The Economics of Groundwater Irrigation in the Indus Basin, Pakistan… · Pakistan. The review...
Transcript of The Economics of Groundwater Irrigation in the Indus Basin, Pakistan… · Pakistan. The review...
The Economics of Groundwater Irrigation in the Indus Basin, Pakistan: Tube-well Adoption, Technical and Irrigation Water Efficiency and Optimal Allocation
Muhammad Arif Watto
M.Sc. (Hons.) Agricultural Extension Education, University of
Agriculture, Faisalabad, Pakistan
Thesis presented for the degree of
Doctor of Philosophy
The University of Western Australia
School of Agricultural and Resource Economics
2015
“When the well is dry, we know the worth of water.” (Benjamin Franklin, 1746)
Declaration for thesis containing published work and/or work prepared for publication
This thesis contains published work and/or work prepared for publication, some of which
has been co-authored. The bibliographical details of the work and where it appears in the
thesis are outlined below. The student must attach to this declaration a statement for each
publication that clarifies the contribution of the student to the work. This may be in the
form of a description of the precise contributions of the student to the published work
and/or a statement of percent contribution by the student. This statement must be signed
by all authors. If signatures from all the authors cannot be obtained, the statement
detailing the student’s contribution to the published work must be signed by the
coordinating supervisor.
Journal Publications
1. Watto, M.A., and Mugera, A.W. (2014). Groundwater depletion in the Indus plains of Pakistan: Imperatives, repercussions and management issues. International Journal of River Basin Management (forthcoming).
M.A. Watto contributed 70%
2. Watto, M.A., Mugera, A.W. and Kingwell, R. (2014). Adoption of tube-well technology under the groundwater depletion risk: Evidences from Punjab, Pakistan.(Submitted for publication to Journal of Hydrology)
M.A. Watto contributed 70%
3. Watto, M.A., and Mugera, A.W. (2014). Wheat farming system performance and irrigation efficiency: A nonparametric metafrontier approach. International Transactions in Operation Research. (Accepted for Publication)
M.A. Watto contributed 70%
4. Watto, M.A., and Mugera, A.W. (2014). Econometric estimation of technical and irrigation efficiency of groundwater irrigated cotton cultivation in Pakistan. Journal of Hydrology: Regional Studies. doi:10.1016/j.ejrh.2014.11.001
M.A. Watto contributed 70%
5. Watto, M.A., and Mugera, A.W. (2014). Measuring production and irrigation efficiencies of rice farms: Evidence from the Punjab, Pakistan. Asian Economic Journal, Vol. 28 (3): 301–322.
M.A. Watto contributed 70%
6. Watto, M.A., and Mugera, A.W. (2014). Irrigation water demand and implications for groundwater pricing in Pakistan.(submitted for publication to Water Policy)
M.A. Watto contributed 70%
Conference Papers
7. Watto, M.A. and Mugera, A.W. 2012. Measuring groundwater irrigation efficiency in Pakistan: A DEA approach using the sub-vector and slack-based models. Presented at 57th Annual AARES Conference at Sydney, Australia 4-9 February, 2013.
M.A. Watto contributed 70%
8. Watto, M.A. and Mugera, A.W. 2014. Does the risk of groundwater depletion drive tube-well technology adoption? A case of Pakistan. HIC-2014, International Conference on Hydroinformatics, New York, USA August 17-21.
M.A. Watto contributed 70%
Student signature…………………………………………………………..
Coordinating supervisor signature………………………………………...
I
Certification
I certify that this thesis has been substantially completed during the course of enrolment
in this degree at The University of Western Australia and has not previously been
submitted or accepted for a degree at this or any other institution. I certify that help
received in preparing this thesis and all sources used have been acknowledged.
Arif Watto
Perth, May, 2015
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Acknowledgements
I would like to express my sincere gratitude and indebtedness to all who guided and
facilitated me during my journey of doctoral study. First, this study was made possible
by the generous support from the University of Agriculture, Faisalabad, Pakistan and
from the University of Western Australia. Many thanks to Professor Iqrar Ahmad Khan
(Vice Chancellor, UAF) and Professor Kadambot Siddique (Director Institute of
Agriculture, UWA) for organizing the UWA Ad Hoc UAF Pakistan programme.
