7407 6624 7090 9242 11038 7063 8903 10149 9389 8377 7454
b S ka te s 152 192 172 184 240 286 183 210 1269 2503 1995 2223
c R a ys 1589 2020 1807 1934 2521 3010 1927 2417 2714 3284 2877 2990
2 Ee ls 780 1297 5057 4576 6417 4619 3732 2729 4389 3454 3302 4059
3 C a tf is he s 8377 9700 11554 9888 13206 11950 10240 13162 12469 13724 15449 12683
4 C lupe ids
a Wo lf he rring 993 2588 2240 2373 2730 1499 1775 2531 3786 4608 5369 3475
b Oil s a rdine 948 1498 2210 97 55 14 577 540 305 95 1114 1595
c Othe r s a rdine s 1084 2735 1825 922 775 807 1187 364 413 464 220 292
d Hils a s ha d 4 14 143 317 1390 933 885 460 375 702 481 386
e Othe r s ha ds 700 1015 615 881 416 374 528 1909 1156 1398 2185 1122
f A nc ho v ie s
i A nc ho v ie lla 238 470 194 242 303 333 68 14936 9123 9175 11426 11493
ii T hris s o c le s 761 817 1186 1512 1625 2466 1106 819 1709 1999 1759 2172
g Othe r c lupe ids 12158 18663 15753 20522 12144 13651 14709 2732 2592 3724 4702 2465
5 B o m ba y duc k 26247 45526 45992 45764 61381 51964 49961 71247 39242 39481 50517 60396
6 Liza rd f is he s 737 257 1321 1349 2163 2719 1251 1559 1325 3707 2498 2175
7 Ha lf a nd F ull 29 46 28 29 46 110 37 40 136 175 40 240
8 F lying f is he s 2 2 3 4 4 1 1 2 2 2 2 2
9 P e rc he s
a R o c k c o ds 110 132 80 159 373 166 198 141 381 69 237 317
b S na ppe rs 127 152 92 184 430 192 228 161 49 435 405 264
c P ig fa c e bre a m s 7 8 5 11 25 11 13 10 8 9 9 16
d Thre a df in 2012 2411 1460 2913 6827 3043 3620 2569 5036 6668 5391 3062
e Othe r pe rc he s 185 223 135 269 629 281 334 238 1053 2262 692 802
10 Go a tf is he s 811 121 603 203 410 984 546 1170 1964 1647 1055 734
11 Thre a df ins 2079 1917 5005 1025 2275 1833 2338 847 550 564 576 1762
12 C ro a ke rs 20194 24234 24001 20312 20501 24472 16515 20825 18187 20894 26003 25152
13 R ibbo n f is he s 8389 8317 9345 5709 9281 9565 10054 6981 10735 9487 10050 16474
14 C a ra ng ids
a Ho rs e m a c ke re l 2219 1637 1096 1051 1695 2015 1145 117 170 333 443 1011
b S c a ds 1 1 1 1 1 1 1 1 18 15 12 9
c Le a the r- ja c ke ts 157 128 112 207 268 362 311 312 541 1097 883 1199T ra c hyno t us 4 4 5 5 4 4 4 4 4 4 4 4
d Othe r C a ra ng ids 242 367 546 285 40 338 301 782 1391 2335 3034 2257C o ryp ha e na 8 12 19 10 2 11 10 26 47 78 102 76Ela c a t e 18 33 49 25 4 30 27 70 125 210 273 203
15 S ilv e rbe llie sLe io g na t hus 547 334 279 215 184 373 205 62 74 392 411 225G a z z a 339 246 239 210 201 455 276 92 119 672 759 444
16 B ig ja we d 499 508 780 294 996 493 533 596 2877 3604 2850 2313
17 P o m fre ts
a B la c k po m fre t 1547 1967 4363 4112 3111 3422 2386 3533 3078 3569 3126 3117
b S ilv e r po m fre t 6186 7869 17452 16448 12442 13691 9544 16896 16133 22229 18971 9276
c C hine s e 1 1 1 1 1 1 1 1 21 1 2 3
18 M a c ke re l
a India n m a c ke re l 2264 1640 1807 788 702 1267 251 251 224 364 853 766
b Othe r
19 S e e r f is he s
a S . c o m m e rs o ni 753 978 1181 1740 1561 2104 1681 1489 2010 2164 1438 2251
b S . g ut t a t us 502 652 787 1160 1041 1403 1121 952 1044 4100 2680 2636
c S . line o la t us
d A c a nt ho c yb iu 1 1 1 1 1 1 1 1 1 1 1 1
2 0 Tunnie s
a E. a f f in is 18 17 30 20 121 108 102 81 213 1370 2294 1580
b A uxis s p p . 1 1 1 1 1 1 1 1 3 9 15 20
c K. p e la m is 3 3 5 3 17 16 15 6 7 8 9 10
d T . t o ng g o l 1 1 1 1 1 1 1 1 1 1 1 1
e Othe r tunnie s 230 222 396 259 1592 1420 1341 996 2557 155 132 43
2 1 B ill f is he s 1 1 1 1 1 1 1 62 166 86 159 153
2 2 B a rra c uda s 18 15 46 197 346 71 29 14 163 242 186 127
2 3 M ulle ts 25 35 232 57 122 44 28 42 31 179 68 212
2 4 Unic o rn c o d 1581 919 353 27 19 240 138 11 60 114 2294 636
2 5 F la t f is he s
a Ha libut 1 1 1 1 1 1 1 41 484 918 638 461
b F lo unde rs 1 1 1 1 1 1 1 93 6 71 137 203
c S o le s 581 574 1884 1480 1997 2639 2126 1993 2911 3097 5209 4352
2 6 C rus ta c e a ns
a P e na e id pra wns 17022 29036 49471 31711 48972 52273 27730 25880 38729 41268 50583 59754
b N o n-P e na e id 57881 81281 77293 79623 52743 64380 55983 62987 46603 36808 45167 63662
c Lo bs te rs 1 289 508 516 723 572 266 462 830 377 1109 2889
d C ra bs 1126 648 62 111 176 594 351 133 541 377 910 570
e S to m a to po ds 1 1 1 1 1 1 1 458 2737 1308 2116 3183
2 7 M o llus c s
2 8 C e pha lo po ds 345 568 3019 709 5431 4535 1409 2091 5460 7575 8808 15074
2 9 M is c e lla ne o us
To ta l 188660 261751 299473 269741 289929 299193 236398 278040 258497 275049 312401 342525
267
S pe c ie s 19 8 6 19 8 7 19 8 8 19 8 9 19 9 0 19 9 1 19 9 2 19 9 3 19 9 4 19 9 5 19 9 6 19 9 7
1 Ela s m o bra nc hs
a S ha rks 7409 9097 10322 8906 7947 9670 9003 7304 5943 5311 5016 5719
b S ka te s 1818 1393 1421 1464 740 444 525 749 307 477 344 722
c R a ys 2992 1849 1570 1722 1280 953 1260 1506 945 1158 982 1053
2 Ee ls 3205 4046 1775 1549 1508 1697 1397 1722 1239 877 1558 1366
3 C a tf is he s 13259 12985 23708 16533 10541 13891 9487 12329 15906 7790 4898 8865
4 C lupe ids
a Wo lf he rring 2267 1693 1341 1071 1575 2396 2612 2303 2655 1742 1720 3335
b Oil s a rdine 711 1095 3572 2171 542 867 181 194 5 1207 1033 534
c Othe r s a rdine s 393 2524 264 1574 2996 3374 3213 5960 12315 6554 5491 5204
d Hils a s ha d 431 530 77 76 185 256 205 261 132 392 103 415
e Othe r s ha ds 1318 2591 1233 873 409 639 163 93 130 84 55 66
f A nc ho v ie s 12937 16056 16207 17173
i A nc ho v ie lla 9294 6627 6963 11009 10790 10768 7214 7441
ii T hris s o c le s 2518 3904 4463 1989 2706 3155 4367 3828
g Othe r c lupe ids 1619 3274 5833 9909 5354 5806 3993 1362 1603 3116 3472 4591
5 B o m ba y duc k 36652 25865 26394 22928 33311 41535 16006 12790 16436 13990 10621 17515
6 Liza rd f is he s 3242 1674 3921 5202 3618 3061 3802 2900 5259 6964 1335 1927
7 Ha lf a nd F ull 68 64 61 86 7 9 72 56 174 81 44 79
8 F lying f is he s 13 24 34 47 56 67 78 88 1 1 3 1
9 P e rc he s
a R o c k c o ds 719 868 1845 2966 2236 1661 2306 2403 1220 1180 634 1650
b S na ppe rs 1017 1036 1211 871 244 602 726 463 55 57 28 1
c P ig fa c e bre a m s 1 3 4 33 58 85 109 21 3 4 8 1
d Thre a df in 5084 6034 13881 12158 12257 12658 15390 13315 11303 11555 7683 8679
e Othe r pe rc he s 2049 2470 2648 3366 655 993 845 903 1119 1452 6886 1074
10 Go a tf is he s 2174 1378 3183 2360 1398 835 563 452 374 286 329 166
11 Thre a df ins 2298 4529 743 2245 1679 1453 1345 502 779 574 526 587
12 C ro a ke rs 28212 25319 26734 26309 26115 26452 28640 27171 28397 30683 25794 25908
13 R ibbo n f is he s 14070 11659 20230 17411 19341 26252 26552 19759 28775 17152 28768 45846
14 C a ra ng ids
a Ho rs e m a c ke re l 3678 731 5600 5035 1665 2651 2150 3473 8785 3285 4005 2316
b S c a ds 2005 2091 3779 8630 7573 3367 5126 2960 3700 3868 2286 5175
c Le a the r- ja c ke ts 446 1107 218 461 120 62 358 247 147 78 67 137T ra c hyno t us 4 4 4 5 4 4 4 4 0 0 0 0
d Othe r C a ra ng ids 3389 1902 2084 5592 2598 2031 1883 2554 5565 12216 3596 4802C o ryp ha e na 114 64 71 188 87 68 64 86Ela c a t e 304 171 188 503 233 182 169 229
15 S ilv e rbe llie s 62 131 79 185Le io g na t hus 52 77 91 16 2 49 19 10G a z z a 110 171 216 40 7 138 57 30
16 B ig ja we d 2565 1042 1640 856 1114 3158 1058 715 1613 2157 1164 1389
17 P o m fre ts
a B la c k po m fre t 2567 5862 9547 6497 5001 5362 2447 4493 1528 5416 2125 1697
b S ilv e r po m fre t 6726 9376 13435 13115 9914 13347 6594 5985 4284 4993 4628 8103
c C hine s e 28 88 13 17 11 5 13 26 71 68 80 181
18 M a c ke re l
a India n m a c ke re l 1078 3961 1197 20320 15226 10094 9880 25011 22598 29398 40080 38138
b Othe r 10 1 1 1
19 S e e r f is he s
a S . c o m m e rs o ni 5147 3026 1755 933 1210 1148 834 1511 1805 2754 4223 2105
b S . g ut t a t us 2086 2885 5355 7389 4947 7741 3395 3655 2565 3105 2720 2285
c S . line o la t us 2 1 1 1
d A c a nt ho c yb iu 1 1 1 1 1 3 5 5 1 1 1 1
2 0 Tunnie s
a E. a f f in is 1474 828 1102 2953 1421 1845 1021 2410 1518 2651 1070 1699
b A uxis s p p . 25 31 36 44 155 468 779 1088 4 72 126 25
c K. p e la m is 10 11 11 13 13 21 16 18 6 1 1 1
d T . t o ng g o l 1 30 25 23 17 14 9 144 289 67 46 83
e Othe r tunnie s 89 198 28 25 105 67 48 110 200 22 62 165
2 1 B ill f is he s 137 88 153 515 485 375 234 402 392 230 423 192
2 2 B a rra c uda s 60 75 116 317 200 695 832 823 430 611 342 528
2 3 M ulle ts 377 219 33 131 30 37 43 29 125 21 27 144
2 4 Unic o rn c o d 357 763 702 494 205 1286 1016 796 402 134 284 508
2 5 F la t f is he s
a Ha libut 649 875 641 889 1038 908 857 497 654 490 427 302
b F lo unde rs 262 331 261 212 134 69 2 2 1 1 1 1
c S o le s 4676 6061 3307 4023 3664 6451 6838 7269 4493 7651 6892 5831
2 6 C rus ta c e a ns
a P e na e id pra wns 52125 53655 33963 45297 63753 66576 65026 63446 53207 41231 53940 50629
b N o n-P e na e id 64550 24554 42218 49698 41121 50568 37427 31721 16712 15173 28773 60506
c Lo bs te rs 2285 803 536 467 901 844 507 267 422 301 1183 855
d C ra bs 406 444 237 236 474 733 755 1155 1728 953 1180 1557
e S to m a to po ds 6918 26390 14549 22288 23790 20481 15623 21485 24092 25803 36916 31681
2 7 M o llus c s 2 10 2 2
2 8 C e pha lo po ds 13987 10464 15006 17526 17871 21418 29102 31129 24877 31151 25638 24167
2 9 M is c e lla ne o us
To ta l 321522 290912 321549 369577 352638 391845 334249 339663 330305 322791 345931 397868
268
S pe c ie s 19 9 8 19 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5
1 Ela s m o bra nc hs
a S ha rks 6886 6299 8646 9201 13419 7433 6447 5191
b S ka te s 260 247 327 258 582 642 805 648
c R a ys 694 781 1170 1401 928 1096 1327 1069
2 Ee ls 1176 1217 1858 2330 2124 2458 2514 2024
3 C a tf is he s 7094 5605 13933 14042 15341 10581 9490 7641
4 C lupe ids
a Wo lf he rring 3148 2422 1622 2508 2807 1904 1503 1210
b Oil s a rdine 1133 978 4285 3546 13292 5524 3899 3139
c Othe r s a rdine s 6287 2436 3441 2934 5106 3442 2662 2143
d Hils a s ha d 558 123 50 209 43 49 57 46
e Othe r s ha ds 3 88 1 1 234 420 394 317
f A nc ho v ie s 27671 12656 15442 17513 19233 16132 19075 15358
i A nc ho v ie lla
ii T hris s o c le s
g Othe r c lupe ids 4225 5734 4830 3634 4332 3065 3678 2962
5 B o m ba y duc k 30486 17125 18480 19970 29709 30813 29309 23598
6 Liza rd f is he s 1628 1055 1395 1620 1854 2287 2905 2339
7 Ha lf a nd F ull 147 105 47 111 198 309 278 224
8 F lying f is he s 1 1 4 1 1 13 1 1
9 P e rc he s
a R o c k c o ds 1740 1329 3989 6258 3838 5561 3949 3180
b S na ppe rs 1 40 198 336 1085 971 800 644
c P ig fa c e bre a m s 1 1 1 221 1 6 52 42
d Thre a df in 7040 10021 9643 21045 23447 29676 27078 21802
e Othe r pe rc he s 545 1229 1432 1878 2718 1704 2053 1653
10 Go a tf is he s 214 667 210 356 581 905 966 778
11 Thre a df ins 508 826 1201 1572 1509 1675 2389 1923
12 C ro a ke rs 33833 24806 27774 29678 31949 29656 30899 24879
13 R ibbo n f is he s 29830 30532 65901 62535 70080 32203 29708 23919
14 C a ra ng ids
a Ho rs e m a c ke re l 1751 2137 4585 6415 8650 8524 3777 3041
b S c a ds 369 766 1167 1613 1129 1501 1650 1329
c Le a the r- ja c ke ts 156 290 332 586 684 2634 2055 1655T ra c hyno t us 0 0 0 0 0 0 0 0
d Othe r C a ra ng ids 6442 3212 2181 3843 3909 7419 5589 4500C o ryp ha e na
Ela c a t e
15 S ilv e rbe llie s 79 22 265 257 314 479 3328 2680Le io g na t hus
G a z z a
16 B ig ja we d 2444 647 1365 1141 947 601 935 753
17 P o m fre ts
a B la c k po m fre t 4719 1133 2007 3272 2943 3477 2569 2068
b S ilv e r po m fre t 9387 7190 6388 6899 6193 3817 3560 2866
c C hine s e 40 69 3 1 51 111 241 194
18 M a c ke re l
a India n m a c ke re l 34973 37194 33423 16512 10257 9278 10034 8079
b Othe r 1 1 1 2 3 1 1 1
19 S e e r f is he s
a S . c o m m e rs o ni 2837 4792 5266 6484 7699 6615 4154 3345
b S . g ut t a t us 7995 3394 1454 3944 6059 4661 2488 2003
c S . line o la t us 1 1 1 1 1 1 1 1
d A c a nt ho c yb iu 1 1 1 1 1 1 1 1
2 0 Tunnie s
a E. a f f in is 1047 2036 2300 2802 3238 3041 2265 1823
b A uxis s p p . 906 478 12 314 16 212 1 1
c K. p e la m is 3 1 5 88 25 131 332 268
d T . t o ng g o l 728 864 415 1636 197 529 606 488
e Othe r tunnie s 29 414 1 1 1235 1998 564 454
2 1 B ill f is he s 203 219 120 117 460 669 426 343
2 2 B a rra c uda s 150 229 566 738 1142 1392 878 707
2 3 M ulle ts 118 17 335 87 58 25 36 29
2 4 Unic o rn c o d 385 325 388 501 886 805 744 599
2 5 F la t f is he s
a Ha libut 76 49 401 137 211 220 488 393
b F lo unde rs 1 1 5 1 1 1 1 1
c S o le s 5697 3938 4503 4084 5421 3162 3537 2848
2 6 C rus ta c e a ns
a P e na e id pra wns 46693 32323 48231 52140 67537 74175 57525 46316
b N o n-P e na e id 68640 44919 43132 49103 44185 54816 42739 34412
c Lo bs te rs 463 303 635 526 417 402 624 502
d C ra bs 476 278 877 749 1294 1724 1638 1318
e S to m a to po ds 37559 17314 10917 9132 8890 9398 7344 5913
2 7 M o llus c s 2 2 124 6 2 2 2 2
2 8 C e pha lo po ds 24578 12089 18300 27591 30124 33051 15355 12363
2 9 M is c e lla ne o us
To ta l 424056 302973 375586 403885 458591 423396 357726 288023
269
Appendix C.6. Marine fisheries catch (t) for Karnataka, 1970-2005 (contd.)
S pe c ie s 19 7 0 19 7 1 19 7 2 19 7 3 19 7 4 19 7 5 19 7 6 19 7 7 19 7 8 19 7 9 19 8 0 19 8 1
1 Ela s m o bra nc hs
a S ha rks 2393 5438 7542 2865 2818 3014 2387 5512 3958 4279 3842 6923
b S ka te s 5 12 17 7 6 7 5 12 10 11 8 16
c R a ys 82 188 261 98 98 105 83 191 138 149 133 240
2 Ee ls 14 16 14 14 12 147 70 5 16 2 4 9
3 C a tf is he s 16153 2726 5428 4170 2927 5832 7112 9111 5665 17388 7331 13221
4 C lupe ids
a Wo lf he rring 111 160 346 220 682 359 164 690 245 237 139 110
b Oil s a rdine 30333 10789 15731 15567 20708 45889 36986 29958 46272 30427 34677 61156
c Othe r s a rdine s 1824 448 1575 1169 227 675 572 173 2548 4372 3356 4966
d Hils a s ha d 19 16 34 50 26 1 21 42 9 9 6 1
e Othe r s ha ds 84 13 5 33 1 9 27 109 30 48 20 33
f A nc ho v ie s
i A nc ho v ie lla 127 88 125 236 51 9 48 167 439 1583 4562 5555
ii T hris s o c le s 905 164 579 264 1075 300 803 799 910 406 690 276
g Othe r c lupe ids 866 333 703 693 954 495 408 1613 864 2095 883 885
5 B o m ba y duc k 48 9 1 17 5 2 6 4 4 5 12 7
6 Liza rd f is he s 131 719 31 19 4 136 311 679 184 272 696 261
7 Ha lf a nd F ull 163 35 27 18 5 31 78 55 41 45 146 214
8 F lying f is he s 1 1 1 8 15 18 25 34 42 44 45 39
9 P e rc he s
a R o c k c o ds 4 6 7 5 7 33 18 65 8 9 37 18
b S na ppe rs 16 37 41 28 39 176 100 349 46 42 194 93
c P ig fa c e bre a m s 2 2 2 2 1 4 2 7 6 5 4 2
d Thre a df in 75 174 194 137 189 843 484 1686 224 203 939 451
e Othe r pe rc he s 23 53 60 42 58 261 150 521 68 63 290 139
10 Go a tf is he s 54 18 111 5 4 5 241 34 62 53 52 35
11 Thre a df ins 35 92 118 14 23 5 5 5 6 4 3 5
12 C ro a ke rs 3302 2689 3604 1781 4669 3354 5345 4875 3458 4116 4792 4044
13 R ibbo n f is he s 251 301 754 139 302 191 520 228 400 1097 1217 219
14 C a ra ng ids
a Ho rs e m a c ke re l 771 401 520 1232 768 650 585 731 200 1015 853 931
b S c a ds 1 1 1 1 1 1 1 1 1 1 1 12
c Le a the r- ja c ke ts 13 5 288 12 72 48 71 487 13 54 54 138T ra c hyno t us 56 68 89 102 115 43 30 17 3 12 207 464
d Othe r C a ra ng ids 22 19 18 14 11 80 60 39 43 2201 3849 2264C o ryp ha e na 1 1 1 1 1 20 21 22 23 21 19 21Ela c a t e 10 7 3 29 54 27 29 42 54 31 372 821
15 S ilv e rbe llie sLe io g na t hus 2337 2706 1355 4674 2995 2244 6791 2879 8487 2743 6442 2881G a z z a 11 12 10 11 10 13 12 12 16 14 11 5
16 B ig ja we d 985 997 1533 3059 2250 896 359 178 396 759 1367 907
17 P o m fre ts
a B la c k po m fre t 79 225 133 257 55 49 93 56 498 56 120 93
b S ilv e r po m fre t 529 1516 900 1732 377 329 622 376 3344 375 813 631
c C hine s e 12 33 20 39 9 7 13 9 74 9 18 14
18 M a c ke re l
a India n m a c ke re l 41543 58383 32499 35632 9661 10857 20036 25215 50232 36869 15935 18423
b Othe r
19 S e e r f is he s
a S . c o m m e rs o ni 759 1247 1374 720 833 368 653 961 791 826 859 1153
b S . g ut t a t us 516 849 935 490 567 251 444 654 538 562 585 785
c S . line o la t us 116 190 209 110 127 56 99 146 120 125 131 175
d A c a nt ho c yb iu 1 1 1 1 1 1 1 1 1 1 1 1
2 0 Tunnie s
a E. a f f in is 4 469 135 125 392 185 513 597 607 1577 771 2345
b A uxis s p p . 1 1 1 1 1 1 1 1 1 1 1 1
c K. p e la m is 1 1 1 1 1 1 1 1 1 1 1 1
d T . t o ng g o l 1 1 1 1 1 1 1 1 1 1 1 1
e Othe r tunnie s 1 1 1 1 1 1 1 1 1 1 1 1
2 1 B ill f is he s 1 1 1 1 1 1 1 1 1 1 68 3
2 2 B a rra c uda s 12 19 32 131 26 12 8 3 22 38 32 70
2 3 M ulle ts 67 51 20 58 31 14 10 7 2 2 1 2
2 4 Unic o rn c o d 1 1 1 1 1 1 1 1 1 1 1 1
2 5 F la t f is he s
a Ha libut 2 2 2 2 1 2 2 2 2 2 1 2
b F lo unde rs 2 2 2 2 1 2 2 2 2 2 1 2
c S o le s 1100 1344 2717 1100 3460 675 1059 1738 3642 1532 1071 960
2 6 C rus ta c e a ns
a P e na e id pra wns 13206 9053 13736 14476 18476 5564 4311 5886 16854 8158 4242 7263
b N o n-P e na e id 2 18 29 2 1 9 17 25 36 11 175 7
c Lo bs te rs 14 18 17 18 16 22 13 7 78 26 151 141
d C ra bs 14 870 141 396 610 1108 63 88 356 1157 3786 1145
e S to m a to po ds 46 2740 448 1246 1926 3490 196 277 1127 3646 11440 15362
2 7 M o llus c s 2 2 2 2 1 2 2 2 2 2 1 2
2 8 C e pha lo po ds 19 14 43 33 29 317 5098 1703 2694 119 167 469
2 9 M is c e lla ne o us
To ta l 119275 105798 94530 93314 77788 89244 97189 99095 155917 128912 117629 156416
270
S pe c ie s 19 8 2 19 8 3 19 8 4 19 8 5 19 8 6 19 8 7 19 8 8 19 8 9 19 9 0 19 9 1 19 9 2 19 9 3
1 Ela s m o bra nc hs
a S ha rks 7228 4624 1989 2091 2724 1884 2374 2098 1180 1376 1041 752
b S ka te s 14 11 45 15 25 5 23 155 399 38 101 15
c R a ys 532 225 198 311 579 259 638 876 752 352 450 272
2 Ee ls 11 1 3 2 11 23 5 61 12 12 14 38
3 C a tf is he s 15731 8848 5187 2055 11254 3641 11676 4806 2083 1386 354 61
4 C lupe ids
a Wo lf he rring 374 188 419 650 398 473 342 273 279 201 214 376
b Oil s a rdine 52376 18318 30384 25554 23509 35527 38070 33647 24250 10792 6534 3938
c Othe r s a rdine s 2994 5137 4555 3587 4201 4877 2656 4302 5190 5161 10366 3300
d Hils a s ha d 12 2 13 2 4 5 5 302 29 141 133 55
e Othe r s ha ds 43 14 1 5 25 94 167 81 392 8 109 16
f A nc ho v ie s
i A nc ho v ie lla 11749 9261 9610 4760 12446 6499 15524 6253 8396 9283 4370 5978
ii T hris s o c le s 887 1324 938 845 1948 2155 2999 1812 2809 2446 3081 1700
g Othe r c lupe ids 573 2636 4337 866 4464 2153 3025 2164 2841 1770 2573 2383
5 B o m ba y duc k 1 9 2 13 25 19 15 10 6 1 6 12
6 Liza rd f is he s 384 1659 824 621 1295 3062 3928 3996 1677 2456 1623 773
7 Ha lf a nd F ull 265 82 82 110 206 158 236 243 133 161 105 269
8 F lying f is he s 27 13 1 8 17 13 8 5 5 5 5 5
9 P e rc he s
a R o c k c o ds 28 30 59 86 29 601 1111 248 293 298 445 977
b S na ppe rs 86 72 65 50 4 24 1 6 25 332 16 53
c P ig fa c e bre a m s 11 16 26 18 7 5 4 3 2 3 2 2
d Thre a df in 454 4664 2125 1785 4085 6142 7858 4886 2202 3139 2714 3891
e Othe r pe rc he s 313 620 913 696 2322 5432 4212 2427 1545 3395 1687 2014
10 Go a tf is he s 2 21 1 214 396 549 1239 706 255 44 168 161
11 Thre a df ins 5 19 18 4 7 4 3 2 6 8 11 14
12 C ro a ke rs 3569 4948 2794 2044 3224 5351 5272 5405 7046 6178 5595 4021
13 R ibbo n f is he s 1009 1913 613 1174 6536 7118 3688 1795 3322 2508 3986 1992
14 C a ra ng ids
a Ho rs e m a c ke re l 361 365 240 467 1439 2005 3207 1313 3105 5030 1780 1100
b S c a ds 44 20 1280 572 1309 1915 6730 6201 5457 12622 14189 3818
c Le a the r- ja c ke ts 356 1162 1210 1072 199 345 467 409 220 153 787 398T ra c hyno t us 703 830 1025 1217 1503 1634 1820 2019 2186 2360 2544 2695
d Othe r C a ra ng ids 1772 2132 3573 5866 13924 15421 10426 5798 5262 6155 5984 4641C o ryp ha e na 22 19 19 19 20 19 19 19 19 19 18 18Ela c a t e 1240 1459 1800 2136 2636 2864 3189 3537 3829 4132 4454 4716
15 S ilv e rbe llie sLe io g na t hus 3704 8534 4670 3706 15765 2924 3461 4123 4441 2731 1946 1957G a z z a 5 11 6 4 16 3 3 3 4 3 1 1
16 B ig ja we d 1273 1377 1187 1233 2126 2011 3902 1434 1807 2201 984 658
17 P o m fre ts
a B la c k po m fre t 3036 1582 1646 2575 1710 2151 1814 2067 2481 3650 2068 1830
b S ilv e r po m fre t 1652 670 757 236 419 541 515 857 1027 199 489 643
c C hine s e 31 5 74 40 1 18 19 196 157 67 67 289
18 M a c ke re l
a India n m a c ke re l 5236 1842 10320 20758 18610 21914 18840 84345 36831 17873 12214 36001
b Othe r
19 S e e r f is he s
a S . c o m m e rs o ni 4654 2819 3471 2236 3062 1679 1430 2021 1093 976 1301 1178
b S . g ut t a t us 1828 584 645 403 202 296 350 764 353 269 170 115
c S . line o la t us 4 30 1 3 1 4 7 11 13 16 18 21
d A c a nt ho c yb iu 1 1 1 1 1 1 1 1 1 1 1 1
2 0 Tunnie s
a E. a f f in is 2119 1145 665 1716 3382 1204 1330 3645 1807 413 5153 278
b A uxis s p p . 29 345 107 143 2214 729 507 326 113 10 629 78
c K. p e la m is 1 23 44 66 92 86 85 83 150 216 283 345
d T . t o ng g o l 1 3 23 532 39 59 755 505 604 896 2 4
e Othe r tunnie s 7 72 136 67 70 37 257 67 72 3 137 1
2 1 B ill f is he s 45 36 5 17 54 127 85 4 9 9 60 17
2 2 B a rra c uda s 63 84 10 77 132 145 537 450 244 374 363 193
2 3 M ulle ts 12 13 59 9 16 55 19 11 39 9 47 40
2 4 Unic o rn c o d 1 1 1 1 1 1 1 1 1 1 1 1
2 5 F la t f is he s
a Ha libut 11 10 1 43 23 177 111 18 26 69 39 73
b F lo unde rs 2 83 326 591 774 929 1192 1719 1808 1830 1888 2145
c S o le s 2107 3021 8406 5726 9300 6197 4814 4088 4278 5955 21884 9673
2 6 C rus ta c e a ns
a P e na e id pra wns 11811 9590 7680 6612 6846 11645 11452 13588 9750 11780 12922 5636
b N o n-P e na e id 43 63 106 147 367 231 33 3 188 171 131 2
c Lo bs te rs 75 32 4 34 27 5 1 3 3 3 10 16
d C ra bs 1539 648 663 878 2549 3258 1009 1243 1390 1544 2494 1454
e S to m a to po ds 15235 9369 13710 13905 21501 57144 32573 40044 28580 25669 30858 22610
2 7 M o llus c s 2 1 1 2 1 1 1 2 2 1 1 1
2 8 C e pha lo po ds 235 1191 464 353 2945 3680 2601 4010 3560 4553 2494 10954
2 9 M is c e lla ne o us
To ta l 157933 113830 129536 121027 193016 227529 218643 261495 186037 163456 174116 146666
271
S pe c ie s 19 9 4 19 9 5 19 9 6 19 9 7 19 9 8 19 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5
1 Ela s m o bra nc hs
a S ha rks 1091 1478 905 606 976 745 850 1003 1247 673 788 917
b S ka te s 21 185 155 7 293 74 4 95 65 27 58 67
c R a ys 511 353 240 232 399 166 259 224 186 154 97 113
2 Ee ls 55 189 180 190 207 218 86 131 101 82 117 136
3 C a tf is he s 207 282 389 111 176 252 149 416 231 216 566 658
4 C lupe ids
a Wo lf he rring 680 543 542 473 208 249 166 373 503 142 177 206
b Oil s a rdine 1675 4429 7329 10125 14364 21653 34334 43137 51654 38213 47184 54825
c Othe r s a rdine s 6097 6449 2054 8346 7954 3179 1014 2639 2049 3954 2694 3130
d Hils a s ha d 163 162 50 38 131 2 95 14 88 25 6 7
e Othe r s ha ds 133 239 7 9 13 1 1 233 1 21 14 17
f A nc ho v ie s 15019 17293 7897 9008 8797 8504 7954 8716 6000 5275 6251 7264
i A nc ho v ie lla
ii T hris s o c le s
g Othe r c lupe ids 3576 4350 2939 1564 3556 1885 1835 2750 1123 1692 1108 1287
5 B o m ba y duc k 1 1 2 1 1 1 1 1 1 1 1 1
6 Liza rd f is he s 2350 2973 3447 2558 4076 2279 3665 4187 1690 3801 2805 3262
7 Ha lf a nd F ull 224 359 252 345 195 308 158 306 263 319 193 224
8 F lying f is he s 1 1 1 1 1 1 1 1 23 1 1 1
9 P e rc he s
a R o c k c o ds 1604 1580 1851 2203 1905 2664 2988 5126 7327 1539 2296 2670
b S na ppe rs 3 1 55 6 183 127 32 23 21 9 6 7
c P ig fa c e bre a m s 1 1 2 46 61 72 1 1 1 7 1 1
d Thre a df in 5603 5916 9337 11806 13859 12110 19092 25801 14710 22661 20469 23806
e Othe r pe rc he s 3398 3722 3288 6120 4997 4148 4144 7001 6644 3668 2939 3418
10 Go a tf is he s 6 5 24 3 35 16 195 13 200 1 1 1
11 Thre a df ins 1 2 1 1 1 1 1 1 1 1 1 1
12 C ro a ke rs 4739 4407 3979 3252 4068 3400 3184 3490 4310 2169 2507 2916
13 R ibbo n f is he s 7668 2153 5114 8107 2785 2738 12278 4741 11929 10891 6586 7652
14 C a ra ng ids
a Ho rs e m a c ke re l 2861 3680 867 1901 3400 3732 1423 2467 2106 3619 7502 8716
b S c a ds 1948 9322 11002 4583 3772 3374 3155 5583 6217 2402 4962 5766
c Le a the r- ja c ke ts 339 423 354 240 386 375 112 472 263 155 169 197T ra c hyno t us
d Othe r C a ra ng ids 6519 7772 9887 10634 9164 8369 5284 5006 3865 3253 3987 4633C o ryp ha e na
Ela c a t e
15 S ilv e rbe llie s 1968 1299 1847 1715 2608 2849 1662 2846 3895 4900 3519 4093Le io g na t hus
G a z z a
16 B ig ja we d 990 1028 710 1077 1654 1110 1313 1326 1088 534 703 818
17 P o m fre ts
a B la c k po m fre t 2399 3381 1115 983 979 972 905 1425 873 1005 1854 2158
b S ilv e r po m fre t 364 442 369 510 435 485 571 329 388 144 103 119
c C hine s e 194 21 2 1 1 1 25 30 1 43 1 1
18 M a c ke re l
a India n m a c ke re l 28057 21128 54987 44663 33601 37653 22846 18538 13980 17869 26517 30812
b Othe r
19 S e e r f is he s
a S . c o m m e rs o ni 1465 2133 1841 2295 2039 2073 3550 2272 4478 4174 5210 6053
b S . g ut t a t us 110 363 339 258 185 281 1184 346 876 377 719 835
c S . line o la t us
d A c a nt ho c yb iu 1 1 8 7 1 1 1 1 1 1 1 1
2 0 Tunnie s
a E. a f f in is 1485 688 416 702 2635 1768 3087 1537 1938 1236 1366 1587
b A uxis s p p . 457 78 67 238 617 141 1271 978 459 157 535 622
c K. p e la m is 1 1 1 1 14 1 1 1 1 1 3 4
d T . t o ng g o l 28 100 35 176 282 373 113 62 59 15 1 1
e Othe r tunnie s 23 2 20 1009 7 1 275 95 225 100 3 4
2 1 B ill f is he s 25 64 163 103 176 18 94 73 36 21 12 14
2 2 B a rra c uda s 767 1612 1281 807 2062 1371 1473 1820 1158 1059 1127 1310
2 3 M ulle ts 64 50 38 66 142 63 42 82 199 126 59 69
2 4 Unic o rn c o d 1 1 1 1 1 1 1 1 1 1 1 1
2 5 F la t f is he s
a Ha libut 55 45 18 105 63 143 135 33 7 1 1 1
b F lo unde rs 1 1 1 1 1 1 1 42 1 2 3 4
c S o le s 7486 4753 6809 8829 6712 6866 14730 11318 8603 10855 6077 7068
2 6 C rus ta c e a ns
a P e na e id pra wns 8593 7915 7315 9610 6625 9003 6723 9069 17920 12169 9228 10732
b N o n-P e na e id 179 1 1 426 94 1 290 5 82 32 25 29
c Lo bs te rs 1 1 1 49 1 2 51 48 81 57 2 2
d C ra bs 1526 879 1315 2380 827 1157 1924 2043 2957 2128 1607 1868
e S to m a to po ds 22349 19383 14903 21340 12900 13592 13127 13148 24138 13072 12870 14968
2 7 M o llus c s 2 2 2 2 2 2 76 1052 241 99 2 2
2 8 C e pha lo po ds 8924 9419 7693 12486 7965 8419 9288 5850 5654 13250 12291 14301
2 9 M is c e lla ne o us
To ta l 154007 153060 173449 192354 168595 168995 187218 198321 212157 188399 197330 229380
272
Appendix C.7. Marine fisheries catch (t) for Kerala, 1970-2005 (contd.)
S pe c ie s 19 7 0 19 7 1 19 7 2 19 7 3 19 7 4 19 7 5 19 7 6 19 7 7 19 7 8 19 7 9 19 8 0 19 8 1
1 Ela s m o bra nc hs
a S ha rks 8478 6217 7577 8569 9593 10152 8840 6141 9435 7349 6242 5491
b S ka te s 281 205 250 284 317 336 292 203 312 244 207 182
c R a ys 2745 2014 2453 2774 3106 3287 2862 1988 3054 2379 2021 1777
2 Ee ls 2 53 7 4 62 16 16 9 52 14 7 5
3 C a tf is he s 25159 26207 18594 22903 42214 43636 20915 11423 12557 16244 17350 14625
4 C lupe ids
a Wo lf he rring 771 404 356 475 757 574 737 503 1155 979 876 840
b Oil s a rdine 168473 169924 93642 107280 89868 92160 113117 107954 106751 101636 60911 126854
c Othe r s a rdine s 5396 9938 6089 54539 27571 31913 31310 19091 10425 13844 9632 6584
d Hils a s ha d 5 8 17 14 11 9 11 33 162 5 12 7
e Othe r s ha ds 11 5 3 34 29 37 42 13 61 31 18 13
f A nc ho v ie s
i A nc ho v ie lla 6986 9449 9570 7811 17125 10841 9115 9295 18872 5700 6795 3705
ii T hris s o c le s 4052 2047 1794 1453 1162 1553 2493 1516 1689 1556 1959 547
g Othe r c lupe ids 1376 1454 1207 1012 1164 946 1072 471 866 586 502 815
5 B o m ba y duc k 1 1 39 39 16 18 18 18 19 1 1 1
6 Liza rd f is he s 1637 2407 2098 1492 11130 15116 162 7430 8595 7637 8815 8704
7 Ha lf a nd F ull 26 85 79 162 291 264 129 258 250 224 316 488
8 F lying f is he s 3 2 2 1 2 2 1 4 6 9 11 14
9 P e rc he s
a R o c k c o ds 266 254 232 456 1056 790 202 812 1376 1162 888 1123
b S na ppe rs 66 64 57 114 264 197 51 203 344 290 222 116
c P ig fa c e bre a m s 66 64 57 114 264 197 51 203 344 290 222 139
d Thre a df in 5129 4867 4463 8762 20332 15192 3878 15629 26479 22347 17078 9853
e Othe r pe rc he s 1132 1075 986 1935 4489 3354 857 3451 5846 4934 3770 1845
10 Go a tf is he s 429 2714 4356 2019 4887 31 4230 345 235 182 1 50
11 Thre a df ins 34 982 35 749 4 141 200 99 48 42 10 72
12 C ro a ke rs 8896 7152 9031 15397 11609 22500 11415 17199 17951 7510 7674 4810
13 R ibbo n f is he s 4326 15147 9379 20880 26566 14391 7016 6844 21546 22372 11311 6098
14 C a ra ng ids
a Ho rs e m a c ke re l 2253 4316 13315 12480 4628 6818 9563 14417 6406 10734 2996 47
b S c a ds 1 1 1 1 1 1 1 1 1 1 1 1457
c Le a the r- ja c ke ts 68 43 733 42 63 128 242 497 132 111 108 274T ra c hyno t us 5 5 2 3 4 6 6 6 17 1 1 1
d Othe r C a ra ng ids 33 3 32 31 64 81 5 72 64 908 975 2580C o ryp ha e na 1 1 82 134 83 58 51 26 17 42 24 23Ela c a t e 104 261 68 45 31 59 97 145 148 104 58 57
15 S ilv e rbe llie sLe io g na t hus 24832 14854 7419 24151 22058 6974 4476 11080 4183 5158 5164 4322G a z z a 29 36 22 5 6 0 0 7 7 7 6 8
16 B ig ja we d 2216 5161 4465 8751 3657 1316 768 1183 2110 363 1072 1344
17 P o m fre ts
a B la c k po m fre t 490 1917 1308 1093 869 727 604 2455 1021 1146 519 939
b S ilv e r po m fre t 553 2167 1479 1236 982 822 681 2774 1155 1295 588 1146
c C hine s e 22 83 57 47 38 32 26 106 44 50 22 15
18 M a c ke re l
a India n m a c ke re l 48041 82936 30952 17282 9094 14158 18234 18368 23068 16167 16152 13981
b Othe r 1 1 1 1 1 1 1 1 1 1 1 1
19 S e e r f is he s
a S . c o m m e rs o ni 721 1156 589 700 2048 1827 2568 1418 1415 2587 1560 1364
b S . g ut t a t us 797 1279 651 774 2263 2020 2838 1567 1564 2860 1724 1505
c S . line o la t us 3 5 3 3 9 8 11 6 6 11 7 5
d A c a nt ho c yb iu 1 1 1 1 1 1 1 1 1 1 1 1
2 0 Tunnie s
a E. a f f in is 797 1963 2406 1745 3859 4101 8699 4564 4313 9907 6865 3518
b A uxis s p p . 220 541 664 481 1064 1130 2399 1258 1189 2732 1893 970
c K. p e la m is 1 1 1 1 1 1 1 1 1 1 1 1
d T . t o ng g o l 4 10 13 10 21 22 47 25 23 54 37 19
e Othe r tunnie s 56 138 169 122 271 288 612 321 303 696 483 248
2 1 B ill f is he s 1 1 1 1 1 1 1 1 1 1 1 111
2 2 B a rra c uda s 69 152 1009 2038 3401 376 451 325 642 415 289 701
2 3 M ulle ts 237 947 134 135 1202 99 43 55 1 56 188 184
2 4 Unic o rn c o d 1 1 1 1 1 1 1 1 1 1 1 1
2 5 F la t f is he s
a Ha libut 2 2 1 1 1 1 2 1 1 1 1 242
b F lo unde rs 2 2 1 1 1 1 2 1 1 1 1 2
c S o le s 15685 15196 9004 11231 16081 9278 5854 8305 10012 6434 5471 7458
2 6 C rus ta c e a ns
a P e na e id pra wns 56738 53995 52777 111339 75316 103334 56587 57713 61971 42333 65528 34059
b N o n-P e na e id 22 2621 1046 1288 1277 1010 90 250 542 108 2169 245
c Lo bs te rs 2 2 1 1 1 41 82 57 52 37 22 76
d C ra bs 820 866 224 2246 3489 2309 2073 6376 2875 10521 406 257
e S to m a to po ds 34 36 9 93 145 96 87 266 120 439 8665 4328
2 7 M o llus c s 2 2 1 1 1 1 2 1 1 1 1 2
2 8 C e pha lo po ds 132 816 515 445 2739 4473 1431 7148 8967 4267 5284 3634
2 9 M is c e lla ne o us
To ta l 400738 454254 301530 457234 428662 429253 337668 351938 380806 337119 285134 279883
273
S pe c ie s 19 8 2 19 8 3 19 8 4 19 8 5 19 8 6 19 8 7 19 8 8 19 8 9 19 9 0 19 9 1 19 9 2 19 9 3
1 Ela s m o bra nc hs
a S ha rks 6374 11324 8493 6597 5885 3924 6696 2836 4273 2691 3226 4060
b S ka te s 515 50 16 16 22 9 45 223 24 98 83 50
c R a ys 2268 1083 1837 1362 1732 1690 2040 3875 5807 2109 1098 1723
2 Ee ls 27 45 26 4 5 16 19 1 46 20 16 62
3 C a tf is he s 13762 22371 15690 6904 10878 5824 12900 6055 3908 2470 1371 789
4 C lupe ids
a Wo lf he rring 928 942 1189 536 611 698 638 1193 622 609 841 1503
b Oil s a rdine 125414 133828 127468 68780 35086 38866 52425 158937 153995 91597 47586 44313
c Othe r s a rdine s 6470 4593 5217 2147 7718 7559 11004 11822 11081 20455 14919 20356
d Hils a s ha d 34 144 172 17 13 7 14 46 275 171 67 76
e Othe r s ha ds 17 21 24 28 2 2 28 17 7 82 413 222
f A nc ho v ie s
i A nc ho v ie lla 11907 47561 35956 31451 23462 14427 39861 38795 23089 39025 42397 44136
ii T hris s o c le s 849 1013 1204 1046 2708 2899 5007 2422 2026 3128 6749 4630
g Othe r c lupe ids 2710 5813 4635 3713 5970 7550 7008 9420 7088 13494 22452 11616
5 B o m ba y duc k 1 1 1 1 1 1 1 1 1 1 1 1
6 Liza rd f is he s 7912 7961 8503 8413 8039 6514 17375 11735 16363 16145 18821 18293
7 Ha lf a nd F ull 880 417 273 576 482 650 1099 547 542 698 358 355
8 F lying f is he s 1 2 3 3 3 3 3 22 42 62 1224 9
9 P e rc he s
a R o c k c o ds 411 560 654 642 424 1182 1163 1859 1424 1026 4033 4833
b S na ppe rs 487 134 207 183 162 500 1141 1536 1651 262 163 401
c P ig fa c e bre a m s 165 147 58 270 365 92 531 285 324 326 117 179
d Thre a df in 13214 10600 27689 32251 48528 29127 32394 63152 77213 45864 49021 72836
e Othe r pe rc he s 1864 3026 7808 7584 8751 6756 6611 5569 15487 10773 13497 20685
10 Go a tf is he s 352 222 102 134 270 855 12739 8893 9872 26664 10103 3292
11 Thre a df ins 170 257 614 208 34 46 131 630 773 932 43 144
12 C ro a ke rs 5170 8917 13122 11517 16161 10199 10970 16852 15506 12488 20789 19383
13 R ibbo n f is he s 9663 961 5600 21827 10263 13294 7756 6172 8376 1868 5418 6503
14 C a ra ng ids
a Ho rs e m a c ke re l 807 342 163 76 1257 1099 3860 573 9387 2835 1534 4484
b S c a ds 1818 4511 4937 3673 30578 6037 16806 31474 35029 55506 61712 40990
c Le a the r- ja c ke ts 468 421 195 225 335 409 224 126 624 113 178 524T ra c hyno t us 1 1 1 1 1 1 1 1 1 1 1 1
d Othe r C a ra ng ids 8021 9007 6549 7172 29672 12247 19892 11000 14287 9407 11423 18488C o ryp ha e na 24 23 23 23 23 23 23 23 23 23 24 24Ela c a t e 58 57 57 57 57 57 57 57 57 57 58 59
15 S ilv e rbe llie sLe io g na t hus 12604 13867 5298 4563 7603 7532 8410 7913 8839 7993 5969 8540G a z z a 7 7 7 7 6 6 6 7 7 7 7 7
16 B ig ja we d 2323 1604 2228 1390 1820 779 1067 1963 3470 890 894 1191
17 P o m fre ts
a B la c k po m fre t 2807 1423 958 929 1700 2170 1063 1322 2303 1486 1520 982
b S ilv e r po m fre t 3303 1319 927 243 605 502 979 1236 1498 289 1815 1793
c C hine s e 19 172 301 16 47 4 44 28 52 1 109 711
18 M a c ke re l
a India n m a c ke re l 9385 10959 10176 15715 18899 8746 38068 73307 67289 46528 33333 52784
b Othe r 1 1 1 1 1 1 1 1 1 2 2 1
19 S e e r f is he s
a S . c o m m e rs o ni 3171 3840 2759 3898 3653 3925 7040 6222 4115 3900 7266 5478
b S . g ut t a t us 1715 2140 2578 3445 543 578 1764 467 499 66 413 273
c S . line o la t us 33 21 11 11 11 11 11 11 11 11 11 12
d A c a nt ho c yb iu 1 47 24 1 54 109 162 214 143 72 1 1
2 0 Tunnie s
a E. a f f in is 4611 3287 3634 5155 7969 5212 6660 12785 21687 7620 10399 8502
b A uxis s p p . 1294 721 1210 2415 4259 2627 3837 4884 4946 2402 3091 2457
c K. p e la m is 44 2 4 38 32 192 55 497 94 71 26 1
d T . t o ng g o l 38 9 29 337 55 241 60 201 80 470 64 56
e Othe r tunnie s 390 950 465 606 459 950 575 793 1209 739 681 810
2 1 B ill f is he s 222 82 277 135 161 132 150 147 210 97 211 218
2 2 B a rra c uda s 574 983 961 780 1134 807 1634 1843 3300 3630 3558 2692
2 3 M ulle ts 48 169 93 956 465 1060 25 116 58 147 87 256
2 4 Unic o rn c o d 1 1 1 1 1 1 1 1 10 8 5 5
2 5 F la t f is he s
a Ha libut 261 233 80 25 24 139 300 89 693 133 461 28
b F lo unde rs 1 66 3 5 30 211 13 186 622 732 1063 122
c S o le s 16491 19141 24039 15103 11888 12394 16479 29650 20695 19668 36375 27116
2 6 C rus ta c e a ns
a P e na e id pra wns 38561 43414 48131 35635 47071 64742 85896 75851 63382 81837 66924 62670
b N o n-P e na e id 94 153 1000 270 132 317 207 26 3 374 83 171
c Lo bs te rs 136 99 72 126 63 174 145 109 175 276 274 53
d C ra bs 501 692 635 1299 1772 3199 2786 3937 6711 6115 6481 7421
e S to m a to po ds 5808 9252 9606 10439 11521 14026 14958 19675 24294 14266 16961 25318
2 7 M o llus c s 1 1 1 1 1 1 1 1 1 1 1 1
2 8 C e pha lo po ds 5105 2524 7345 11062 18970 9496 19701 34935 35899 27823 40543 37394
2 9 M is c e lla ne o us
To ta l 332311 393533 401340 332047 390447 312847 482562 674569 691522 588656 578365 592083
274
S pe c ie s 19 9 4 19 9 5 19 9 6 19 9 7 19 9 8 19 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5
1 Ela s m o bra nc hs
a S ha rks 4183 2797 2572 1767 2094 1808 1735 2102 2038 3511 2624 2284
b S ka te s 145 142 217 508 563 529 180 911 320 271 365 318
c R a ys 2118 1464 1911 1912 1727 1553 1066 1725 1918 1281 784 683
2 Ee ls 144 261 349 315 318 189 97 230 300 162 112 97
3 C a tf is he s 544 331 413 205 227 262 108 156 407 271 178 155
4 C lupe ids
a Wo lf he rring 1560 1013 1367 1453 1407 397 248 407 669 722 773 672
b Oil s a rdine 1660 13970 31946 98818 82017 149534 250493 161873 225583 271121 230004 199942
c Othe r s a rdine s 17608 48352 7032 16435 20968 30387 6200 2756 6122 16107 18149 15777
d Hils a s ha d 1 29 1 1 1 1 1 6 1 1 1 1
e Othe r s ha ds 89 60 1 35 85 11 8 21 19 212 6 5
f A nc ho v ie s 41417 43399 37263 34003 42185 30095 26608 21631 30383 30497 34945 30378
i A nc ho v ie lla
ii T hris s o c le s
g Othe r c lupe ids 14037 10275 21623 10055 11241 8432 3998 7672 3427 3604 2537 2206
5 B o m ba y duc k 1 1 1 1 1 1 1 1 1 1 1 1
6 Liza rd f is he s 13153 13501 11467 8066 9164 6776 8167 8624 7340 11035 13840 12048
7 Ha lf a nd F ull 487 3746 490 744 674 735 633 614 1348 2274 663 577
8 F lying f is he s 6 4 17 8 1 4 6 1 124 2 1 1
9 P e rc he s
a R o c k c o ds 2949 5514 7090 5058 5724 4099 4678 6893 9751 4520 5987 5212
b S na ppe rs 243 86 314 1458 1537 1825 2606 1861 2003 1017 776 676
c P ig fa c e bre a m s 486 478 714 1786 1000 676 86 307 525 107 146 127
d Thre a df in 47991 31618 54744 26990 29715 29295 39306 36781 34031 21607 48090 41861
e Othe r pe rc he s 13938 13406 12513 14654 7181 7421 6680 12907 17129 8337 10104 8795
10 Go a tf is he s 416 187 88 119 382 129 66 6 23 1 185 161
11 Thre a df ins 98 15 1 10 342 162 39 52 70 202 122 106
12 C ro a ke rs 18243 10709 18770 10629 14315 8039 9739 6951 6710 9397 5960 5188
13 R ibbo n f is he s 16490 4864 22842 20026 17479 17279 19989 32691 24907 15493 13166 11445
14 C a ra ng ids
a Ho rs e m a c ke re l 4712 6468 3406 5222 5559 10505 2351 1393 2633 5222 3871 3365
b S c a ds 41771 87321 45026 22336 45233 22590 12832 27664 21914 15412 20434 17763
c Le a the r- ja c ke ts 250 216 392 1854 464 462 1046 82 383 604 347 302T ra c hyno t us
d Othe r C a ra ng ids 16918 13704 15858 23641 17133 13759 14243 13811 21279 17141 13762 11963C o ryp ha e na
Ela c a t e
15 S ilv e rbe llie s 4620 4298 4805 5054 5455 6503 4745 6286 7155 5433 4331 3770Le io g na t hus
G a z z a
16 B ig ja we d 1246 601 2353 1919 3219 1744 1539 936 424 625 463 403
17 P o m fre ts
a B la c k po m fre t 1971 1308 2584 1773 1976 1527 196 621 640 295 1142 994
b S ilv e r po m fre t 1016 480 1300 1042 922 445 620 2407 444 204 140 122
c C hine s e 734 5 1 22 152 109 1 1 26 1 11 10
18 M a c ke re l
a India n m a c ke re l 119524 82295 134030 86991 64837 86146 35128 21398 24060 35920 55285 48059
b Othe r 1 1 1 1 1 1 1 1 1 1 1 1
19 S e e r f is he s
a S . c o m m e rs o ni 6071 6091 4967 4173 5860 3007 4948 3740 6240 8728 8771 7625
b S . g ut t a t us 165 103 53 270 114 53 200 132 143 44 212 184
c S . line o la t us 1 1 19 6 1 1 1 1 14 1 1 1
d A c a nt ho c yb iu 1 1 1 1 3 15 37 144 11 14 8 7
2 0 Tunnie s
a E. a f f in is 6114 7243 6719 7369 5365 8833 7742 8024 9792 10539 7861 6834
b A uxis s p p . 8423 3204 10021 7106 3877 3928 3870 6980 6613 9758 4029 3502
c K. p e la m is 1 1 124 174 192 218 638 837 1081 645 341 296
d T . t o ng g o l 223 83 231 117 151 749 520 1669 358 1050 380 330
e Othe r tunnie s 617 976 1613 2138 2604 4770 3749 2022 1527 1928 2230 1939
2 1 B ill f is he s 155 116 289 758 419 477 875 1087 1741 1702 643 559
2 2 B a rra c uda s 3030 4902 4315 3689 10312 2948 3109 6820 3691 4526 4895 4255
2 3 M ulle ts 728 750 326 80 124 52 356 206 163 31 102 88
2 4 Unic o rn c o d 1 1 1 1 1 1 1 1 1 1 1 1
2 5 F la t f is he s
a Ha libut 316 423 601 128 5 5 19 261 60 99 8 7
b F lo unde rs 119 146 15 52 44 29 31 33 31 2 69 60
c S o le s 22458 12722 16087 21581 17800 26843 17556 15761 20911 22462 16843 14661
2 6 C rus ta c e a ns
a P e na e id pra wns 75550 44742 47472 58203 60535 43273 57479 46334 42627 43190 30808 26798
b N o n-P e na e id 108 188 140 447 54 2643 9809 7018 10079 10552 8689 7558
c Lo bs te rs 483 104 119 283 68 542 562 275 410 401 273 238
d C ra bs 5209 2178 3793 11148 7444 5111 6188 4372 4919 5829 5698 4960
e S to m a to po ds 21837 12419 9655 25721 9715 13759 12426 7066 7733 4909 5813 5060
2 7 M o llus c s 1460 504 2237 781 1028 697 489 766 620 435 1116 971
2 8 C e pha lo po ds 40540 46535 34581 39701 34483 33093 32284 31133 30491 28475 43180 37591
2 9 M is c e lla ne o us
To ta l 584383 546350 586878 588842 555495 594481 618328 526459 603366 637934 631280 548993
275
Appendix C.8. Marine fisheries catch (t) for Lakshadweep Islands, 1970-2005 (contd.)
S pe c ie s 19 7 0 19 7 1 19 7 2 19 7 3 19 7 4 19 7 5 19 7 6 19 7 7 19 7 8 19 7 9 19 8 0 19 8 1
1 Ela s m o bra nc hs
a S ha rks 296 218 234 283 406 523 526 433 365 643 411 347
b S ka te s
c R a ys 32 24 27 31 45 59 58 49 41 71 45 40
2 Ee ls
3 C a tf is he s 2 2 2 2 2 2 2 2 2 2 2 2
4 C lupe ids
a Wo lf he rring
b Oil s a rdine
c Othe r s a rdine s 4 4 4 4 4 4 4 4 4 4 4 4
d Hils a s ha d
e Othe r s ha ds
f A nc ho v ie s
i A nc ho v ie lla
ii T hris s o c le s
g Othe r c lupe ids
5 B o m ba y duc k
6 Liza rd f is he s
7 Ha lf a nd F ull 15 8 19 95 26 26 32 56 131 91 90 101
8 F lying f is he s 19 13 17 40 41 27 40 29 30 14 26 14
9 P e rc he s 77 61 95 165 196 229 219 238 229 275 416 407
a R o c k c o ds
b S na ppe rs 34 26 42 72 85 100 96 103 100 120 181 172
c P ig fa c e bre a m s
d Thre a df in
e Othe r pe rc he s 2 2 2 2 4 4 3 3 4 4 6 42
10 Go a tf is he s 51 16 20 66 57 61 96 47 55 53 43 46
11 Thre a df ins
12 C ro a ke rs
13 R ibbo n f is he s
14 C a ra ng ids
a Ho rs e m a c ke re l 30 19 30 60 59 56 92 62 54 52 72 94
b S c a ds
c Le a the r- ja c ke tsT ra c hyno t us
d Othe r C a ra ng idsC o ryp ha e na
Ela c a t e
15 S ilv e rbe llie sLe io g na t hus 2 2 2 2 2 9 8 8 10 10 8 9G a z z a
16 B ig ja we d
17 P o m fre ts
a B la c k po m fre t
b S ilv e r po m fre t
c C hine s e
18 M a c ke re l
a India n m a c ke re l
b Othe r
19 S e e r f is he s
a S . c o m m e rs o ni 22 18 20 11 35 24 34 15 15 9 7 18
b S . g ut t a t us 33 27 31 16 53 37 51 24 23 13 12 27
c S . line o la t us
d A c a nt ho c yb iu
2 0 Tunnie s
a E. a f f in is 6 7 5 10 12 17 13 12 17 25 16 20
b A uxis s p p . 27 36 26 49 60 89 63 56 85 126 80 100
c K. p e la m is 417 565 395 763 938 1376 983 873 1326 1957 1243 1554
d T . t o ng g o l 27 36 26 49 60 89 63 56 85 126 80 100
e Othe r tunnie s 59 80 56 107 132 195 139 123 187 276 176 219
2 1 B ill f is he s 16 16 17 16 16 16 17 16 15 15 15 15
2 2 B a rra c uda s 6 7 8 11 17 16 20 14 16 10 13 11
2 3 M ulle ts
2 4 Unic o rn c o d
2 5 F la t f is he s
a Ha libut
b F lo unde rs
c S o le s
2 6 C rus ta c e a ns
a P e na e id pra wns
b N o n-P e na e id
c Lo bs te rs
d C ra bs
e S to m a to po ds
2 7 M o llus c s
2 8 C e pha lo po ds 14 26 28 37 27 34 66 37 41 29 21 26
2 9 M is c e lla ne o us
To ta l 1188 1214 1102 1890 2277 2990 2623 2259 2836 3923 2967 3367
276
S pe c ie s 19 8 2 19 8 3 19 8 4 19 8 5 19 8 6 19 8 7 19 8 8 19 8 9 19 9 0 19 9 1 19 9 2 19 9 3
1 Ela s m o bra nc hs
a S ha rks 436 494 498 459 377 101 567 780 504 657 714 772
b S ka te s
c R a ys 38 154 224 208 142 146 264 185 157 197 210 217
2 Ee ls
3 C a tf is he s 2 2 3 3 4 6 3 3 4 5 5 6
4 C lupe ids
a Wo lf he rring
b Oil s a rdine
c Othe r s a rdine s 4 4 4 4 4 3 3 3 4 4 4 4
d Hils a s ha d
e Othe r s ha ds
f A nc ho v ie s
i A nc ho v ie lla
ii T hris s o c le s
g Othe r c lupe ids
5 B o m ba y duc k
6 Liza rd f is he s
7 Ha lf a nd F ull 78 171 55 35 38 28 60 61 73 97 123 149
8 F lying f is he s 22 22 13 5 12 13 17 31 33 39 46 53
9 P e rc he s 456 504 289 187 277 512 426 281 449 283 273 263
a R o c k c o ds
b S na ppe rs 248 180 174 132 142 225 141 175 161 168 231 286
c P ig fa c e bre a m s
d Thre a df in
e Othe r pe rc he s 54 39 55 20 54 146 89 152 201 125 121 120
10 Go a tf is he s 54 63 60 82 177 219 83 66 142 197 221 252
11 Thre a df ins
12 C ro a ke rs
13 R ibbo n f is he s
14 C a ra ng ids
a Ho rs e m a c ke re l 191 131 40 45 45 52 43 40 48 51 53 55
b S c a ds
c Le a the r- ja c ke tsT ra c hyno t us
d Othe r C a ra ng idsC o ryp ha e na
Ela c a t e
15 S ilv e rbe llie sLe io g na t hus 10 10 13 15 19 28 15 17 18 24 26 29G a z z a
16 B ig ja we d
17 P o m fre ts
a B la c k po m fre t
b S ilv e r po m fre t
c C hine s e
18 M a c ke re l
a India n m a c ke re l
b Othe r
19 S e e r f is he s
a S . c o m m e rs o ni 36 21 21 22 13 18 27 20 19 28 37 46
b S . g ut t a t us 53 31 31 30 20 28 41 30 28 42 55 69
c S . line o la t us
d A c a nt ho c yb iu
2 0 Tunnie s
a E. a f f in is 27 29 36 35 109 213 245 319 374 451 533 615
b A uxis s p p . 132 146 184 168 229 321 303 346 369 419 470 521
c K. p e la m is 2066 2287 2866 2639 3318 4368 3919 4286 4417 4886 5349 5810
d T . t o ng g o l 132 146 184 169 199 247 211 220 217 232 246 259
e Othe r tunnie s 291 322 404 372 426 515 426 434 417 385 402 417
2 1 B ill f is he s 38 29 37 66 40 32 26 19 26 30 34 37
2 2 B a rra c uda s 9 17 12 7 7 5 6 4 17 16 15 14
2 3 M ulle ts
2 4 Unic o rn c o d
2 5 F la t f is he s
a Ha libut
b F lo unde rs
c S o le s
2 6 C rus ta c e a ns
a P e na e id pra wns
b N o n-P e na e id
c Lo bs te rs
d C ra bs
e S to m a to po ds
2 7 M o llus c s
2 8 C e pha lo po ds 44 31 35 32 42 51 31 43 66 85 91 97
2 9 M is c e lla ne o us
To ta l 4419 4835 5240 4734 5694 7278 6946 7516 7744 8418 9258 10093
277
S pe c ie s 19 9 4 19 9 5 19 9 6 19 9 7 19 9 8 19 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5
1 Ela s m o bra nc hs
a S ha rks 1079 1363 1024 807 860 883 698 698 698 698 698 698
b S ka te s
c R a ys 293 353 259 194 197 95 92 92 92 92 92 92
2 Ee ls
3 C a tf is he s 5 5 10 5 7 6 5 5 5 5 5 5
4 C lupe ids
a Wo lf he rring
b Oil s a rdine
c Othe r s a rdine s 4 4 4 4 4 5 4 4 4 4 4 4
d Hils a s ha d
e Othe r s ha ds
f A nc ho v ie s
i A nc ho v ie lla
ii T hris s o c le s
g Othe r c lupe ids
5 B o m ba y duc k
6 Liza rd f is he s
7 Ha lf a nd F ull 180 186 113 149 301 354 287 287 287 287 287 287
8 F lying f is he s 62 81 23 88 53 130 117 117 117 117 117 117
9 P e rc he s 181 78 38 374 518 584 535 535 535 535 535 535
a R o c k c o ds
b S na ppe rs 248 206 230 447 689 559 313 313 313 313 313 313
c P ig fa c e bre a m s
d Thre a df in
e Othe r pe rc he s 81 54 57 90 125 133 125 125 125 125 125 125
10 Go a tf is he s 203 196 115 244 368 368 289 289 289 289 289 289
11 Thre a df ins
12 C ro a ke rs
13 R ibbo n f is he s
14 C a ra ng ids
a Ho rs e m a c ke re l 58 62 37 139 91 118 95 95 95 95 95 95
b S c a ds
c Le a the r- ja c ke tsT ra c hyno t us
d Othe r C a ra ng idsC o ryp ha e na
Ela c a t e
15 S ilv e rbe llie sLe io g na t hus 23 25 48 23 33 32 24 24 24 24 24 24G a z z a
16 B ig ja we d
17 P o m fre ts
a B la c k po m fre t
b S ilv e r po m fre t
c C hine s e
18 M a c ke re l
a India n m a c ke re l
b Othe r
19 S e e r f is he s
a S . c o m m e rs o ni 57 52 20 25 37 58 83 83 83 83 83 83
b S . g ut t a t us 87 78 30 38 55 89 124 124 124 124 124 124
c S . line o la t us
d A c a nt ho c yb iu
2 0 Tunnie s
a E. a f f in is 638 737 709 735 1129 1007 753 753 753 753 753 753
b A uxis s p p . 511 590 630 650 961 823 616 616 616 616 616 616
c K. p e la m is 5549 6240 6219 5721 8230 6858 5132 5132 5132 5132 5132 5132
d T . t o ng g o l 238 296 314 293 483 457 342 342 342 342 342 342
e Othe r tunnie s 416 408 371 381 571 498 402 402 402 402 402 402
2 1 B ill f is he s 41 58 34 65 62 85 69 69 69 69 69 69
2 2 B a rra c uda s 13 16 21 89 48 85 84 84 84 84 84 84
2 3 M ulle ts
2 4 Unic o rn c o d
2 5 F la t f is he s
a Ha libut
b F lo unde rs
c S o le s
2 6 C rus ta c e a ns
a P e na e id pra wns
b N o n-P e na e id
c Lo bs te rs
d C ra bs
e S to m a to po ds
2 7 M o llus c s
2 8 C e pha lo po ds 74 79 151 69 98 114 96 96 96 96 96 96
2 9 M is c e lla ne o us
To ta l 10042 11166 10455 10628 14919 13342 10284 10284 10284 10284 10284 10284
278
Appendix C.9. Marine fisheries catch (t) for Tamil Nadu, 1970-2005 (contd.)
S pe c ie s 19 7 0 19 7 1 19 7 2 19 7 3 19 7 4 19 7 5 19 7 6 19 7 7 19 7 8 19 7 9 19 8 0 19 8 1
1 Ela s m o bra nc hs
a S ha rks 3803 3639 2763 2789 4704 4490 4197 4179 3463 2942 3522 2963
b S ka te s 210 200 152 153 258 246 228 227 187 154 190 159
c R a ys 18389 17595 13335 13444 22577 21596 20025 19834 16338 13509 16631 13944
2 Ee ls 259 186 59 155 356 141 805 312 438 166 114 123
3 C a tf is he s 8451 9149 6710 12574 12330 9532 6448 20046 6913 7459 5310 4864
4 C lupe ids
a Wo lf he rring 3183 2623 2065 2851 1439 1619 1806 2278 1599 1671 2444 1632
b Oil s a rdine 40 40 130 40 188 340 480 657 33 919 290 175
c Othe r s a rdine s 14924 20939 18735 23374 13660 31834 22088 24171 19385 30253 27155 21919
d Hils a s ha d 13 170 151 9 12 108 19 316 148 37 34 47
e Othe r s ha ds 2055 2492 1858 1210 603 1035 2046 5324 3837 2509 2797 3152
f A nc ho v ie s
i A nc ho v ie lla 6797 4587 3896 8167 9512 9720 6906 12324 6858 10052 11905 7930
ii T hris s o c le s 3680 4415 4697 4324 4112 2795 7338 2769 4346 5036 4578 5796
g Othe r c lupe ids 2044 1949 2765 3324 2492 4833 13911 2441 2802 3239 1662 4517
5 B o m ba y duc k 2 12 43 211 104 1 7 13 7 1 5 3
6 Liza rd f is he s 1432 1768 1157 1111 1429 1309 1054 754 1448 1989 1473 1867
7 Ha lf a nd F ull 715 437 359 605 3496 1325 629 1449 699 567 679 734
8 F lying f is he s 1850 7443 1148 5580 643 1481 1081 484 1006 1453 1003 2205
9 P e rc he s
a R o c k c o ds 588 618 677 533 855 884 582 887 1033 668 768 704
b S na ppe rs 595 626 684 539 866 895 588 898 1046 676 777 709
c P ig fa c e bre a m s 823 866 948 746 1198 1238 815 1242 1448 935 1075 989
d Thre a df in 1805 1900 2077 1636 2627 2716 1786 2725 3175 2052 2358 2160
e Othe r pe rc he s 3105 3269 3574 2813 4519 4672 3072 4687 5461 3530 4057 3715
10 Go a tf is he s 1374 1607 1643 1288 2340 1999 802 1097 2584 1923 1416 1432
11 Thre a df ins 2268 2047 1136 2303 1048 1709 2491 2099 929 469 825 303
12 C ro a ke rs 11860 6964 7798 13525 11877 12885 13532 18136 18743 25162 25646 16856
13 R ibbo n f is he s 6012 9684 9343 8633 7409 15896 16721 4229 26397 19121 7131 6818
14 C a ra ng ids
a Ho rs e m a c ke re l 5755 7541 6327 5045 4593 3977 3256 2735 2056 1358 685 14
b S c a ds 1 1 1 1 1 1 1 1 1 1 1 1134
c Le a the r- ja c ke ts 1072 1193 930 756 1364 974 567 1349 894 767 1008 545T ra c hyno t us 126 10 7 4 2 3 4 6 6 7 8 9
d Othe r C a ra ng ids 21 21 26 8 2 5708 7318 5986 3330 7088 5369 11417C o ryp ha e na 157 36 21 29 68 69 68 71 71 70 70 69Ela c a t e 149 28 249 67 89 91 90 95 96 95 96 95
15 S ilv e rbe llie sLe io g na t hus 31962 23970 27031 28222 28555 25714 37973 23491 39954 57157 50120 65290G a z z a 87 6 78 43 33 29 41 25 41 56 47 60
16 B ig ja we d 1154 811 1693 3612 869 2338 1006 994 1133 1850 1260 1078
17 P o m fre ts
a B la c k po m fre t 1649 826 453 1705 677 1308 835 660 832 959 1372 691
b S ilv e r po m fre t 450 226 123 466 185 357 227 180 227 261 375 188
c C hine s e 9 5 3 10 4 8 5 4 5 6 8 4
18 M a c ke re l
a India n m a c ke re l 1795 2651 6976 7932 2336 5208 9204 5223 1338 3200 6557 3511
b Othe r 1 1 1 1 1 1 1 1 1 1 1 1
19 S e e r f is he s
a S . c o m m e rs o ni 2323 4034 4826 4663 4135 3307 2996 5334 3905 4286 5874 4393
b S . g ut t a t us 244 424 507 490 435 348 315 561 411 451 618 462
c S . line o la t us 7 13 15 15 13 11 10 17 13 14 19 14
d A c a nt ho c yb iu 1 1 1 1 1 1 1 1 1 1 1 1
2 0 Tunnie s
a E. a f f in is 556 744 469 448 1200 1278 2055 2389 863 2338 3076 2745
b A uxis s p p . 14 19 12 12 30 32 52 60 22 59 78 69
c K. p e la m is 4 4 3 3 7 8 13 15 6 15 19 17
d T . t o ng g o l 1 1 1 1 1 1 1 1 1 1 1 1
e Othe r tunnie s 121 161 101 97 259 276 445 516 187 505 666 594
2 1 B ill f is he s 1 1 1 1 1 1 1 1 1 1 845 133
2 2 B a rra c uda s 1217 778 887 770 708 1346 1364 1567 1977 1330 523 850
2 3 M ulle ts 908 1035 334 1854 314 2009 370 1240 1118 320 429 539
2 4 Unic o rn c o d 1 1 1 1 1 1 1 1 1 1 1 1
2 5 F la t f is he s
a Ha libut 1 1 1 1 1 1 1 1 1 1 1 262
b F lo unde rs 1 1 1 1 1 1 1 1 1 1 1 37
c S o le s 793 873 649 871 1490 1002 1165 1197 2080 3103 2747 1537
2 6 C rus ta c e a ns
a P e na e id pra wns 5578 4610 6135 5779 9673 14593 11422 10920 17385 13639 12003 17228
b N o n-P e na e id 638 79 186 1649 55 730 218 212 325 1197 1250 895
c Lo bs te rs 243 327 389 460 493 593 673 377 770 452 118 305
d C ra bs 5102 7508 11665 9623 11389 17735 21029 14526 12228 7812 8100 11761
e S to m a to po ds 117 171 266 219 259 296 315 343 361 384 398 407
2 7 M o llus c s
2 8 C e pha lo po ds 91 493 311 545 1149 3789 1884 1847 1406 2661 1978 2223
2 9 M is c e lla ne o us
To ta l 158626 163831 158585 187342 181050 228447 234386 215524 223372 247938 229566 234296
279
S pe c ie s 19 8 2 19 8 3 19 8 4 19 8 5 19 8 6 19 8 7 19 8 8 19 8 9 19 9 0 19 9 1 19 9 2 19 9 3
1 Ela s m o bra nc hs
a S ha rks 3816 4103 3506 2369 5290 6650 5596 6548 1346 3780 10099 7093
b S ka te s 574 1000 211 241 90 1173 137 76 67 179 602 122
c R a ys 13381 20973 13268 9401 9652 17715 13122 10440 9496 12206 10583 13406
2 Ee ls 205 281 422 216 221 294 130 211 176 344 383 335
3 C a tf is he s 7749 6024 5936 2850 2398 3023 2712 2588 2089 3362 3125 3172
4 C lupe ids
a Wo lf he rring 2400 2516 2441 1882 2801 3244 3344 3430 2303 2778 3413 3128
b Oil s a rdine 976 1226 1105 2699 5559 802 1898 11778 30286 29102 28565 32741
c Othe r s a rdine s 20966 34469 24238 19524 27536 42440 31109 24204 18743 29506 31637 31151
d Hils a s ha d 157 523 1042 234 280 249 189 20 19 12 28 153
e Othe r s ha ds 3611 5661 4796 2190 1907 3983 2009 861 1112 770 1581 1802
f A nc ho v ie s
i A nc ho v ie lla 8900 9622 14091 8065 13078 13658 26468 13900 12033 17470 17162 7627
ii T hris s o c le s 4422 5014 7137 6281 4913 9409 4315 5421 4578 7518 4545 5760
g Othe r c lupe ids 4987 6644 5238 4688 3785 9676 10359 11893 12508 11618 9724 6371
5 B o m ba y duc k 2 6 4 7 8 11 13 179 44 6 35 41
6 Liza rd f is he s 2475 2386 3089 2722 2622 5344 4130 5039 7124 10386 10143 6777
7 Ha lf a nd F ull 743 1232 851 663 610 564 1514 882 1051 945 1167 927
8 F lying f is he s 1456 973 1994 564 1150 905 2814 10623 751 4771 3020 1286
9 P e rc he s
a R o c k c o ds 1658 2138 2085 2475 1910 1964 1985 1310 1357 4366 3247 3774
b S na ppe rs 1536 1387 1021 1298 691 1794 1227 772 798 1087 1259 1545
c P ig fa c e bre a m s 2891 2956 2341 2706 3336 2476 3355 2279 4868 6793 5372 8406
d Thre a df in 4788 4504 4073 3859 5726 8814 6542 10538 15503 14432 11375 11158
e Othe r pe rc he s 5011 7635 7175 6441 6978 8972 8634 9169 7827 8612 11909 12441
10 Go a tf is he s 1867 3157 2486 3011 3110 4937 14527 12053 13590 13672 8132 8736
11 Thre a df ins 486 505 672 551 665 918 569 820 1193 985 737 396
12 C ro a ke rs 28225 17136 17973 10872 10263 15207 15160 16636 16955 18633 16244 14408
13 R ibbo n f is he s 5730 4964 11002 7356 18577 8882 2630 2515 2941 12507 13254 3235
14 C a ra ng ids
a Ho rs e m a c ke re l 137 52 68 92 76 10 289 572 34 147 18 5175
b S c a ds 579 725 2413 222 508 2402 1720 3039 7541 2427 2454 2313
c Le a the r- ja c ke ts 633 3226 1482 2607 538 1259 1558 632 659 1222 1429 981T ra c hyno t us 11 12 13 14 15 16 17 19 20 21 22 23
d Othe r C a ra ng ids 5542 6532 9597 9501 6672 8264 11939 11674 13902 13011 12321 10175C o ryp ha e na 69 71 71 72 70 70 71 71 74 72 71 70Ela c a t e 96 100 101 102 100 102 103 104 108 107 106 104
15 S ilv e rbe llie sLe io g na t hus 67304 80990 56403 51831 63628 60157 60619 47765 52023 49869 47802 56990G a z z a 60 70 48 43 51 47 47 35 38 35 33 39
16 B ig ja we d 896 475 1221 875 701 433 930 319 945 284 503 223
17 P o m fre ts
a B la c k po m fre t 1002 838 1048 265 861 235 164 347 590 479 176 492
b S ilv e r po m fre t 693 857 515 216 386 1530 1588 1636 1380 1987 1351 1656
c C hine s e 96 20 1 3 143 133 145 8 6 3 3 9
18 M a c ke re l
a India n m a c ke re l 4003 5459 6045 5667 9341 12166 8228 8912 6647 14174 28900 11388
b Othe r 1 1 1 1 1 1 1 1 22 13 1 1
19 S e e r f is he s
a S . c o m m e rs o ni 3939 3752 5362 2353 2461 2658 3224 2914 2783 3322 4624 3524
b S . g ut t a t us 464 287 357 711 304 381 421 365 213 287 257 275
c S . line o la t us 73 158 153 21 37 49 472 63 2 37 13 41
d A c a nt ho c yb iu 1 7 41 35 29 24 19 14 9 3 2 3
2 0 Tunnie s
a E. a f f in is 2269 1407 1572 730 1161 2813 2399 1985 2206 3007 2681 1449
b A uxis s p p . 334 377 200 44 438 190 301 786 316 552 512 123
c K. p e la m is 5 32 51 12 22 32 42 1 52 8 279 6311
d T . t o ng g o l 1 6 119 96 69 46 252 15 10 4 68 54
e Othe r tunnie s 285 41 425 460 469 178 365 554 792 463 258 76
2 1 B ill f is he s 232 115 227 243 140 94 207 65 76 99 190 172
2 2 B a rra c uda s 1609 1310 1704 1220 1935 2866 3302 3936 4486 5327 4726 4935
2 3 M ulle ts 675 600 619 614 346 752 537 442 454 1260 801 609
2 4 Unic o rn c o d 1 1 1 1 117 95 73 49 27 3 3 2
2 5 F la t f is he s
a Ha libut 445 342 157 410 250 450 480 555 1516 1196 422 557
b F lo unde rs 9 17 47 29 20 12 5 9 80 85 71 35
c S o le s 3727 3627 2221 1690 1913 1953 2465 3201 3205 3663 2948 2427
2 6 C rus ta c e a ns
a P e na e id pra wns 18137 17701 20857 15523 21659 23576 21251 22592 26125 25675 27440 25440
b N o n-P e na e id 484 362 2000 227 280 44 558 70 93 884 235 245
c Lo bs te rs 400 463 801 613 463 697 171 221 501 510 684 530
d C ra bs 16231 13276 12144 9118 8404 10149 9107 7888 9379 13998 12056 13851
e S to m a to po ds 2686 1146 925 398 681 879 1298 454 199 191 315 295
2 7 M o llus c s
2 8 C e pha lo po ds 4275 5159 5407 6361 5827 5336 5483 7552 11104 12752 21657 11156
2 9 M is c e lla ne o us
To ta l 266412 296649 272612 215584 263275 313205 304339 293048 316374 373019 382772 347739
280
S pe c ie s 19 9 4 19 9 5 19 9 6 19 9 7 19 9 8 19 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5
1 Ela s m o bra nc hs
a S ha rks 3574 8152 3413 11750 5272 2674 6510 4454 5402 3692 11550 8244
b S ka te s 199 576 558 1114 530 827 204 177 810 442 899 641
c R a ys 11278 12951 13478 17058 15321 11025 12253 9482 10791 13391 12684 9048
2 Ee ls 565 345 487 1015 833 992 599 356 653 852 397 284
3 C a tf is he s 2580 3609 3982 3793 3947 2975 5557 2782 3364 2828 4441 3168
4 C lupe ids
a Wo lf he rring 2690 2127 3812 3946 3552 2543 2544 2838 3460 2702 3110 2217
b Oil s a rdine 39167 32918 48380 74812 84475 57086 58859 46643 33223 44742 63931 45572
c Othe r s a rdine s 38181 59447 66243 67887 45739 47716 42832 40317 57856 59336 34207 24383
d Hils a s ha d 40 424 356 60 205 130 94 25 117 1 14 10
e Othe r s ha ds 923 2616 1383 3049 1848 2103 3470 411 460 801 656 468
f A nc ho v ie s 21792 20276 27491 28787 25482 16029 20458 22314 23045 15864 15416 10989
i A nc ho v ie lla
ii T hris s o c le s
g Othe r c lupe ids 12236 10627 12814 15369 12373 12786 15045 14099 14179 8142 7906 5636
5 B o m ba y duc k 9 30 20 7 1 7 2 35 1 1 33 23
6 Liza rd f is he s 10551 9252 11972 4656 2715 2857 3739 3650 6113 4510 5340 3809
7 Ha lf a nd F ull 1410 1350 2589 2307 4700 4081 5755 2786 3057 2085 1994 1422
8 F lying f is he s 203 4411 998 114 3382 1781 2142 5991 5843 4995 2783 1984
9 P e rc he s
a R o c k c o ds 2879 3092 3279 4649 3864 3267 3722 4552 2566 4164 3907 2787
b S na ppe rs 1552 1872 2653 2632 2625 2746 2648 1414 1160 3258 4864 3470
c P ig fa c e bre a m s 9847 9944 11364 12449 10650 10163 11177 13831 11492 10561 11837 8444
d Thre a df in 13078 14141 13163 6591 5598 4852 3655 6147 6799 5342 5535 3949
e Othe r pe rc he s 9133 13258 11844 12520 10373 10032 10494 10950 11790 13355 11562 8248
10 Go a tf is he s 8383 5454 5721 5810 6641 11152 7686 6442 5856 4943 6890 4915
11 Thre a df ins 804 676 680 960 424 332 643 492 687 333 569 406
12 C ro a ke rs 15683 17487 14834 12862 12443 10629 11926 9493 10753 9084 9201 6564
13 R ibbo n f is he s 5647 2634 5435 1765 3998 2537 5034 3953 5706 5540 4585 3269
14 C a ra ng ids
a Ho rs e m a c ke re l 415 344 1598 252 220 482 800 950 644 501 558 398
b S c a ds 3957 3513 4158 1707 1833 2348 1184 2629 5707 3457 3615 2577
c Le a the r- ja c ke ts 2458 1071 1769 1762 1722 1694 1176 944 1268 2808 3447 2457T ra c hyno t us
d Othe r C a ra ng ids 22978 20805 17848 22593 18281 18615 18685 20043 20914 18130 22996 16392C o ryp ha e na
Ela c a t e
15 S ilv e rbe llie s 62418 55067 50949 52968 42633 38846 37750 34898 40509 28757 38387 27384Le io g na t hus
G a z z a
16 B ig ja we d 253 596 405 748 669 183 345 450 656 91 288 206
17 P o m fre ts
a B la c k po m fre t 727 1803 933 769 812 1047 1554 2318 1211 730 961 686
b S ilv e r po m fre t 1612 2086 2609 3001 1776 1399 1561 1455 2366 1506 1997 1425
c C hine s e 21 17 46 1 1 4 2 13 68 100 125 89
18 M a c ke re l
a India n m a c ke re l 20142 25859 18359 21308 15897 16976 12062 13316 21060 11040 23337 16635
b Othe r 1 3 1 1 3 1 1 1 16 5 1 1
19 S e e r f is he s
a S . c o m m e rs o ni 4934 5515 3951 6841 9418 8210 14337 6867 8526 7576 7762 5533
b S . g ut t a t us 1257 842 494 677 479 354 188 129 277 221 131 94
c S . line o la t us 35 27 37 672 116 135 57 36 1 1 1 1
d A c a nt ho c yb iu 1 1 1 15 20 10 2 8 14 7 22 16
2 0 Tunnie s
a E. a f f in is 2726 2511 2577 3925 3682 4087 4591 3764 3880 2652 4406 3141
b A uxis s p p . 293 786 463 707 1434 903 1008 555 1004 832 582 415
c K. p e la m is 53 24 82 430 53 305 2030 417 367 465 540 385
d T . t o ng g o l 292 847 344 102 88 841 122 587 1247 320 776 553
e Othe r tunnie s 17 170 274 80 105 27 1672 416 457 1861 1638 1168
2 1 B ill f is he s 217 173 401 467 839 1021 284 1600 770 686 1243 886
2 2 B a rra c uda s 4273 5899 6189 5807 5579 7170 8975 6178 9314 5377 5289 3770
2 3 M ulle ts 590 924 894 1787 852 1346 1096 990 645 852 617 441
2 4 Unic o rn c o d 1 1 2 3 8 1 8 1 1 1 1 1
2 5 F la t f is he s
a Ha libut 685 696 491 285 505 138 259 304 640 530 267 190
b F lo unde rs 44 30 1 47 30 10 44 31 38 5 2 2
c S o le s 2147 2888 2894 3491 2410 2113 2481 1597 2117 1836 1843 1315
2 6 C rus ta c e a ns
a P e na e id pra wns 33880 30826 29774 29460 30313 25243 24190 17550 22489 16446 17104 12202
b N o n-P e na e id 2815 744 167 1006 325 53 2201 6007 3740 2031 3293 2349
c Lo bs te rs 644 331 279 382 1092 280 160 177 211 216 265 189
d C ra bs 11597 13020 10711 12978 14134 13097 15386 11562 15401 16513 15608 11134
e S to m a to po ds 586 820 1077 720 954 546 368 515 863 1512 1234 880
2 7 M o llus c s 250 609 1030 1175 720 384 94 275 4247 1287 1113 794
2 8 C e pha lo po ds 18330 17077 19203 11945 8596 13791 10869 8895 12180 14190 18185 12980
2 9 M is c e lla ne o us
To ta l 413052 433593 446962 484078 432589 382979 402587 359113 408030 363505 401948 286637
281
Appendix C.10. Marine fisheries catch (t) for Puducherry, 1970-2005 (contd.)