Second, I’m highly indebted to my coordinating supervisor Dr Amin W. Mugera for his
guidance early from my writing the research proposal and to the completion of this
dissertation. The patience, kindness and friendship I received from my supervisor made
my PhD experience an enjoyable one. I never impaired by diminution but always
remained committed and motivated throughout my doctoral study under his supervision.
I would always remember his indefatigable assistance for the completion of this
dissertation.
I also benefitted immensely from Professor Ross Kingwell from his remarkable
knowledge. I really acknowledge his assistance and value his suggestions to improve
this dissertation. I owe many thanks to the former and present heads of school Ben
White and David Pannell for their timely support and guidance. Many thanks to
Atakelty Hailu, Jo Pluske, Ram Pandat and Chunbo Ma, members of the School of
Agricultural and Resource Economics (SARE) postgraduate progress review committee
for their keen interest in my studies, research endeavours and personal well-being. I also
want to thank all SARE staff for the encouragement, support and friendship I received
from all of them. I am thankful to Deborah Swindles, Emma Smith and Theresa Goh for
their all types of administrative support. I also want to acknowledge the friendship and
wonderful time I had with Tas Thamo, Manoj Thibbotuwawa, Khalid Bashir, Donkor
Adai, and Katrina Davis in the ‘Cabinet Room’. Let me mention my other SARE
colleagues especially Masood Azeem, Luke Abatania, Tran Doc Lap, Alison Wilson
and Veronique Florec for their friendship and moral support. I will forever cherish the
friendship that you accorded me.
I also owe a debt of gratitude to my teachers Dr Tanvir Ali, Dr Munir Ahmad, Dr Babar
Shahbaz, Dr Gazanfar Ali and Dr Amir Shah who were guiding lights for me. I also
want to acknowledge my friendship with Arbab, Mudasar, Umar, Asim, Ayaz and many
more who made my stay at Perth memorable and many thanks to Dr Shoukat Ali, Dr
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Sajid and Asif Iqbal from Pakistan who kept sending best wishes to me at every
occasion.
Finally, I want to express my sincerest gratitude to my mother, brothers Asif and Bilal
and sisters for their unwavering moral support and best wishes.
V
Abstract
This PhD study explored the economics of groundwater irrigation in the Indus basin of
Pakistan where groundwater exploitation is escalating due to high irrigation water
demands. Recent trends in groundwater withdrawals for irrigation and increases in
number of tube-wells have brought into greater prominence the challenge to control
groundwater over-exploitation. Besides this, hydrological assessments indicate that
groundwater extraction rates have exceeded the annual recharge rates the available
literature highlights the inefficient use of water resources in the irrigation sector.
This study had four main objectives: 1) to review the causes and consequences of
groundwater overdrafting in the region; 2) to investigate farmers’ adoption decisions
regarding tube-well technology; 3) to analyse irrigation water use efficiency for
different crop enterprises; and 4) to estimate the derived demand for irrigation. Data
used for analyses come from a survey of 200 rural households that predominately use
groundwater for irrigation in the arid to semi-arid plains of the Punjab province of
Pakistan.
The review found that groundwater expansion in the Indus basin was mainly as a result
of the rigidity of the surface water allocation system, increased crop intensities during
the Green Revolution and the division of the Indus river tributaries under the Indus
Water Treaty in the 1960s. Later, overexploitation of groundwater was as a result of
increase in population and lack of effective groundwater management policies.
A moment-based approach was used to analyse farmers’ decisions to adopt tube-well
technology when groundwater table is declining. The estimation procedure consisted of
two steps. First, the moments of profit distribution were computed using an expected
utility maximization framework. In the next step, the estimated moments were
incorporated into a probit model to estimate their impact on tube-well adoption
decisions. Analysis of tube-well adoption decision reveals that farmers are not risk-
neutral. The results indicate that the probability of tube-well adoption increases
significantly with increase in expected mean and variance of profit. The non-significant
third moment (skewness) indicates that downside profit risk does not have significant
impact on tube-well adoption. The highly significant fourth moment (kurtosis) indicates
that adoption of tube-well technology decreases significantly in the presence of extreme
events.