S pe c ie s 19 7 0 19 7 1 19 7 2 19 7 3 19 7 4 19 7 5 19 7 6 19 7 7 19 7 8 19 7 9 19 8 0 19 8 1
1 Ela s m o bra nc hs
a S ha rks 313 566 343 223 176 101 120 242 133 159 305 273
b S ka te s 4 9 6 3 2 2 1 4 3 3 4 4
c R a ys 315 568 345 223 177 100 119 243 133 153 304 271
2 Ee ls 25 139 1 3 6 6 9 7 64 127 11 18
3 C a tf is he s 215 485 101 170 124 86 96 190 226 71 110 142
4 C lupe ids
a Wo lf he rring 230 249 131 249 51 42 57 55 95 146 86 73
b Oil s a rdine 1 1 1 1 1 1 1 1 1 1 1 1
c Othe r s a rdine s 1517 1627 619 429 1066 888 1666 1003 1016 1750 2402 1157
d Hils a s ha d 3 5 10 15 22 26 27 25 23 23 22 11
e Othe r s ha ds 64 143 80 135 153 235 110 37 93 11 47 111
f A nc ho v ie s
i A nc ho v ie lla 2147 628 375 596 613 350 161 475 447 303 252 503
ii T hris s o c le s 697 304 378 531 460 286 512 351 221 376 340 448
g Othe r c lupe ids 434 95 121 57 63 42 115 177 241 302 240 340
5 B o m ba y duc k 2 2 2 2 2 2 2 2 2 2 2 2
6 Liza rd f is he s 161 372 172 144 49 69 153 143 177 363 225 343
7 Ha lf a nd F ull 1 23 3 10 19 47 2 3 4 5 23 49
8 F lying f is he s 219 443 36 15 97 121 149 3 412 748 3 529
9 P e rc he s
a R o c k c o ds 6 9 6 8 4 8 16 7 9 19 13 18
b S na ppe rs 1 3 1 1 2 2 3 1 3 4 3 4
c P ig fa c e bre a m s 7 12 7 10 4 11 20 10 12 25 17 24
d Thre a df in 160 299 171 246 108 264 487 236 285 606 408 568
e Othe r pe rc he s 193 364 207 299 133 322 592 287 346 738 497 691
10 Go a tf is he s 151 178 93 169 144 156 112 44 146 282 211 216
11 Thre a df ins 38 22 21 28 48 22 38 7 36 19 8 1
12 C ro a ke rs 698 527 770 873 475 332 631 358 502 425 451 460
13 R ibbo n f is he s 279 86 344 372 209 266 388 124 119 113 157 187
14 C a ra ng ids
a Ho rs e m a c ke re l 202 904 266 195 418 367 357 309 272 244 210 172
b S c a ds 1 1 1 1 1 1 1 1 1 1 1 140
c Le a the r- ja c ke ts 3 20 13 8 2 2 2 4 28 24 2 28T ra c hyno t us
d Othe r C a ra ng ids 1 1 1 1 1 518 460 428 190 472 422 1299C o ryp ha e na 1 2 2 3 4 4 5 4 4 4 4 4Ela c a t e 5 6 6 6 6 6 6 6 6 6 6 6
15 S ilv e rbe llie sLe io g na t hus 959 799 581 762 473 799 701 451 499 1035 959 1360G a z z a
16 B ig ja we d 120 71 24 3 23 47 177 244 126 16 41 62
17 P o m fre ts
a B la c k po m fre t 122 51 76 93 36 16 51 58 54 40 213 136
b S ilv e r po m fre t 30 13 18 22 10 5 13 15 14 10 54 38
c C hine s e
18 M a c ke re l
a India n m a c ke re l 442 585 2836 2270 2053 1917 1448 345 154 371 391 272
b Othe r
19 S e e r f is he s
a S . c o m m e rs o ni 36 34 11 20 30 10 13 15 18 46 38 42
b S . g ut t a t us 36 34 11 20 30 10 13 15 18 46 38 45
c S . line o la t us 1 1 1 1 1 1 1 1 1 1 1 1
d A c a nt ho c yb iu
2 0 Tunnie s
a E. a f f in is 1 2 1 1 1 1 1 2 3 4 4 5
b A uxis s p p .
c K. p e la m is 1 1 1 1 1 1 1 1 1 1 1 1
d T . t o ng g o l 1 1 1 1 1 1 1 1 1 1 1 1
e Othe r tunnie s 2 13 1 4 7 4 1 2 3 1 22 42
2 1 B ill f is he s 1 1 1 1 1 1 1 1 1 1 1 15
2 2 B a rra c uda s 30 87 3 1 25 23 14 8 21 19 48 43
2 3 M ulle ts 67 9 21 27 2 49 6 19 35 39 70 7
2 4 Unic o rn c o d 1 1 1 1 1 1 1 1 1 1 1 1
2 5 F la t f is he s
a Ha libut 1 1 1 1 2 2 1 1 1 1 1 8
b F lo unde rs 1 1 1 1 2 2 1 1 1 1 1 1
c S o le s 91 160 92 299 91 195 370 108 146 225 213 262
2 6 C rus ta c e a ns
a P e na e id pra wns 648 389 250 50 57 104 148 153 327 723 668 472
b N o n-P e na e id 1 1 7 12 4 3 3 3 95 98 58 75
c Lo bs te rs 1 1 1 1 2 39 48 28 3 7 6 7
d C ra bs 133 240 575 271 382 407 751 411 337 336 242 329
e S to m a to po ds 1 1 1 1 2 2 1 1 1 1 1 1
2 7 M o llus c s 1 1 1 1 2 2 1 1 1 1 1 1
2 8 C e pha lo po ds 13 70 31 28 54 92 309 86 49 72 57 62
2 9 M is c e lla ne o us
To ta l 10836 10663 9179 8916 7932 8416 10495 6759 7164 10622 9916 11387
282
S pe c ie s 19 8 2 19 8 3 19 8 4 19 8 5 19 8 6 19 8 7 19 8 8 19 8 9 19 9 0 19 9 1 19 9 2 19 9 3
1 Ela s m o bra nc hs
a S ha rks 148 62 413 320 21 77 276 72 26 20 82 62
b S ka te s 4 4 13 20 22 23 22 22 18 12 14 14
c R a ys 421 379 265 352 231 271 246 230 384 156 315 169
2 Ee ls 3 44 3 5 5 8 23 18 14 6 2 87
3 C a tf is he s 29 75 106 61 1606 47 57 34 7 14 74 56
4 C lupe ids
a Wo lf he rring 103 108 70 51 101 108 104 24 54 32 35 31
b Oil s a rdine 1 129 845 1240 1676 676 187 2670 5639 1221 2649 972
c Othe r s a rdine s 1219 3115 1060 2868 1026 2101 1488 1169 739 1009 2058 727
d Hils a s ha d 1 17 16 14 12 10 9 3 157 238 270 230
e Othe r s ha ds 91 304 278 86 23 2 3 2 2 2 5 6
f A nc ho v ie s
i A nc ho v ie lla 338 290 554 448 727 311 320 133 174 59 143 81
ii T hris s o c le s 404 353 672 587 385 579 254 287 352 604 359 221
g Othe r c lupe ids 434 450 534 690 532 613 588 495 451 637 353 97
5 B o m ba y duc k 2 2 3 3 4 5 6 6 7 7 8 7
6 Liza rd f is he s 346 425 204 257 383 372 101 178 228 197 281 174
7 Ha lf a nd F ull 166 131 9 1 2 2 3 3 14 24 2 5
8 F lying f is he s 154 297 414 485 222 181 865 995 113 413 196 61
9 P e rc he s
a R o c k c o ds 46 3 17 5 9 14 3 5 5 20 39 17
b S na ppe rs 4 23 5 22 26 62 110 51 25 15 2 5
c P ig fa c e bre a m s 4 69 20 55 17 5 71 25 7 23 46 9
d Thre a df in 762 1209 496 1262 864 115 690 408 398 324 386 181
e Othe r pe rc he s 1062 639 764 892 462 1012 399 283 548 278 196 282
10 Go a tf is he s 156 109 67 227 120 183 256 631 617 315 141 113
11 Thre a df ins 32 14 36 34 54 48 29 45 44 34 21 41
12 C ro a ke rs 810 667 1160 725 691 715 712 373 267 217 258 178
13 R ibbo n f is he s 95 49 197 34 113 101 2045 86 26 33 86 82
14 C a ra ng ids
a Ho rs e m a c ke re l 142 112 77 41 6 5 3 1 9 16 2 614
b S c a ds 1409 68 735 279 101 146 20 80 87 3 2 6
c Le a the r- ja c ke ts 18 28 34 17 28 28 17 12 8 5 5 1T ra c hyno t us
d Othe r C a ra ng ids 628 476 856 714 396 434 888 543 592 358 293 194C o ryp ha e na 4 5 5 5 5 5 5 5 5 5 5 4Ela c a t e 6 6 6 6 6 6 7 7 7 6 6 6
15 S ilv e rbe llie sLe io g na t hus 777 2518 1448 2127 1686 1317 566 887 739 621 992 787G a z z a
16 B ig ja we d 40 28 19 7 117 74 104 132 168 148 188 202
17 P o m fre ts
a B la c k po m fre t 151 73 215 52 2 34 52 18 54 18 19 10
b S ilv e r po m fre t 21 77 60 2 59 39 26 59 20 151 3 13
c C hine s e
18 M a c ke re l
a India n m a c ke re l 465 848 873 1127 1385 2025 570 1086 1043 787 1612 2956
b Othe r
19 S e e r f is he s
a S . c o m m e rs o ni 61 89 259 106 98 132 74 76 57 74 104 53
b S . g ut t a t us 4 19 10 14 6 4 1 1 5 8 2 2
c S . line o la t us 1 1 1 1 1 1 1 1 1 1 1 1
d A c a nt ho c yb iu
2 0 Tunnie s
a E. a f f in is 19 5 54 8 51 5 87 8 22 209 113 9
b A uxis s p p .
c K. p e la m is 1 93 58 21 11 62 57 93 53 14 89 94
d T . t o ng g o l 1 1 1 1 1 1 1 35 28 21 160 265
e Othe r tunnie s 27 10 29 14 54 35 16 23 29 35 50 187
2 1 B ill f is he s 75 10 14 30 80 5 26 13 20 13 47 23
2 2 B a rra c uda s 43 26 68 71 18 39 279 51 151 42 72 94
2 3 M ulle ts 6 68 5 12 66 73 14 2 64 12 17 8
2 4 Unic o rn c o d 1 1 1 1 41 42 40 35 30 25 21 14
2 5 F la t f is he s
a Ha libut 9 4 58 50 48 20 21 4 19 58 30 38
b F lo unde rs 1 11 11 11 9 8 9 7 5 5 4 2
c S o le s 325 411 443 326 377 305 220 243 292 407 278 154
2 6 C rus ta c e a ns
a P e na e id pra wns 467 416 1270 1148 836 764 666 781 1563 970 697 250
b N o n-P e na e id 25 14 94 12 84 50 17 34 22 25 37 43
c Lo bs te rs 39 45 8 19 28 24 33 16 19 31 5 7
d C ra bs 1324 875 1152 673 502 310 648 156 548 631 499 505
e S to m a to po ds 9 10 8 5 2 147 2 15 11 5 2 2
2 7 M o llus c s 1 1 2 2 2 2 2 2 2 2 2 2
2 8 C e pha lo po ds 125 167 60 72 75 104 49 206 202 318 336 55
2 9 M is c e lla ne o us
To ta l 13061 15484 16155 17715 15512 13880 13386 12876 16190 10932 13713 10504
283
S pe c ie s 19 9 4 19 9 5 19 9 6 19 9 7 19 9 8 19 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5
1 Ela s m o bra nc hs
a S ha rks 483 1 34 2 8 32 271 87 32 62 84 50
b S ka te s 1 10 1 1 1 1 1 1 31 1 1 1
c R a ys 137 94 74 71 269 413 339 163 231 294 148 87
2 Ee ls 1 1 2 2 84 8 1 1 1 1 1 1
3 C a tf is he s 80 26 70 34 142 52 18 169 155 102 190 112
4 C lupe ids
a Wo lf he rring 68 104 103 66 40 53 58 150 26 33 106 62
b Oil s a rdine 2917 1301 7818 11691 6889 5121 2940 1652 1368 6111 7310 4294
c Othe r s a rdine s 1006 1336 3846 1441 901 1933 546 1530 1661 1228 2211 1299
d Hils a s ha d 1 1 4 1 1 1 1 1 1 1 1 1
e Othe r s ha ds 11 46 11 37 6 86 10 2 2 2 1 1
f A nc ho v ie s 406 1360 1273 1145 255 534 832 184 403 300 237 139
i A nc ho v ie lla
ii T hris s o c le s
g Othe r c lupe ids 170 277 555 215 348 234 198 379 486 262 289 170
5 B o m ba y duc k 1 24 1 1 1 1 1 1 1 1 1 1
6 Liza rd f is he s 52 53 55 34 56 282 18 69 321 42 144 85
7 Ha lf a nd F ull 9 31 11 35 1 1 354 2 2 1 5 3
8 F lying f is he s 1 56 14 19 183 24 1787 238 698 84 101 59
9 P e rc he s
a R o c k c o ds 1 1 3 1 10 45 78 15 1 9 74 44
b S na ppe rs 1 1 1 1 1 1 18 1 321 351 134 79
c P ig fa c e bre a m s 25 6 7 2 11 1 122 8 18 20 54 32
d Thre a df in 236 186 82 59 69 246 41 192 706 170 329 194
e Othe r pe rc he s 242 249 179 153 79 208 187 163 294 462 275 162
10 Go a tf is he s 47 28 136 64 62 930 393 109 310 51 458 270
11 Thre a df ins 7 17 19 21 23 32 21 20 6 6 32 19
12 C ro a ke rs 137 375 235 95 250 249 133 363 310 159 176 104
13 R ibbo n f is he s 29 23 24 29 42 4 33 92 191 54 78 46
14 C a ra ng ids
a Ho rs e m a c ke re l 1 3 1 1 10 1 1 1 15 3 5 3
b S c a ds 1 28 11 1 25 1 1 1 19 17 23 14
c Le a the r- ja c ke ts 1 1 2 24 3 1 12 30 9 18 7 4T ra c hyno t us
d Othe r C a ra ng ids 665 904 1060 1222 251 516 587 1170 626 455 401 235C o ryp ha e na
Ela c a t e
15 S ilv e rbe llie s 585 597 425 135 446 451 525 374 1818 267 611 359Le io g na t hus
G a z z a
16 B ig ja we d
17 P o m fre ts
a B la c k po m fre t 10 1097 14 26 5 10 1 1 70 3 8 4
b S ilv e r po m fre t 11 8 6 1 24 13 1 14 2 1 1
c C hine s e
18 M a c ke re l
a India n m a c ke re l 1699 3505 2854 3616 2028 2881 1619 1579 4155 1817 2827 1661
b Othe r 1 1 1 1
19 S e e r f is he s
a S . c o m m e rs o ni 113 151 208 81 102 543 283 143 593 99 233 137
b S . g ut t a t us 2 18 22 173 3 59 24 6 6 37 14 8
c S . line o la t us
d A c a nt ho c yb iu
2 0 Tunnie s
a E. a f f in is 31 52 1 436 10 136 265 215 310 191 81 47
b A uxis s p p . 1 1 1 111 20 90 106 7 472 33 10 6
c K. p e la m is 24 1 289 15 1 228 346 154 19 14 217 127
d T . t o ng g o l 1 1 1 43 1 1 1 150 12 30 30 17
e Othe r tunnie s 1 1 8 1 1 1 1 3 15 8 49 29
2 1 B ill f is he s 10 44 15 1 14 51 107 26 97 52 36 21
2 2 B a rra c uda s 77 57 77 43 41 110 40 56 244 49 46 27
2 3 M ulle ts 10 17 6 21 55 7 40 48 24 16 74 43
2 4 Unic o rn c o d 1 1 1 1 6 1 1 1 1 1 1 1
2 5 F la t f is he s
a Ha libut 8 27 5 1 1 15 1 1 61 14 13 8
b F lo unde rs 1 1 1 1 1 1 1 1 1 1 5 3
c S o le s 167 203 92 92 107 113 46 111 106 258 129 76
2 6 C rus ta c e a ns
a P e na e id pra wns 891 501 399 115 743 397 363 179 729 92 334 197
b N o n-P e na e id 3 468 1 1 69 1 1 31 1 1 1 1
c Lo bs te rs 13 1 1 1 1 1 1 8 1 1 5 3
d C ra bs 297 417 173 379 265 153 121 349 395 147 413 243
e S to m a to po ds 1 1 49 1 1 1 1 1 1 1 1 1
2 7 M o llus c s 1 170 198 0 80 1 2 6 1 17 59 35
2 8 C e pha lo po ds 44 45 42 26 51 250 108 2051 2525 1867 768 452
2 9 M is c e lla ne o us
To ta l 10732 13937 20524 21795 14071 16536 13017 12301 19916 15320 18847 11077
284
Appendix C.11. Marine fisheries catch (t) for Andhra Pradesh, 1970-2005 (contd.)
S pe c ie s 19 7 0 19 7 1 19 7 2 19 7 3 19 7 4 19 7 5 19 7 6 19 7 7 19 7 8 19 7 9 19 8 0 19 8 1
1 Ela s m o bra nc hs
a S ha rks 2906 3126 4948 6074 8714 7039 5030 4453 6180 4875 3823 3858
b S ka te s 370 398 630 773 1110 895 640 565 787 616 483 490
c R a ys 2007 2159 3419 4197 6021 4862 3473 3064 4273 3343 2624 2672
2 Ee ls 506 134 134 357 627 2357 280 550 1397 310 415 546
3 C a tf is he s 3070 3417 4439 5646 6801 7068 8379 7079 4239 4778 3335 5678
4 C lupe ids
a Wo lf he rring 1206 1198 1875 2367 2091 2542 1613 1038 1095 830 978 956
b Oil s a rdine 81 89 97 112 517 114 98 110 127 138 156 169
c Othe r s a rdine s 15747 16605 6330 10713 28899 28728 20387 9355 6669 5257 12125 13530
d Hils a s ha d 55 663 184 40 53 61 246 35 52 66 84 34
e Othe r s ha ds 736 640 2575 2499 2152 810 1594 1410 1171 929 1103 1156
f A nc ho v ie s
i A nc ho v ie lla 4152 1109 1673 4369 9048 6127 9929 7628 6777 5008 5381 12102
ii T hris s o c le s 1080 793 927 2233 1737 1546 1548 1192 1583 2920 6377 2172
g Othe r c lupe ids 2214 4997 7418 8248 9347 6562 7384 2015 1557 2142 4775 4254
5 B o m ba y duc k 181 648 248 198 115 313 188 818 954 610 532 727
6 Liza rd f is he s 122 141 613 510 371 310 227 1094 1365 1735 1328 1368
7 Ha lf a nd F ull 266 173 14 3 178 21 12 115 52 85 84 25
8 F lying f is he s 383 206 43 94 92 83 78 72 56 60 37 91
9 P e rc he s
a R o c k c o ds 5 11 11 12 18 37 15 20 16 24 40 47
b S na ppe rs 31 68 67 71 114 232 89 126 93 145 245 281
c P ig fa c e bre a m s 1 1 1 1 3 6 3 4 3 4 7 9
d Thre a df in 302 662 650 699 1108 2257 861 1228 904 1401 2382 2738
e Othe r pe rc he s 501 1096 1076 1158 1834 3736 1427 2032 1497 2321 3944 4543
10 Go a tf is he s 311 467 543 291 424 925 756 394 433 536 498 914
11 Thre a df ins 805 1970 2657 1842 3102 2355 2478 873 1389 1776 2065 1037
12 C ro a ke rs 5354 7469 8848 10015 17184 14982 14884 12730 7231 11100 13544 9414
13 R ibbo n f is he s 5338 6186 2991 3378 10850 10188 10925 7286 4777 5390 13619 7064
14 C a ra ng ids
a Ho rs e m a c ke re l 2085 2068 2023 2325 2660 3046 2675 3413 1736 2709 1506 237
b S c a ds 2905 2932 2939 3164 3230 3068 3093 3004 3057 2997 3067 3032
c Le a the r- ja c ke ts 167 444 461 948 1485 1559 1598 452 577 378 618 390T ra c hyno t us 3 3 4 4 6 5 1 1 1 1 1 1
d Othe r C a ra ng ids 30 12 11 10 7 5 4 67 75 48 641 1218C o ryp ha e na 61 14 88 32 93 219 78 117 21 6 6 6Ela c a t e 17 2 32 50 2 22 12 6 46 249 464 664
15 S ilv e rbe llie sLe io g na t hus 3325 2819 3612 3709 6716 14451 5297 7380 2809 4509 5393 12999G a z z a 8 4 1 4 17 130 66 60 62 60 71 170
16 B ig ja we d 1276 1055 1697 1298 2661 3224 2350 1421 883 1198 1349 1099
17 P o m fre ts
a B la c k po m fre t 1742 2973 3342 3565 3621 4823 3690 2095 2084 1731 2086 2391
b S ilv e r po m fre t 872 1487 1671 1783 1810 2412 1845 1048 1042 866 1042 1198
c C hine s e 26 45 51 54 54 73 56 31 31 27 32 39
18 M a c ke re l
a India n m a c ke re l 1723 1167 4502 2262 1590 1387 1830 887 2187 2229 5399 2802
b Othe r
19 S e e r f is he s
a S . c o m m e rs o ni 905 848 1478 1106 1237 1397 910 845 686 1434 786 931
b S . g ut t a t us 2061 1934 3370 2523 2820 3184 2076 1927 1564 3270 1791 2124
c S . line o la t us 9 8 14 11 12 14 9 9 7 14 8 11
d A c a nt ho c yb iu
2 0 Tunnie s
a E. a f f in is 61 133 225 69 342 316 160 209 155 203 199 167
b A uxis s p p . 2 6 9 3 15 13 7 9 7 9 9 7
c K. p e la m is 2 6 9 3 15 13 7 9 7 9 9 7
d T . t o ng g o l 1 1 1 1 2 2 1 1 1 1 1 1
e Othe r tunnie s 45 99 168 51 254 235 119 155 115 151 148 124
2 1 B ill f is he s 168 170 170 183 187 178 179 174 177 174 178 176
2 2 B a rra c uda s 7 58 73 16 17 104 164 92 37 53 77 107
2 3 M ulle ts 185 681 143 337 2570 1224 1220 213 306 201 39 245
2 4 Unic o rn c o d 33 37 41 48 53 54 59 61 66 68 74 77
2 5 F la t f is he s
a Ha libut 14 14 13 15 15 14 15 14 14 14 16 61
b F lo unde rs 352 337 327 356 374 345 368 336 348 338 384 1386
c S o le s 119 99 261 247 306 391 77 850 448 767 416 1510
2 6 C rus ta c e a ns
a P e na e id pra wns 6548 11186 6247 10709 13531 9089 11809 7608 9998 10488 7695 8732
b N o n-P e na e id 2468 361 531 877 3901 4477 3042 6203 1907 3759 5908 2086
c Lo bs te rs 13 12 34 48 130 131 4 3 26 42 14 1
d C ra bs 89 83 237 337 909 621 360 719 493 1116 2015 2020
e S to m a to po ds 25 24 68 96 260 155 90 180 123 279 411 385
2 7 M o llus c s
2 8 C e pha lo po ds 868 143 81 81 229 194 331 512 383 663 675 689
2 9 M is c e lla ne o us
To ta l 75948 85690 86349 102230 163641 160726 136147 105391 86155 96456 122509 122966
285
S pe c ie s 19 8 2 19 8 3 19 8 4 19 8 5 19 8 6 19 8 7 19 8 8 19 8 9 19 9 0 19 9 1 19 9 2 19 9 3
1 Ela s m o bra nc hs
a S ha rks 6298 8688 8945 8427 7996 5300 6269 5462 4767 3171 7347 6684
b S ka te s 310 522 823 345 1011 834 609 406 348 576 854 336
c R a ys 2074 2167 3775 2135 2246 2079 2261 5178 4570 2336 2489 2280
2 Ee ls 713 611 933 779 636 697 1018 735 845 1070 1679 1566
3 C a tf is he s 4276 4662 7402 7014 5319 4269 3585 3665 2822 2490 3277 4185
4 C lupe ids
a Wo lf he rring 1064 1647 1729 1369 1083 1125 1251 806 1222 1050 887 1042
b Oil s a rdine 182 195 209 223 1049 124 1103 4883 823 1522 3725 1895
c Othe r s a rdine s 11753 14947 17920 19347 14168 10456 14586 24032 18472 7365 7556 10415
d Hils a s ha d 126 53 48 148 200 709 83 75 66 215 287 932
e Othe r s ha ds 2117 3223 6117 2104 5071 5247 4173 1581 618 6843 3355 2469
f A nc ho v ie s
i A nc ho v ie lla 5667 7012 3841 2292 9152 11043 7348 2787 8351 8942 5408 5602
ii T hris s o c le s 2411 3290 2920 3445 4458 2774 2865 2894 2321 2810 4365 4748
g Othe r c lupe ids 4258 4400 5707 6948 4985 3858 3490 3842 3602 4751 5169 7253
5 B o m ba y duc k 1310 918 870 491 902 607 587 940 1239 1045 512 655
6 Liza rd f is he s 2228 1472 1644 1685 2624 2033 1698 1094 1396 705 964 1536
7 Ha lf a nd F ull 41 56 117 32 47 77 111 53 133 25 54 36
8 F lying f is he s 144 63 33 66 15 12 19 429 409 389 376 357
9 P e rc he s
a R o c k c o ds 47 10 20 28 58 66 35 84 247 89 79 431
b S na ppe rs 443 1364 2881 2307 854 816 1124 1100 452 487 1163 937
c P ig fa c e bre a m s 9 9 8 8 9 7 7 7 7 7 7 6
d Thre a df in 3302 3846 2900 1908 3056 3027 1465 1483 1380 1808 2872 2642
e Othe r pe rc he s 5800 6183 9218 6587 6302 7952 4542 3245 2585 3406 3967 5060
10 Go a tf is he s 2043 1850 1281 1849 3605 5165 3504 2801 3080 4286 2295 2118
11 Thre a df ins 2746 1585 2877 2477 1579 1532 1527 1784 2086 1373 1734 1835
12 C ro a ke rs 11798 14937 10870 9075 13253 11223 8756 7840 11544 9915 11497 12396
13 R ibbo n f is he s 5805 9095 5490 3797 4621 6403 3015 4113 3478 10018 6226 6421
14 C a ra ng ids
a Ho rs e m a c ke re l 605 691 650 447 380 1025 438 293 257 873 841 925
b S c a ds 2318 3228 1624 932 1547 1590 526 1348 1692 508 2462 2256
c Le a the r- ja c ke ts 500 1398 1672 688 306 245 603 307 119 154 349 322T ra c hyno t us 1 1 1 1 1 1 1 1 1 1 1 1
d Othe r C a ra ng ids 2072 2955 2674 1730 3025 2872 2904 1935 2485 1512 1831 2626C o ryp ha e na 6 6 6 6 6 6 6 6 6 6 6 6Ela c a t e 865 1069 1269 1468 1670 1872 2072 2279 2472 2672 2914 3121
15 S ilv e rbe llie sLe io g na t hus 6810 10410 6717 7104 7860 11146 4475 3689 4499 3379 6063 7880G a z z a 87 132 84 88 96 134 53 43 52 38 68 88
16 B ig ja we d 1102 1450 802 675 717 1532 663 251 486 671 605 464
17 P o m fre ts
a B la c k po m fre t 2872 2586 8600 1998 1082 1048 662 2499 2287 1298 1558 2304
b S ilv e r po m fre t 2520 3762 4639 1863 1757 1575 3425 2208 3059 2706 2950 4048
c C hine s e 204 34 82 8 6 32 12 16 19 16 10 6
18 M a c ke re l
a India n m a c ke re l 2538 5569 5452 3082 20198 12131 11347 5583 3264 4407 22648 21965
b Othe r
19 S e e r f is he s
a S . c o m m e rs o ni 2115 2022 2292 1942 1891 1055 2298 2532 1419 1041 1312 1947
b S . g ut t a t us 2755 3192 4534 1973 2140 2462 2420 2397 1892 2431 4428 3994
c S . line o la t us 20 28 37 12 96 14 8 3 1 6 5 4
d A c a nt ho c yb iu
2 0 Tunnie s
a E. a f f in is 553 620 690 1190 617 1058 1095 966 855 639 359 798
b A uxis s p p . 7 7 9 20 14 11 8 5 1 3 3 6
c K. p e la m is 7 7 7 17 26 14 2 2 3 3 15 2
d T . t o ng g o l 1 1 1 1 1 1 1 1 1 1 1 1
e Othe r tunnie s 144 27 31 296 210 62 68 8 8 8 31 241
2 1 B ill f is he s 190 303 56 168 252 18 30 114 58 3 170 431
2 2 B a rra c uda s 244 207 134 364 272 426 336 385 665 283 405 347
2 3 M ulle ts 534 143 126 463 76 421 2130 549 540 259 1272 954
2 4 Unic o rn c o d 80 84 88 91 95 98 102 106 110 113 119 122
2 5 F la t f is he s
a Ha libut 78 129 135 98 187 264 181 137 115 157 240 326
b F lo unde rs 99 180 36 121 167 63 23 12 54 80 106 81
c S o le s 968 1255 635 836 1198 735 646 751 645 1214 1571 1680
2 6 C rus ta c e a ns
a P e na e id pra wns 12320 13100 11450 9929 15691 8532 9900 10032 10939 13619 13934 20409
b N o n-P e na e id 5775 7251 1541 1501 3297 1206 1778 1930 2261 2420 1891 1509
c Lo bs te rs 11 26 16 12 12 5 1 1 5 13 12 4
d C ra bs 1283 3939 2552 2137 4751 2929 2734 3241 2640 4509 4934 5068
e S to m a to po ds 396 791 790 537 515 514 509 2654 2286 4086 2467 3473
2 7 M o llus c s
2 8 C e pha lo po ds 802 672 607 745 1045 1015 700 663 1280 586 871 1761
2 9 M is c e lla ne o us
To ta l 127848 160080 158617 127426 165501 143547 127185 128270 123339 126410 154593 173007
286
S pe c ie s 19 9 4 19 9 5 19 9 6 19 9 7 19 9 8 19 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5
1 Ela s m o bra nc hs
a S ha rks 5694 3541 4846 3789 3152 7244 5234 3487 1705 2030 2379 1889
b S ka te s 242 410 232 332 255 318 599 97 58 169 171 136
c R a ys 1748 1654 1851 3389 2373 5187 3654 4284 3481 6914 3964 3147
2 Ee ls 1048 1000 920 1064 1042 1771 2027 1654 1189 1814 2163 1718
3 C a tf is he s 3612 3877 3263 3304 3139 4458 4366 4423 2665 3611 4557 3618
4 C lupe ids
a Wo lf he rring 1298 1762 1211 1513 2591 2250 1536 1073 857 2195 1673 1327
b Oil s a rdine 6851 6854 19932 33375 21723 12331 20145 12698 1115 5924 2724 2160
c Othe r s a rdine s 12804 6731 16499 17963 14597 33451 21309 18773 27080 14120 25235 20013
d Hils a s ha d 1153 523 829 790 794 997 419 206 97 6177 2857 2265
e Othe r s ha ds 17819 7283 3522 2785 8208 5709 1474 983 2080 1637 1853 1469
f A nc ho v ie s 8519 11826 13106 15883 24805 17206 11731 10186 6434 9258 10703 8488
i A nc ho v ie lla
ii T hris s o c le s
g Othe r c lupe ids 12850 7192 10146 8603 7194 8803 9089 5523 6143 6843 8441 6694
5 B o m ba y duc k 453 2406 1444 993 767 1074 730 2291 1830 731 3122 2476
6 Liza rd f is he s 1146 940 980 1141 1082 777 1131 1330 2157 2119 2796 2220
7 Ha lf a nd F ull 91 45 131 206 71 347 82 102 453 317 395 313
8 F lying f is he s 2 37 23 11 7 1 4 41 1 44 1 1
9 P e rc he s
a R o c k c o ds 197 167 102 35 121 64 105 68 27 66 108 85
b S na ppe rs 664 891 237 233 424 996 393 289 266 326 649 515
c P ig fa c e bre a m s 6 88 39 1 4 1 1 1 21 1 1 1
d Thre a df in 1828 1817 1439 1758 2028 1329 1288 1577 2144 2840 3322 2637
e Othe r pe rc he s 4126 4040 3848 2635 3403 3501 3517 3819 3664 4464 5275 4188
10 Go a tf is he s 1926 1314 1003 2223 2721 4506 3728 2703 3404 5030 4923 3908
11 Thre a df ins 1693 1816 1451 1458 1217 1698 1481 768 663 689 940 746
12 C ro a ke rs 9199 9101 7456 8293 8790 11786 8577 6187 5897 9762 10696 8491
13 R ibbo n f is he s 6419 8503 6661 11769 8877 20993 14580 7701 19172 16237 10451 8288
14 C a ra ng ids
a Ho rs e m a c ke re l 654 888 1729 1568 1319 2279 1231 766 1363 1757 1720 1364
b S c a ds 1509 2653 3787 4375 3472 2138 2579 1853 1304 3909 4480 3552
c Le a the r- ja c ke ts 428 845 431 513 713 705 687 520 553 688 648 514T ra c hyno t us
d Othe r C a ra ng ids 4056 7245 3281 3568 3961 4916 5999 4567 5936 6835 5570 4417C o ryp ha e na
Ela c a t e
15 S ilv e rbe llie s 5003 5236 4149 5212 5546 5250 3816 4551 5162 4247 3552 2820Le io g na t hus
G a z z a
16 B ig ja we d 292 352 213 195 164 640 622 542 352 393 218 173
17 P o m fre ts
a B la c k po m fre t 1859 3414 1916 3235 2283 1798 3098 2345 1890 3872 3647 2896
b S ilv e r po m fre t 2490 3926 2804 2926 1393 3239 2892 3375 2858 2802 3204 2544
c C hine s e 26 18 77 16 10 22 256 172 144 216 366 291
18 M a c ke re l
a India n m a c ke re l 15692 15036 9464 16461 15592 20484 10358 9847 14825 23546 21131 16758
b Othe r
19 S e e r f is he s
a S . c o m m e rs o ni 2072 1758 3854 3103 2837 3515 3769 5183 3698 2638 4899 3885
b S . g ut t a t us 2806 2244 2265 1630 1551 3551 1934 2760 2654 3468 3810 3022
c S . line o la t us 13 64 38 293 1 1 2 6 1 1 1 1
d A c a nt ho c yb iu 1 1 15 1 1 6 22 1 1 3 1 1
2 0 Tunnie s
a E. a f f in is 812 772 2622 3634 1887 1783 1624 1313 1544 645 1137 901
b A uxis s p p . 1 1 36 1 2 10 103 54 339 65 25 20
c K. p e la m is 1 1 1 61 23 126 288 110 486 244 130 103
d T . t o ng g o l 35 72 21 66 2 342 30 17 490 2318 1838
e Othe r tunnie s 290 14 123 22 66 469 696 276 124 465 474 376
2 1 B ill f is he s 207 61 283 292 458 394 1458 812 675 889 4294 3406
2 2 B a rra c uda s 558 677 846 933 1010 726 733 1111 695 1348 1017 807
2 3 M ulle ts 4722 224 1523 204 1259 1400 1126 1080 1762 1062 2512 1995
2 4 Unic o rn c o d
2 5 F la t f is he s
a Ha libut 201 155 156 131 137 140 174 108 145 277 216 172
b F lo unde rs 188 85 91 65 284 72 103 160 87 43 47 38
c S o le s 1049 1035 927 2008 2402 1163 1387 1121 1300 1967 1388 1102
2 6 C rus ta c e a ns
a P e na e id pra wns 16005 14345 15608 14567 19538 25470 23384 16839 16774 18323 17584 13951
b N o n-P e na e id 2179 938 4516 3602 3215 5320 2771 1510 3057 4547 1824 1447
c Lo bs te rs 4 5 5 1 13 25 14 3 17 14 40 32
d C ra bs 3515 3189 2416 3699 3525 3173 2972 3133 5222 5380 7253 5758
e S to m a to po ds 1888 2245 851 1127 1152 1623 1272 728 762 947 830 659
2 7 M o llus c s 1 1 1 1
2 8 C e pha lo po ds 1963 1328 1813 1072 1326 1540 1077 1275 2433 2116 2324 1845
2 9 M is c e lla ne o us
To ta l 171907 152573 167082 198083 194592 238782 193989 156418 168785 196517 206062 163482
287
Appendix C.12. Marine fisheries catch (t) for Orissa, 1950-2005 (contd.)
S pe c ie s 19 5 0 19 5 1 19 5 2 19 5 3 19 5 4 19 5 5 19 5 6 19 5 7 19 5 8 19 5 9 19 6 0 19 6 1
1 Ela s m o bra nc hs
a S ha rks 38 38 153 118 195 209 183 175 158 106 195 137
b S ka te s 2 2 5 2 8 10 9 3 2 1 1 1
c R a ys 17 17 65 50 84 88 78 76 67 46 83 59
2 Ee ls 10 10 9 9 9 9 9 8 5 4 3 3
3 C a tf is he s 87 87 68 31 2 85 116 198 106 112 106 107
4 C lupe ids
a Wo lf he rring 35 35 82 58 205 336 208 213 176 106 186 135
b Oil s a rdine 2 2 2 2 2 2 3 3 2 1 1 1
c Othe r s a rdine s 3124 3124 2778 4650 6521 2135 1383 1467 1319 489 1231 1774
d Hils a s ha d 69 69 70 68 69 82 97 104 40 87 18 163
e Othe r s ha ds 220 220 222 216 223 231 227 251 200 21 5 45
f A nc ho v ie s
i A nc ho v ie lla 63 63 180 232 185 318 364 613 270 94 128 199
ii T hris s o c le s 14 14 41 50 44 77 113 155 137 13 28 22
g Othe r c lupe ids 259 259 179 279 134 225 1029 414 322 187 145 172
5 B o m ba y duc k 4 4 16 5 7 31 88 5 3 3 2 2
6 Liza rd f is he s 5 5 5 5 5 5 5 11 7 5 3 8
7 Ha lf a nd F ull 2 2 2 2 2 2 2 3 2 3 4 4
8 F lying f is he s 1 1 1 1 1 1 1 3 2 1 1 1
9 P e rc he s
a R o c k c o ds 3 3 3 2 2 2 3 3 2 3 1 1
b S na ppe rs 16 16 19 13 7 9 14 23 7 20 9 11
c P ig fa c e bre a m s 1 1 1 1 1 1 1 3 2 1 1 1
d Thre a df in 13 13 17 10 7 9 11 17 5 15 7 9
e Othe r pe rc he s 159 159 196 132 70 94 134 11 80 208 101 118
10 Go a tf is he s 3 3 4 7 8 11 14 6 16 23 5 19
11 Thre a df ins 214 214 565 281 54 111 406 40 155 78 117 70
12 C ro a ke rs 51 51 169 3 108 123 434 150 64 31 49 30
13 R ibbo n f is he s 17 17 30 18 90 157 722 191 78 21 29 29
14 C a ra ng ids
a Ho rs e m a c ke re l 105 105 43 24 8 7 34 101 2 10 71 222
b S c a ds 1 1 1 1 1 1 1 3 2 1 1 1
c Le a the r- ja c ke ts 3 3 3 3 3 3 3 8 10 9 12 9T ra c hyno t us
d Othe r C a ra ng ids 1 1 1 1 1 1 1C o ryp ha e na 1 1 1 1 1 1 1 3 2 1 1 1Ela c a t e 2 2 2 2 2 2 2 5 3 3 2 2
15 S ilv e rbe llie sLe io g na t hus 6 6 25 14 33 45 79 619 151 258 154 776G a z z a 5 5 5 5 5 5 5 25 24 16 14 50
16 B ig ja we d jum pe r 4 4 16 10 21 29 45 3 18 3 2 3
17 P o m fre ts
a B la c k po m fre t 13 13 30 9 10 27 41 119 124 36 40 42
b S ilv e r po m fre t 47 47 111 32 39 100 154 441 458 134 148 153
c C hine s e 2 2 2 2 2 2 2 3 2 1 1 1
18 M a c ke re l
a India n m a c ke re l 40 40 3 24 6 3 20 213 61 94 46 21
b Othe r m a c ke re ls 1 1 1 1 1 1 1 3 2 1 1 1
19 S e e r f is he s
a S . c o m m e rs o ni 6 6 18 6 11 16 7 16 5 4 8 8
b S . g ut t a t us 124 124 424 129 251 376 172 371 129 103 190 191
c S . line o la t us 2 2 5 2 3 5 3 3 2 1 2 2
d A c a nt ho c yb ium
2 0 Tunnie s
a E. a f f in is 4 4 6 17 19 15 22 35 12 16 2 3
b A uxis s p p . 1 1 1 1 1 1 1 3 2 1 1 1
c K. p e la m is 1 1 1 1 1 1 1 3 2 1 1 1
d T . t o ng g o l 1 1 1 1 1 1 1 3 2 1 1 1
e Othe r tunnie s 4 4 7 23 23 16 25 49 17 23 3 4
2 1 B ill f is he s 1 1 1 1 1 1 1 3 2 1 1 1
2 2 B a rra c uda s 1 1 1 1 1 1 1 25 77 26 12 9
2 3 M ulle ts 21 21 13 6 2 10 8 6 87 1 23 43
2 4 Unic o rn c o d
2 5 F la t f is he s
a Ha libut
b F lo unde rs
c S o le s 4 4 4 4 4 4 4 8 33 67 52 146
2 6 C rus ta c e a ns
a P e na e id pra wns 23 23 84 28 380 290 1624 729 220 212 236 444
b N o n-P e na e id 218 218 309 146 439 514 757 45 31 1 1 1
c Lo bs te rs 1 1 1 1 1 1 1 3 2 1 1 1
d C ra bs 3 3 3 3 4 4 5 6 7 1 1 2
e S to m a to po ds 2 2 2 2 2 2 2 3 2 1 1 1
2 7 M o llus c s
2 8 C e pha lo po ds 3 3 3 2 4 5 7 3 2 1 1 1
To ta l 5080 5080 6012 6752 9325 5855 8689 7000 4719 2711 3494 5265
288
S pe c ie s 19 6 2 19 6 3 19 6 4 19 6 5 19 6 6 19 6 7 19 6 8 19 6 9 19 7 0 19 7 1 19 7 2 19 7 3
1 Ela s m o bra nc hs
a S ha rks 147 146 204 251 101 154 171 273 962 312 574 703
b S ka te s 1 1 1 1 1 1 1 1 1 2 1 1
c R a ys 63 62 87 107 43 66 73 117 412 133 246 301
2 Ee ls 4 4 4 4 4 6 8 7 10 13 27 59
3 C a tf is he s 107 214 261 117 84 120 217 111 382 315 378 469
4 C lupe ids
a Wo lf he rring 104 87 213 200 71 229 723 549 542 274 162 384
b Oil s a rdine 1 1 1 1 1 1 1 228 100 2 17 34
c Othe r s a rdine s 1258 1286 1979 1933 2416 1374 3879 3611 2458 2076 1202 1830
d Hils a s ha d 10 236 66 75 34 47 56 41 217 98 28 136
e Othe r s ha ds 18 3 41 44 48 51 43 19 36 71 5 166
f A nc ho v ie s
i A nc ho v ie lla 287 304 346 773 176 261 389 345 170 133 233 287
ii T hris s o c le s 18 101 145 62 41 110 439 73 217 76 104 48
g Othe r c lupe ids 169 123 79 79 60 251 745 629 1422 339 170 762
5 B o m ba y duc k 2 10 6 3 2 20 47 20 11 16 12 28
6 Liza rd f is he s 3 6 28 2 5 4 1 20 105 14 9 4
7 Ha lf a nd F ull 1 4 5 7 5 6 1 2 2 1 2 1
8 F lying f is he s 1 1 1 1 1 19 1 1 1 1 1 1
9 P e rc he s
a R o c k c o ds 1 1 1 1 1 1 1 1 1 2 1 1
b S na ppe rs 7 4 6 3 1 2 3 2 3 14 5 11
c P ig fa c e bre a m s 1 1 1 1 1 1 1 1 1 2 1 1
d Thre a df in 5 3 4 2 1 2 3 2 3 11 3 8
e Othe r pe rc he s 72 40 64 2 5 22 27 3 33 154 46 116
10 Go a tf is he s 8 10 2 7 9 19 32 30 26 8 10 6
11 Thre a df ins 1 3 9 3 9 68 46 32 239 95 68 129
12 C ro a ke rs 68 118 74 83 33 43 199 116 355 430 282 184
13 R ibbo n f is he s 54 80 55 42 29 59 99 41 91 64 34 62
14 C a ra ng ids
a Ho rs e m a c ke re l 94 214 35 29 118 210 675 346 682 93 66 119
b S c a ds 1 1 1 1 1 1 1 1 1 1 1 1
c Le a the r- ja c ke ts 17 14 15 18 19 35 49 82 65 63 54 137T ra c hyno t us
d Othe r C a ra ng idsC o ryp ha e na 1 1 1 2 2 2 3 2 1 1 1 1Ela c a t e 2 6 5 6 5 9 11 8 8 11 10 12
15 S ilv e rbe llie sLe io g na t hus 598 612 399 210 592 2159 840 824 1162 482 1009 585G a z z a 52 46 27 21 15 18 16 9 10 6 5 6
16 B ig ja we d jum pe r 4 57 18 23 44 5 11 4 69 11 29 8
17 P o m fre ts
a B la c k po m fre t 31 51 34 61 25 104 75 65 97 170 122 169
b S ilv e r po m fre t 114 191 127 225 91 388 279 242 360 634 451 631
c C hine s e 1 1 1 1 1 1 1 1 1 2 1 1
18 M a c ke re l
a India n m a c ke re l 17 26 38 473 17 153 15 24 1345 383 80 68
b Othe r m a c ke re ls 1 1 1 1 1 1 1 1 1 1 1 1
19 S e e r f is he s
a S . c o m m e rs o ni 6 7 9 14 4 16 16 20 24 12 10 25
b S . g ut t a t us 145 172 233 342 85 373 400 492 577 298 238 601
c S . line o la t us 1 1 2 4 1 4 4 6 6 3 3 6
d A c a nt ho c yb ium
2 0 Tunnie s
a E. a f f in is 7 64 4 42 26 19 3 1 7 16 8 15
b A uxis s p p . 1 1 1 1 1 1 1 1 1 1 1 1
c K. p e la m is 1 1 1 1 1 1 1 1 1 1 1 1
d T . t o ng g o l 1 1 1 1 1 1 1 1 1 1 1 1
e Othe r tunnie s 11 87 6 58 36 26 4 2 1 22 12 22
2 1 B ill f is he s 1 1 1 1 1 1 1 1 1 1 1 1
2 2 B a rra c uda s 1 12 1 1 1 3 24 12 5 9 5 1
2 3 M ulle ts 28 4 5 18 14 10 6 7 29 21 45 14
2 4 Unic o rn c o d
2 5 F la t f is he s
a Ha libut
b F lo unde rs
c S o le s 126 147 100 77 46 13 33 37 70 11 42 30
2 6 C rus ta c e a ns
a P e na e id pra wns 736 1321 613 502 367 2078 3471 1322 1042 573 430 906
b N o n-P e na e id 1 1 1 1 1 1 1 1 1 3 7 14
c Lo bs te rs 1 1 1 1 1 1 1 1 1 2 1 1
d C ra bs 3 4 3 2 1 2 3 2 1 2 1 33
e S to m a to po ds 1 1 1 1 1 1 1 1 1 2 1 1
2 7 M o llus c s
2 8 C e pha lo po ds 1 1 1 1 1 1 1 1 1 10 1 1
To ta l 4421 5905 5374 5938 4697 8580 13156 9788 13369 7504 6261 9150
289
S pe c ie s 19 7 4 19 7 5 19 7 6 19 7 7 19 7 8 19 7 9 19 8 0 19 8 1 19 8 2 19 8 3 19 8 4 19 8 5
1 Ela s m o bra nc hs
a S ha rks 1053 1013 2491 1635 2954 3883 3228 2891 2510 1702 1525 1665
b S ka te s 1 1 1 1 1 1 1 1 5 17 40 66
c R a ys 452 435 1077 703 1272 1685 1393 1158 438 128 483 600
2 Ee ls 86 55 1 3 4 3 16 30 105 17 12 618
3 C a tf is he s 808 2689 2400 1464 2246 1697 2705 7683 5048 5449 7449 6379
4 C lupe ids
a Wo lf he rring 219 347 456 667 926 1472 1264 1107 977 884 728 412
b Oil s a rdine 3 51 98 145 188 243 281 327 370 414 465 82
c Othe r s a rdine s 1160 848 1462 1088 2171 2406 1637 3919 3522 4489 2854 3344
d Hils a s ha d 2182 4336 4834 2614 6680 8925 4407 1802 964 359 1435 497
e Othe r s ha ds 126 69 114 436 732 321 40 305 272 216 179 58
f A nc ho v ie s
i A nc ho v ie lla 100 338 299 431 1009 452 234 210 551 2385 1765 2249
ii T hris s o c le s 175 163 94 175 151 264 288 278 180 99 57 158
g Othe r c lupe ids 352 745 681 690 1148 1281 2230 1771 1363 2618 2053 2726
5 B o m ba y duc k 14 42 77 76 271 402 327 63 155 267 261 192
6 Liza rd f is he s 4 8 1 7 6 65 233 136 417 348 159 278
7 Ha lf a nd F ull 1 4 1 6 11 25 40 2 2 1 7 8
8 F lying f is he s 1 1 2 2 3 4 15 12 10 8 4 1
9 P e rc he s
a R o c k c o ds 1 3 2 1 3 3 4 1 236 7 14 20
b S na ppe rs 3 20 2 6 18 16 33 13 47 24 10 26
c P ig fa c e bre a m s 1 1 1 1 1 1 1 1 1 1 6 5
d Thre a df in 2 15 2 4 13 12 25 9 1050 629 554 404
e Othe r pe rc he s 32 208 31 67 184 166 357 131 929 421 246 657
10 Go a tf is he s 20 19 1 1 3 3 364 131 135 262 108 505
11 Thre a df ins 222 184 295 574 1611 1934 1386 715 116 19 54 100
12 C ro a ke rs 173 540 402 441 6507 6941 3525 2693 4691 15364 20363 18094
13 R ibbo n f is he s 28 84 115 154 290 551 803 860 379 850 1065 1615
14 C a ra ng ids
a Ho rs e m a c ke re l 18 33 42 51 59 292 390 59 198 167 265 208
b S c a ds 1 1 1 1 1 1 1 84 51 181 31 57
c Le a the r- ja c ke ts 125 123 209 342 704 641 537 169 83 45 123 68T ra c hyno t us
d Othe r C a ra ng idsC o ryp ha e na 1 12 8 6 3 1 5 8 10 18 22 14Ela c a t e 9 180 124 94 3 1 83 122 163 275 352 217
15 S ilv e rbe llie sLe io g na t hus 398 684 453 327 317 1423 861 917 1418 699 432 836G a z z a 4 7 5 3 4 14 9 9 14 7 5 8
16 B ig ja we d jum pe r 22 8 1 25 15 6 79 62 10 18 22 24
17 P o m fre ts
a B la c k po m fre t 193 523 2710 304 1508 2740 2349 984 567 442 361 234
b S ilv e r po m fre t 717 1945 10086 1129 5611 10198 8740 3661 2895 2996 2471 1880
c C hine s e 1 1 6 1 4 6 6 3 5 12 13 13
18 M a c ke re l
a India n m a c ke re l 133 103 375 173 169 274 229 711 531 1719 356 498
b Othe r m a c ke re ls 1 1 1 1 1 1 1 1 1 1 1 1
19 S e e r f is he s
a S . c o m m e rs o ni 29 17 34 24 36 88 54 79 606 134 191 192
b S . g ut t a t us 678 411 788 566 869 2079 1268 2095 1178 709 683 366
c S . line o la t us 7 4 8 6 9 21 13 22 34 19 3 122
d A c a nt ho c yb ium
2 0 Tunnie s
a E. a f f in is 2 6 31 14 221 12 12 91 347 24 10 47
b A uxis s p p . 1 1 1 1 1 1 1 1 1 7 16 4
c K. p e la m is 1 1 1 1 1 1 1 1 2 1 3 4
d T . t o ng g o l 1 1 1 1 1 1 1 1 1 1 1 1
e Othe r tunnie s 3 8 43 19 305 16 17 125 1 9 16 23
2 1 B ill f is he s 1 1 1 1 1 1 1 1 3 4 3 2
2 2 B a rra c uda s 3 3 1 3 3 4 7 8 10 26 22 33
2 3 M ulle ts 24 33 6 6 4 28 1 3 4 5 1 2
2 4 Unic o rn c o d
2 5 F la t f is he s
a Ha libut
b F lo unde rs
c S o le s 94 11 7 102 129 162 85 59 344 392 394 814
2 6 C rus ta c e a ns
a P e na e id pra wns 615 925 850 1112 3212 3819 1390 1713 2605 2451 2579 3122
b N o n-P e na e id 26 79 124 24 15 44 39 71 276 23 26 329
c Lo bs te rs 1 1 1 1 1 1 1 4 48 4 1 2
d C ra bs 18 11 28 8 5 8 214 186 78 224 124 151
e S to m a to po ds 1 1 1 1 1 1 228 183 145 239 57 206
2 7 M o llus c s
2 8 C e pha lo po ds 1 3 32 23 5 18 120 72 246 144 72 111
To ta l 10347 17353 30919 15765 41621 54658 41580 37750 36347 47972 50553 50351
290
S pe c ie s 19 8 6 19 8 7 19 8 8 19 8 9 19 9 0 19 9 1 19 9 2 19 9 3 19 9 4 19 9 5 19 9 6 19 9 7
1 Ela s m o bra nc hs
a S ha rks 3888 1335 1493 1802 2017 1306 3342 1423 586 367 1737 1045
b S ka te s 48 402 73 42 21 23 15 27 32 7 12 25
c R a ys 688 1105 705 362 339 347 516 1020 150 322 712 631
2 Ee ls 646 238 476 839 1098 1148 609 918 279 494 685 465
3 C a tf is he s 5365 4970 5359 5772 5089 4233 4662 7674 4298 3148 3075 3577
4 C lupe ids
a Wo lf he rring 742 997 625 1259 1225 825 896 899 1206 675 574 891
b Oil s a rdine 9 1 47 120 11 3 32 104 69 5 119 342
c Othe r s a rdine s 3039 6423 2504 2640 4730 1286 806 1516 2052 2379 3432 2050
d Hils a s ha d 421 539 120 735 1218 1937 486 1608 2399 2684 3619 1766
e Othe r s ha ds 117 310 228 104 192 42 243 151 122 22 190 413
f A nc ho v ie s 1057 1797 2392 2862
i A nc ho v ie lla 4591 1611 818 525 535 1230 2402 1270
ii T hris s o c le s 71 165 121 203 164 244 63 171
g Othe r c lupe ids 6042 1602 1950 2090 3407 1254 1667 2251 1525 1150 2075 2219
5 B o m ba y duc k 102 451 202 120 171 461 650 391 821 810 5963 524
6 Liza rd f is he s 250 376 292 387 94 151 177 434 63 138 188 153
7 Ha lf a nd F ull 8 2 1 15 8 1 2 3 2 8 29 33
8 F lying f is he s 1 1 1 83 59 30 4 2 1 1 1 1
9 P e rc he s
a R o c k c o ds 4 25 4 6 21 14 7 1 10 1 5 8
b S na ppe rs 266 49 28 10 1 7 13 24 1 2 1 13
c P ig fa c e bre a m s 5 3 3 4 4 2 2 7 1 4 7 15
d Thre a df in 614 392 591 340 288 299 128 192 132 138 139 292
e Othe r pe rc he s 1188 2648 2052 1426 2484 1398 1075 1529 644 728 1027 539
10 Go a tf is he s 323 231 300 190 103 99 77 190 141 127 151 308
11 Thre a df ins 166 191 109 81 220 135 319 161 4 10 150 156
12 C ro a ke rs 18601 19699 18369 12156 24268 13176 13111 20700 14976 9814 10815 8819
13 R ibbo n f is he s 1138 1897 1857 3008 3969 2383 1926 1911 2109 3039 3101 4477
14 C a ra ng ids
a Ho rs e m a c ke re l 153 156 128 609 667 293 396 551 709 723 481 809
b S c a ds 254 46 18 47 19 183 132 57 12 26 106 133
c Le a the r- ja c ke ts 25 132 40 137 391 145 182 138 166 197 357 463T ra c hyno t us
d Othe r C a ra ng ids 400 694 1023 673C o ryp ha e na 21 11 15 33 25 29 33 32Ela c a t e 318 177 231 516 396 453 509 502
15 S ilv e rbe llie s 116 376 308 439Le io g na t hus 263 485 503 281 240 871 445 541G a z z a 3 5 5 2 2 9 4 5
16 B ig ja we d jum pe r 13 30 129 45 221 53 137 141 32 24 116 434
17 P o m fre ts
a B la c k po m fre t 896 1293 149 1139 1277 1616 1496 1822 1347 1600 1141 718
b S ilv e r po m fre t 3301 2836 2078 4365 4540 3000 2835 5004 6349 3349 1843 2891
c C hine s e 15 1 34 74 216 41 46 40 25 38 33 140
18 M a c ke re l
a India n m a c ke re l 877 1743 235 155 63 486 658 825 493 78 268 215
b Othe r m a c ke re ls 1 1 1 1 1 1 1 1
19 S e e r f is he s
a S . c o m m e rs o ni 218 798 509 1468 806 1089 1598 608 776 423 1374 1003
b S . g ut t a t us 745 948 601 731 993 955 676 1580 1987 717 1047 698
c S . line o la t us 243 361 473 622 775 846 945 1063
d A c a nt ho c yb ium
2 0 Tunnie s
a E. a f f in is 284 214 117 24 48 61 40 54 17 133 329 60
b A uxis s p p . 6 1 1 1 1 1 1 1 14 1 1 1
c K. p e la m is 1 1 1 1 1 1 1 1 1 1 1 1
d T . t o ng g o l 1 2 2 2 2 2 2 2
e Othe r tunnie s 31 2 28 39 1 5 54 54 33 1 23 7
2 1 B ill f is he s 1 6 1 1 1 30 58 58 10 4 101 82
2 2 B a rra c uda s 80 287 7 4 64 31 24 20 74 22 11 30
2 3 M ulle ts 35 18 14 12 8 4 17 28 1 8 6 240
2 4 Unic o rn c o d
2 5 F la t f is he s
a Ha libut 1 1 1
b F lo unde rs
c S o le s 906 675 366 198 153 308 341 576 326 392 529 365
2 6 C rus ta c e a ns
a P e na e id pra wns 3658 2503 1962 2972 4514 2328 3067 3285 2591 5453 3632 3004
b N o n-P e na e id 275 157 159 13 40 423 381 418 227 380 269 630
c Lo bs te rs 9 6 3 1 1 1 1 1 1 2 1 1
d C ra bs 226 421 249 480 184 233 614 780 280 815 820 758
e S to m a to po ds 573 1246 1240 606 130 871 1129 1326 415 878 931 1239
2 7 M o llus c s
2 8 C e pha lo po ds 153 112 40 98 16 53 67 68 31 96 30 115
To ta l 61911 60338 47669 48998 67532 46457 49119 64161 49119 44300 54980 46774
291
S pe c ie s 19 9 8 19 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5
1 Ela s m o bra nc hs
a S ha rks 1614 1803 1185 2264 766 1008 1105 1418
b S ka te s 2 3 18 11 35 1 1 1
c R a ys 729 812 1647 2042 1233 656 369 473
2 Ee ls 431 632 628 601 1174 1318 809 1038
3 C a tf is he s 4494 4568 8032 5471 4225 6573 4900 6285
4 C lupe ids
a Wo lf he rring 762 578 1151 1337 1359 665 529 678
b Oil s a rdine 485 201 1438 203 100 319 94 120
c Othe r s a rdine s 4222 7862 6653 9135 3932 4251 6271 8038
d Hils a s ha d 950 1448 2076 6157 3802 1488 1928 2471
e Othe r s ha ds 176 30 437 34 23 10 95 122
f A nc ho v ie s 2509 2324 4337 2771 4000 3999 4826 6185
i A nc ho v ie lla
ii T hris s o c le s
g Othe r c lupe ids 2066 1820 2997 3269 3575 3750 3394 4350
5 B o m ba y duc k 1300 1040 489 237 1027 512 627 804
6 Liza rd f is he s 122 178 239 202 381 279 743 952
7 Ha lf a nd F ull 27 1 5 3 7 34 1 1
8 F lying f is he s 1 1 1 1 5 1 1 1
9 P e rc he s
a R o c k c o ds 11 39 29 16 7 38 18 23
b S na ppe rs 1 65 136 131 27 38 78 100
c P ig fa c e bre a m s 15 14 4 1 1 3 1 1
d Thre a df in 436 542 695 719 233 418 723 927
e Othe r pe rc he s 469 1416 2165 1462 707 899 683 877
10 Go a tf is he s 390 850 1102 655 468 1126 1632 2093
11 Thre a df ins 161 148 264 134 254 204 260 333
12 C ro a ke rs 6542 8156 15138 9652 9539 9526 12534 16078
13 R ibbo n f is he s 3130 6300 7541 5349 8679 7072 7962 10205
14 C a ra ng ids
a Ho rs e m a c ke re l 708 565 919 868 1593 1605 1503 1926
b S c a ds 230 714 232 155 534 396 629 807
c Le a the r- ja c ke ts 686 759 1163 1748 1245 1133 1296 1661T ra c hyno t us
d Othe r C a ra ng ids 1076 954 1294 910 1325 1736 1440 1846C o ryp ha e na
Ela c a t e
15 S ilv e rbe llie s 581 883 1349 1638 1463 834 1627 2087Le io g na t hus
G a z z a
16 B ig ja we d jum pe r 251 157 625 258 198 228 341 437
17 P o m fre ts
a B la c k po m fre t 1028 951 1872 1348 1553 2512 3342 4287
b S ilv e r po m fre t 1813 1068 2656 2765 2067 2133 2059 2641
c C hine s e 39 129 147 188 271 419 355 455
18 M a c ke re l
a India n m a c ke re l 372 1056 236 1005 2695 2519 1701 2180
b Othe r m a c ke re ls
19 S e e r f is he s
a S . c o m m e rs o ni 1799 2836 2661 545 1355 1037 992 1272
b S . g ut t a t us 1071 533 1246 1944 953 1304 649 832
c S . line o la t us
d A c a nt ho c yb ium
2 0 Tunnie s
a E. a f f in is 280 444 228 50 57 140 103 132
b A uxis s p p . 1 1 1 1 1 3 1 1
c K. p e la m is 1 1 1 1 1 1 576 738
d T . t o ng g o l
e Othe r tunnie s 7 2 1 84 1 16 9 12
2 1 B ill f is he s 217 14 5 362 34 26 58 74
2 2 B a rra c uda s 12 44 39 10 494 35 44 57
2 3 M ulle ts 25 48 12 8 87 325 372 478
2 4 Unic o rn c o d
2 5 F la t f is he s
a Ha libut 1 1 1 1 1 1 1 1
b F lo unde rs
c S o le s 207 666 857 627 510 493 591 758
2 6 C rus ta c e a ns
a P e na e id pra wns 2315 4356 6952 4154 5022 5707 9872 12656
b N o n-P e na e id 325 736 2612 1107 1068 1507 1431 1835
c Lo bs te rs 1 9 1 1 1 1 1 1
d C ra bs 452 947 1336 1007 1336 1387 1366 1752
e S to m a to po ds 551 788 1395 661 433 372 90 116
2 7 M o llus c s 3 1 1 1 1
2 8 C e pha lo po ds 174 213 368 284 176 412 1014 1300
To ta l 45271 59706 86613 73589 70036 70474 81048 103919
292
Appendix C.13. Marine fisheries catch (t) for West Bengal, 1950-2005 (contd.)
S pe c ie s 19 5 0 19 5 1 19 5 2 19 5 3 19 5 4 19 5 5 19 5 6 19 5 7 19 5 8 19 5 9 19 6 0 19 6 1
1 Ela s m o bra nc hs
a S ha rks 1 1 3 2 4 4 4 4 5 7 17 17
b S ka te s 1 3 4 10 10
c R a ys 1 1 2 2 2 2 2 3 7 7
2 Ee ls 1 1 1
3 C a tf is he s 2 2 1 1 1 2 2 9 16 35 47 70
4 C lupe ids
a Wo lf he rring 1 1 2 1 4 7 4 18 32 40 100 105
b Oil s a rdine 1 1 2 2
c Othe r s a rdine s 61 61 54 90 126 41 27 14 2 1 5 10
d Hils a s ha d 1 1 1 1 1 2 2 1 1 4 1 13
e Othe r s ha ds 4 4 4 4 4 4 4 5 5 1 1 4
f A nc ho v ie s
i A nc ho v ie lla 1 1 3 4 4 6 7 52 97 70 132 302
ii T hris s o c le s 1 1 1 1 2 36 70 12 41 46
g Othe r c lupe ids 5 5 3 5 3 4 20 134 248 295 324 556
5 B o m ba y duc k 1 2 33 65 76 82 172
6 Liza rd f is he s 1 1 1
7 Ha lf a nd F ull 1 6 8
8 F lying f is he s
9 P e rc he s
a R o c k c o ds 1 1 2
b S na ppe rs 1 1 1
c P ig fa c e bre a m s
d Thre a df in 1 1 5 3 6
e Othe r pe rc he s 3 3 4 3 1 2 3 2 1 3 2 3
10 Go a tf is he s 1 1 1
11 Thre a df ins 4 4 11 5 1 2 8 14 19 19 42 36
12 C ro a ke rs 1 1 3 2 2 8 78 148 147 328 293
13 R ibbo n f is he s 1 2 3 14 126 239 132 264 365
14 C a ra ng ids
a Ho rs e m a c ke re l 2 2 1 1 1 1 1 1
b S c a ds
c Le a the r- ja c ke ts 1 1 1T ra c hyno t us
d Othe r C a ra ng ids 1 1 1C o ryp ha e na
Ela c a t e
15 S ilv e rbe llie sLe io g na t hus 1 1 2 2 2 8 7 48G a z z a
16 B ig ja we d jum pe r 1 1 1 1 1 1 1
17 P o m fre ts
a B la c k po m fre t 1 1 1 1 1 5 9 6 9 13
b S ilv e r po m fre t 1 1 2 1 1 2 3 18 33 19 31 46
c C hine s e 1 1 1
18 M a c ke re l
a India n m a c ke re l 1 1 1 1 1 1 1 1 1 3 2 1
b Othe r m a c ke re ls
19 S e e r f is he s
a S . c o m m e rs o ni 1 1 1
b S . g ut t a t us 2 2 8 3 5 7 3 3 3 5 13 20
c S . line o la t us 1 1 1
d A c a nt ho c yb ium
2 0 Tunnie s
a E. a f f in is 1 1 3 1 1
b A uxis s p p .
c K. p e la m is
d T . t o ng g o l
e Othe r tunnie s
2 1 B ill f is he s
2 2 B a rra c uda s
2 3 M ulle ts 9 17 1 13 34
2 4 Unic o rn c o d
2 5 F la t f is he s
a Ha libut
b F lo unde rs
c S o le s 1 1 4 5 20
2 6 C rus ta c e a ns
a P e na e id pra wns 2 1 7 6 31 98 165 325 514 1402
b N o n-P e na e id 4 4 6 3 9 10 15 182 350 3 10 22
c Lo bs te rs
d C ra bs 1 3 1 2 2
e S to m a to po ds 1 1 1
2 7 M o llus c s
2 8 C e pha lo po ds 1 3 4 5 7
To ta l 98 98 117 131 181 114 168 858 1548 1251 2038 3659
293
S pe c ie s 19 6 2 19 6 3 19 6 4 19 6 5 19 6 6 19 6 7 19 6 8 19 6 9 19 7 0 19 7 1 19 7 2 19 7 3
1 Ela s m o bra nc hs
a S ha rks 12 12 26 39 25 19 17 43 143 40 91 105
b S ka te s 8 7 15 24 16 11 10 27 86 23 53 63
c R a ys 5 4 10 15 9 7 7 17 57 17 36 41
2 Ee ls 1 1 1 1 2 1 1 1 2 2 1 2
3 C a tf is he s 46 85 164 90 102 72 105 87 284 197 295 347
4 C lupe ids
a Wo lf he rring 53 42 160 188 104 164 424 523 534 238 156 347
b Oil s a rdine 2 2 2 2 3 2 2 2 1 1 1 1
c Othe r s a rdine s 4 4 11 13 25 7 16 24 17 13 9 12
d Hils a s ha d 1 12 5 7 5 3 3 4 23 10 3 13
e Othe r s ha ds 2 1 4 6 9 5 3 2 5 8 1 21
f A nc ho v ie s
i A nc ho v ie lla 288 282 505 1405 503 364 442 637 326 226 436 501
ii T hris s o c le s 26 133 300 159 166 215 706 188 584 179 276 119
g Othe r c lupe ids 360 243 247 309 366 747 1809 2482 5810 1225 681 2849
5 B o m ba y duc k 133 425 401 228 197 1245 2351 1599 874 1231 1005 2169
6 Liza rd f is he s 1 1 4 1 2 1 1 5 22 2 1 2
7 Ha lf a nd F ull 2 5 10 14 16 9 2 4 4 2 2 1
8 F lying f is he s
9 P e rc he s
a R o c k c o ds 1 1 1 1 2 1 1 1 2 2 1 2
b S na ppe rs 1 1 1 1 2 1 1 1 2 2 1 2
c P ig fa c e bre a m s
d Thre a df in 3 1 3 1 2 1 1 1 2 8 3 7
e Othe r pe rc he s 1 1 2 1 2 1 1 1 2 5 1 3
10 Go a tf is he s 1 1 1 1 2 1 2 2 2 2 1 2
11 Thre a df ins 1 1 5 2 8 33 18 19 143 48 43 77
12 C ro a ke rs 438 700 693 967 616 387 1459 1384 3961 4080 3314 2047
13 R ibbo n f is he s 464 628 678 643 717 695 959 647 1474 924 541 925
14 C a ra ng ids
a Ho rs e m a c ke re l 1 1 1 1 2 1 1 1 1 1 1 1
b S c a ds
c Le a the r- ja c ke ts 2 1 2 4 5 5 5 14 12 10 10 23T ra c hyno t us
d Othe r C a ra ng ids 1 1 1 1 2 1 1 1 1 1 2 2C o ryp ha e na
Ela c a t e
15 S ilv e rbe llie sLe io g na t hus 25 23 24 15 68 123 38 61 82 28 76 41G a z z a
16 B ig ja we d jum pe r 1 6 3 5 14 1 2 1 13 2 6 2
17 P o m fre ts
a B la c k po m fre t 6 10 10 21 14 29 17 24 34 50 45 59
b S ilv e r po m fre t 23 35 37 81 51 107 64 89 124 186 163 219
c C hine s e 1 1 1 1 2 1 1 1 2 2 1 2
18 M a c ke re l
a India n m a c ke re l 1 1 2 37 2 9 1 2 109 28 7 6
b Othe r m a c ke re ls
19 S e e r f is he s
a S . c o m m e rs o ni 1 1 1 2 2 1 1 2 3 1 1 3
b S . g ut t a t us 10 11 24 43 17 36 31 63 77 35 31 73
c S . line o la t us 1 1 1 1 2 1 1 1 1 1 1 1
d A c a nt ho c yb ium
2 0 Tunnie s
a E. a f f in is 2 13 1 15 16 5 1 1 1 6 3 6
b A uxis s p p .
c K. p e la m is
d T . t o ng g o l
e Othe r tunnie s
2 1 B ill f is he s
2 2 B a rra c uda s
2 3 M ulle ts 15 2 4 18 20 6 3 7 27 17 43 14
2 4 Unic o rn c o d
2 5 F la t f is he s
a Ha libut
b F lo unde rs
c S o le s 11 12 13 12 11 2 3 6 10 2 7 5
2 6 C rus ta c e a ns
a P e na e id pra wns 1536 2540 1862 1896 2188 6014 8221 5079 3758 1754 1625 3209
b N o n-P e na e id 25 15 19 21 29 21 21 25 37 139 414 796
c Lo bs te rs
d C ra bs 3 4 5 4 2 4 3 2 3 2 3 63
e S to m a to po ds 1 1 1 1 2 1 1 1 2 2 1 2
2 7 M o llus c s
2 8 C e pha lo po ds 10 1 5 4 2 9 18 2 5 138 10 12
To ta l 3527 5270 5268 6306 5344 10372 16776 13089 18660 10887 9408 14198
294
S pe c ie s 19 7 4 19 7 5 19 7 6 19 7 7 19 7 8 19 7 9 19 8 0 19 8 1 19 8 2 19 8 3 19 8 4 19 8 5
1 Ela s m o bra nc hs
a S ha rks 229 160 398 31 118 213 80 408 679 274 371 145
b S ka te s 136 94 236 17 70 128 48 220 273 120 48 206
c R a ys 91 64 158 13 46 86 33 135 648 329 199 338
2 Ee ls 4 2 2 16 24 33 34 39 47 14 67 6
3 C a tf is he s 870 2093 1254 190 278 245 1023 5728 11776 1864 3102 2230
4 C lupe ids
a Wo lf he rring 315 342 289 124 266 446 261 265 566 786 307 237
b Oil s a rdine 1 1 1 1 1 1 1 1 1 1 1 1
c Othe r s a rdine s 12 6 14 11 5 4 4 4 4 35 21 36
d Hils a s ha d 340 462 920 69 236 723 608 2395 1004 868 1831 5956
e Othe r s ha ds 25 10 17 30 34 42 19 39 73 36 255 41
f A nc ho v ie s
i A nc ho v ie lla 280 656 765 277 638 231 152 632 808 1074 3413 894
ii T hris s o c le s 690 1062 851 213 339 80 31 59 99 149 50 246
g Othe r c lupe ids 2102 3046 2061 822 1438 1663 637 2157 2376 261 1171 514
5 B o m ba y duc k 1718 3568 2594 1198 1672 1327 396 554 1542 3945 1888 935
6 Liza rd f is he s 2 2 2 2 2 2 1 1 1 1 1 2
7 Ha lf a nd F ull 3 10 10 14 13 9 4 1 2 2 3 2
8 F lying f is he s
9 P e rc he s
a R o c k c o ds 4 3 11 26 44 63 3 9 14 12 13 12
b S na ppe rs 2 2 2 2 2 2 1 1 1 1 1 2
c P ig fa c e bre a m s
d Thre a df in 2 12 2 87 147 209 10 196 385 549 824 1085
e Othe r pe rc he s 2 7 22 50 85 121 6 48 53 25 123 62
10 Go a tf is he s 2 20 35 72 79 95 91 97 110 119 149 173
11 Thre a df ins 192 115 110 35 160 301 263 402 173 51 100 4
12 C ro a ke rs 2809 6317 6493 1556 3263 1603 507 348 1391 1674 4174 1288
13 R ibbo n f is he s 669 1360 807 331 834 319 134 144 168 172 4928 3647
14 C a ra ng ids
a Ho rs e m a c ke re l 1 1 1 1 1 1 1 1 1 1 44 37
b S c a ds
c Le a the r- ja c ke ts 34 23 43 8 47 73 123 56 73 352 34 18T ra c hyno t us
d Othe r C a ra ng ids 3 30 20 14 2 78 46 23 4 5 10 2C o ryp ha e na
Ela c a t e
15 S ilv e rbe llie sLe io g na t hus 41 51 350 20 547 168 48 37 31 92 133 592G a z z a
16 B ig ja we d jum pe r 6 2 2 2 2 2 1 1 1 1 1 2
17 P o m fre ts
a B la c k po m fre t 97 191 200 38 112 340 275 10 330 583 389 453
b S ilv e r po m fre t 362 711 744 136 414 1267 1024 3558 4549 7118 3355 1402
c C hine s e 2 2 2 2 2 2 1 694 84 49 35 15
18 M a c ke re l
a India n m a c ke re l 16 8 8 10 9 8 7 6 6 6 28 6
b Othe r m a c ke re ls
19 S e e r f is he s
a S . c o m m e rs o ni 5 2 13 1 2 14 9 961 616 439 95 31
b S . g ut t a t us 132 55 314 24 62 344 210 51 153 252 122 74
c S . line o la t us 1 2 3 3 1 3 2 2 2 2 2 2
d A c a nt ho c yb ium
2 0 Tunnie s
a E. a f f in is 1 2 6 11 15 16 17 19 23 24 27 39
b A uxis s p p .