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Both parametric (stochastic frontier analysis) and non-parametric (data envelopment
analysis) approaches were used to estimate technical and irrigation water use efficiency
separately for tube-well owners (adopters) and water buyers (non-adopters) for different
crops viz. wheat, cotton and rice. Results indicate that the average technical efficiency
(TE) scores are fairly high but there are still inefficiencies in the production of all crops.
The TE efficiency distribution for both tube-well owners is right skewed suggesting that
tube-well owners are slightly more efficient compared to water buyers. The highest
level of TE is in rice production and lowest is in cotton farming. The estimated mean
irrigation water efficiency (IWE) of tube-well owners and water buyers are less than
their respective technical efficiencies. On average, tube-well owners are more irrigation
water-use efficient than water buyers. Again the lowest IWE is in cotton farming and
the highest is in rice farming. The IWE estimates of all the three crops (wheat, cotton
and rice) suggest that there is considerable potential to improve irrigation water use
efficiency for both tube-well owners and water buyers.
The derived demand for groundwater was estimated using the Positive Mathematical
Programming (PMP) approach. The estimates indicate that the actual crop water
requirement is lower than the amount of groundwater that is being extracted for
irrigation. Given the limited land available for different crops, additional irrigation
water supply would not increase farm profit. Therefore, producers would not respond to
any pricing policy unless their current groundwater extraction rate is constrained to
certain limits. It is suggested that by introducing groundwater pricing at Rs. 0.04/ m3 for
water sellers and Rs. 0.036/m3 for water buyers could induce them to reduce their
current groundwater extraction rates by 2%.
Results from this study have policy implications. First, farmers adopt the tube-well
technology to overcome crop production risk and variability in farm profits. However,
tube-well adoption does not necessarily improve irrigation water use efficiency nor
conserve the groundwater resources. Therefore, tube-well adoption must be
accompanied by complimentary policies that promote efficient use of groundwater for
irrigation, such as adoption of sprinkler or drip irrigation technologies, and limit
extraction in order to ensure the sustainability of groundwater resources. Second, there
is need for policies that educate farmers on actual crop water requirements as a way to
promote irrigation water use efficiency. This may involve extending extension advice
from crop management to groundwater management or creating a separate water
extension wing. Third, to ensure sustainable groundwater use, there is need for policies
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that will constrain groundwater extraction. This could involve metering of groundwater
extraction and pricing to induce farmers to reduce irrigation water demands. Finally,
additional policies are also required to improve equity of access for water buyers who
generally face more irrigation water uncertainties being located down the water supply
chain.
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Table of Contents
Certification ....................................................................................................................... I
Acknowledgements ......................................................................................................... III
Abstract ............................................................................................................................ V
Table of Contents ............................................................................................................ IX
List of Tables ............................................................................................................... XIII
List of Figures ............................................................................................................... XV
1. Introduction ............................................................................................................. 1
1.1 Overview ........................................................................................................... 1
1.2 Context and Problem Statement ........................................................................ 2
1.3 Conceptual Framework ..................................................................................... 5
1.4 Research Objectives .......................................................................................... 6
1.5 Data Description and Research Design ............................................................. 7
1.6 Contribution to Scholarship and Originality ..................................................... 8
1.7 Thesis Organisation ........................................................................................... 9
2. Imperatives and Repercussions of Groundwater Depletion ............................. 11
Abstract ....................................................................................................................... 11
2.1 Introduction ..................................................................................................... 12
2.2 Groundwater from Menace to Mainstay ......................................................... 13
2.3 Warabandi- A Rigid Irrigation Delivery System ............................................ 15
2.4 The Green Revolution ..................................................................................... 16
2.5 The Indus Water Treaty and Beyond the Indus Water Treaty ........................ 17
2.6 Myopic Groundwater Policies ........................................................................ 18
2.7 Population Growth and Water Demands ........................................................ 20
2.8 Lack of Surface Water Developments ............................................................ 21
2.9 Groundwater Overuse Externalities ................................................................ 22
2.10 Environmental Externalities ............................................................................ 23
2.10.1 Soil Salinization ...................................................................................... 23
2.10.2 Land Subsidence ..................................................................................... 24