c K. p e la m is
d T . t o ng g o l
e Othe r tunnie s
2 1 B ill f is he s
2 2 B a rra c uda s
2 3 M ulle ts 32 32 3 4 4 2 1 1 97 21 15 51
2 4 Unic o rn c o d
2 5 F la t f is he s
a Ha libut
b F lo unde rs
c S o le s 21 2 14 35 44 42 4 5 4 46 53 85
2 6 C rus ta c e a ns
a P e na e id pra wns 3150 3414 3309 942 1066 695 219 308 403 495 2743 325
b N o n-P e na e id 2069 4478 4189 452 1168 273 69 1580 1191 2050 12781 3783
c Lo bs te rs
d C ra bs 52 22 22 33 29 30 25 78 134 446 156 311
e S to m a to po ds 2 2 2 2 2 2 3 9 16 40 32 7
2 7 M o llus c s
2 8 C e pha lo po ds 9 51 48 67 56 30 6 6 8 22 59 10
To ta l 16534 28493 26345 6997 13381 11335 6438 21288 29922 24406 43154 25306
295
S pe c ie s 19 8 6 19 8 7 19 8 8 19 8 9 19 9 0 19 9 1 19 9 2 19 9 3 19 9 4 19 9 5 19 9 6 19 9 7
1 Ela s m o bra nc hs
a S ha rks 182 153 46 56 401 1379 2596 2831 1764 1914 1928 3361
b S ka te s 4 23 33 38 24 3 3 74 102 381 102 278
c R a ys 160 479 249 926 767 1037 649 987 418 705 848 871
2 Ee ls 103 7 13 7 8 1 9 268 2 38 138 80
3 C a tf is he s 2222 3049 963 6297 4675 6751 8292 7193 4805 4815 5334 5488
4 C lupe ids
a Wo lf he rring 266 163 56 1083 921 1032 894 935 938 988 765 454
b Oil s a rdine 2 3 4 6 7 5 3 875 1 20 121 197
c Othe r s a rdine s 108 3 3 62 3 344 48 206 209 115 1302 239
d Hils a s ha d 1589 1332 574 10539 10493 20158 17357 23118 22987 15940 21405 26772
e Othe r s ha ds 96 155 49 408 126 4 4 4 13 1 1 1
f A nc ho v ie s 6613 7449 4346 6385
i A nc ho v ie lla 2331 1565 1118 1429 2507 3482 5561 4786
ii T hris s o c le s 47 51 173 501 431 391 811 2063
g Othe r c lupe ids 198 781 295 1094 1757 1357 1947 1658 2138 2841 2259 1611
5 B o m ba y duc k 588 809 450 3404 10788 8294 17457 17858 6244 12042 6311 5640
6 Liza rd f is he s 1 1 1 1 2 1 2 2 1 29 21 97
7 Ha lf a nd F ull 2 2 2 2 1 1 2 2 5 7 7 2
8 F lying f is he s
9 P e rc he s
a R o c k c o ds 9 7 5 6 5 3 2 114 13 3 1 10
b S na ppe rs 1 1 1 1 2 1 2 2 1 1 1 1
c P ig fa c e bre a m s 1 1 1 1
d Thre a df in 1144 1140 1225 1848 2202 2351 2680 2926 68 453 168 517
e Othe r pe rc he s 102 67 131 241 51 197 362 144 121 531 635 333
10 Go a tf is he s 166 154 155 224 256 264 292 311 351 611 1017 296
11 Thre a df ins 38 25 20 1026 457 1103 1891 750 713 631 1406 379
12 C ro a ke rs 1295 6439 2147 468 2616 6614 4205 7330 4754 7419 10161 8173
13 R ibbo n f is he s 1788 3060 1417 681 927 1470 2336 3617 1406 2111 2833 1740
14 C a ra ng ids
a Ho rs e m a c ke re l 32 28 21 20 15 98 3 344 118 505 588 444
b S c a ds 1 76 1 31
c Le a the r- ja c ke ts 13 42 59 70 61 243 370 173 176 188 297 204T ra c hyno t us
d Othe r C a ra ng ids 5 10 5 103 11 31 46 82 1353 591 1046 359C o ryp ha e na
Ela c a t e
15 S ilv e rbe llie s 878 1020 1267 2239Le io g na t hus 233 90 21 86 230 567 253 309G a z z a
16 B ig ja we d jum pe r 1 1 1 1 2 1 2 2 1 19 1 37
17 P o m fre ts
a B la c k po m fre t 72 123 178 540 1380 1274 1171 1069 444 831 539 373
b S ilv e r po m fre t 2397 1836 462 3188 3177 2914 3096 4085 1376 2588 1945 1568
c C hine s e 18 93 160 312 101 37 72 122 57 144 233 303
18 M a c ke re l
a India n m a c ke re l 3 1 1 1 1 1 24 47 248 612 198 1255
b Othe r m a c ke re ls 1 26 17 1
19 S e e r f is he s
a S . c o m m e rs o ni 31 11 19 8 7 14 98 75 24 37 1 2
b S . g ut t a t us 116 117 120 874 869 1522 880 555 828 582 988 694
c S . line o la t us 2 1 1 1 1 1 1 1 1 1 5 1
d A c a nt ho c yb ium
2 0 Tunnie s
a E. a f f in is 51 64 72 94 109 123 35 13 43 42 97 114
b A uxis s p p . 1 1 1 1
c K. p e la m is
d T . t o ng g o l 2 1 8 1
e Othe r tunnie s 1 1 20 1
2 1 B ill f is he s 1 6 1 1
2 2 B a rra c uda s 1 17 13 1
2 3 M ulle ts 1 2 1 178 315 2880 709 833 327 928 173 174
2 4 Unic o rn c o d
2 5 F la t f is he s
a Ha libut
b F lo unde rs
c S o le s 119 230 25 8 3 18 14 118 47 141 177 96
2 6 C rus ta c e a ns
a P e na e id pra wns 504 337 229 736 3688 1778 4036 4192 1318 3519 3933 3141
b N o n-P e na e id 1349 767 1785 1646 2402 2010 2777 3895 1790 3460 3968 1959
c Lo bs te rs 1 12 1 6
d C ra bs 121 145 69 37 36 184 41 215 263 550 243 251
e S to m a to po ds 8 59 2 11 20 157 41 142 1 10 5 1
2 7 M o llus c s
2 8 C e pha lo po ds 9 31 7 55 122 94 187 415 151 229 382 608
To ta l 17528 23458 12370 38314 51972 70190 81262 94744 63121 75184 77261 76792
296
S pe c ie s 19 9 8 19 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5
1 Ela s m o bra nc hs
a S ha rks 1769 553 729 1006 1995 2266 2131 2174
b S ka te s 150 45 60 53 48 68 114 116
c R a ys 667 746 551 632 507 633 281 287
2 Ee ls 136 69 115 154 800 766 502 512
3 C a tf is he s 5616 3665 3771 6081 10087 9639 8286 8451
4 C lupe ids
a Wo lf he rring 545 464 579 811 1983 2031 2901 2954
b Oil s a rdine 283 23 122 90 43 5 0 0
c Othe r s a rdine s 234 1998 329 49 947 673 1104 1124
d Hils a s ha d 15907 7895 8786 11192 19801 32224 61049 62159
e Othe r s ha ds 1 1 4 10 1 1 1 1
f A nc ho v ie s 7648 5665 8655 10088 13260 11111 8845 9006
i A nc ho v ie lla
ii T hris s o c le s
g Othe r c lupe ids 2553 2129 3331 2701 6053 5405 6327 6442
5 B o m ba y duc k 16054 5875 9635 16845 28694 44741 42667 43442
6 Liza rd f is he s 244 312 191 189 770 384 366 373
7 Ha lf a nd F ull 8 1 1 1 1 1 1 1
8 F lying f is he s
9 P e rc he s
a R o c k c o ds 14 177 6 2 37 1 1 1
b S na ppe rs 1 1 1 1 3 1 24 25
c P ig fa c e bre a m s 1 1 1 5 1 1 1 1
d Thre a df in 578 179 77 417 1250 1665 708 722
e Othe r pe rc he s 259 782 441 477 1062 2017 940 958
10 Go a tf is he s 710 584 448 20 1475 733 1921 1959
11 Thre a df ins 384 214 298 227 794 656 1077 1098
12 C ro a ke rs 10566 8449 10104 11120 16524 17526 13085 13346
13 R ibbo n f is he s 3378 1937 3904 4954 7737 7596 6334 6449
14 C a ra ng ids
a Ho rs e m a c ke re l 621 249 612 703 1658 1796 1317 1341
b S c a ds 650 185 83 183 5 572 1 1
c Le a the r- ja c ke ts 443 289 232 156 285 395 564 575T ra c hyno t us
d Othe r C a ra ng ids 360 192 122 147 717 235 549 559C o ryp ha e na
Ela c a t e
15 S ilv e rbe llie s 528 528 571 1594 1157 903 460 469Le io g na t hus
G a z z a
16 B ig ja we d jum pe r 1 15 1 38 10 54 38 39
17 P o m fre ts
a B la c k po m fre t 1447 877 882 710 1781 1530 2624 2677
b S ilv e r po m fre t 1646 1444 1849 2316 5556 5220 4178 4262
c C hine s e 261 264 244 361 1220 686 735 750
18 M a c ke re l
a India n m a c ke re l 872 595 307 389 1557 4512 1509 1536
b Othe r m a c ke re ls 1 1 1 1 1 1 1 1
19 S e e r f is he s
a S . c o m m e rs o ni 33 1 1 15 3 1 1 1
b S . g ut t a t us 905 603 690 726 2091 1613 1332 1357
c S . line o la t us 1 1 1 1 1 1 1 1
d A c a nt ho c yb ium
2 0 Tunnie s
a E. a f f in is 101 68 6 7 87 21 59 60
b A uxis s p p . 1 1 1 147 77 102 5 5
c K. p e la m is 5 1 1 1
d T . t o ng g o l 1 1 1 1 1 1 1 1
e Othe r tunnie s 1 1 1 1 1 1 1 1
2 1 B ill f is he s 1 7 1 1 1 6 1 1
2 2 B a rra c uda s 1 5 2 1 1 1 1 1
2 3 M ulle ts 194 182 249 298 186 211 163 166
2 4 Unic o rn c o d
2 5 F la t f is he s
a Ha libut
b F lo unde rs
c S o le s 345 133 141 256 498 809 314 320
2 6 C rus ta c e a ns
a P e na e id pra wns 3332 2875 4632 9305 10069 13206 7696 7839
b N o n-P e na e id 9209 5572 9243 13203 18852 21612 15081 15361
c Lo bs te rs 98 27 1 1 32 23 55 56
d C ra bs 855 487 455 1265 1754 2779 1825 1861
e S to m a to po ds 1 1 11 1 1 1 1 1
2 7 M o llus c s
2 8 C e pha lo po ds 649 493 480 894 774 1754 1258 1284
To ta l 90270 56867 72960 99847 162258 198192 198437 202129
297
Appendix C.14. Marine fisheries catch (t) for Andaman and Nicobar Islands, 1950-2005. The reported
data for year 1978 was erroneous and not supported by the historical information. So, it was replaced by
the interpolated values between the adjacent years (contd.).
S pe c ie s 19 5 0 19 5 1 19 5 2 19 5 3 19 5 4 19 5 5 19 5 6 19 5 7 19 5 8 19 5 9 19 6 0 19 6 1
1 Ela s m o bra nc hs
a S ha rks 1 1 1 1 1 1 1 2 2 2
b S ka te s 1 1 1 1 1 1 1 1
c R a ys 1 1 1 1 1 1 1 1 1 1
2 Ee ls 1 1 1 1 1 1
3 C a tf is he s 1 1 1 1 1 1 1 1 1 1
4 C lupe ids
a Wo lf he rring 1 1 1 1 1 1 1 1
b Oil s a rdine
c Othe r s a rdine s 1 2 3 4 5 6 13 10 15 17 13
d Hils a s ha d 1 1 1 1 1 1 1 1 1
e Othe r s ha ds 1 1 1 1 1 1
f A nc ho v ie s
i A nc ho v ie lla 1 1 1 1 2 2 5 6 8 9 10
ii T hris s o c le s 1 1 1 1 5 7 14
g Othe r c lupe ids 1 1 1 1 1 2 2 3 2 2
5 B o m ba y duc k
6 Liza rd f is he s
7 Ha lf a nd F ull 1 1 1 1 1 1 2 2 2 2 2
8 F lying f is he s 1 1 1 1 1 1 1
9 P e rc he s 1 2 5 7 9 12 15 22 18 23 31 31
a R o c k c o ds
b S na ppe rs
c P ig fa c e bre a m s
d Thre a df in
e Othe r pe rc he s
10 Go a tf is he s 1 1 1 1 1 1 1 1 1
11 Thre a df ins 1 1 1 1 1 1 1 1 1
12 C ro a ke rs 1 1 1 1 1 1 1 1 1
13 R ibbo n f is he s 1 1 1 1 1 1 1 1 1
14 C a ra ng ids
a Ho rs e m a c ke re l 0 1 2 3 4 5 6 7 5 10 9 9
b S c a ds
c Le a the r- ja c ke tsT ra c hyno t us
d Othe r C a ra ng ids 1 1 1 1 1 1 1 1 1C o ryp ha e na
Ela c a t e
15 S ilv e rbe llie sLe io g na t hus 1 2 2 4 4 5 5 5 5 4 9G a z z a
16 B ig ja we d jum pe r
17 P o m fre ts
a B la c k po m fre t 1 1 1 1 1 1 1 1
b S ilv e r po m fre t 1 1 1 1 1 1 1 1 1
c C hine s e
18 M a c ke re l
a India n m a c ke re l 1 1 1 1 2 2 3 6 9 5 3
b Othe r m a c ke re ls 1 1 1 1 1 2 2 2 1
19 S e e r f is he s
a S . c o m m e rs o ni 1 2 2 3 3 4 5 5 4 4 3
b S . g ut t a t us 1 2 2 2 3 4 4 4 4 3 3
c S . line o la t us
d A c a nt ho c yb ium
2 0 Tunnie s
a E. a f f in is
b A uxis s p p .
c K. p e la m is 1 1 1 1 1 2
d T . t o ng g o l
e Othe r tunnie s 1 1 1 1 1 1 1
2 1 B ill f is he s 1 1 1 1 1 1 1
2 2 B a rra c uda s 1 1 1 1 1 1 1 2 2 4 2
2 3 M ulle ts 1 1 1 1 1 1 2 2 6 4 2
2 4 Unic o rn c o d
2 5 F la t f is he s
a Ha libut
b F lo unde rs
c S o le s
2 6 C rus ta c e a ns
a P e na e id pra wns 1 1 1 1 1 1 1 1 1 1 1
b N o n-P e na e id 1 1 1 1 1 1
c Lo bs te rs 1 1 1 1 1 1
d C ra bs 1 1 1 1 1 1 1 1
e S to m a to po ds
2 7 M o llus c s
2 8 C e pha lo po ds 1 1 1 1 1 1
To ta l 1 13 25 37 48 60 75 94 90 122 128 130
298
S pe c ie s 19 6 2 19 6 3 19 6 4 19 6 5 19 6 6 19 6 7 19 6 8 19 6 9 19 7 0 19 7 1 19 7 2 19 7 3
1 Ela s m o bra nc hs
a S ha rks 2 3 3 6 10 12 11 13 20 22 19 35
b S ka te s 1 1 1 1 1 1 2 2 2 2 3 4
c R a ys 1 1 1 1 1 1 2 2 2 2 3 4
2 Ee ls 1 1 1 1 1 1 1 1 1 1 1 1
3 C a tf is he s 2 3 3 5 9 11 10 12 12 17 21 11
4 C lupe ids
a Wo lf he rring 2 2 2 4 5 7 6 7 9 9 11 4
b Oil s a rdine
c Othe r s a rdine s 13 14 12 20 30 38 31 38 36 39 55 57
d Hils a s ha d 1 1 1 1 1 1 1 1 1 1 1 1
e Othe r s ha ds 2 2 1 1 2 2 1 1 1 5 10 4
f A nc ho v ie s
i A nc ho v ie lla 12 12 12 18 27 34 28 34 32 29 59 57
ii T hris s o c le s 17 18 16 25 38 47 38 47 44 49 56 59
g Othe r c lupe ids 3 4 4 7 11 14 11 14 5 7 11 4
5 B o m ba y duc k
6 Liza rd f is he s
7 Ha lf a nd F ull 3 3 3 4 5 5 5 5 7 14 11 9
8 F lying f is he s 1 1 1 1 1 1 1 1 1 1 1 1
9 P e rc he s 29 29 25 38 56 69 56 67 88 97 119 139
a R o c k c o ds
b S na ppe rs
c P ig fa c e bre a m s
d Thre a df in
e Othe r pe rc he s
10 Go a tf is he s 1 1 1 1 1 1 1 1 1 1 1 1
11 Thre a df ins 1 1 1 1 1 1 1 1 1 1 1 1
12 C ro a ke rs 1 1 1 1 1 1 1 1 1 1 1 1
13 R ibbo n f is he s 1 1 1 1 1 1 1 1 1 1 1 1
14 C a ra ng ids
a Ho rs e m a c ke re l 8 10 11 18 28 37 32 39 44 52 77 90
b S c a ds
c Le a the r- ja c ke tsT ra c hyno t us
d Othe r C a ra ng ids 1 1 1 1 1 1 1 1 1 1 1 1C o ryp ha e na
Ela c a t e
15 S ilv e rbe llie sLe io g na t hus 8 8 7 12 17 21 17 21 33 50 74 67G a z z a
16 B ig ja we d jum pe r
17 P o m fre ts
a B la c k po m fre t 1 1 1 1 1 1 1 1 1 2 1 1
b S ilv e r po m fre t 2 2 2 2 3 3 2 3 5 10 8 7
c C hine s e
18 M a c ke re l
a India n m a c ke re l 10 9 7 10 15 17 14 16 14 15 33 68
b Othe r m a c ke re ls 2 2 2 3 4 4 3 4 4 3 8 17
19 S e e r f is he s
a S . c o m m e rs o ni 7 6 5 9 13 15 12 15 21 20 34 40
b S . g ut t a t us 6 5 5 8 12 14 11 13 19 18 32 38
c S . line o la t us
d A c a nt ho c yb ium
2 0 Tunnie s
a E. a f f in is
b A uxis s p p .
c K. p e la m is 1 1 1 2 4 4 4 5 6 8 6 9
d T . t o ng g o l
e Othe r tunnie s 1 1 1 1 1 1 1 1 2 2 2 3
2 1 B ill f is he s 1 1 1 1 1 1 1 1 1 1 1 1
2 2 B a rra c uda s 2 3 2 4 6 8 6 7 10 15 16 11
2 3 M ulle ts 2 5 5 10 17 23 19 25 63 64 95 108
2 4 Unic o rn c o d
2 5 F la t f is he s
a Ha libut
b F lo unde rs
c S o le s
2 6 C rus ta c e a ns
a P e na e id pra wns 1 1 2 3 5 7 6 7 12 15 16 11
b N o n-P e na e id 1 1 1 1 1 1 1 1 1 1 1 1
c Lo bs te rs 1 1 1 1 1 1 1 1 1 1 1 1
d C ra bs 1 1 1 1 1 1 1 1 1 1 1 1
e S to m a to po ds
2 7 M o llus c s
2 8 C e pha lo po ds 1 1 1 1 1 1 1 1 1 1 1 1
To ta l 155 158 147 224 331 410 341 412 510 581 796 871
299
S pe c ie s 19 7 4 19 7 5 19 7 6 19 7 7 19 7 8 19 7 9 19 8 0 19 8 1 19 8 2 19 8 3 19 8 4 19 8 5
1 Ela s m o bra nc hs
a S ha rks 33 50 76 91 87 82 52 29 31 246 293 319
b S ka te s 4 7 9 11 11 11 7 4 4 31 37 40
c R a ys 4 7 9 11 11 11 7 4 4 31 37 33
2 Ee ls 1 1 1 1 1 1 1 1 1 1 1 1
3 C a tf is he s 21 20 25 35 50 64 37 23 38 34 131 132
4 C lupe ids
a Wo lf he rring 18 31 29 32 33 34 18 15 13 22 43 45
b Oil s a rdine
c Othe r s a rdine s 55 58 76 73 87 100 176 156 628 593 714 755
d Hils a s ha d 1 1 1 1 1 1 1 1 1 1 67 67
e Othe r s ha ds 7 11 25 21 27 32 18 15 14 20 18 17
f A nc ho v ie s
i A nc ho v ie lla 53 69 104 88 89 90 126 135 182 195 248 274
ii T hris s o c le s 73 67 72 73 73 72 72 110 168 77 132 135
g Othe r c lupe ids 18 8 9 9 17 25 21 254 535 713 1035 1248
5 B o m ba y duc k
6 Liza rd f is he s
7 Ha lf a nd F ull 9 14 31 45 56 67 30 10 19 10 61 63
8 F lying f is he s 1 1 1 1 1 1 1 2 6 6 6 6
9 P e rc he s 170 205 186 248 264 280 346 199 374 608 934 969
a R o c k c o ds
b S na ppe rs
c P ig fa c e bre a m s
d Thre a df in
e Othe r pe rc he s
10 Go a tf is he s 1 1 3 5 18 30 42 51 62 71 89 98
11 Thre a df ins 1 1 1 1 1 1 1 1 1 14 44 44
12 C ro a ke rs 1 1 7 6 6 6 5 4 4 4 32 32
13 R ibbo n f is he s 1 1 1 1 2 2 10 14 15 34 60 81
14 C a ra ng ids
a Ho rs e m a c ke re l 82 94 110 114 108 102 97 79 87 78 85 83
b S c a ds
c Le a the r- ja c ke tsT ra c hyno t us
d Othe r C a ra ng ids 1 1 1 10 58 106 107 116 98 183 322 327C o ryp ha e na
Ela c a t e
15 S ilv e rbe llie sLe io g na t hus 70 104 153 123 107 91 117 286 972 196 412 448G a z z a
16 B ig ja we d jum pe r
17 P o m fre ts
a B la c k po m fre t 3 4 8 8 7 7 3 1 5 11 8 7
b S ilv e r po m fre t 13 14 31 30 29 27 14 5 20 46 32 34
c C hine s e
18 M a c ke re l
a India n m a c ke re l 30 80 55 76 66 56 133 92 225 187 360 302
b Othe r m a c ke re ls 8 20 13 19 16 14 22 24 34 37 48 54
19 S e e r f is he s
a S . c o m m e rs o ni 33 38 42 53 54 55 44 46 51 61 106 105
b S . g ut t a t us 30 36 40 49 49 50 41 43 47 56 98 99
c S . line o la t us
d A c a nt ho c yb ium
2 0 Tunnie s
a E. a f f in is
b A uxis s p p .
c K. p e la m is 6 6 9 26 30 35 32 20 18 50 110 106
d T . t o ng g o l
e Othe r tunnie s 1 2 3 6 7 8 8 5 5 12 27 28
2 1 B ill f is he s 1 1 1 1 1 1 1 1 1 23 98 96
2 2 B a rra c uda s 26 22 43 65 69 72 49 24 46 29 136 133
2 3 M ulle ts 110 110 132 165 153 142 134 81 128 172 264 275
2 4 Unic o rn c o d
2 5 F la t f is he s
a Ha libut
b F lo unde rs
c S o le s
2 6 C rus ta c e a ns
a P e na e id pra wns 42 37 51 57 66 75 62 27 65 73 217 217
b N o n-P e na e id 1 1 1 1 1 1 1 1 1 1 1 2
c Lo bs te rs 1 1 1 1 1 1 1 1 2 3 10 9
d C ra bs 1 1 1 1 1 1 1 15 29 10 32 32
e S to m a to po ds
2 7 M o llus c s
2 8 C e pha lo po ds 1 1 1 1 1 1 1 1 1 1 1 1
To ta l 938 1126 1361 1563 1659 1756 1839 1899 3936 3944 6351 6719
300
S pe c ie s 19 8 6 19 8 7 19 8 8 19 8 9 19 9 0 19 9 1 19 9 2 19 9 3 19 9 4 19 9 5 19 9 6 19 9 7
1 Ela s m o bra nc hs
a S ha rks 327 320 307 289 268 252 515 739 963 829 918 733
b S ka te s 41 39 37 34 31 28 57 82 107 91 101 82
c R a ys 59 87 116 146 175 213 236 267 298 325 349 359
2 Ee ls 1 1 1 1 1 1 1 1 1 1 1 1
3 C a tf is he s 187 242 299 354 410 482 516 728 939 830 778 715
4 C lupe ids
a Wo lf he rring 84 127 171 216 262 309 268 307 346 316 313 283
b Oil s a rdine
c Othe r s a rdine s 998 1231 1457 1681 1902 2121 2289 2534 2780 2913 3057 3093
d Hils a s ha d 206 362 529 701 879 1058 2684 1775 866 1438 1463 1314
e Othe r s ha ds 20 21 22 23 24 24 24 25 25 25 24 23
f A nc ho v ie s
i A nc ho v ie lla 348 415 479 541 601 661 785 941 1098 967 972 912
ii T hris s o c le s 157 174 188 201 212 222 227 239 251 251 254 249
g Othe r c lupe ids 1688 2117 2540 2960 3377 3792 2826 2893 2960 2815 2799 2657
5 B o m ba y duc k
6 Liza rd f is he s
7 Ha lf a nd F ull 121 184 251 318 387 457 371 356 340 311 312 324
8 F lying f is he s 7 7 8 8 8 9 9 9 9 9 9 8
9 P e rc he s 1328 1684 2039 2393 2747 3209 3564 3304 3044 2739 2710 2548
a R o c k c o ds
b S na ppe rs
c P ig fa c e bre a m s
d Thre a df in
e Othe r pe rc he s
10 Go a tf is he s 121 141 162 181 200 225 221 223 226 225 222 212
11 Thre a df ins 70 97 124 152 179 216 236 311 387 294 303 277
12 C ro a ke rs 211 411 621 838 1059 1330 1512 922 332 356 351 329
13 R ibbo n f is he s 116 151 188 224 260 297 326 367 408 431 457 467
14 C a ra ng ids
a Ho rs e m a c ke re l 93 100 106 110 113 171 173 172 171 152 144 123
b S c a ds
c Le a the r- ja c ke tsT ra c hyno t us
d Othe r C a ra ng ids 402 468 529 586 642 1033 1104 1157 1210 1132 1124 1000C o ryp ha e na
Ela c a t e
15 S ilv e rbe llie sLe io g na t hus 637 826 1017 1208 1400 1647 2031 2037 2043 1985 1972 1859G a z z a
16 B ig ja we d jum pe r
17 P o m fre ts
a B la c k po m fre t 50 97 145 197 249 76 94 104 114 108 124 103
b S ilv e r po m fre t 179 338 506 680 857 259 323 358 394 373 422 353
c C hine s e
18 M a c ke re l
a India n m a c ke re l 472 649 830 1014 1199 1386 1670 1583 1495 1375 1384 1190
b Othe r m a c ke re ls 61 65 69 71 73 75 74 76 78 77 76 72
19 S e e r f is he s
a S . c o m m e rs o ni 127 145 161 176 190 203 230 290 349 321 361 315
b S . g ut t a t us 117 134 149 163 176 188 212 267 322 296 334 291
c S . line o la t us
d A c a nt ho c yb ium
2 0 Tunnie s
a E. a f f in is
b A uxis s p p .
c K. p e la m is 120 129 136 141 144 148 147 151 154 152 150 143
d T . t o ng g o l
e Othe r tunnie s 64 103 145 187 231 275 417 663 908 856 854 807
2 1 B ill f is he s 107 112 115 117 117 117 114 113 113 108 105 97
2 2 B a rra c uda s 196 262 327 393 460 527 619 751 883 806 787 670
2 3 M ulle ts 397 520 644 769 895 1055 1257 1763 2269 2293 2333 2091
2 4 Unic o rn c o d
2 5 F la t f is he s
a Ha libut
b F lo unde rs
c S o le s
2 6 C rus ta c e a ns
a P e na e id pra wns 240 255 268 277 284 300 317 333 349 360 367 361
b N o n-P e na e id 38 79 121 165 212 266 304 351 398 379 347 536
c Lo bs te rs 12 14 15 16 17 20 21 22 24 25 26 25
d C ra bs 46 63 78 93 110 130 138 252 366 477 582 659
e S to m a to po ds
2 7 M o llus c s
2 8 C e pha lo po ds 1 1 1 1 1 1 1 105 208 198 194 192
To ta l 9448 12173 14901 17624 20352 22786 25912 26570 27229 26642 27082 25473
301
S pe c ie s 19 9 8 19 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5
1 Ela s m o bra nc hs
a S ha rks 1125 651 1086 1086 1086 1086 1086 1086
b S ka te s 126 72 271 271 271 271 271 271
c R a ys 420 357 377 377 377 377 377 377
2 Ee ls 1 1 1 1 1 1 1 1
3 C a tf is he s 623 446 581 581 581 581 581 581
4 C lupe ids
a Wo lf he rring 304 253 269 269 269 269 269 269
b Oil s a rdine
c Othe r s a rdine s 3505 3889 3026 3026 3026 3026 3026 3026
d Hils a s ha d 651 355 329 329 329 329 329 329
e Othe r s ha ds 25 21 22 22 22 22 22 22
f A nc ho v ie s
i A nc ho v ie lla 686 1011 963 963 963 963 963 963
ii T hris s o c le s 272 232 584 584 584 584 584 584
g Othe r c lupe ids 2851 2372 2528 2528 2528 2528 2528 2528
5 B o m ba y duc k
6 Liza rd f is he s
7 Ha lf a nd F ull 282 179 1035 1035 1035 1035 1035 1035
8 F lying f is he s 9 7 8 8 8 8 8 8
9 P e rc he s 2141 3854 6419 6419 6419 6419 6419 6419
a R o c k c o ds
b S na ppe rs
c P ig fa c e bre a m s
d Thre a df in
e Othe r pe rc he s
10 Go a tf is he s 231 184 182 182 182 182 182 182
11 Thre a df ins 290 494 490 490 490 490 490 490
12 C ro a ke rs 399 94 71 71 71 71 71 71
13 R ibbo n f is he s 533 453 523 523 523 523 523 523
14 C a ra ng ids
a Ho rs e m a c ke re l 120 100 106 106 106 106 106 106
b S c a ds
c Le a the r- ja c ke tsT ra c hyno t us
d Othe r C a ra ng ids 1017 1670 797 797 797 797 797 797C o ryp ha e na
Ela c a t e
15 S ilv e rbe llie sLe io g na t hus 1575 1261 1773 1773 1773 1773 1773 1773G a z z a
16 B ig ja we d jum pe r
17 P o m fre ts
a B la c k po m fre t 682 573 568 568 568 568 568 568
b S ilv e r po m fre t 1151 1523 2114 2114 2114 2114 2114 2114
c C hine s e
18 M a c ke re l
a India n m a c ke re l 970 901 1535 1535 1535 1535 1535 1535
b Othe r m a c ke re ls 78 65 69 69 69 69 69 69
19 S e e r f is he s
a S . c o m m e rs o ni 410 452 498 498 498 498 498 498
b S . g ut t a t us 378 418 460 460 460 460 460 460
c S . line o la t us
d A c a nt ho c yb ium
2 0 Tunnie s
a E. a f f in is
b A uxis s p p .