2.10.3 Seawater Intrusion ................................................................................... 24
2.10.4 Drying of Wetlands and Vegetation ........................................................ 25
2.11 Economic Externalities ................................................................................... 25
2.12 Spatial Externalities ........................................................................................ 26
2.13 Groundwater Management Problems .............................................................. 28
2.13.1 Institutional Impediments ....................................................................... 28
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2.13.2 Lack of Entitlements and Informal Groundwater Marketing .................. 29
2.13.3 Irrigation Efficiencies and Water Productivity ....................................... 30
2.14 Conclusions ..................................................................................................... 32
3. The Groundwater Depletion Risk and Tube-well Technology Adoption ........ 35
Abstract ....................................................................................................................... 35
3.1 Introduction ..................................................................................................... 35
3.2 Literature Review on Irrigation Technology Adoption .................................. 38
3.2.1 Adoption of Tube-well Technology in Pakistan ......................................... 40
3.3 Theoretical Framework ................................................................................... 42
3.3.1 Empirical Estimation Procedure ................................................................. 44
3.4 Data Descriptions ............................................................................................ 47
3.4.1 Salient Features of Study Districts .............................................................. 47
3.4.2 Data Descriptions ........................................................................................ 49
3.5 Results and Discussion ................................................................................... 52
3.6 Conclusions ..................................................................................................... 56
4. The Efficiency of Irrigation Water Use and its Determinants .......................... 59
4.1 Technical and Irrigation Efficiency of Wheat Farms in Pakistan: A
Nonparametric Meta-frontier Approach ..................................................................... 61
Abstract ................................................................................................................... 63
4.1.1 Introduction ................................................................................................. 63
4.1.2 Methodological Framework ........................................................................ 66
4.1.3 Methodological Framework ........................................................................ 69
4.1.4 Study Areas, Data and Variable Definitions ............................................... 73
4.1.5 Empirical Results and Discussion ............................................................... 75
4.1.6 Conclusion .................................................................................................. 82
4.2 Econometric Approach to Estimating Technical and Irrigation Efficiency in
Cotton Farming in Pakistan ......................................................................................... 85
Abstract ................................................................................................................... 87
4.2.1 Introduction ................................................................................................. 87
4.2.2 Conceptual and Methodological Framework .............................................. 91
4.2.3 Study Area and Data ................................................................................... 96
4.2.4 Estimation Results ...................................................................................... 99
4.2.5 Discussion and Conclusions ..................................................................... 104
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4.3 Measuring Production and Irrigation Efficiencies of Rice Farms: Evidence
from Punjab, Pakistan ............................................................................................... 109
Abstract ................................................................................................................. 111
4.3.1 Introduction ............................................................................................... 111
4.3.2 Review of Literature ................................................................................. 113
4.3.3 Methodological Framework ...................................................................... 115
4.3.4 Study Area and Data ................................................................................. 120
4.3.5 Results and Discussion .............................................................................. 123
4.3.6 Conclusion ................................................................................................ 129
5. Derived Demand for Irrigation Water .............................................................. 131
Abstract ..................................................................................................................... 131
5.1 Introduction ................................................................................................... 131
5.2 Irrigation Water Pricing and Demand ........................................................... 134
5.3 Theoretical Framework ................................................................................. 136
5.3.1 Approaches to Derive Demand for Irrigation Water ................................. 136
5.3.2 Method of Analysis-Positive Mathematical Programming (PMP) ........... 137
5.4 Study Areas and Data Description ................................................................ 139
5.4.1 Nature of Irrigation Water Demand in the Central and South Punjab ...... 139
5.5 Results and Discussion .................................................................................. 142
5.6 Conclusions ................................................................................................... 149
6. Conclusions .......................................................................................................... 151
6.1 Summary ....................................................................................................... 151
6.2 Methods ......................................................................................................... 152
6.3 Main Results ................................................................................................. 153
6.4 Synthesis of Main Findings ........................................................................... 155
6.5 Policy Recommendations .............................................................................. 157
6.6 Limitations and Future Research Needs ....................................................... 158
7. References ............................................................................................................ 159
8. Appendix .............................................................................................................. 177
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List of Tables
Table 3.1: Summary statistics of the variables for cotton and wheat crops .................... 50
Table 3.2: Summary statistics of the variables used in the probability model ................ 51
Table 3.3: Estimation of the results for the probability of adopting a tube-well ............ 53
Table 3.4: Marginal effects of the explanatory variables ................................................ 55
Table 4.1.1: Descriptive statistics of the variables used in the DEA analysis ................ 75
Table 4.1.2: Metafrontier and groupfrontier technical efficiency frequency distribution
......................................................................................................................................... 76
Table 4.1.3: Average groupfrontier and metafrontier technical efficiency scores and the
technology gap ratio ........................................................................................................ 77
Table 4.1.4: Frequency distribution of irrigation water use efficiency under the
metafrontier and groupfrontiers ...................................................................................... 78
Table 4.1.5: Spearman’s rank correlation among technical efficiency and the sub-vector
irrigation water use efficiencies ...................................................................................... 79
Table 4.1.6: Bootstrap truncated estimates of the determinants of technical and
irrigation water use efficiency ......................................................................................... 81
Table 4.2.1: Summary statistics of the variables used in the empirical model ............... 98
Table 4.2.2: Restricted and unrestricted model parameter estimates ............................ 100
Table 4.2.3: Inefficiency model estimates .................................................................... 101
Table 4.2.4: Proportion of farms satisfying the monotonicity and quasi-concavity
conditions ...................................................................................................................... 101
Table 4.2.5: Partial production elasticities for the sample mean from the unrestricted
and restricted models .................................................................................................... 102
Table 4.2.6: Frequency distribution of technical and irrigation water use efficiency for
tube-well owners from the unrestricted and the restricted models ............................... 103
Table 4.2.7: Frequency distribution of technical and irrigation water use efficiency for
water buyers from the unrestricted and the restricted models....................................... 103
Table 4.3.1: Descriptive statistics of the variables used in the DEA analysis .............. 121
Table 4.3.2: Summary statistics of variables included in the truncated regression ...... 123
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Table 4.3.3: Frequency distribution of technical, scale, cost and allocative efficiencies
....................................................................................................................................... 124
Table 4.3.4: Distribution of returns to scale for tube wells owners and water buyers .. 124
Table 4.3.5: Frequency distribution of sub-vector and slack-based water use efficiencies
....................................................................................................................................... 125
Table 4.3.6: Spearman’s rank correlation among technical efficiency and the sub-vector
and slack-based irrigation water use efficiencies .......................................................... 126
Table 4.3.7: Paired samples t-test demonstrating the difference between technical and
irrigation water use efficiencies .................................................................................... 126
Table 4.3.8: Bootstrap truncated estimates of the determinants of technical and
irrigation water use efficiency....................................................................................... 128
Table 5.1: Number of sample households that grew wheat and cotton in the Lodhran and
Jhang districts during 2010-2011 .................................................................................. 140
Table 5.2: Area allocation to different crops, yield, irrigation water requirements and
crop prices ..................................................................................................................... 141
Table 5.3: Input cost for different farm operations in Rs.ha-1 ....................................... 142
Table 5.4: PMP step 1, water sellers ............................................................................. 143
Table 5.5: PMP step 1, water buyers ............................................................................ 143
Table 5.6: PMP Step 2, dual multipliers, yield slope coefficient and the intercept
coefficient...................................................................................................................... 144
Table 5.7: PMP Step 3, water sellers ............................................................................ 145
Table 5.8: PMP step 3, water buyers ............................................................................ 146
Table 5.9: Percent change in water demand given and the percent change in shadow
price ............................................................................................................................... 148
XV
List of Figures
Figure 1.1: Conceptual framework for the study .............................................................. 5