c K. p e la m is 154 128 136 136 136 136 136 136
d T . t o ng g o l
e Othe r tunnie s 3413 1012 370 370 370 370 370 370
2 1 B ill f is he s 101 99 288 288 288 288 288 288
2 2 B a rra c uda s 912 758 488 488 488 488 488 488
2 3 M ulle ts 1823 1324 1614 1614 1614 1614 1614 1614
2 4 Unic o rn c o d
2 5 F la t f is he s
a Ha libut
b F lo unde rs
c S o le s
2 6 C rus ta c e a ns
a P e na e id pra wns 406 333 339 339 339 339 339 339
b N o n-P e na e id 868 902 60 60 60 60 60 60
c Lo bs te rs 29 24 25 25 25 25 25 25
d C ra bs 835 639 840 840 840 840 840 840
e S to m a to po ds
2 7 M o llus c s
2 8 C e pha lo po ds 169 101 100 100 100 100 100 100
To ta l 29563 27207 30946 30946 30946 30946 30946 30946
302
Appendix D.1. Marine fishing effort (hp days) for Gujarat, 1950-2005 (except industrial trawlers). Values
in bold represent interpolated and extrapolated data. An additional row for the year 2005 shows the data
that were not included in the analysis (details in Chapter 2; contd.)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
Fis
hin
g
effo
rt (
hp
da
ys)
1950 734479 2470 2470 7 0.18 236 734479
1951 756692 2545 2545 7 0.18 236 756692
1952 778905 2619 2619 7 0.18 236 778905 0 0 0 0
1953 801778 2695 2694 7 0.18 236 801118 1 3 220 660
1954 826122 2773 2769 7 0.18 236 823330 4 3 220 2792
1955 850694 2851 2844 7 0.18 236 845543 7 3 220 5151 0 0
1956 886277 2928 2918 7 0.18 236 867756 7 4 220 5417 3 13104
1957 1182379 3079 2993 7 0.18 236 889969 23 4 220 18999 63 273411
1958 1472873 3230 3068 7 0.18 236 912182 42 4 220 35481 120 525210
1959 1764605 3380 3142 7 0.18 236 934394 60 4 220 53395 178 776816
1960 2314786 3531 3217 7 0.18 236 956607 79 4 220 72715 235 1285464
1961 3790236 3600 2960 7 0.18 236 880186 159 4 220 153332 481 2756719
1962 4537380 3757 3000 7 0.18 236 892080 187 5 220 187719 570 3457581
1963 6834280 4104 3125 7 0.18 236 929250 242 5 220 251210 737 5653820
1964 7917209 4177 3125 7 0.18 236 929250 259 5 220 279161 793 6708798
1965 9069807 4357 3210 7 0.18 236 954526 282 5 220 314531 865 7800750
1966 9877397 4455 3256 7 0.18 236 968204 294 5 220 339492 905 8569701
1967 10711364 4538 3281 7 0.18 236 975638 308 5 220 367195 949 9368531
1968 11783991 4708 3366 7 0.18 236 999783 329 6 220 404117 1013 10380092
1969 11279422 4629 3393 7 0.18 235 1006662 303 6 220 383368 933 9889391
1970 11848448 4945 3691 7 0.18 235 1093835 307 6 220 400313 947 10354300
1971 12039930 5053 3818 7 0.18 235 1130189 302 6 220 405460 933 10504281
1972 13802570 5269 3876 7 0.18 235 1145908 340 6 220 469998 1053 12186664
1973 15968660 5684 4103 7 0.18 234 1211796 386 6 220 547824 1195 14209040
1974 18670861 6100 4286 7 0.18 234 1264404 443 7 220 645093 1371 16761363
1975 23881522 6604 4307 7 0.18 234 1269152 561 7 220 837812 1736 21774557
1976 25404023 7059 4676 7 0.18 234 1376315 581 7 220 890936 1802 23136771
1977 27677219 7405 4860 7 0.18 233 1428693 621 7 220 974755 1924 25273771
1978 33491551 8693 5673 7 0.18 233 1665956 736 7 220 1184290 2284 30641305
1979 37921933 9522 6165 7 0.18 233 1808367 818 7 220 1347147 2539 34766419
1980 39719977 9482 6023 7 0.18 233 1764691 843 8 220 1419728 2616 36535558
1981 40798631 9802 6195 7 0.18 232 1813004 973 8 220 1676315 2634 37309312
1982 44949566 11014 6998 7 0.18 232 2045655 1174 8 220 2066662 2842 40837249
1983 47990645 11774 7529 7 0.18 232 2200877 1324 8 220 2330839 2921 43458928
1984 51942966 12306 7749 7 0.18 232 2265188 1500 8 220 2640038 3057 47037740
1985 55904833 12876 8018 7 0.18 232 2343822 1673 8 220 2944480 3185 50616532
1986 62547600 13811 8498 7 0.18 232 2484135 1854 8 220 3263040 3459 56800425
1987 69263667 14680 8965 7 0.18 232 2620649 1776 8 220 3125760 3939 63517258
1988 87953779 15969 8966 7 0.18 232 2620941 1806 8 220 3178560 5197 82154278
1989 96448041 16384 8735 7 0.18 232 2553415 1822 8 233 3506097 5827 90388528
1990 105167606 16817 8677 7 0.18 232 2536461 1838 9 247 3851070 6302 98780076
1991 119202226 17849 8825 7 0.18 232 2579724 1862 9 260 4231977 7162 112390525
1992 134383082 18515 8745 7 0.18 232 2556338 1857 9 273 4562649 7913 127264095
1993 162144872 19836 8323 7 0.18 232 2432979 1813 9 273 4578278 9700 155133614
1994 183102817 21018 8370 7 0.18 232 2446718 1814 10 273 4704609 10834 175951490
1995 213321023 22663 8646 7 0.18 232 2527399 1817 10 273 4836400 12200 205957225
1996 228024599 23522 8851 7 0.18 232 2587324 1827 10 273 4987710 12844 220449564
1997 244690448 24616 8918 7 0.18 232 2606910 1854 10 273 5187956 13844 236895583
1998 264815321 25985 9222 7 0.18 232 2695775 1899 11 273 5443484 14864 256676063
1999 273504148 26275 8819 7 0.18 232 2577970 1895 11 273 5561351 15561 265364826
2000 285822143 28706 10170 7 0.18 232 2972894 1813 11 273 5444439 16723 277404810
2001 293676381 29506 10414 7 0.18 232 3044220 1807 11 273 5426421 17285 285205740
2002 301670776 30098 10430 7 0.18 232 3048898 1805 11 273 5420415 17863 293201464
2003 311392713 33213 12365 7 0.18 232 3614537 2476 11 273 7435428 18372 300342748
2004 311392713 33213 12365 7 0.18 232 3614537 2476 11 273 7435428 18372 300342748
2005 311392713 33213 12365 7 0.18 232 3614537 2476 11 273 7435428 18372 300342748
2005 330348642 24152 3729 7 0.18 232 1090061 7380 11 273 22162140 13043 307096441
Year
Total
fishing
effort
(hp days)
Total
number
of
vessels
Vessels without engines Vessels with engines
To
tal
#
Cre
w s
ize
Av
era
ge
ma
le
ener
gy
ou
tpu
t
(hp
/da
y)
Fis
hin
g d
ay
s
Fis
hin
g e
ffo
rt
(hp
da
ys)
O utboard engines Inboard engines
303
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
1950
1951
1952
1953
1954
1955 0 0 0 0
1956 3 16 273 13104
1957 63 16 273 273411
1958 120 16 273 525210
1959 178 16 273 776816
1960 235 20 273 1285464
1961 0 0 0 0 481 21 273 2756719
1962 3 48 205 29520 567 22 273 3428061
1963 263 49 205 2663432 474 23 273 2990388
1964 388 51 205 4039988 405 24 273 2668809
1965 484 52 205 5183915 381 25 273 2616835
1966 546 54 205 6004379 358 26 273 2565322
1967 602 55 205 6782851 347 27 273 2585680
1968 665 56 205 7683417 349 28 273 2696674
1969 628 58 205 7436963 306 29 273 2452428
1970 649 59 205 7878922 298 30 273 2475377
1971 649 61 205 8065770 284 31 273 2438511
1972 742 62 205 9424518 311 33 273 2762146
1973 850 63 205 11051488 345 34 273 3157552 0 0 0 0
1974 966 65 205 12837446 380 35 273 3591192 25 54 249 332726
1975 1214 66 205 16474821 465 36 273 4527950 57 54 249 771787
1976 1251 68 205 17335105 468 37 273 4693784 82 54 249 1107882
1977 1328 69 205 18786985 487 38 273 5022724 109 54 249 1464063
1978 1568 70 205 22630522 565 39 273 5984921 151 54 249 2025862
1979 1735 72 205 25542023 615 40 273 6692070 188 54 249 2532326
1980 1781 73 205 26725686 622 41 273 6945874 213 54 249 2863998
1981 1745 75 205 26683889 665 42 273 7610730 224 54 249 3014693
1982 1835 76 205 28589487 755 43 273 8864250 252 54 249 3383512
1983 1841 79 205 29880005 812 45 273 9977670 268 54 249 3601253
1984 1883 82 205 31791933 885 47 273 11356493 289 54 249 3889314
1985 1919 86 205 33659860 956 49 273 12788412 310 54 249 4168260
1986 2062 89 205 37515513 1050 51 273 14619150 347 54 249 4665762 0 0 0 0
1987 2230 92 205 42029228 1124 53 273 16263156 359 54 249 4827114 226 8 220 397760
1988 2522 95 205 49180576 1756 55 273 26366340 427 54 249 5741442 492 8 220 865920
1989 2712 98 205 54657818 1770 57 273 27542970 486 54 249 6534756 859 8 233 1652984
1990 2814 102 205 58552305 1946 59 273 31344222 498 54 249 6696108 1044 9 247 2187441
1991 3055 105 205 65563164 2211 61 273 36819783 510 54 249 6857460 1386 9 260 3150118
1992 3456 108 205 76427280 2315 63 273 39815685 524 54 249 7045704 1618 9 273 3975426
1993 3951 111 205 89955627 3058 63 273 52594542 530 54 249 7126380 2161 9 273 5457065
1994 4634 114 205 108534073 3110 63 273 53488890 545 54 249 7328070 2545 10 273 6600458
1995 5685 117 205 136864598 3143 63 273 54056457 562 54 249 7556652 2810 10 273 7479518
1996 6027 121 205 149036409 3205 63 273 55122795 600 54 249 8067600 3012 10 273 8222760
1997 6390 124 205 162188184 3275 63 273 56326725 628 54 249 8444088 3551 10 273 9936586
1998 6749 127 205 175710215 3482 63 273 59886918 635 61 249 9618663 3998 11 273 11460267
1999 6787 127 205 176699545 3764 63 273 64737036 663 68 249 11170887 4347 11 273 12757358
2000 6948 127 205 180891180 3375 63 273 58046625 1238 75 249 22965519 5162 11 273 15501486
2001 7029 127 205 183000015 3007 63 273 51717393 1665 81 249 33719580 5584 11 273 16768752
2002 7163 127 205 186488705 3031 63 273 52130169 1665 88 249 36552578 6004 11 273 18030012
2003 7402 127 205 192711070 3082 63 273 53007318 1498 95 249 35435190 6390 11 273 19189170
2004 7402 127 205 192711070 3082 63 273 53007318 1498 95 249 35435190 6390 11 273 19189170
2005 7402 127 205 192711070 3082 63 273 53007318 1498 95 249 35435190 6390 11 273 19189170
2005 8002 127 205 208332070 2363 63 273 40641237 2425 95 249 57363375 253 11 273 759759
Gillnetters
Year
Dolnetters O thers
Vessels with engines
Trawlers
304
Appendix D.2. Marine fishing effort (hp days) for Daman and Diu, 1950-2005 (except industrial
trawlers). Values in bold represent interpolated and extrapolated data. An additional row for the year 2005
shows the data that were not included in the analysis and total effort includes the effort of sub-categories,
'other vessels with engines' and 'dolnetters' (details in Chapter 2; contd.)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
1950 70082 473 473 4 0.18 206 70082
1951 72202 487 487 4 0.18 206 72202
1952 74321 501 501 4 0.18 206 74321
1953 76440 515 515 4 0.18 206 76440
1954 78560 530 530 4 0.18 206 78560
1955 80679 544 544 4 0.18 206 80679
1956 82798 558 558 4 0.18 206 82798
1957 84917 573 573 4 0.18 206 84917
1958 87037 587 587 4 0.18 206 87037
1959 89156 601 601 4 0.18 206 89156 0 0
1960 117939 618 615 4 0.18 206 91275 3 26664
1961 144603 621 615 4 0.18 206 91275 6 53328
1962 169977 615 607 4 0.18 206 89985 8 79992
1963 195881 609 598 4 0.18 206 88694 11 107187
1964 279223 609 589 4 0.18 206 87403 20 191820
1965 396941 613 581 4 0.18 206 86112 32 310828
1966 604821 625 572 4 0.18 206 84822 53 519999
1967 606042 616 563 4 0.18 206 83531 53 522511
1968 757050 622 554 4 0.18 206 82240 68 674810
1969 884538 623 546 4 0.18 207 84270 77 800268
1970 1010287 623 537 4 0.18 207 86215 86 924072
1971 1134319 608 513 4 0.18 208 85466 95 1048852
1972 1260165 592 488 5 0.18 209 84437 104 1175728
1973 1388393 577 464 5 0.18 210 83123 113 1305270
1974 1512531 562 440 5 0.18 210 81522 122 1431009
1975 1646483 546 415 5 0.18 211 79631 131 1566852
1976 1783457 531 391 5 0.18 212 77448 140 1706009
1977 1937612 580 431 5 0.18 212 88116 149 1849496
1978 2804132 645 431 6 0.18 213 90885 214 2713247
1979 3679854 710 431 6 0.18 214 93638 279 3586216
1980 4529154 775 431 6 0.18 214 96405 0 344 4432749
1981 4891377 804 418 6 0.18 215 96218 18 10 120 21000 368 4774160
1982 5269414 834 405 6 0.18 216 95875 37 10 120 42000 393 5131539
1983 5641540 864 392 6 0.18 217 95375 55 10 120 63000 417 5483165
1984 6108137 893 379 6 0.18 217 94630 74 10 120 84000 441 5929506
1985 6586602 923 366 7 0.18 217 93512 92 10 120 105000 465 6388090
1986 7069397 954 354 7 0.18 217 92531 111 10 120 126000 490 6850866
1987 7547521 984 342 7 0.18 217 91409 129 10 120 147000 514 7309112
1988 8031316 1020 334 7 0.18 217 91368 147 10 120 168000 538 7771948
1989 8529474 1055 327 7 0.18 217 89326 166 10 120 189000 562 8251148
1990 8921419 1090 319 7 0.18 217 87283 184 10 120 210000 587 8624136
1991 9299082 1125 312 7 0.18 217 85241 203 10 120 231000 611 8982842
1992 9466977 1160 304 7 0.18 217 83199 221 10 120 252000 635 9131778
1993 9604987 1196 297 7 0.18 217 81156 239 10 120 273000 659 9250831
1994 9734888 1231 289 7 0.18 217 79114 258 10 120 294000 684 9361775
1995 9830588 1266 282 7 0.18 217 77071 276 10 120 315000 708 9438517
1996 9905652 1301 274 7 0.18 217 75029 295 10 120 336000 732 9494623
1997 9960080 1337 267 7 0.18 217 72987 313 10 120 357000 756 9530093
1998 10004572 1372 259 7 0.18 217 70944 332 10 120 378000 781 9555628
1999 10303763 1407 252 7 0.18 217 68902 350 10 120 399000 805 9835861
2000 10303763 1407 252 7 0.18 217 68902 350 10 120 399000 805 9835861
2001 10303763 1407 252 7 0.18 217 68902 350 10 120 399000 805 9835861
2002 10303763 1407 252 7 0.18 217 68902 350 10 120 399000 805 9835861
2003 10303763 1407 252 7 0.18 217 68902 350 10 120 399000 805 9835861
2004 10303763 1407 252 7 0.18 217 68902 350 10 120 399000 805 9835861
2005 10303763 1407 252 7 0.18 217 68902 350 10 120 399000 805 9835861
2005 6877992 1427 211 7 0.18 217 57692 654 10 120 745560 562 6074740
To
tal
#
Cre
w s
ize
Av
era
ge
ma
le
ener
gy
ou
tpu
t
(hp
/da
y)
Fis
hin
g d
ay
s
Year
Total fishing
effort
(hp days)
Total
number
of
vessels
Vessels without engines Vessels with engines
Fis
hin
g e
ffo
rt
(hp
da
ys)
O utboard engines Inboard engines
305
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959 0 0 0 0
1960 3 48 202 26664
1961 6 48 202 53328
1962 8 48 202 79992
1963 11 48 202 107187 0 0 0 0
1964 19 48 202 186060 1 48 120 5760
1965 31 49 202 305068 1 48 120 5760
1966 52 49 202 514239 1 48 120 5760
1967 52 49 202 516751 1 48 120 5760
1968 67 49 202 669050 0 0 0 0 1 48 120 5760
1969 64 50 208 667508 12 54 203 127000 1 48 120 5760
1970 64 50 215 691700 21 54 204 226612 1 48 120 5760
1971 66 50 221 729998 29 54 205 313094 1 48 120 5760
1972 68 50 228 777557 36 54 206 392411 1 48 120 5760
1973 70 51 234 832009 42 54 207 467500 1 48 120 5760
1974 73 51 241 887836 48 54 208 537413 1 48 120 5760
1975 75 51 247 952901 54 54 209 608191 1 48 120 5760
1976 78 51 254 1022315 60 54 210 677935 1 48 120 5760
1977 82 52 260 1095794 66 54 211 747042 1 53 125 6659
1978 116 52 267 1602425 97 54 212 1103208 1 58 131 7614
1979 148 52 273 2103643 130 54 213 1473951 1 63 136 8622
1980 176 52 273 2514654 167 54 214 1908409 1 69 141 9686
1981 191 53 273 2743120 175 54 215 2009431 2 74 147 21609
1982 207 53 274 2988261 184 54 216 2119323 2 79 152 23955
1983 222 53 274 3221283 193 54 217 2235471 2 84 157 26411
1984 238 53 274 3471135 201 54 226 2429395 2 89 163 28976
1985 255 54 274 3738045 208 54 235 2618394 2 94 168 31651
1986 271 54 274 3975005 217 54 245 2841425 2 99 173 34436
1987 287 54 274 4212251 225 54 254 3059531 2 104 179 37329
1988 305 54 275 4479154 231 54 264 3252461 2 110 184 40333
1989 321 54 275 4716988 239 54 273 3490715 2 115 189 43446
1990 339 54 275 4984515 246 54 273 3592953 2 120 195 46668
1991 356 54 275 5237650 253 54 273 3695192 2 125 200 50000
1992 373 54 264 5273954 259 54 273 3782825 3 125 200 75000
1993 390 54 254 5290768 266 54 273 3885063 3 125 200 75000
1994 409 54 243 5314079 272 54 273 3972696 3 125 200 75000
1995 427 54 232 5303188 278 54 273 4060329 3 125 200 75000
1996 445 54 221 5271661 284 54 273 4147962 3 125 200 75000
1997 463 54 211 5219498 290 54 273 4235595 3 125 200 75000
1998 482 54 200 5157400 296 54 273 4323228 3 125 200 75000
1999 500 54 200 5350000 302 54 273 4410861 3 125 200 75000
2000 500 54 200 5350000 302 54 273 4410861 3 125 200 75000
2001 500 54 200 5350000 302 54 273 4410861 3 125 200 75000
2002 500 54 200 5350000 302 54 273 4410861 3 125 200 75000
2003 500 54 200 5350000 302 54 273 4410861 3 125 200 75000
2004 500 54 200 5350000 302 54 273 4410861 3 125 200 75000
2005 500 54 200 5350000 302 54 273 4410861 3 125 200 75000
2005 315 54 202 3404205 170 54 273 2482935 4 125 200 100000
Trawlers Gillnetters
Year
Vessels with engines
Liners Purse seiners
306
Appendix D.3. Marine fishing effort (hp days) for Goa, 1950-2005 (except industrial trawlers). Values in
bold represent interpolated and extrapolated data. An additional row for the year 2005 shows the data that
were not included in the analysis and total effort includes the effort of sub-category, 'other vessels with
engines' (details in Chapter 2; contd.)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
1950 399681 2695 2695 4 0.18 206 399681
1951 411767 2776 2776 4 0.18 206 411767
1952 423854 2858 2858 4 0.18 206 423854
1953 435940 2939 2939 4 0.18 206 435940
1954 448027 3021 3021 4 0.18 206 448027
1955 460113 3102 3102 4 0.18 206 460113
1956 472199 3184 3184 4 0.18 206 472199
1957 484286 3265 3265 4 0.18 206 484286
1958 496372 3347 3347 4 0.18 206 496372
1959 508458 3428 3428 4 0.18 206 508458 0 0
1960 559329 3514 3510 4 0.18 206 520545 4 38784
1961 559329 3514 3510 4 0.18 206 520545 4 38784
1962 566511 3465 3460 4 0.18 206 513183 6 53328
1963 576758 3417 3410 4 0.18 206 505822 7 70936
1964 583980 3370 3361 4 0.18 206 498461 9 85519
1965 629990 3326 3311 4 0.18 206 491100 15 138890
1966 652995 3279 3261 4 0.18 206 483738 18 169257
1967 649931 3230 3212 4 0.18 206 476377 18 173554
1968 775877 3193 3162 4 0.18 206 469016 31 306861
1969 1187386 3183 3113 4 0.18 207 480594 71 706792
1970 1663492 3173 3063 4 0.18 207 491685 110 1171808
1971 1935487 3053 2924 4 0.18 208 487417 129 1448070
1972 2216731 2933 2785 5 0.18 209 481544 147 1735187
1973 2510888 2813 2647 5 0.18 210 474051 166 2036838
1974 2817759 2693 2508 5 0.18 210 464921 185 2352838
1975 3137219 2572 2369 5 0.18 211 454139 204 2683080
1976 3469193 2452 2230 5 0.18 212 441689 222 3027505
1977 3923733 2699 2458 5 0.18 212 502526 241 3421208
1978 4442333 2724 2458 6 0.18 213 518318 266 3924015
1979 4983899 2748 2457 6 0.18 214 534019 0 0 0 0 291 4449880
1980 5535214 2813 2456 6 0.18 214 549802 40 10 120 45980 316 4939432
1981 6495761 2835 2382 6 0.18 215 548732 81 10 120 91960 373 5855070
1982 7496223 2858 2308 6 0.18 216 546776 121 10 120 137940 430 6811506
1983 8542334 2881 2233 6 0.18 217 543927 161 10 120 183920 486 7814487
1984 9669344 2904 2159 6 0.18 217 539679 202 10 120 229900 543 8899765
1985 10852016 2926 2084 7 0.18 217 533303 242 10 120 275880 600 10042833
1986 11490935 2920 2016 7 0.18 217 527703 282 10 120 321860 621 10641372
1987 12164982 2914 1948 7 0.18 217 521306 323 10 120 367840 643 11275836
1988 12902686 3009 1982 7 0.18 217 541985 363 10 120 413820 664 11946881
1989 13666347 3105 2016 7 0.18 217 551248 403 10 120 459800 686 12655299
1990 14436987 3201 2050 7 0.18 217 560511 444 10 120 505780 707 13370696
1991 15382206 3256 2036 7 0.18 217 556683 484 10 120 551760 736 14273763
1992 15658978 3310 2022 7 0.18 217 552855 524 10 120 597740 764 14508383
1993 16252915 3380 2008 7 0.18 217 549027 565 10 120 643720 807 15060168
1994 16810628 3449 1994 7 0.18 217 545199 605 10 120 689700 850 15575729
1995 16982978 3505 1980 7 0.18 217 541372 645 10 120 735680 880 15705927
1996 18103911 3564 1910 7 0.18 217 522232 686 10 120 781660 968 16800018
1997 19122018 3622 1840 7 0.18 217 503093 726 10 120 827640 1056 17791285
1998 19217348 3748 1890 7 0.18 217 516764 766 10 120 873620 1092 17826965
1999 19326464 4093 2194 7 0.18 217 599883 807 10 120 919600 1092 17806981
2000 19882620 3938 1963 7 0.18 217 536723 847 10 120 965580 1128 18380316
2001 19921377 3978 1963 7 0.18 217 536723 887 10 120 1011560 1128 18373094
2002 20008970 4022 1963 7 0.18 217 536723 928 10 120 1057540 1131 18414706
2003 20102504 4065 1963 7 0.18 217 536723 968 10 120 1103520 1134 18462260
2004 20102504 4065 1963 7 0.18 217 536723 968 10 120 1103520 1134 18462260
2005 20102504 4065 1963 7 0.18 217 536723 968 10 120 1103520 1134 18462260
2005 18935166 2565 532 7 0.18 217 145459 932 10 120 1062480 1101 17727226
To
tal
#
Cre
w s
ize
Av
era
ge
ma
le
ener
gy
ou
tpu
t
(hp
/da
y)
Fis
hin
g d
ay
s
Year
Total fishing
effort
(hp days)
Total
number
of
vessels
Vessels without engines Vessels with engines
Fis
hin
g e
ffo
rt
(hp
da
ys)
O utboard engines Inboard engines
307
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959 0 0 0 0
1960 4 48 202 38784
1961 4 48 202 38784
1962 6 48 202 53328
1963 7 50 202 70936 0 0 0 0
1964 7 52 202 73999 2 48 120 11520
1965 10 55 202 110090 5 48 120 28800
1966 12 57 202 132000 6 48 120 37257
1967 11 59 202 135591 7 48 120 37963
1968 20 61 202 240920 0 0 0 0 11 48 120 65940
1969 41 61 208 518615 2 61 203 31042 27 48 120 157135
1970 67 61 215 875859 7 62 204 84908 37 48 120 211041
1971 81 61 221 1102216 10 62 205 132541 37 48 120 213313
1972 96 61 228 1334760 14 63 206 183234 38 48 120 217193
1973 110 61 234 1580586 18 63 207 236252 38 48 120 220000
1974 124 62 241 1839492 22 64 208 291331 39 48 120 222014
1975 139 62 247 2111356 26 64 209 348297 39 48 120 223426
1976 153 62 254 2396106 30 64 210 407030 39 48 120 224369
1977 168 62 260 2693701 34 65 211 467444 39 53 125 260063
1978 187 62 267 3076832 39 65 212 542296 40 58 131 304886
1979 206 62 273 3477613 44 66 213 619877 41 63 136 352390
1980 231 62 273 3912293 46 66 214 649383 39 69 141 377756
1981 269 62 273 4566029 49 67 215 692851 55 74 147 596190
1982 307 62 274 5217318 51 67 216 730141 72 79 152 864047
1983 344 62 274 5867437 52 67 217 762883 90 84 157 1184167
1984 382 62 274 6517255 54 68 226 822964 108 89 163 1559546
1985 419 62 274 7167382 55 68 235 882171 126 94 168 1993280
1986 432 63 274 7396877 53 69 245 890093 137 99 173 2354402
1987 444 63 274 7629853 51 69 254 899949 147 104 179 2746035
1988 457 63 275 7865845 50 69 264 911424 157 110 184 3169612
1989 470 63 275 8104498 48 70 273 924279 167 115 189 3626523
1990 483 63 275 8345530 47 70 273 907035 176 120 195 4118131
1991 502 63 275 8678334 47 71 273 901182 188 125 200 4694247
1992 519 63 264 8633939 47 71 273 913395 198 125 200 4961048
1993 546 64 254 8803522 47 72 273 925435 213 125 200 5331211
1994 574 64 243 8936210 48 72 273 936964 228 125 200 5702555
1995 604 65 232 9064445 48 72 273 951546 228 125 200 5689936
1996 675 65 221 9737626 52 73 273 1027349 241 125 200 6035043
1997 747 66 211 10339667 55 73 273 1100632 254 125 200 6350987
1998 783 66 200 10370672 56 74 273 1118315 254 125 200 6337978
1999 793 67 200 10588794 54 74 273 1099372 245 125 200 6118815
2000 829 67 200 11159268 55 75 273 1116911 244 125 200 6104137
2001 838 68 200 11376996 54 75 273 1099014 236 125 200 5897084
2002 850 68 200 11622118 53 75 273 1078650 229 125 200 5713938
2003 861 69 200 11865143 52 75 273 1059129 222 125 200 5537989
2004 861 69 200 11865143 52 75 273 1059129 222 125 200 5537989
2005 861 69 200 11865143 52 75 273 1059129 222 125 200 5537989
2005 841 70 200 11771612 48 75 273 974881 199 125 200 4963933
Trawlers Gillnetters
Year
Vessels with engines
Liners Purse seiners
308
Appendix D.4. Marine fishing effort (hp days) for Maharashtra, 1950-2005 (except industrial trawlers).
Values in bold represent interpolated and extrapolated data. An additional row for the year 2005 shows
the data that were not included in the analysis (details in Chapter 2; contd.)
To
tal
#
Po
wer (
hp
)
Fis
hin
g
da
ys
Fis
hin
g
eff
ort
(hp
da
ys)
To
tal
#
Po
wer (
hp
)F
ish
ing
da
ys
Fis
hin
g
eff
ort
(hp
da
ys)
1950 759547 5121 5121 4 0.18 206 759547 0 0 0 0
1951 836702 5589 5574 4 0.18 206 826802 15 3 220 9900
1952 1013351 6209 6028 4 0.18 206 894056 181 3 220 119295
1953 1190001 6828 6481 4 0.18 206 961311 347 3 220 228690
1954 1386081 7447 6935 4 0.18 206 1028566 512 3 220 357515
1955 1594736 8066 7388 4 0.18 206 1095821 678 3 220 498914 0 0
1956 2246567 8741 7842 4 0.18 206 1163076 741 4 220 573553 158 509938
1957 3215506 9415 8295 4 0.18 206 1230331 781 4 220 634269 339 1350906
1958 4139002 9932 8749 4 0.18 206 1297586 712 4 220 604818 471 2236598
1959 5194009 10448 9202 4 0.18 206 1364841 656 4 220 582399 590 3246769
1960 6208077 9857 8548 4 0.18 206 1267839 610 4 220 564820 699 4375418
1961 7574016 9318 7894 4 0.18 206 1170838 593 4 220 571540 831 5831638
1962 9617046 9380 7850 4 0.18 206 1164312 574 5 220 574682 956 7878052
1963 11372452 9411 7806 4 0.18 206 1157786 546 5 220 566945 1059 9647721
1964 13515993 9498 7762 4 0.18 206 1151260 537 5 220 578844 1198 11785889
1965 15777580 9584 7718 4 0.18 206 1144734 529 5 220 589595 1337 14043250
1966 18458156 9704 7674 4 0.18 206 1138208 528 5 220 609301 1502 16710647
1967 20583368 9744 7630 4 0.18 206 1131682 507 5 220 603975 1606 18847712
1968 22790801 9783 7586 4 0.18 206 1125156 487 6 220 598892 1710 21066754
1969 31058325 10409 7542 4 0.18 207 1164522 589 6 220 746670 2278 29147133
1970 31092284 10213 7498 4 0.18 207 1203636 518 6 220 676419 2197 29212229
1971 30989568 10017 7454 4 0.18 208 1242493 455 6 220 611504 2108 29135570
1972 32365675 10266 7410 5 0.18 209 1281088 445 6 220 614879 2411 30469708
1973 35099562 10442 7456 5 0.18 210 1335537 435 6 220 617490 2550 33146535
1974 38014877 10617 7502 5 0.18 210 1390844 425 7 220 619337 2690 36004696
1975 41127130 10792 7548 5 0.18 211 1447013 415 7 220 620420 2829 39059697
1976 44452397 10968 7594 5 0.18 212 1504049 405 7 220 620740 2969 42327608
1977 48007320 11143 7640 5 0.18 212 1561959 395 7 220 620296 3108 45825065
1978 57395505 11673 7644 6 0.18 213 1611785 385 7 220 619089 3645 55164631
1979 67239015 12203 7647 6 0.18 214 1661945 375 7 220 617117 4181 64959953
1980 76027586 12733 7651 6 0.18 214 1712439 365 8 220 614382 4718 73700765
1981 75216729 12646 7654 6 0.18 215 1763268 355 8 220 610883 4638 72842578
1982 74394443 12559 7658 6 0.18 216 1814432 345 8 220 606621 4557 71973390
1983 78259240 12769 7661 6 0.18 217 1865932 335 8 220 588903 4773 75804405
1984 86011566 12978 7665 6 0.18 217 1916002 325 8 220 571185 4989 83524379
1985 94105240 13188 7668 7 0.18 217 1961804 314 8 220 553467 5206 91589970
1986 98783723 13398 7672 7 0.18 217 2007647 304 8 220 535748 5422 96240327
1987 103559911 13607 7675 7 0.18 217 2053531 294 8 220 518030 5638 100988350
1988 111843712 14005 7679 7 0.18 217 2099455 284 8 220 500312 6043 109243944
1989 120359844 14403 7682 7 0.18 217 2100412 274 8 233 527649 6447 117731782
1990 128212355 14801 7686 7 0.18 217 2101369 264 9 247 553427 6852 125557558
1991 135607312 15199 7689 7 0.18 217 2102326 254 9 260 577447 7256 132927539
1992 140827216 16832 8927 7 0.18 217 2440820 244 9 273 599508 7661 137786887
1993 140831349 17375 9315 7 0.18 217 2546771 265 9 273 669191 7796 137615387
1994 140748276 17918 9702 7 0.18 217 2652721 286 10 273 741741 7930 137353814
1995 141421863 18212 9813 7 0.18 217 2683016 276 10 273 733312 8124 138005535
1996 142553790 18506 9924 7 0.18 217 2713311 265 10 273 723450 8318 139117030
1997 143569704 18800 10034 7 0.18 217 2743606 255 10 273 712155 8511 140113943
1998 144468656 19094 10145 7 0.18 217 2773901 244 11 273 699426 8705 140995330
1999 148159310 19441 10256 7 0.18 217 2804196 286 11 273 839339 8899 144515776
2000 148159310 19441 10256 7 0.18 217 2804196 286 11 273 839339 8899 144515776
2001 148159310 19441 10256 7 0.18 217 2804196 286 11 273 839339 8899 144515776
2002 148159310 19441 10256 7 0.18 217 2804196 286 11 273 839339 8899 144515776
2003 148159310 19441 10256 7 0.18 217 2804196 286 11 273 839339 8899 144515776
2004 148159310 19441 10256 7 0.18 217 2804196 286 11 273 839339 8899 144515776
2005 148159310 19441 10256 7 0.18 217 2804196 286 11 273 839339 8899 144515776
2005 206318026 23508 7073 7 0.18 217 1933900 3382 11 273 10156146 13053 194227980
To
tal
#
Crew
siz
e
Av
era
ge m
ale
en
erg
y o
utp
ut
(hp
/da
y)
Fis
hin
g d
ay
s
Fis
hin
g e
ffo
rt
(hp
da
ys)
O utboard engines Inboard engines
Year
Total
fishing
effort
(hp days)
Total
number
of
vessels
Vessels without engines Vessels with engines
309
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
1950
1951
1952
1953
1954
1955 0 0 0 0
1956 158 16 202 509938
1957 339 20 202 1350906
1958 471 24 202 2236598
1959 590 27 202 3246769
1960 699 31 202 4375418
1961 0 0 0 0 831 35 202 5831638
1962 231 48 202 2235217 726 39 202 5642835
1963 379 50 202 3836503 681 42 202 5811218
1964 510 52 202 5392366 688 46 202 6393523
1965 630 55 202 6931116 708 50 202 7112134
1966 755 57 202 8640823 747 54 202 8069824
1967 846 59 202 10051744 761 57 202 8795967
1968 932 61 202 11480292 778 61 202 9586462
1969 1275 63 208 16642953 1003 61 203 12504180
1970 1256 64 215 17343870 941 62 204 11868359
1971 1226 66 221 17883684 882 62 205 11251887 0 0 0 0
1972 685 68 228 10540837 476 63 206 6143835 26 30 217 169260
1973 766 69 234 12419531 480 63 207 6268201 25 30 217 163537
1974 852 71 241 14527010 482 64 208 6360402 24 30 217 156118
1975 943 72 247 16880201 481 64 209 6419296 23 30 217 147004
1976 1037 74 254 19496611 477 64 210 6443730 21 30 217 136195
1977 1137 76 260 22394324 471 65 211 6432537 19 30 217 123690
1978 1334 77 267 27503209 562 65 212 7756051 24 30 217 158623
1979 1531 79 273 33011087 655 66 213 9146025 30 30 217 197553
1980 1728 79 273 37406765 751 66 214 10603949 37 30 217 240481
1981 1699 80 273 36924816 750 67 215 10706490 39 30 217 253650
1982 1670 80 274 36437676 749 67 216 10803411 41 30 217 266220
1983 1750 80 274 38327773 797 67 217 11616599 46 30 217 295945
1984 1829 80 274 40232747 845 68 226 12945924 50 30 217 328647
1985 1909 81 274 42152632 895 68 235 14371429 56 30 217 362277
1986 1989 81 274 44087462 946 69 245 15896756 61 30 217 397517
1987 2069 81 274 46037271 998 69 254 17525601 67 30 217 434367
1988 2218 81 275 49546898 1086 69 264 19881595 75 30 217 488044 0 0 0 0
1989 2364 82 275 53008872 1173 70 273 22387251 84 30 217 543960 9 125 200 229412
1990 2510 82 275 56498185 1263 70 273 24241685 93 30 217 602822 18 125 200 458824
1991 2656 82 275 60014899 1354 71 273 26155879 102 30 217 664629 28 125 200 688235
1992 2802 82 264 61045752 1448 71 273 28130536 112 30 217 729381 37 125 200 917647
1993 2849 83 254 59742642 1491 72 273 29150370 118 30 217 770234 46 125 200 1147059
1994 2896 83 243 58347104 1535 72 273 30192894 125 30 217 812021 55 125 200 1376471
1995 2965 83 232 57279718 1592 72 273 31490024 132 30 217 861076 64 125 200 1605882
1996 3034 83 221 56082064 1650 73 273 32819031 140 30 217 911507 73 125 200 1835294
1997 3103 84 211 54752868 1708 73 273 34180241 148 30 217 963313 83 125 200 2064706
1998 3172 84 200 53290857 1767 74 273 35573983 156 30 217 1016495 92 125 200 2294118
1999 3241 84 200 54450290 1828 74 273 37000585 165 30 217 1071051 101 125 200 2523529
2000 3241 84 200 54450290 1828 74 273 37000585 165 30 217 1071051 101 125 200 2523529
2001 3241 84 200 54450290 1828 74 273 37000585 165 30 217 1071051 101 125 200 2523529
2002 3241 84 200 54450290 1828 74 273 37000585 165 30 217 1071051 101 125 200 2523529
2003 3241 84 200 54450290 1828 74 273 37000585 165 30 217 1071051 101 125 200 2523529
2004 3241 84 200 54450290 1828 74 273 37000585 165 30 217 1071051 101 125 200 2523529
2005 3241 84 200 54450290 1828 74 273 37000585 165 30 217 1071051 101 125 200 2523529
2005 4219 84 200 70879200 2550 75 273 52211250 253 30 217 1647030 156 125 200 3900000
Trawlers Gillnetters
Vessels with engines
Liners Purse seiners
Year
310
To
tal
#
Po
wer (
hp
)
Fis
hin
g
da
ys
Fis
hin
g
eff
ort
(hp
da
ys)
To
tal
#
Po
wer (
hp
)
Fis
hin
g
da
ys
Fis
hin
g
eff
ort
(hp
da
ys)
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971 0 0 0 0
1972 1224 54 206 13615776
1973 1279 54 207 14295266
1974 1332 54 208 14961166
1975 1383 54 209 15613195
1976 1433 54 210 16251071
1977 1481 54 211 16874514
1978 1725 54 212 19746749
1979 1965 54 213 22605289
1980 2202 54 214 25449570
1981 2150 54 215 24957622
1982 2098 54 216 24466083
1983 2182 54 217 25564088
1984 2264 54 246 30017061
1985 2345 54 274 34703633
1986 2425 54 274 35858593
1987 2504 54 274 36991111
1988 2664 54 273 39327407
1989 2817 54 273 41562287
1990 2968 54 273 43756043
1991 3116 54 270 45403897
1992 3262 54 267 46963570
1993 3291 54 263 46805081
1994 3318 54 260 46625325
1995 3370 54 257 46768834
1996 3420 54 257 47469134
1997 3470 54 257 48152815
1998 3518 54 257 48819877
1999 3565 54 257 49470320
2000 3565 54 257 49470320
2001 3565 54 257 49470320
2002 3565 54 257 49470320
2003 3565 54 257 49470320
2004 3565 54 257 49470320
2005 3565 54 257 49470320
2005 4409 54 257 61188102 1466 11 273 4402398
Dolnetters O thers
Vessels with engines
Year
311
Appendix D.5. Marine fishing effort (hp days) for Karnataka, 1950-2005 (except industrial trawlers).
Values in bold represent interpolated and extrapolated data. An additional row for the year 2005 shows
the data that were not included in the analysis (details in Chapter 2; contd.)
To
tal
#
Po
wer (
hp
)
Fis
hin
g
da
ys
Fis
hin
g
eff
ort
(hp
da
ys)
To
tal
#
Po
wer (
hp
)F
ish
ing
da
ys
Fis
hin
g
eff
ort
(hp
da
ys)
1950 2232558 5021 5021 13 0.18 190 2232558
1951 2307572 5190 5190 13 0.18 190 2307572
1952 2382586 5359 5359 13 0.18 190 2382586
1953 2457600 5528 5528 13 0.18 190 2457600
1954 2532614 5696 5696 13 0.18 190 25326141955 2607628 5865 5865 13 0.18 190 26076281956 2682642 6034 6034 13 0.18 190 2682642 0 0
1957 2775896 6205 6203 13 0.18 190 2757656 2 18240
1958 2914750 6380 6371 13 0.18 190 2832670 9 82080
1959 3099204 6561 6540 13 0.18 190 2907684 21 191520
1960 3304763 6497 6449 13 0.18 190 2867003 48 437760
1961 3410002 6421 6357 13 0.18 190 2826322 64 583680
1962 3542764 6427 6348 13 0.18 190 2822284 79 720480
1963 3839685 6451 6339 13 0.18 190 2818245 112 1021440
1964 4446687 6509 6330 13 0.18 190 2814207 179 1632480
1965 4871288 6547 6321 13 0.18 190 2810168 226 2061120
1966 5733650 6633 6312 13 0.18 190 2806130 321 2927520
1967 6678092 6728 6303 13 0.18 190 2802092 425 3876000
1968 7626428 6828 6293 13 0.18 190 2798053 535 4828375
1969 9393832 6984 6284 13 0.18 191 2813132 700 6580700
1970 13261814 7342 6275 13 0.18 193 2828155 1067 10433660
1971 13935113 7358 6266 13 0.18 194 2843123 1092 11091990
1972 14692209 7380 6257 13 0.18 195 2858035 1123 11834174
1973 15785793 7431 6248 13 0.18 197 2872893 1183 12912900
1974 17302869 7825 6563 13 0.18 198 3037852 1262 14265017
1975 19464368 8269 6879 13 0.18 199 3204730 1390 16259638
1976 20831726 8630 7194 13 0.18 200 3373526 1436 17458200
1977 23259051 9034 7509 13 0.18 202 3544240 1525 19714811
1978 27475550 9596 7825 13 0.18 203 3716873 1771 23758677
1979 31141799 10105 8140 13 0.18 204 3891425 1965 27250375
1980 35769225 10963 8698 13 0.18 206 4184643 0 0 0 0 2265 31584583
1981 37522528 11766 9256 13 0.18 207 4481255 92 12 225 247500 2418 32793772
1982 40976607 12651 9814 13 0.18 208 4781263 183 12 225 495000 2654 35700344
1983 43217693 13911 10831 13 0.18 210 5309436 275 12 225 742500 2805 37165757
1984 46623028 15256 11847 13 0.18 211 5843793 367 12 225 990000 3042 39789235
1985 49664131 15439 11850 13 0.18 212 5881122 458 12 225 1237500 3131 42545509
1986 52598325 15680 11852 13 0.18 213 5918467 550 12 225 1423125 3278 45256732
1987 55445723 15910 11855 13 0.18 215 5955828 642 12 225 1717581 3414 47772314
1988 58692366 16163 11857 13 0.18 216 5993204 733 12 225 2028400 3572 50670762
1989 59417768 16415 11860 13 0.18 216 5994518 825 13 225 2355581 3730 51067669
1990 59371832 16507 11860 13 0.18 216 5994518 917 13 225 2699125 3730 50678189
1991 58699092 16656 11860 13 0.18 216 5994518 1008 13 205 2787117 3788 49917457
1992 59352490 16776 11712 13 0.18 216 5919713 1100 14 205 3129940 3964 50302837
1993 65095244 17624 11886 13 0.18 216 6007407 1145 14 205 3349627 4594 55738210
1994 66972654 17985 11952 13 0.18 216 6041019 1189 15 205 3576552 4844 57355083
1995 76296340 18595 11533 13 0.18 216 5829240 1642 15 205 5071477 5420 65395624
1996 85008392 19901 11746 13 0.18 216 5936646 2094 15 205 6640010 6061 72431736
1997 91461506 21697 11958 13 0.18 216 6044052 2547 16 205 8282151 7192 77135304
1998 89277122 22346 12974 13 0.18 216 6557579 2999 16 205 9997900 6373 72721644
1999 96530656 22862 12608 13 0.18 216 6372335 3452 17 205 11787257 6803 78371064
2000 102422414 22968 12241 13 0.18 216 6187091 3494 17 205 12215374 7232 84019949
2001 108320494 23073 11875 13 0.18 216 6001847 3536 17 205 12650348 7662 89668299
2002 113593311 23179 11508 13 0.18 216 5816604 3579 17 205 12471073 8092 95305635
2003 113593311 23179 11508 13 0.18 216 5816604 3579 17 205 12471073 8092 95305635
2004 113593311 23179 11508 13 0.18 216 5816604 3579 17 205 12471073 8092 95305635
2005 113593311 23179 11508 13 0.18 216 5816604 3579 17 205 12471073 8092 95305635
2005 80535474 15655 7577 13 0.18 216 3829719 3705 17 192 12093120 4373 64612635
To
tal
#
Crew
siz
e
Av
era
ge m
ale
en
erg
y o
utp
ut
(hp
/da
y)
Fis
hin
g d
ay
s
Fis
hin
g e
ffo
rt
(hp
da
ys)
O utboard engines Inboard engines
Year
Total
fishing
effort
(hp days)
Total
number
of
vessels
Vessels without engines Vessels with engines
312
To
tal
#
Po
wer (
hp
)
Fis
hin
g
da
ys
Fis
hin
g
eff
ort
(hp
da
ys)
To
tal
#
Po
wer (
hp
)
Fis
hin
g
da
ys
Fis
hin
g
eff
ort
(hp
da
ys)
To
tal
#
Po
wer (
hp
)
Fis
hin
g
da
ys
Fis
hin
g
eff
ort
(hp
da
ys)
To
tal
#
Po
wer (
hp
)
Fis
hin
g
da
ys
Fis
hin
g
eff
ort
(hp
da
ys)
1950
1951
1952
1953
1954
1955
1956 0 0 0 0
1957 2 48 190 18240
1958 9 48 190 82080
1959 21 48 190 191520
1960 48 48 190 437760
1961 64 48 190 583680
1962 79 48 190 720480
1963 112 48 190 1021440
1964 179 48 190 1632480
1965 226 48 190 2061120
1966 321 48 190 2927520
1967 425 48 190 3876000
1968 535 48 190 4828375
1969 700 49 190 6580700
1970 1067 51 191 10433660
1971 1092 53 191 11091990
1972 1123 55 192 11834174
1973 1183 57 192 12912900
1974 1262 59 192 14265017 0 0 0 0
1975 1388 61 193 16223638 2 100 180 36000
1976 1416 63 193 17098200 20 100 180 360000
1977 1397 64 194 17410811 128 100 180 2304000
1978 1577 66 194 20272677 194 100 180 3486000
1979 1705 68 194 22582375 0 0 0 0 0 0 0 0 259 100 180 4668000
1980 1833 70 195 24994788 104 30 235 734335 3 30 65 5460 325 100 180 5850000
1981 1858 70 195 25387712 235 30 232 1635140 6 30 65 10920 320 100 180 5760000
1982 1913 71 196 26566978.8 366 30 229 2510985 8 30 65 16380 367 100 180 6606000
1983 1930 72 196 27236160 496 30 221 3283757 11 30 65 21840 368 100 180 6624000
1984 2033 73 196 29147527.6 627 30 212 3990407 14 30 65 27300 368 100 180 6624000
1985 1984 79 197 30943257.6 722 30 204 4412451 22 30 65 42900 396 100 180 7128000
1986 1982 86 197 33417709.2 763 30 195 4467161 31 30 65 59475 390 100 180 7020000
1987 1962 92 198 35570667.6 803 30 187 4501389 39 30 65 76050 391 100 180 7038000
1988 1963 98 198 38090052 844 30 178 4515135 48 30 65 92625 393 100 180 7074000
1989 1964 98 198 38109456 884 30 170 4508400 56 30 65 109200 395 100 180 7110000
1990 1938 98 198 37604952 884 30 170 4508400 56 30 65 109200 395 100 180 7110000
1991 1909 98 198 37037847 871 30 170 4440411 55 30 65 107553 363 100 180 6541835
1992 1896 98 198 36791389 936 30 170 4772380 67 30 65 130733 355 100 180 6390746
1993 2065 98 198 40069260 1122 30 170 5722200 82 30 65 159900 374 100 180 6732000
1994 2098 98 198 40709592 1189 30 170 6063900 85 30 65 165750 378 100 180 6804000
1995 2471 98 198 47947284 1639 30 170 8358900 90 30 65 175500 327 100 180 5886000
1996 2709 98 198 52565436 1935 30 170 9868500 48 30 65 93600 354 100 180 6372000
1997 2636 98 198 51148944 3041 30 170 15509100 51 30 65 100100 357 100 180 6426000
1998 2506 98 198 48626424 3385 30 170 17263500 55 30 65 106600 360 100 180 6480000
1999 2683 98 198 52065783 3566 30 170 18185325 58 30 65 113100 432 100 180 7767000
2000 2861 98 198 55505142 3747 30 170 19107150 61 30 65 119600 503 100 180 9054000
2001 3038 98 198 58944501 3927 30 170 20028975 65 30 65 126100 575 100 180 10341000
2002 3215 98 198 62383860 4108 30 170 20950800 68 30 65 132600 646 100 180 11628000
2003 3215 98 198 62383860 4108 30 170 20950800 68 30 65 132600 646 100 180 11628000
2004 3215 98 198 62383860 4108 30 170 20950800 68 30 65 132600 646 100 180 11628000
2005 3215 98 198 62383860 4108 30 170 20950800 68 30 65 132600 646 100 180 11628000
2005 2515 98 198 48801060 1254 30 170 6395400 28 30 65 54600 505 100 180 9090000
Trawlers Gillnetters
Vessels with engines
Liners Purse seiners
Year
313
To
tal
#
Po
wer (
hp
)
Fis
hin
g
da
ys
Fis
hin
g
eff
ort
(hp
da
ys)
To
tal
#
Po
wer (
hp
)
Fis
hin
g
da
ys
Fis
hin
g
eff
ort
(hp
da
ys)
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984 0 0 0 0
1985 7 12 225 18900
1986 113 12 225 292388
1987 219 12 225 586208
1988 325 12 225 898950
1989 431 13 225 1230613
1990 457 13 225 1345637
1991 590 13 225 1789812
1992 710 14 225 2217589
1993 951 14 225 3054850
1994 1094 15 225 3611841
1995 893 15 225 3027940
1996 1015 15 225 3532200
1997 1107 16 225 3951160
1998 67 16 225 245120
1999 64 17 225 239856
2000 61 17 225 234057
2001 58 17 225 227723
2002 55 17 225 210375
2003 55 17 225 210375
2004 55 17 225 210375
2005 55 17 225 210375
2005 71 17 225 271575
Dolnetters O thers
Vessels with engines
Year
314
Appendix D.6. Marine fishing effort (hp days) for Kerala, 1950-2005 (except industrial trawlers). Values
in bold represent interpolated and extrapolated data. An additional row for the year 2005 shows the data
that were not included in the analysis (details in Chapter 2; contd.)
To
tal
#
Po
wer (
hp
)
Fis
hin
g
da
ys
Fis
hin
g
eff
ort
(hp
da
ys)
To
tal
#
Po
wer (
hp
)
Fis
hin
g
Fis
hin
g
eff
ort
(hp
da
ys)
1950 3601997 14253 14253 9 0.18 156 3601997
1951 3723024 14732 14732 9 0.18 156 3723024
1952 3844052 15211 15211 9 0.18 156 3844052
1953 3965079 15690 15690 9 0.18 156 3965079
1954 4086106 16169 16169 9 0.18 156 4086106
1955 4207133 16647 16647 9 0.18 156 4207133 0 0
1956 4395412 17136 17126 9 0.18 156 4328160 9 67252
1957 4583691 17624 17605 9 0.18 156 4449187 18 134504
1958 4771970 18112 18084 9 0.18 156 4570214 28 201756
1959 4960249 18600 18563 9 0.18 156 4691241 37 269008
1960 5317034 19755 19709 9 0.18 156 4980774 46 336260
1961 7199335 21118 20854 9 0.18 156 5270307 264 1929028
1962 9081636 22482 22000 9 0.18 156 5559840 482 3521796
1963 10915723 23655 22955 9 0.18 156 5801160 700 5114563
1964 12749810 24827 23910 9 0.18 156 6042479 918 6707331
1965 14583897 26000 24865 9 0.18 156 6283799 1135 8300099
1966 16417985 27173 25820 9 0.18 156 6525118 1353 9892867
1967 18252072 28346 26774 9 0.18 156 6766438 1571 11485634
1968 19869450 29519 27729 9 0.18 156 7007757 1790 12861693
1969 24639873 30689 28684 9 0.18 172 7969337 2005 16670536
1970 24688686 31545 29639 9 0.18 187 8978872 1906 15709813
1971 31704757 33209 30594 9 0.18 203 10036362 2615 21668395
1972 28354719 32243 30114 9 0.18 218 10634943 2129 17719777
1973 30161115 31874 29633 9 0.18 234 11209401 2241 18951714
1974 32001110 31506 29153 9 0.18 249 11759737 2353 20241373
1975 33873560 31137 28673 9 0.18 265 12285951 2464 21587609
1976 35777321 30768 28192 9 0.18 280 12788042 2576 22989278
1977 37711249 30399 27712 9 0.18 296 13266012 2687 24445238
1978 41198631 30031 27232 10 0.18 311 15244287 0 0 0 0 2799 25954344
1979 42422885 29694 26751 10 0.18 305 14686482 50 11 225 123750 2893 27612653
1980 46484739 30455 26271 10 0.18 299 14139052 1146 11 225 2836350 3038 29509337
1981 48582615 30613 25360 10 0.18 293 13375075 2242 11 225 5548950 3011 29658590
1982 50295716 30749 24450 10 0.18 287 12630767 3338 11 225 8261550 2961 29403399
1983 53453512 31024 23539 10 0.18 281 11906127 4434 11 225 10974150 3051 30573235
1984 56647815 31299 22629 10 0.18 275 11201157 5530 11 225 13686750 3141 31759908
1985 59350510 31523 21718 10 0.18 269 10515856 6626 11 225 16399350 3179 32435304
1986 63528380 32383 21327 10 0.18 263 10096202 7722 11 225 19111950 3334 34320228
1987 70964812 33213 20936 10 0.18 263 9911102 8818 13 225 25131300 3459 35922409
1988 77733686 33918 20545 10 0.18 258 9522608 9914 14 225 31972650 3459 36238429
1989 84572378 34397 20545 10 0.18 252 9319212 10209 16 225 36753600 3643 38499566
1990 100622534 34792 20545 10 0.18 247 9115817 10505 16 225 37816800 3742 53689917
1991 144217310 35082 20545 10 0.18 241 8912421 10800 35 205 77490000 3737 57814889
1992 154763553 40896 25769 10 0.18 231 10702946 11374 35 205 81608450 3754 62452157
1993 165235866 43021 27104 10 0.18 221 10757578 12144 35 205 87129613 3773 67348676
1994 177401502 44670 27873 10 0.18 210 10548537 12913 35 205 92650775 3884 74202190
1995 210241485 48574 28456 10 0.18 200 10244160 15912 35 205 114167703 4206 85829622
1996 233563639 49302 26170 10 0.18 200 9421200 18911 35 205 135684631 4221 88457808
1997 258513868 50105 23884 10 0.18 200 8598240 21910 35 205 157201559 4311 92714069
1998 280351482 50762 21598 10 0.18 200 7775280 24909 35 205 178718488 4256 93857714
1999 277987392 50226 21598 10 0.18 200 7775280 24428 35 205 175273292 4200 94938820
2000 313060822 55148 21854 10 0.18 200 7867440 29144 35 205 209108200 4150 96085182
2001 314719677 55501 21956 10 0.18 200 7904160 29395 35 205 210909125 4150 95906392
2002 322258993 55861 21956 10 0.18 200 7904160 29395 35 205 210909125 4510 103445708
2003 322258993 55861 21956 10 0.18 200 7904160 29395 35 205 210909125 4510 103445708
2004 322258993 55861 21956 10 0.18 200 7904160 29395 35 205 210909125 4510 103445708
2005 322258993 55861 21956 10 0.18 200 7904160 29395 35 205 210909125 4510 103445708
2005 232364375 29177 9522 10 0.18 200 3427920 14151 35 205 101533425 5504 127403030
O utboard engines Inboard engines
Year
Total
fishing
effort
(hp days)
To
tal
nu
mb
er o
f
vess
els
Vessels without engines
To
tal
#
Crew
siz
e
Av
era
ge m
ale
en
erg
y o
utp
ut
(hp
/da
y)
Fis
hin
g d
ay
s
Fis
hin
g e
ffo
rt
(hp
da
ys)
Vessels with engines
315
To
tal
#
Po
wer (
hp
)
Fis
hin
g
da
ys
Fis
hin
g
eff
ort
(hp
da
ys)
To
tal
#
Po
wer (
hp
)
Fis
hin
g
da
ys
Fis
hin
g
eff
ort
(hp
da
ys)
To
tal
#
Po
wer (
hp
)
Fis
hin
g
da
ys
Fis
hin
g
eff
ort
(hp
da
ys)
To
tal
#
Po
wer (
hp
)
Fis
hin
g
da
ys
Fis
hin
g
eff
ort
(hp
da
ys)
1950
1951
1952
1953
1954
1955 0 0 0 0
1956 9 43 170 67252
1957 18 43 170 134504
1958 28 43 170 201756
1959 37 43 170 269008
1960 46 43 170 336260
1961 264 43 170 1929028
1962 482 43 170 3521796
1963 700 43 170 5114563
1964 918 43 170 6707331
1965 1135 43 170 8300099
1966 1353 43 170 9892867
1967 1571 43 170 11485634 0 0 0 0
1968 1759 43 170 12861693 30 12 264 95568
1969 1945 50 170 16574968 60 12 264 191136
1970 1816 50 171 15518677 91 12 264 286704
1971 2495 50 171 21381691 121 12 264 450050
1972 1978 51 172 17269726 151 14 261 645079
1973 2060 52 172 18306636 181 17 258 870464
1974 2141 52 173 19370909 211 19 254 1124881
1975 2223 53 173 20462728 241 21 251 1407006
1976 2304 54 174 21582272 272 24 248 1715515
1977 2386 55 174 22729723 302 26 245 2049082
1978 2467 55 175 23905262 332 28 242 2406383 0 0 0 0
1979 2549 56 175 25109071 334 30 239 2423211 10 81 120 97200
1980 2630 57 176 26341329 362 33 235 2786094 37 81 120 359640
1981 2553 58 176 25989270 372 35 232 3023007 0 0 0 0 60 81 120 583200
1982 2476 59 177 25613761 382 36 229 3149208 8 41 65 21320 53 81 120 515160
1983 2541 59 177 26712147 384 37 221 3133879 8 41 65 21320 53 86 120 546499
1984 2607 60 178 27834158 386 38 212 3111711 8 41 65 21320 53 91 120 577889
1985 2630 61 178 28515606 382 39 204 3033213 8 41 65 20978 52 96 120 599567
1986 2749 62 179 30269600 392 40 195 3058579 8 41 65 21672 53 100 120 643371
1987 2843 62 179 31786249 398 41 187 3045600 8 41 65 22162 54 105 120 681864
1988 2835 63 180 32172299 389 42 178 2917356 8 41 65 21855 53 110 120 695545
1989 2977 64 180 34294598 402 43 170 2936799 9 41 65 22711 54 115 120 746283
1990 3050 90 180 49404207 405 43 170 2957694 9 41 65 23029 54 115 120 748426
1991 3038 93 189 53566791 396 43 170 2897717 9 41 65 22713 53 115 120 730177
1992 3044 97 198 58215836 391 43 170 2856863 8 41 65 22540 52 115 120 716884
1993 3052 100 207 63119641 386 43 170 2820234 8 41 65 22396 51 115 120 704763
1994 3135 103 216 69877921 390 43 170 2852336 9 41 65 22795 51 115 120 709850
1995 3388 107 225 81176291 415 43 170 3036641 9 41 65 24421 55 115 120 752624
1996 3393 110 225 83816546 410 43 170 2997100 9 41 65 24252 54 115 120 739800
1997 3458 113 225 88001881 412 43 170 3011720 9 41 65 24518 54 115 120 740400
1998 3407 116 225 89232769 400 43 170 2926264 9 41 65 23965 52 115 120 716496
1999 3357 120 225 90399373 389 43 170 2843873 9 41 65 23427 50 115 120 693536
2000 3311 123 225 91623665 379 43 170 2768043 9 41 65 22935 49 115 120 672357
2001 3305 123 225 91467844 373 43 170 2727686 9 41 65 22730 48 115 120 659933
2002 3563 123 225 98605500 397 43 170 2903438 9 41 65 24331 51 115 120 699689
2003 3563 123 225 98605500 397 43 170 2903438 9 41 65 24331 51 115 120 699689
2004 3563 123 225 98605500 397 43 170 2903438 9 41 65 24331 51 115 120 699689
2005 3563 123 225 98605500 397 43 170 2903438 9 41 65 24331 51 115 120 699689
2005 3982 135 225 120953250 428 43 170 3128680 10 41 65 26650 54 115 120 745200
Trawlers Liners Purse seiners
Year
Gillnetters
Vessels with engines
316
To
tal
#
Po
wer (
hp
)
Fis
hin
g
da
ys
Fis
hin
g
eff
ort
(hp
da
ys)
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979 0 0 0 0
1980 9 11 225 22275
1981 26 11 225 63113
1982 42 11 225 103950
1983 64 11 225 159390
1984 87 11 225 214830
1985 107 11 225 265940
1986 132 11 225 327006
1987 156 11 225 386535
1988 174 11 225 431373
1989 202 11 225 499175
1990 225 11 225 556562
1991 241 11 225 597491
1992 259 11 225 640034
1993 275 11 225 681643
1994 299 11 225 739288
1995 339 11 225 839646
1996 356 11 225 880110
1997 378 11 225 935550
1998 387 11 225 958220
1999 395 11 225 978610
2000 403 11 225 998181
2001 415 11 225 1028200
2002 490 11 225 1212750
2003 490 11 225 1212750
2004 490 11 225 1212750
2005 490 11 225 1212750
2005 1030 11 225 2549250
Vessels with engines
O thers
Year
317
Appendix D.7. Marine fishing effort (hp days) for Lakshadweep Islands, 1950-2008 (except industrial
trawlers). Values in bold represent interpolated and extrapolated data.
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
1950 32659 36 36 25 0.18 202 32659
1951 32441 36 36 25 0.18 202 32441
1952 32728 36 36 25 0.18 202 32728
1953 33015 36 36 25 0.18 202 33015
1954 33301 37 37 25 0.18 202 33301
1955 33588 37 37 25 0.18 202 33588
1956 33875 37 37 25 0.18 202 33875
1957 34162 38 38 25 0.18 202 34162
1958 34448 38 38 25 0.18 202 34448 0 0 0 0
1959 37615 39 38 25 0.18 202 34735 1 24 120 2880
1960 47502 43 39 25 0.18 202 35022 4 24 120 12480
1961 57388 47 39 25 0.18 202 35308 8 24 120 22080
1962 82658 67 56 25 0.18 202 50978 11 24 120 31680
1963 107927 88 73 25 0.18 202 66647 14 24 120 41280
1964 133197 108 91 25 0.18 202 82317 0 0 0 0 18 24 120 50880
1965 171956 136 108 25 0.18 202 97986 7 7 286 13490 21 24 120 60480
1966 235675 172 125 25 0.18 202 113655 14 7 286 26980 33 24 120 95040
1967 299394 208 143 25 0.18 202 129325 20 7 286 40470 45 24 120 129600
1968 337194 235 160 25 0.18 202 144994 27 7 286 53960 48 24 120 138240
1969 374993 262 177 25 0.18 202 160664 34 7 286 67449 51 24 120 146880
1970 432541 296 194 25 0.18 202 176333 41 7 286 80939 61 24 120 175269
1971 490089 330 212 25 0.18 202 192003 47 7 286 94429 71 24 120 203657
1972 547637 363 229 25 0.18 202 207672 54 7 286 107919 81 24 120 232046
1973 605185 397 246 25 0.18 202 223341 61 7 286 121409 90 24 120 260434
1974 662733 431 263 25 0.18 202 239011 68 7 286 134899 100 24 120 288823
1975 720280 465 281 25 0.18 202 254680 74 7 286 148389 110 24 120 317211
1976 777828 499 298 25 0.18 202 270350 81 7 286 161879 120 24 120 345600
1977 873948 546 315 25 0.18 202 286019 88 7 286 175368 143 24 120 412560
1978 970067 594 333 25 0.18 202 301689 95 7 286 188858 167 24 120 479520
1979 1066186 641 350 25 0.18 202 317358 101 7 286 202348 190 24 120 546480
1980 1162306 688 367 25 0.18 202 333028 108 7 286 215838 213 24 120 613440
1981 1202025 716 384 25 0.18 202 348697 115 7 286 229328 217 24 120 624000
1982 1241744 743 402 25 0.18 202 364366 122 7 286 242818 220 24 120 634560
1983 1281463 771 419 25 0.18 202 380036 128 7 286 256308 224 24 120 645120
1984 1321183 799 436 25 0.18 202 395705 135 7 286 269798 228 24 120 655680
1985 1368237 827 453 25 0.18 202 411375 142 7 286 290622 231 24 120 666240
1986 1415990 854 471 25 0.18 202 427044 149 7 286 312146 235 24 120 676800
1987 1453882 878 488 25 0.18 202 442714 155 8 286 334368 235 24 120 676800
1988 1517486 904 495 25 0.18 202 448837 162 8 286 357289 247 24 120 711360
1989 1581012 920 502 25 0.18 202 454961 164 8 286 370186 255 25 120 755865
1990 1643786 935 508 25 0.18 202 461084 165 8 286 380982 262 26 120 801720
1991 2277572 1150 515 25 0.18 202 467208 225 8 286 531164 410 26 120 1279200
1992 2419236 1160 447 25 0.18 202 405518 298 8 286 718918 415 26 120 1294800
1993 2504978 1375 740 25 0.18 202 671328 225 9 286 554450 410 26 120 1279200
1994 2839535 1521 780 25 0.18 202 707616 298 9 286 749759 443 26 120 1382160
1995 2847156 1492 743 25 0.18 202 673868 300 9 286 769288 450 26 120 1404000
1996 2854942 1464 706 25 0.18 202 640120 301 9 286 788982 457 26 120 1425840
1997 2862895 1435 668 25 0.18 202 606372 303 9 286 808842 464 26 120 1447680
1998 2871013 1407 631 25 0.18 202 572625 304 10 286 828868 471 26 120 1469520
1999 2879296 1378 594 25 0.18 202 538877 306 10 286 849059 478 26 120 1491360
2000 3057932 1490 677 25 0.18 202 614174 314 10 286 886877 499 26 120 1556880
2001 3220733 1602 760 25 0.18 202 689472 322 10 286 908861 520 26 120 1622400
2002 3383534 1713 843 25 0.18 202 764770 329 10 286 930844 541 26 120 1687920
2003 3546335 1825 926 25 0.18 202 840067 337 10 286 952828 562 26 120 1753440
2004 3709136 1937 1009 25 0.18 202 915365 345 10 286 974811 583 26 120 1818960
2005 3871937 2049 1092 25 0.18 202 990662 353 10 286 996795 604 26 120 1884480
2006 4034738 2160 1175 25 0.18 202 1065960 360 10 286 1018778 625 26 120 1950000
2007 4197539 2272 1258 25 0.18 202 1141258 368 10 286 1040762 646 26 120 2015520
2008 4360340 2384 1341 25 0.18 202 1216555 376 10 286 1062745 667 26 120 2081040
O utboard engines Inboard engines
Year
Total
fishing
effort
(hp days)
Total
number
of
vessels
Vessels without engines Vessels with engines
To
tal
#
Cre
w s
ize
Av
era
ge
ma
le
ener
gy
ou
tpu
t
(hp
/da
y)
Fis
hin
g d
ay
s
Fis
hin
g e
ffo
rt
(hp
da
ys)
318
Appendix D.8. Marine fishing effort (hp days) for Tamil Nadu, 1950-2005 (except industrial trawlers).
Values in bold represent interpolated and extrapolated data. An additional row for the year 2005 shows
the data that were not included in the analysis and total effort includes the effort of sub-categories, 'purse
seiners' and 'dolnetters' (details in Chapter 2; contd.)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
1950 8406288 19459 19459 8 0.18 300 8406288 0 0
1951 8748662 20241 20237 8 0.18 300 8742480 4 6182
1952 9087776 21021 21015 8 0.18 300 9078672 5 9104
1953 9427284 21800 21794 8 0.18 300 9414864 7 12420
1954 9767185 22580 22572 8 0.18 300 9751056 8 16129
1955 10115706 23360 23350 8 0.18 300 10087248 10 28458
1956 10460225 24139 24128 8 0.18 300 10423440 11 36785
1957 10830965 24922 24907 8 0.18 300 10759632 15 71333
1958 11240184 25708 25685 8 0.18 300 11095824 23 144360
1959 11718684 26500 26463 8 0.18 300 11432016 37 286668
1960 12269756 27357 27307 8 0.18 300 11796552 50 473204
1961 12858530 28220 28151 8 0.18 300 12161088 69 697442
1962 13537224 29099 28995 8 0.18 300 12525624 104 1011600
1963 14472752 29998 29838 8 0.18 300 12890160 160 1582592
1964 15056468 30872 30682 8 0.18 300 13254696 190 1801772
1965 16199936 31782 31526 8 0.18 300 13619232 256 2580704
1966 18124022 32687 32370 8 0.18 300 13983768 317 4140254
1967 20209121 33648 33214 8 0.18 300 14348304 434 5860817
1968 22226780 34603 34058 8 0.18 300 14712840 545 7513940
1969 25066907 35621 34901 8 0.18 299 15018742 720 10048165
1970 27279017 36600 35745 8 0.18 298 15321808 855 11957208
1971 29619052 37590 36589 8 0.18 297 15622039 1001 13997012
1972 31169558 38517 37433 8 0.18 295 15919435 1084 15250123
1973 36618147 39710 38277 8 0.18 294 16213996 1433 20404151
1974 41353659 40853 39121 8 0.18 293 16505721 1732 24847938
1975 43313830 41836 39964 8 0.18 292 16794611 1872 26519218
1976 45301179 42820 40808 8 0.18 291 17080666 2012 28220513
1977 47314424 43804 41652 8 0.18 290 17363886 2152 29950539
1978 48585942 44461 42216 8 0.18 288 17527945 0 0 0 0 2245 31057997
1979 51641777 45469 42779 8 0.18 287 17690110 254 7 228 404005 2436 33547662
1980 54619059 46477 43343 8 0.18 286 17850381 507 7 228 808010 2627 35960668
1981 56652202 46280 42705 8 0.18 285 17515696 761 7 228 1212015 2814 37924491
1982 58659763 46083 42066 8 0.18 284 17183157 1015 7 228 1616020 3002 39860586
1983 61655671 45885 41428 8 0.18 283 16852762 1268 7 228 2020025 3189 42782884
1984 64710983 45688 40789 8 0.18 281 16524513 1522 7 228 2424030 3377 45762440
1985 72571568 45491 40151 8 0.18 280 16198409 1776 7 228 2828035 3564 53545124
1986 74645452 45294 39512 8 0.18 279 15874451 2030 7 228 3232040 3752 555389611987 74715928 45096 38874 8 0.18 251 14050553 2283 7 228 3636045 3939 57029330
1988 74708412 44899 38235 8 0.18 223 12278140 2537 7 228 4040050 4127 583902221989 74954160 44702 37597 8 0.18 195 10557212 2791 7 228 4444055 4314 59952893
1990 77307061 44504 36958 8 0.18 195 10377934 3044 7 228 4848060 4502 62081067
1991 94168390 44307 36320 8 0.18 195 10198656 3298 14 228 10128983 4689 73840752
1992 119669897 39117 28539 8 0.18 195 8013751 5152 14 228 15823080 5426 95833066
1993 143676264 39712 27638 8 0.18 195 7760750 5246 14 228 16111778 6828 119803736
1994 167479019 40307 26737 8 0.18 195 7507750 5340 14 228 16400475 8230 143570795
1995 174113939 42732 28179 8 0.18 195 7912551 5990 14 228 18398016 8563 147803372
1996 180752143 45157 29620 8 0.18 195 8317352 6641 14 228 20395557 8896 152039234
1997 187393266 47583 31062 8 0.18 195 8722153 7291 14 228 22393098 9230 156278014
1998 194036995 50008 32503 8 0.18 195 9126955 7942 14 228 24390639 9563 160519402
1999 200683062 52433 33945 8 0.18 195 9531756 8592 14 228 26388180 9896 164763126
2000 200683062 52433 33945 8 0.18 195 9531756 8592 14 228 26388180 9896 164763126
2001 200683062 52433 33945 8 0.18 195 9531756 8592 14 228 26388180 9896 164763126
2002 200683062 52433 33945 8 0.18 195 9531756 8592 14 228 26388180 9896 164763126
2003 200683062 52433 33945 8 0.18 195 9531756 8592 14 228 26388180 9896 164763126
2004 200683062 52433 33945 8 0.18 195 9531756 8592 14 228 26388180 9896 1647631262005 200683062 52433 33945 8 0.18 195 9531756 8592 14 228 26388180 9896 164763126
2005 198446645 54420 24231 8 0.18 195 6804065 22478 14 228 69035558 7711 122607023
Year
Total
fishing
effort
(hp days)
Total
number
of
vessels
Vessels without engines Vessels with engines
To
tal
#
Cre
w s
ize
Av
era
ge
ma
le
ener
gy
ou
tpu
t
(hp
/da
y)
Fis
hin
g d
ay
s
Fis
hin
g e
ffo
rt
(hp
da
ys)
O utboard engines Inboard engines
319
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
1950 0 0 0 0
1951 4 6 281 6182
1952 5 6 281 9104
1953 7 7 281 12420
1954 0 0 0 0 8 7 281 16129
1955 4 15 281 16452 6 8 281 12006
1956 6 15 281 25836 5 8 281 10949
1957 10 22 281 58282 5 9 281 13051
1958 16 29 281 125975 7 9 281 18386
1959 26 35 281 259044 10 10 281 27623
1960 37 42 281 436674 13 10 281 36530
1961 56 42 281 660912 13 10 281 36530
1962 80 42 281 944160 24 10 281 67440
1963 126 42 281 1487052 34 10 281 95540
1964 141 42 281 1664082 49 10 281 137690
1965 207 42 281 2443014 49 10 281 137690
1966 264 53 281 3931752 53 14 281 208502
1967 379 53 281 5644447 55 14 281 216370
1968 490 53 281 7297570 55 14 281 216370
1969 665 53 279 9831282 55 14 282 216883
1970 800 53 277 11739812 55 14 282 217397
1971 946 53 275 13779102 55 14 283 217910
1972 1029 54 273 15031699 55 14 284 218423
1973 1378 54 271 20185214 55 14 284 218937
1974 1677 55 269 24628488 55 14 285 219450
1975 1773 55 267 26095244 99 15 286 423975
1976 1868 56 265 27568739 144 16 286 651774
1977 1965 56 262 29047484 187 17 287 903055
1978 2019 57 260 29904140 226 18 288 1153857
1979 2161 57 258 32068396 275 19 288 1479266 0 0 0 0
1980 2295 58 256 34115310 324 20 289 1832590 8 7 228 12768
1981 2430 58 254 35837982 339 21 290 2015200 45 7 228 71309
1982 2566 58 252 37523719 355 21 290 2206379 82 7 228 130488
1983 2700 60 250 40186477 370 22 291 2406204 119 7 228 190203
1984 2835 61 248 42897326 385 23 292 2614744 157 7 228 250370
1985 2969 69 246 50402134 400 24 292 2832066 195 7 228 310923
1986 3104 70 241 52108916 415 25 293 3058236 233 7 228 371808
1987 3238 70 236 53742265 430 26 255 2854083 271 7 228 432982
1988 3372 71 231 55299877 445 27 216 2595940 310 7 228 494406
1989 3506 71 226 56251209 460 32 216 3146853 0 0 0 0 348 7 228 554832
1990 3599 71 226 57746303 470 36 216 3686094 50 10 75 37675 383 7 228 610995
1991 3693 83 226 68854363 479 41 216 4246188 100 10 75 74751 417 7 228 665450
1992 4215 94 226 89537217 545 46 216 5371431 165 10 75 123854 502 7 228 800563
1993 5236 94 226 111240859 673 50 216 7321617 265 10 75 198776 653 7 228 1042484
1994 6237 94 226 132500399 799 55 216 9488099 383 10 75 287012 812 7 228 1295284
1995 6418 94 226 136350749 819 55 216 9723780 459 10 75 344027 868 7 228 1384816
1996 6600 94 226 140204247 838 55 216 9959769 535 10 75 400948 924 7 228 1474270
1997 6781 94 226 144060541 858 55 216 10196033 610 10 75 457785 980 7 228 1563655
1998 6963 94 226 147919334 878 55 216 10432541 686 10 75 514547 1036 7 228 1652980
1999 7145 94 226 151780366 898 55 216 10669269 762 10 75 571242 1092 7 228 1742249
2000 7145 94 226 151780366 898 55 216 10669269 762 10 75 571242 1092 7 228 1742249
2001 7145 94 226 151780366 898 55 216 10669269 762 10 75 571242 1092 7 228 1742249
2002 7145 94 226 151780366 898 55 216 10669269 762 10 75 571242 1092 7 228 1742249
2003 7145 94 226 151780366 898 55 216 10669269 762 10 75 571242 1092 7 228 1742249
2004 7145 94 226 151780366 898 55 216 10669269 762 10 75 571242 1092 7 228 1742249
2005 7145 94 226 151780366 898 55 216 10669269 762 10 75 571242 1092 7 228 1742249
2005 5300 94 226 112593200 655 55 216 7781400 781 10 75 585750 918 7 228 1465128
Year
O thers
Vessels with engines
Trawlers Gillnetters Liners
320
Appendix D.9. Marine fishing effort (hp days) for Puducherry, 1950-2005 (except industrial trawlers).
Values in bold represent interpolated and extrapolated data. An additional row for the year 2005 shows
the data that were not included in the analysis (details in Chapter 2).
To
tal
#
Po
wer (
hp
)
Fis
hin
g
da
ys
Fis
hin
g
eff
ort
(hp
da
ys)
To
tal
#
Po
wer (
hp
)
Fis
hin
g
da
ys
Fis
hin
g
eff
ort
(hp
da
ys)
1950 394740 860 860 9 0.18 300 394740
1951 411393 896 896 9 0.18 300 411393
1952 428045 933 933 9 0.18 300 428045
1953 444698 969 969 9 0.18 300 444698
1954 461350 1005 1005 9 0.18 300 461350
1955 478003 1041 1041 9 0.18 300 478003
1956 494655 1078 1078 9 0.18 300 494655
1957 511308 1114 1114 9 0.18 300 511308
1958 527960 1150 1150 9 0.18 300 527960
1959 544613 1187 1187 9 0.18 300 544613
1960 561265 1223 1223 9 0.18 300 561265 0 0 0 0
1961 602084 1261 1259 9 0.18 300 577918 2 43 281 24166
1962 630819 1298 1295 9 0.18 300 594570 3 43 281 36249
1963 659555 1336 1332 9 0.18 300 611223 4 43 281 48332
1964 688290 1373 1368 9 0.18 300 627875 5 43 281 60415
1965 704943 1409 1404 9 0.18 300 644528 5 43 281 60415
1966 852260 1456 1440 9 0.18 300 661180 16 43 281 191080
1967 889932 1494 1477 9 0.18 300 677833 17 44 281 212099
1968 928671 1531 1513 9 0.18 300 694485 18 46 281 234185
1969 1009747 1572 1549 9 0.18 299 708372 23 47 279 301375
1970 1368609 1635 1586 9 0.18 298 722130 49 48 277 646479
1971 1731821 1697 1622 9 0.18 297 735758 75 48 275 996064
1972 1885321 1743 1658 9 0.18 295 749256 85 49 273 1136065
1973 1916644 1781 1694 9 0.18 294 762625 87 49 271 1154019
1974 2013253 1825 1731 9 0.18 293 775864 94 49 269 1237388
1975 2108320 1868 1767 9 0.18 292 788974 101 49 267 1319345
1976 2287893 1880 1764 9 0.18 291 784308 116 49 265 1503585
1977 2464453 1891 1760 9 0.18 290 779654 131 49 262 1684799
1978 2637998 1903 1757 9 0.18 288 775012 146 49 260 1862986
1979 2808529 1914 1753 ## 0.18 287 770383 161 49 258 2038146
1980 2976045 1926 1750 9 0.18 286 765765 176 49 256 2210280
1981 3518392 2253 2042 9 0.18 285 889854 211 49 254 2628539
1982 4052636 2580 2334 9 0.18 284 1012900 246 49 252 3039735
1983 4578775 2907 2626 9 0.18 283 1134905 281 49 250 3443870
1984 5096810 3234 2918 9 0.18 281 1255867 316 49 248 3840943
1985 5606742 3561 3210 9 0.18 280 1375788 351 49 246 4230954
1986 6052940 3887 3501 9 0.18 279 1494666 386 49 241 4558274
1987 6325209 4214 3793 9 0.18 251 1456765 421 49 236 4868444
1988 6555318 4541 4085 9 0.18 223 1393854 456 49 231 5161464
1989 6743266 4868 4377 9 0.18 195 1305932 491 49 226 5437334
1990 7217947 5195 4669 9 0.18 195 1393023 0 0 0 0 526 49 226 5824924
1991 8714524 5854 4961 9 0.18 195 1480114 332 14 228 1021896 561 49 226 6212514
1992 7770477 5771 4950 9 0.18 195 1476833 355 14 228 1133160 466 49 226 5160484
1993 8409872 6295 5425 9 0.18 195 1618549 360 14 228 1149120 510 49 226 5642203
1994 9049267 6818 5900 9 0.18 195 1760265 365 14 228 1165080 553 49 226 6123922
1995 9237506 7127 6179 9 0.18 195 1843624 393 14 228 1254456 554 49 226 6139426
1996 9425744 7436 6459 9 0.18 195 1926983 421 14 228 1343832 556 49 226 6154929
1997 9613983 7744 6738 9 0.18 195 2010342 449 14 228 1433208 557 49 226 6170433
1998 9802221 8053 7018 9 0.18 195 2093701 477 14 228 1522584 559 49 226 6185936
1999 9990460 8362 7297 9 0.18 195 2177060 505 14 228 1611960 560 49 226 6201440
2000 9990460 8362 7297 9 0.18 195 2177060 505 14 228 1611960 560 49 226 6201440
2001 9990460 8362 7297 9 0.18 195 2177060 505 14 228 1611960 560 49 226 6201440
2002 9990460 8362 7297 9 0.18 195 2177060 505 14 228 1611960 560 49 226 6201440
2003 9990460 8362 7297 9 0.18 195 2177060 505 14 228 1611960 560 49 226 6201440
2004 9990460 8362 7297 9 0.18 195 2177060 505 14 228 1611960 560 49 226 6201440
2005 9990460 8362 7297 9 0.18 195 2177060 505 14 228 1611960 560 49 226 6201440
2005 14758835 4457 1524 9 0.18 195 454685 2306 14 228 7360752 627 49 226 6943398
O utboard engines Inboard engines
Year
Total
fishing
effort
(hp days)
Total
number
of
vessels
Vessels without engines Vessels with engines
To
tal
#
Crew
siz
e
Av
era
ge m
ale
en
erg
y o
utp
ut
(hp
/da
y)
Fis
hin
g d
ay
s
Fis
hin
g e
ffo
rt
(hp
da
ys)
321
Appendix D.10. Marine fishing effort (hp days) for Andhra Pradesh, 1950-2005 (except industrial
trawlers). Values in bold represent interpolated and extrapolated data. An additional row for the year 2005
shows the data that were not included in the analysis and total effort includes the effort of sub-category,
'other vessels with engines' (details in Chapter 2; contd.)
To
tal
#
Po
wer (
hp
)
Fis
hin
g
da
ys
Fis
hin
g
eff
ort
(hp
da
ys)
To
tal
#
Po
wer (
hp
)
Fis
hin
g
Fis
hin
g
eff
ort
(hp
da
ys)
1950 5072467 15484 15484 7 0.18 260 5072467
1951 5242902 16004 16004 7 0.18 260 5242902
1952 5413337 16524 16524 7 0.18 260 5413337
1953 5583772 17044 17044 7 0.18 260 5583772 0 0
1954 5766083 17566 17565 7 0.18 260 5754207 0 0 0 0 1 11876
1955 6181059 18111 18085 7 0.18 260 5924642 5 10 200 10000 21 246417
1956 6601635 18659 18605 7 0.18 260 6095077 13 10 200 25600 41 480958
1957 7022211 19206 19125 7 0.18 260 6265512 21 10 200 41200 60 715499
1958 7442787 19754 19646 7 0.18 260 6435947 28 10 200 56800 80 950040
1959 7863363 20302 20166 7 0.18 260 6606382 36 10 200 72400 100 1184581
1960 8048967 20133 19969 7 0.18 260 6541844 44 10 200 88000 120 1419122
1961 8325035 20008 19772 7 0.18 260 6477307 97 10 200 194065 139 1653663
1962 8807849 20515 20206 7 0.18 260 6619515 150 10 200 300129 159 1888205
1963 9290663 21022 20640 7 0.18 260 6761724 203 10 200 406194 179 2122746
1964 9773477 21529 21074 7 0.18 260 6903932 256 10 200 512258 199 2357287
1965 10256290 22036 21508 7 0.18 260 7046140 309 10 200 618323 218 2591828
1966 10739104 22543 21942 7 0.18 260 7188348 362 10 200 724387 238 2826369
1967 11690772 23086 22377 7 0.18 260 7330556 415 10 200 830452 294 3529764
1968 12611381 23629 22811 7 0.18 260 7472764 468 10 200 936516 350 4202100
1969 12451449 24082 23245 7 0.18 260 7614973 521 10 200 1042581 316 3793896
1970 12291518 24535 23679 7 0.18 260 7757181 574 10 200 1148645 282 3385692
1971 12029099 24990 24113 7 0.18 260 7899389 627 10 200 1254710 250 2875000
1972 11858149 25441 24547 7 0.18 260 8041597 680 10 200 1360774 214 2455778
1973 13079069 26518 25500 7 0.18 260 8353800 733 10 200 1466839 284 3258431
1974 14299989 27594 26453 7 0.18 260 8666003 786 10 200 1572903 355 4061083
1975 15446829 28671 27406 7 0.18 260 8978206 839 10 200 1678968 425 4789655
1976 16569224 29747 28359 7 0.18 260 9290408 893 10 200 1785032 495 5493784
1977 18469684 30904 29312 7 0.18 260 9602611 946 10 200 1891097 646 6975976
1978 20925495 33353 31546 7 0.18 260 10334360 999 10 200 1997161 809 8593973
1979 22267388 35640 33779 7 0.18 275 11704539 1052 10 200 2103226 809 8459623
1980 23352440 37960 36013 7 0.18 275 12478505 1105 10 200 2209290 842 8664645
1981 23752673 38058 36024 7 0.18 275 12482403 1158 10 200 2315355 876 8954916
1982 25190238 38155 36036 7 0.18 275 12486301 1211 10 200 2421419 909 10282518
1983 26659369 38253 36047 7 0.18 275 12490199 1264 10 200 2527484 942 11641686
1984 28155642 38350 36058 7 0.18 275 12494097 1317 10 200 2633548 976 13027997
1985 29674633 38448 36069 7 0.18 275 12497995 1370 10 200 2739613 1009 14437025
1986 31313878 38552 36081 7 0.18 275 12501893 1423 10 200 2845677 1049 159663081987 33156457 38668 36092 7 0.18 275 12505791 1476 10 200 2951742 1100 17698924
1988 45523178 39400 36103 7 0.18 275 12509690 1529 10 200 3057806 1768 299556821989 59592768 44405 40284 7 0.18 275 13958291 1582 10 200 3163871 2539 42470606
1990 73447939 49410 44464 7 0.18 275 15406892 1635 10 200 3269935 3311 54771112
1991 87070467 54415 48645 7 0.18 275 16855493 1688 10 200 3376000 4082 66838974
1992 87584469 54415 48645 7 0.18 275 16855493 1688 10 200 3376000 4082 67352977
1993 88673734 56103 50333 7 0.18 275 17440385 1688 10 200 3376000 4082 67857349
1994 103256273 57862 50920 7 0.18 275 17643665 2101 10 200 4201333 4842 81411275
1995 118128276 59622 51506 7 0.18 275 17846945 2513 10 200 5026667 5602 95254665
1996 133289743 61381 52093 7 0.18 275 18050225 2926 10 200 5852000 6362 109387519
1997 148740675 63140 52680 7 0.18 275 18253505 3339 10 200 6677333 7122 123809837
1998 164481071 64900 53266 7 0.18 275 18456785 3751 10 200 7502667 7882 138521620
1999 180510931 66659 53853 7 0.18 275 18660065 4164 10 200 8328000 8642 153522867
2000 180510931 66659 53853 7 0.18 275 18660065 4164 10 200 8328000 8642 153522867
2001 180510931 66659 53853 7 0.18 275 18660065 4164 10 200 8328000 8642 153522867
2002 180510931 66659 53853 7 0.18 275 18660065 4164 10 200 8328000 8642 153522867
2003 180510931 66659 53853 7 0.18 275 18660065 4164 10 200 8328000 8642 153522867
2004 180510931 66659 53853 7 0.18 275 18660065 4164 10 200 8328000 8642 1535228672005 180510931 66659 53853 7 0.18 275 18660065 4164 10 200 8328000 8642 153522867
2005 72425683 41039 24386 7 0.18 275 8449749 14112 10 200 28224000 2541 35751934
To
tal
#
Crew
siz
e
Av
era
ge m
ale
en
erg
y o
utp
ut
(hp
/da
y)
Fis
hin
g d
ay
s
Year
Total
fishing
effort
(hp days)
Total
number
of
vessels
Vessels without engines Vessels with engines
Fis
hin
g e
ffo
rt
(hp
da
ys)
O utboard engines Inboard engines
322
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
1950
1951
1952
1953 0 0 0 0
1954 1 46 261 11876
1955 21 46 261 246417
1956 41 46 261 480958
1957 60 46 261 715499
1958 80 46 261 950040
1959 100 46 261 1184581
1960 120 46 261 1419122
1961 139 46 261 1653663
1962 159 46 261 1888205
1963 179 46 261 2122746
1964 199 46 261 2357287
1965 218 46 261 2591828
1966 238 46 261 2826369
1967 294 46 261 3529764
1968 350 46 261 4202100
1969 316 46 261 3793896
1970 282 46 261 3385692
1971 250 46 250 2875000 0 0 0 0
1972 213 46 250 2450778 1 25 200 5000
1973 282 46 250 3248431 2 25 200 10000
1974 352 46 250 4046083 3 25 200 15000
1975 421 46 246 4769655 4 25 200 20000
1976 491 46 242 5473784 4 25 200 20000
1977 640 46 239 6945976 6 25 200 30000
1978 801 46 235 8553973 8 25 200 40000
1979 801 46 231 8414623 8 25 225 45000
1980 834 46 227 8619645 8 25 225 45000
1981 868 46 223 8909916 8 25 225 45000
1982 901 52 219 10237518 8 25 225 45000
1983 934 58 216 11596686 8 25 225 45000
1984 968 63 212 12982997 8 25 225 45000
1985 1001 69 208 14392025 8 25 225 45000
1986 1041 75 204 15921308 8 25 225 45000
1987 1092 81 200 17653924 8 25 225 45000
1988 1760 87 196 29910682 8 25 225 45000
1989 2507 88 193 42287959 32 25 225 182647
1990 3254 89 189 54450818 57 25 225 320294
1991 4001 90 185 66381033 81 25 225 457941
1992 3976 91 185 66757389 106 25 225 595588
1993 3952 92 185 67124114 130 25 225 733235
1994 4687 93 185 80540392 155 25 225 870882
1995 5423 94 185 94246135 179 25 225 1008529
1996 6158 95 185 108241342 204 25 225 1146176
1997 6894 96 185 122526014 228 25 225 1283824
1998 7629 97 185 137100149 253 25 225 1421471
1999 8365 98 185 151963749 277 25 225 1559118
2000 8365 98 185 151963749 277 25 225 1559118
2001 8365 98 185 151963749 277 25 225 1559118
2002 8365 98 185 151963749 277 25 225 1559118
2003 8365 98 185 151963749 277 25 225 1559118
2004 8365 98 185 151963749 277 25 225 1559118
2005 8365 98 185 151963749 277 25 225 1559118
2005 1802 98 185 32736934 424 25 225 2385000 20 10 200 40000
Trawlers Gillnetters
Year
Vessels with engines
Liners Purse seiners
323
Appendix D.11. Marine fishing effort (hp days) for Orissa, 1950-2005 (except industrial trawlers). Values
in bold represent interpolated and extrapolated data. An additional row for the year 2005 shows the data
that were not included in the analysis and total effort includes the effort of sub-categories, 'dolnetters' and
'other vessels with engines' (details in Chapter 2; contd.)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
1950 599900 2178 2178 9 0.18 170 599900
1951 620057 2251 2251 9 0.18 170 620057
1952 640213 2325 2325 9 0.18 170 640213
1953 660370 2398 2398 9 0.18 170 660370
1954 680527 2471 2471 9 0.18 170 680527
1955 700683 2544 2544 9 0.18 170 700683
1956 720840 2617 2617 9 0.18 170 720840 0 0
1957 743447 2693 2691 9 0.18 170 740997 2 2450
1958 768371 2767 2764 9 0.18 170 761153 3 7218
1959 789115 2840 2837 9 0.18 170 781310 3 7805
1960 857143 3085 3082 9 0.18 170 848750 3 8393
1961 925171 3330 3327 9 0.18 170 916191 3 8980
1962 1001774 3579 3572 9 0.18 170 983632 7 18143
1963 1102390 3833 3817 9 0.18 170 1051072 16 51318
1964 1196086 4087 4061 9 0.18 170 1118513 25 77573
1965 1293151 4341 4306 9 0.18 170 1185953 35 107198
1966 1393584 4595 4551 9 0.18 170 1253394 44 140190
1967 1497387 4849 4796 9 0.18 170 1320835 53 176552
1968 1710625 5103 5041 9 0.18 170 1388275 62 322350
1969 1798054 5347 5286 9 0.18 173 1478729 61 319325
1970 1886514 5591 5531 9 0.18 175 1571315 60 315199
1971 1977073 5835 5776 9 0.18 178 1666033 59 311039
1972 2297466 6108 6020 9 0.18 181 1762884 87 534582
1973 2613407 6381 6265 9 0.18 183 1861867 115 751540
1974 2932337 6654 6510 9 0.18 186 1962982 144 969355
1975 3256048 6927 6755 9 0.18 189 2066230 172 1189818
1976 3585172 7200 7000 9 0.18 192 2171610 200 1413562
1977 5057709 7444 7049 9 0.18 194 2217501 395 2840208
1978 5499843 7591 7150 9 0.18 197 2280403 441 3219440
1979 5711468 7705 7250 9 0.18 200 2343781 455 3367687
1980 5923838 7819 7350 9 0.18 202 2408027 469 3515811
1981 6449621 7999 7449 9 0.18 205 2473142 550 3976480
1982 6977957 8180 7549 9 0.18 208 2539125 631 4438832
1983 7730566 9222 8549 9 0.18 210 2912811 673 4817754
1984 7990359 10205 9550 9 0.18 213 3295208 0 0 0 0 655 4695151
1985 8499034 11224 10550 9 0.18 212 3621868 5 26 180 23400 669 4853766
1986 8882280 11377 10653 9 0.18 211 3638532 45 26 180 212073 679 5031675
1987 10428366 12910 12019 9 0.18 210 4083996 131 26 180 621655 760 5722715
1988 12368924 14569 13470 9 0.18 209 4553399 228 27 180 1089425 871 6726100
1989 13398976 14751 13488 9 0.18 208 4535812 352 27 180 1693440 911 7169724
1990 16787485 15292 13488 9 0.18 207 4512141 820 27 180 3971782 984 8303563
1991 18663378 15604 13340 9 0.18 205 4439219 1263 27 180 6158847 1001 8065313
1992 20208921 15773 13149 9 0.18 204 4352582 1573 27 180 7722000 1051 8134340
1993 26635605 15331 11627 9 0.18 203 3828200 2350 27 180 11613273 1355 11194133
1994 29844264 15639 11521 9 0.18 202 3773215 2453 28 180 12202560 1665 13868489
1995 29529119 15493 11415 9 0.18 201 3718602 2490 28 180 12470112 1587 13340405
1996 29236737 15347 11310 9 0.18 200 3664359 2528 28 180 12740112 1509 12832266
1997 28679269 15201 11204 9 0.18 200 3630150 2565 28 180 12928608 1432 12120511
1998 28133386 15055 11099 9 0.18 200 3595941 2603 28 180 13117104 1354 11420341
1999 27596805 14909 10993 9 0.18 200 3561732 2640 28 180 13305600 1276 10729473
2000 27596805 14909 10993 9 0.18 200 3561732 2640 28 180 13305600 1276 10729473
2001 27596805 14909 10993 9 0.18 200 3561732 2640 28 180 13305600 1276 10729473
2002 27596805 14909 10993 9 0.18 200 3561732 2640 28 180 13305600 1276 10729473
2003 27596805 14909 10993 9 0.18 200 3561732 2640 28 180 13305600 1276 10729473
2004 27596805 14909 10993 9 0.18 200 3561732 2640 28 180 13305600 1276 10729473
2005 27596805 14909 10993 9 0.18 200 3561732 2640 28 180 13305600 1276 10729473
2005 57056176 23740 15444 9 0.18 200 5003856 4719 28 180 23783760 3577 28268560
To
tal
#
Cre
w s
ize
Av
era
ge
ma
le
ener
gy
ou
tpu
t
(hp
/da
y)
Fis
hin
g d
ay
s
Year
Total
fishing
effort
(hp days)
Total
number
of
vessels
Vessels without engines Vessels with engines
Fis
hin
g e
ffo
rt
(hp
da
ys)
O utboard engines Inboard engines
324
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
1950
1951
1952
1953
1954
1955
1956 0 0 0 0
1957 0 0 0 0 2 7 175 2450
1958 1 55 80 4400 2 8 175 2818
1959 1 55 84 4620 2 9 175 3185
1960 1 55 88 4840 2 10 175 3553
1961 1 55 92 5060 2 11 175 3920
1962 1 55 96 5280 6 12 175 12863
1963 4 58 100 23000 12 13 175 28318
1964 4 60 100 24000 21 14 175 53573
1965 4 63 100 25000 31 15 175 82198
1966 4 65 100 26000 40 16 175 114190
1967 4 68 100 27000 49 18 175 149552
1968 4 70 100 28000 58 29 175 294350
1969 4 73 101 29414 57 29 174 289910
1970 4 72 103 29760 56 29 174 285439
1971 4 72 104 30104 55 29 173 280935
1972 35 72 106 269436 52 30 173 265146
1973 62 72 107 478598 53 30 172 272942
1974 88 72 109 681447 56 30 172 287908
1975 112 72 110 883793 59 30 171 306026
1976 137 71 111 1087732 63 30 171 325830
1977 279 71 113 2240166 116 30 170 600042
1978 319 71 114 2586212 122 31 169 633228
1979 335 71 116 2743943 120 31 169 623744
1980 350 71 117 2897333 119 31 168 618478
1981 353 70 119 2950828 197 31 168 1025652
1982 356 70 120 3004640 275 31 167 1434192
1983 395 70 121 3365494 278 31 167 1452260
1984 377 70 123 3237900 279 32 166 1457251
1985 376 70 124 3271200 293 33 166 1582566
1986 387 70 126 3405600 292 34 165 1626075
1987 431 70 127 3835900 329 35 164 1886815
1988 512 70 129 4608000 359 36 164 2118100
1989 542 70 130 4932200 369 37 163 2237524 0 0 0 0
1990 732 70 131 6734400 250 38 163 1556563 2 96 75 12600
1991 574 70 133 5338200 423 39 162 2701913 4 96 75 25200
1992 439 70 134 4125229 607 41 162 3971311 5 96 75 37800
1993 754 70 136 7162054 594 42 161 3981679 7 96 75 50400
1994 890 70 137 8545194 766 43 161 5258495 9 96 75 64800
1995 819 70 139 7943037 758 44 160 5319968 11 96 75 77400
1996 755 70 140 7398193 742 45 160 5344073 13 96 75 90000
1997 697 70 140 6826796 721 45 160 5191115 14 96 75 102600
1998 643 70 140 6299066 695 45 160 5006075 16 96 75 115200
1999 592 70 140 5806400 666 45 160 4795273 18 96 75 127800
2000 592 70 140 5806400 666 45 160 4795273 18 96 75 127800
2001 592 70 140 5806400 666 45 160 4795273 18 96 75 127800
2002 592 70 140 5806400 666 45 160 4795273 18 96 75 127800
2003 592 70 140 5806400 666 45 160 4795273 18 96 75 127800
2004 592 70 140 5806400 666 45 160 4795273 18 96 75 127800
2005 592 70 140 5806400 666 45 160 4795273 18 96 75 127800
2005 1340 70 140 13132000 1760 45 160 12672000 28 96 75 201600 22 28 180 110880
Trawlers Gillnetters
Year
Vessels with engines
Liners Purse seiners
325
Appendix D.12. Marine fishing effort (hp days) for West Bengal, 1950-2005 (except industrial trawlers).
Values in bold represent interpolated and extrapolated data. The data for 1957 (given below), seemed to
be an anomaly and were not used in the analysis (interpolated value between adjacent year was used). An
additional row for 2005 shows the data that were not included in the analysis (see Chapter 2; contd.)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
1950 4105 42 42 9 0.18 60 4105
1951 4552 44 44 9 0.18 64 4552
1952 5019 45 45 9 0.18 69 5019
1953 5507 46 46 9 0.18 73 5507
1954 6015 48 48 9 0.18 78 6015
1955 6542 49 49 9 0.18 82 6542
1956 7090 51 51 9 0.18 86 7090
1957 7658 52 52 9 0.18 91 7658 0 0
1958 112237 66 54 9 0.18 95 8246 12 103991
1959 216417 78 55 9 0.18 99 8854 23 207563
1960 325372 116 82 9 0.18 104 13698 35 311674
1961 435242 154 108 9 0.18 108 18918 46 416325
1962 572523 337 280 9 0.18 113 51009 57 521514
1963 712779 520 452 9 0.18 117 85536 68 627243
1964 856011 703 624 9 0.18 121 122500 79 733511
1965 1002219 886 796 9 0.18 126 161900 90 840318
1966 1151402 1069 967 9 0.18 130 203737 102 947665
1967 1294709 1253 1139 9 0.18 130 239935 113 1054773
1968 1438016 1436 1311 9 0.18 130 276134 125 1161882
1969 1706748 1655 1483 9 0.18 130 312332 172 1394416
1970 1982581 1873 1655 9 0.18 127 339594 218 1642987
1971 2266855 2091 1827 9 0.18 123 364999 264 1901855
1972 2559664 2309 1999 9 0.18 120 388548 311 2171115
1973 2861105 2528 2171 9 0.18 117 410241 357 2450864
1974 3171276 2746 2342 9 0.18 113 430078 0 0 0 0 403 2741198
1975 3516953 2980 2514 9 0.18 110 448058 16 12 140 26682 450 3042213
1976 3871551 3214 2686 9 0.18 107 464181 32 12 140 53365 496 3354005
1977 4235167 3448 2858 9 0.18 103 478449 48 12 140 80047 543 3676671
1978 4270846 3683 3030 9 0.18 100 490860 64 12 140 106729 589 3673257
1979 5389040 4437 3546 9 0.18 100 574371 79 12 140 133412 812 4681257
1980 6546474 5191 4061 9 0.18 100 657882 95 12 140 160094 1034 5728498
1981 7066400 5317 4064 9 0.18 100 658324 111 12 140 186776 1143 6221300
1982 7593990 5443 4066 9 0.18 100 658766 127 12 140 213459 1250 6721765
1983 8129612 5569 4069 9 0.18 100 659207 143 12 140 240141 1357 7230264
1984 8673565 5694 4072 9 0.18 100 659649 159 12 140 266824 1463 7747092
1985 10148972 5818 4075 9 0.18 100 660091 175 13 140 317965 1569 9170917
1986 11737424 5942 4077 9 0.18 100 660533 191 14 140 373553 1674 10703338
1987 12001889 5909 4080 9 0.18 100 660975 206 15 140 433588 1622 10907326
1988 14562176 6122 4083 9 0.18 100 661417 222 16 140 498071 1817 13402689
1989 17478031 6337 4086 9 0.18 100 661858 238 17 140 567000 2013 16249173
1990 20747028 6560 4088 9 0.18 100 662300 254 18 140 640376 2218 19444352
1991 22954949 6784 4091 9 0.18 100 662742 270 18 140 680400 2423 21611807
1992 23296929 6838 4091 9 0.18 100 662742 270 18 140 680400 2477 21953787
1993 23724118 7162 4361 9 0.18 100 706482 270 18 140 680400 2531 22337236
1994 24084876 6946 4091 9 0.18 100 662742 270 18 140 680400 2585 22741734
1995 27466414 7368 4243 9 0.18 100 687334 270 18 140 680400 2856 26098680
1996 30976381 7791 4395 9 0.18 100 711925 270 18 140 680400 3126 29584056
1997 34616880 8213 4546 9 0.18 100 736517 270 18 140 680400 3397 33199963
1998 38863794 8824 4698 9 0.18 100 761108 458 18 140 1154790 3667 36947895
1999 43243828 9435 4850 9 0.18 100 785700 647 18 140 1629180 3938 40828948
2000 43243828 9435 4850 9 0.18 100 785700 647 18 140 1629180 3938 40828948
2001 43243828 9435 4850 9 0.18 100 785700 647 18 140 1629180 3938 40828948
2002 43243828 9435 4850 9 0.18 100 785700 647 18 140 1629180 3938 40828948
2003 43243828 9435 4850 9 0.18 100 785700 647 18 140 1629180 3938 40828948
2004 43243828 9435 4850 9 0.18 100 785700 647 18 140 1629180 3938 40828948
2005 43243828 9435 4850 9 0.18 100 785700 647 18 140 1629180 3938 40828948
2005 84441252 18646 10041 9 0.18 100 1626642 1776 18 140 4475520 6829 78339090
To
tal
#
Cre
w s
ize
Av
era
ge
ma
le
ener
gy
ou
tpu
t
(hp
/da
y)
Fis
hin
g d
ay
s
Fis
hin
g e
ffo
rt
(hp
da
ys)
O utboard engines Inboard engines
Year
Total
fishing
effort
(hp days)
Total
number
of
vessels
Vessels without engines Vessels with engines
326
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
To
tal
#
Po
wer
(h
p)
Fis
hin
g
da
ys
Fis
hin
g
effo
rt (
hp
da
ys)
1950
1951
1952
1953
1954
1955
1956
1957 0 0 0 0
1958 2 8 145 2320
1959 3 10 145 4221
1960 4 12 145 6661
1961 5 14 145 9640
1962 6 17 145 13159
1963 6 19 145 17216
1964 7 21 145 21813
1965 8 23 145 26950
1966 9 25 145 32625
1967 11 25 145 38063
1968 12 25 145 43500
1969 48 25 145 174363
1970 84 26 146 316199
1971 120 27 146 467553
1972 156 27 147 628519
1973 193 28 147 799196
1974 229 29 148 979678
1975 265 30 148 1170062
1976 301 31 149 1370445
1977 337 31 149 1580922 0 0 0 0
1978 266 32 150 1284780 107 12 140 179760
1979 430 33 150 2126025 127 12 140 265847
1980 593 34 150 3006510 147 12 140 351935
1981 642 35 150 3332557 174 12 140 438022
1982 690 35 150 3666267 201 12 140 524109
1983 738 36 150 4008010 229 12 140 610196
1984 785 37 150 4358083 257 12 140 696284
1985 832 45 150 5615152 286 12 140 782371
1986 878 53 150 6980818 316 12 140 868458
1987 767 61 150 7018050 287 12 140 954545
1988 911 69 150 9427326 350 12 140 1040633
1989 0 0 0 0 1055 77 150 12187722 412 12 140 1126720
1990 33 65 140 298977 1176 85 150 14997838 464 12 140 1212807
1991 63 75 140 665018 1299 86 150 16713164 517 12 140 1298895
1992 83 85 140 989954 1283 87 150 16644120 514 12 140 1384982
1993 99 95 140 1312075 1270 87 150 16619361 0 0 0 0 511 12 140 1471069
1994 111 105 140 1629446 1257 88 150 16597374 3 96 75 23027 509 12 140 1557156
1995 140 115 140 2251537 1444 89 150 19220750 7 96 75 48419 586 12 140 1643244
1996 170 125 140 2971217 1629 90 150 21873198 10 96 75 75580 663 12 140 1729331
1997 200 135 140 3788300 1814 90 150 24557418 14 96 75 104097 740 12 140 1815418
1998 232 145 140 4702653 1998 91 150 27275329 19 96 75 133678 817 12 140 1901505
1999 263 155 140 5714180 2182 92 150 30028336 23 96 75 164108 894 12 140 1987593
2000 263 155 140 5714180 2182 92 150 30028336 23 96 75 164108 894 12 140 1987593
2001 263 155 140 5714180 2182 92 150 30028336 23 96 75 164108 894 12 140 1987593
2002 263 155 140 5714180 2182 92 150 30028336 23 96 75 164108 894 12 140 1987593
2003 263 155 140 5714180 2182 92 150 30028336 23 96 75 164108 894 12 140 1987593
2004 263 155 140 5714180 2182 92 150 30028336 23 96 75 164108 894 12 140 1987593
2005 263 155 140 5714180 2182 92 150 30028336 23 96 75 164108 894 12 140 1987593
2005 610 165 140 14091000 4355 93 150 60752250 66 96 75 475200 1798 12 140 3020640
O thersTrawlers Gillnetters
Vessels with engines
Liners
Year
327
To
tal
#
Po
wer (
hp
)
Fis
hin
g
da
ys
Fis
hin
g
eff
ort
(hp
da
ys)
1950
1951
1952
1953
1954
1955
1956
1957 0 0 0 0
1958 10 68 145 101671
1959 21 68 145 203342
1960 31 68 145 305013
1961 41 68 145 406684
1962 51 68 145 508355
1963 62 68 145 610027
1964 72 68 145 711698
1965 82 68 145 813369
1966 93 68 145 915040
1967 103 68 145 1016711
1968 113 68 145 1118382
1969 123 68 145 1220053
1970 134 68 146 1326788
1971 144 68 146 1434303
1972 154 68 147 1542596
1973 165 68 147 1651668
1974 175 68 148 1761520
1975 185 68 148 1872151
1976 195 68 149 1983560
1977 206 68 149 2095749
1978 216 68 150 2208717
1979 224 68 150 2289385
1980 232 68 150 2370053
1981 240 68 150 2450721
1982 248 68 150 2531390
1983 255 68 150 2612058
1984 263 68 150 2692726
1985 271 68 150 2773394
1986 279 68 150 2854062
1987 287 68 150 2934730
1988 287 68 150 2934730
1989 287 68 150 2934730
1990 287 68 150 2934730
1991 287 68 150 2934730
1992 287 68 150 2934730
1993 287 68 150 2934730
1994 287 68 150 2934730
1995 287 68 150 2934730
1996 287 68 150 2934730
1997 287 68 150 2934730
1998 287 68 150 2934730
1999 287 68 150 2934730
2000 287 68 150 2934730
2001 287 68 150 2934730
2002 287 68 150 2934730
2003 287 68 150 2934730
2004 287 68 150 2934730
2005 287 68 150 2934730
2005
Carrier and launch vessels
Vessels with engines
Year
328
Appendix D.13. Marine fishing effort (hp days) for Andaman and Nicobar Islands, 1950-2005 (except
industrial trawlers). Values in bold represent interpolated and extrapolated data.
To
tal
#
Po
wer (
hp
)
Fis
hin
g
da
ys
Fis
hin
g
eff
ort
(hp
da
ys)
To
tal
#
Po
wer (
hp
)
Fis
hin
g
da
ys
Fis
hin
g
eff
ort
(hp
da
ys)
1950 158 1 1 3 0.18 292 158
1951 473 3 3 3 0.18 292 473
1952 1355 9 9 3 0.18 292 1355
1953 2237 14 14 3 0.18 292 2237
1954 3119 20 20 3 0.18 292 3119
1955 9337 25 25 7 0.18 292 9337
1956 11395 31 31 7 0.18 292 11395
1957 13453 37 37 7 0.18 292 13453
1958 15512 42 42 7 0.18 292 15512
1959 17570 48 48 7 0.18 292 17570
1960 19628 53 53 7 0.18 292 19628
1961 21686 59 59 7 0.18 292 21686
1962 23745 65 65 7 0.18 292 23745
1963 25803 70 70 7 0.18 292 25803
1964 27861 76 76 7 0.18 292 27861
1965 29919 81 81 7 0.18 292 29919
1966 31978 87 87 7 0.18 292 31978
1967 34036 93 93 7 0.18 292 34036 0 0 0 0
1968 47614 103 98 7 0.18 292 36094 5 12 192 11520
1969 50184 109 104 7 0.18 292 38152 5 12 192 12032
1970 52755 115 109 7 0.18 292 40211 5 12 192 12544
1971 55325 121 115 7 0.18 292 42269 6 12 192 13056
1972 57895 126 120 7 0.18 292 44327 6 12 192 13568
1973 60465 132 126 7 0.18 292 46385 6 12 192 14080
1974 63036 138 132 7 0.18 292 48444 6 12 192 14592
1975 65606 144 137 7 0.18 292 50502 7 12 192 15104
1976 68176 150 143 7 0.18 292 52560 7 12 192 15616
1977 103509 245 238 7 0.18 292 87381 7 12 192 16128
1978 140634 340 332 7 0.18 292 122202 8 12 192 18432
1979 177759 436 427 7 0.18 292 157023 9 12 192 20736
1980 214884 531 521 7 0.18 292 191844 0 0 0 0 10 12 192 23040
1981 287856 646 616 7 0.18 292 226665 11 7 228 17991 19 12 192 43200
1982 360829 761 711 7 0.18 292 261486 23 7 228 35983 28 12 192 63360
1983 433801 875 805 7 0.18 292 296307 34 7 228 53974 36 12 192 83520
1984 506773 990 900 7 0.18 292 331128 45 7 228 71965 45 12 192 103680
1985 634507 1042 891 7 0.18 292 327974 56 7 228 89956 94 12 192 216576
1986 718464 1074 883 7 0.18 292 324821 68 7 228 107948 124 12 192 285696
1987 802422 1107 874 7 0.18 292 321667 79 7 228 125939 154 12 192 354816
1988 817260 1110 866 7 0.18 292 318514 90 7 228 143930 154 12 192 354816
1989 832097 1113 857 7 0.18 292 315360 101 7 228 161921 154 12 192 354816
1990 881495 1130 849 7 0.18 292 312206 113 7 228 179913 169 12 192 389376
1991 930893 1148 840 7 0.18 292 309053 124 7 228 197904 184 12 192 423936
1992 930893 1148 840 7 0.18 292 309053 124 7 228 197904 184 12 192 423936
1993 976515 1272 964 7 0.18 292 354675 124 7 228 197904 184 12 192 423936
1994 1219426 1570 1180 7 0.18 292 434146 160 7 228 255360 230 12 192 529920
1995 1219426 1570 1180 7 0.18 292 434146 160 7 228 255360 230 12 192 529920
1996 1219426 1570 1180 7 0.18 292 434146 160 7 228 255360 230 12 192 529920
1997 1219426 1570 1180 7 0.18 292 434146 160 7 228 255360 230 12 192 529920
1998 1219426 1570 1180 7 0.18 292 434146 160 7 228 255360 230 12 192 529920
1999 1219426 1570 1180 7 0.18 292 434146 160 7 228 255360 230 12 192 529920
2000 1399939 1772 1290 7 0.18 292 474433 264 7 228 420546 219 12 192 504960
2001 1580452 1974 1399 7 0.18 292 514720 367 7 228 585732 208 12 192 480000
2002 1742005 2177 1509 7 0.18 292 555007 471 7 228 750918 198 12 192 436080
2003 1941479 2379 1618 7 0.18 292 595295 574 7 228 916104 187 12 192 430080
2004 2121992 2581 1728 7 0.18 292 635582 678 7 228 1081290 176 12 192 405120
2005 2302505 2783 1837 7 0.18 292 675869 781 7 228 1246476 165 12 192 380160
O utboard engines Inboard engines
Year
Total
fishing
effort
(hp days)
Total
number
of
vessels
Vessels without engines Vessels with engines
To
tal
#
Crew
siz
e
Av
era
ge m
ale
en
erg
y o
utp
ut
(hp
/da
y)
Fis
hin
g d
ay
s
Fis
hin
g e
ffo
rt
(hp
da
ys)
329
Appendix E. Intrinsic rate of growth (r), which was estimated using a relationship between r and adult
mean weight (w bar) or using natural mortality denoted with an asterisk sign (see section 3.2.2). All r
values in bold, represent the geometric mean of species within a taxonomic category (bold text). Data on
adult weight and trophic level was assembled using FishBase for fishes, and SeaLifeBase and the Sea
Around Us database for invertebrates. Miscellaneous sources were used for natural mortality data (Jagadis
et al. 2010; James and Thirumilu 1993; Kagwade 1993; Sukumaran 1987; Thomas and Nasser 2009).
# Categories/Species Trophic level Adult mean weight (gms)
and Natural mortality*
Intrinsic rate
of growth (r)
1 Elasmobranchs
a Sharks 0.50
Chiloscyllium plagiosum 4.0 6,272 0.94
Chiloscyllium punctatum 4.1 19,112 0.70
Rhizoprionodon acutus 4.3 16,143 0.73
Carcharhinus sorrah 4.2 19,484 0.70
Carcharhinus macloti 4.2 14,429 0.76
Carcharhinus melanopterus 3.9 32,879 0.61
Carcharhinus hemiodon 4.2 84,149 0.48
Carcharhinus brevipinna 4.2 96,290 0.46
Carcharhinus dussumieri 3.9 18,654 0.71
Carcharhinus limbatus 4.2 90,686 0.47
Carcharhinus leucas 4.3 318,840 0.34
Carcharhinus sealei 4.2 6,036 0.95
Galeocerdo cuvieri 4.5 525,431 0.30
Eusphyra blochii 4.2 345,809 0.33
Sphyrna lewini 4.1 179,153 0.39
Stegostoma fasciatum 3.1 454,229 0.31
Sphyrna mokarran 4.3 754,620 0.27
Sphyrna zygaena 4.5 674,925 0.28
Chaenogaleus macrostoma 4.2 10,863 0.81
Hemipristis elongata 4.3 144,199 0.42
Loxodon macrorhinus 4.0 10,242 0.83
Nebrius ferrugineus 4.1 337,296 0.33
Negaprion acutidens 4.1 560,060 0.29
Mustelus mosis 3.9 36,026 0.60
Triaenodon obesus 4.2 46,616 0.56
b Skates 0.31
Rhynchobatus djiddensis 3.6 193,331 0.39
Anoxypristis cuspidata 4.5 1,048,869 0.25
Pristis pectinata 4.5 579,842 0.29
Rhinobatus granulatus 3.5 227,373 0.37
Rhina ancylostoma 3.6 193,981 0.38
Pristis microdon 3.9 1,428,429 0.23
c Rays 0.59
Aetobatus narinari 3.2 326,524 0.34
Rhinoptera javanica 3.3 12,733 0.78
Himantura uarnak 3.6 159,771 0.40
Himantura bleekeri 12,562 0.78
Himantura fluviatilis 10,863 0.81
Himantura jenkinsii 32,911 0.61
Himantura marginatus 60,684 0.52
Amphotistius kuhlii 3.2 16,118 0.74
Pastinachus sephen 3.7 64,765 0.51
330
# Categories/Species Trophic level Adult mean weight (gms)
and Natural mortality*
Intrinsic rate
of growth (r)
Gymnura poecilura 162,712 0.40
Gymnura micrura 3.6 27,558 0.64
Mobula diabolus 3.7 10,864 0.81
Aetomylaeus maculatus 84,149 0.48
Dasyatis bleekeri 12,562 0.78
Dasyatis sephen 3.7 64,765 0.51
Dasyatis kuhlii 3.2 16,118 0.74
2 Eels 0.66
Congresox talabonoides 4.3 162,712 0.40
Gymnothorax pseudothyrsoidea 3.7 5,635 0.97
Anguilla bengalensis bengalensis 10,579 0.82
Muraenosox bagio 4.0 7,813 0.89
Muraenesox cinereus 4.1 111,494 0.44
3 Catfishes 1.20
Tachysurus sona 4.0 2,226 1.23
Tachysurus dussumieri 4.0 1,929 1.28
Tachysurus caelatus 4.0 1,032 1.50
Tachysurus thalassinus 3.1 3,408 1.10
Arius sagor 3.1 1,032 1.50
Plotosus canius 3.9 13,141 0.78
4 Clupeids
a Wolf herring (Chirocentrus dorab ) 4.5 5,221 0.99
b Oil sardine (Sardinella longiceps ) 2.4 234 2.21
c Other sardines 2.31
Sardinella gibbosa 2.9 122 2.62
Sardinella melanura 320 2.04
d Hilsa shad (Hilsa ilisha ) 2.0 2,408 1.21
e Other shads 1.23
Hilsa toli 2.5 8,290 0.87
Ilisha elongata 3.8 580 1.74
f Anchovies
i Anchoviella 2.38
Coilia dussumieri 3.3 201 2.30
Coilia ramcarati 186 2.35
Setipinna brevifilis 203 2.29
Setipinna tenuifilis 3.6 125 2.60
ii Thrissocles 2.87
Thryssa malabarica 63 3.11
Thryssa gautamiensis 117 2.65
g Other clupeids 2.89
Opisthopterus tardoore 3.4 94 2.80
Pellona ditchela 74 2.98
5 Bombay duck (Harpodon nehereus ) 4.2 172 2.39
6 Lizard fishes 1.58
Saurida tumbil 4.4 736 1.64
Saurida undosquamis 4.5 1,598 1.34
Saurida micropectoralis 4.2 686 1.67
Trachinocephalus myops 4.4 642 1.70
7 Half beaks and Full beaks 1.73
Rhynchorhamphus malabaricus 2.2 490 1.82
Strongylura strongylura 4.2 725 1.65
331
# Categories/Species Trophic level Adult mean weight (gms)
and Natural mortality*
Intrinsic rate
of growth (r)
8 Flying fishes 2.02
Cypselurus comatus 313 2.05
Exocoetus volitans 3.0 313 2.05
Hirundichthys coromandelensis 373 1.96
9 Perches
a Rock cods 0.77
Epinephelus tauvina 4.1 19,523 0.70
Epinephelus malabaricus 3.8 179,594 0.39
Epinephelus bleekeri 7,460 0.90
Epinephelus epistictus 4.0 6,281 0.94
Epinephelus flavocaeruleus 4.2 10,931 0.81
Epinephelus morrhua 4.0 4,274 1.04
Epinephelus undulosus 3.7 5,458 0.97
Epinephelus fuscoguttatus 4.1 11,891 0.80
Epinephelus chlorostigma 4.0 4,832 1.01
Cephalopholis sonnerati 3.8 4,233 1.04
Promicrops lanceolatus 4.0 285,752 0.35
b Snappers 0.75
Lutjanus johni 4.2 16,607 0.73
Lutjanus argentimaculatus 3.6 15,702 0.74
Lutjanus bohar 4.1 8,000 0.88
Lutjanus rivulatus 4.1 12,163 0.79
Lutjanus sebae 4.3 30,183 0.62
Lutjanus sanguineus 4.5 18,574 0.71
Lutjanus malabaricus 4.5 9,891 0.83
c Pig face breams 0.90
Lethrinus nebulosus 3.3 8,675 0.86
Lethrinus elongatus 3.8 7,558 0.90
Lethrinus mahsena 3.4 6,272 0.94
Lethrinella miniatus 3.5 6,395 0.93
Lethrinus frenatus 3.3 8,675 0.86
d Threadfin breams 2.12
Nemipterus japonicus 3.8 276 2.12
e Other perches 0.77
Argyrops spinifer 4.5 20,278 0.69
Lates calcarifer 4.4 47,225 0.56
Lobotes surinamensis 4.0 12,024 0.79
Mylio berda 6,846 0.92
Pomadasys hasta 3.4 6,207 0.94
10 Goatfishes 1.91
Upeneus vittatus 3.5 205 2.29
Upeneus moluccensis 3.6 275 2.12
Upeneus taeniopterus 3.5 280 2.11
Parupeneus indicus 3.5 1,748 1.31
11 Threadfins 0.52
Eleutheronema tetradactylum 4.4 110,462 0.45
Polynemus indicus 3.9 33,750 0.61
12 Croakers 0.77
Johnius elongatus 4.1 867 1.57
Otolithoides biauritus 4.1 87,418 0.47
Otolithoides pama 43,547 0.57
Protonibea diacanthus 3.5 17,422 0.72
Daysciaena albida 7,966 0.88
332
# Categories/Species Trophic level Adult mean weight (gms)
and Natural mortality*
Intrinsic rate
of growth (r)
13 Ribbon fishes 1.04
Trichiurus lepturus 4.5 2,538 1.19
Lepturacanthus pantului 4.1 8,493 0.87
Euplurogrammus muticus 4.2 3,795 1.07
Euplurogrammus glossodon 4.5 3,795 1.07
14 Carangids
a Horse mackerel (Megalaspis cordyla ) 4.4 2,090 1.25
b Scads 1.78
Decapterus kurroides 3.4 735 1.64
Caranx kalla 3.3 332 2.02
Selar crumenopthalmus 4.1 947 1.54
Selar boops 3.5 244 2.19
Atule mate 4.5 766 1.62
c Leather-jackets 0.83
Scomberoides lysan 4.5 6,452 0.93
Scomberoides commersonianus 4.5 16,238 0.73
i Trachynotus 0.97
Trachinotus blochii 3.7 7,555 0.90
Trachinotus botla 3.2 4,045 1.05
d Other Carangids 0.66
Caranx sexfasciatus 4.5 19,460 0.70
Caranx carangus 3.5 32,722 0.61
Caranx ignobilis 4.2 122,701 0.43
Caranx melampygus 4.5 27,495 0.64
Carangoides chrysophrys 4.4 5,599 0.97
Carangoides ferdau 4.5 11,636 0.80
Elagatis bipinnulata 3.6 29,312 0.63
Alectis indicus 4.1 23,691 0.67
Alectis ciliaris 3.8 39,973 0.58
i Coryphaena (Coryphaena hippurus ) 4.4 41,338 0.58
ii Elacate (Rachycentron canadum ) 4.0 26,300 0.65
15 Silverbellies
a Leiognathus 2.36
Leiognathus brevirostris 3.0 110 2.69
Leiognathus equulus 3.0 389 1.94
Leiognathus bindus 2.5 142 2.52
b Gazza 3.41
Gazza minuta 4.2 44 3.41
16 Big jawed jumper (Lactarius lactarius ) 4.0 397 1.93
17 Pomfrets
a Black pomfret (Apolectus niger ) 4.0 42,310 0.57
b Silver pomfret (Pampus argenteus ) 3.1 3,960 1.06
c Chinese pomfrets (Pampus chinensis ) 3.6 2,977 1.14
18 Mackerel
a Indian mackerel (Rastrelliger kanagurta ) 3.2 372 1.96
b Other mackerels 1.91
Rastrelliger faughni 3.4 408 1.91
19 Seer fishes
a Scomberomorus commersoni 4.5 43,932 0.57
b Scomberomorus guttatus 4.3 4,685 1.01
c Scomberomorus lineolatus 4.5 2,874 1.15
d Acanthocybium spp. 0.54
Acanthocybium solandri 4.4 53,835 0.54
333
# Categories/Species Trophic level Adult mean weight (gms)
and Natural mortality*
Intrinsic rate
of growth (r)
20 Tunnies
a Euthynnus affinis 4.5 12,804 0.78
b Auxis spp. 1.28
Auxis thazard 4.3 1,740 1.31
Auxis rochei 4.1 2,058 1.26
c Katsuwonus pelamis 3.8 19,265 0.70
d Thunnus tonggol 4.5 21,429 0.68
e Other tunnies 0.40
Thunnus albacares 4.3 166,859 0.40
21 Bill fishes 0.36
Istiophorus gladius 4.5 68,508 0.50
Xiphias gladius 4.5 955,205 0.25
22 Barracudas 0.65
Sphyraena jello 4.5 16,486 0.73
Sphyraena barracuda 4.5 41,320 0.58
23 Mullets 1.20
Mugil cephalus 2.1 1,128 1.47
Mugil macrolepis 2.6 3,435 1.10
Mugil seheli 2.3 5,903 0.95
Mugil tade 2.0 1,366 1.40
Mugil waigiensis 2.3 2,785 1.16
24 Unicorn cod (Bregmaceros macclellendii ) 3.3 3.50
25 Flatfishes
a Halibut 1.19
Psettodes erumei 4.4 2,565 1.19
b Flounders 1.37
Pseudorhombus arsius 4.2 1,993 1.27
Bothus pantherinus 3.5 2,180 1.24
Chascanopsetta lugubris 3.5 725 1.65
c Soles 1.52
Cynoglossus dubius 3.5 1,406 1.39
Cynoglossus bilineatus 3.5 961 1.53
Cynoglossus arel 3.3 1,091 1.48
Cynoglossus dispar 3.5 669 1.68
26 Crustaceans
a Penaeid Prawns
Penaeus monodon 2.6 240 2.19
b Non Penaeid Prawns 2.6 240 2.19
c Crabs 1.90
Portunus pelagicus 3.0 420 1.90
d Lobsters
Panulirus polyphagus 2.7 0.34* 0.68
e Stomatopod
Oratosquilla nepa 3.1 0.52* 1.04
27 Mollusc except Cephalopods 1.42
Xancus pyrum 2.0 0.28* 0.55
Paphia malabarica 2.0 1.82* 3.64
28 Cephalopods 0.83
Octopus vulgaris 4.1 10,000 0.83