Figure 1.2: Map showing study districts (Jhang and Lodhran) in red colour ................... 8
Figure 2.1: Historical development of tube-wells and share of groundwater in irrigated
agriculture in Pakistan ..................................................................................................... 14
Figure 2.2: The red depression highlights groundwater depletion rates with expanding
sphere towards Pakistan side ........................................................................................... 18
Figure 2.3: Groundwater use trends in Pakistan ............................................................. 20
Figure 2.4: Different water shortage versus population growth projections ................... 21
Figure 2.5: Storage loss in the capacity of different dams in Pakistan ........................... 22
Figure 2.6: Province wise soil salinity status for the period 2001-04 ............................. 24
Figure 2.7: Variability of the groundwater recharge from head to tail of the LBD canal
command ......................................................................................................................... 27
Figure 2.8: Water productivity as a ratio of total GDP to the total annual water
withdrawals ..................................................................................................................... 31
Figure 2.9: Yield (in kg/ha) in the selected countries ..................................................... 32
Figure 4.1.1: Graphical representation of the technical and sub-vector irrigation water
use efficiency .................................................................................................................. 71
Figure 4.1.2: Cumulative distribution of meta-frontier and group-frontier technical
efficiency ......................................................................................................................... 77
Figure 4.1.3: Cumulative distribution of metafrontier and groupfrontier irrigation water
use efficiency .................................................................................................................. 80
Figure 4.2.1: Historical trends in cotton production and consumption in Pakistan ........ 89
Figure 4.2.2: Graphical representation of irrigation water use efficiency ...................... 91
Figure 4.2.3: Technical efficiency estimates from the restricted and the unrestricted
models ........................................................................................................................... 104
Figure 4.2.4: Irrigation water use efficiency estimates from the restricted and the
unrestricted models ....................................................................................................... 104
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Figure 4.3.1: Graphical representation of the sub-vector and the slack-based input
oriented efficiency models ............................................................................................ 117
Figure 4.3.2: Cumulative distribution for technical, sub-vector and slack-based
irrigation water use efficiencies .................................................................................... 126
Figure 5.1: Derived demand for groundwater for irrigation for water sellers .............. 147
Figure 5.2: Derived demand for groundwater for irrigation for water buyers .............. 147
1
CHAPTER 1 1. Introduction
1.1 Overview
Water is a critical resource for a variety of users including irrigators, residential
customers and industrial producers. It is expected that the future water demand and
supply relationship will be greatly influenced by human actions that would greatly
affect water availability and accessibility (Barnett et al., 2005, Laghari et al., 2012,
Sharma et al., 2010, Karagiannis et al., 2003, Hanjra and Gichuki, 2008, Tilman et al.,
2002, Vörösmarty et al., 2000, Archer et al., 2010). Consequently, one third of the
population living in developing countries will be facing water scarcity by 2025 (Molden
et al., 2001). Indeed, water scarcity is also projected to greatly increase inter-sectoral
water competition. Hence, addressing and managing water scarcity has become a
critical policy challenge on international and national agendas. Worldwide, growing
water scarcity raises two important concerns on how to: (i) use available water
resources more efficiently and sustainably and; (ii) find possible ways to address and
manage water scarcity to meet inter-sectoral multiple water demands equitably.
The agriculture sector is by far the largest user of global freshwater withdrawals.
Agricultural intensification has led to a rapid increase in irrigation water demands in
many parts of the world (Yang et al., 2003, Koundouri et al., 2006, Jara-Rojas et al.,
2013, Biswas, 1993). The sector withdraws about 70% of the global water resources and
this is expected to further increase by 28% in 2025 relative to 1995 under the business
as usual scenario1(Rosegrant et al., 2002). Nevertheless, increasing inter-sectoral water
demands and growing awareness of environmental and in-stream water values have
increased pressure to reduce water diversions for the agriculture sector (Seo et al., 2008,
Hussain and Hanjra, 2004, Malano et al., 2004, Wichelns, 2002). Because of rapid
population growth, water demands for industrial and domestic consumption, together
with livestock consumption, would dramatically increase by 62% in 2025 relative to
1995 (Rosegrant et al., 2002). Within these prospects, increasing water scarcity would
require a re-allocation of water resources from low to high valued users and agriculture
1Based on the assumption that current trends in agricultural water use can be extrapolated—that reservoirs will be constructed as in the past, Shiklomanov (1999) projected that the world’s irrigated area will expand by 30% and irrigation water withdrawal will increase by 28% from 1995 to 2025.
Introduction
2
would obviously be perceived as an inefficient water user (Donohew, 2009).
Consequently, irrigated agriculture would be under great pressure to improve on its
water efficiency and productivity (Wichelns, 2002, Malano et al., 2004). Many studies
from different parts of the world already suggest considerable scope for improving
efficiency and productivity of water in irrigated agriculture (Cai et al., 2003, Gleick,
2000, Molden et al., 2010).
1.2 Context and Problem Statement
As in many parts of the world, water scarcity is one of the key factors constraining
agricultural development in the Indus river basin of Pakistan. Pakistan’s current per
capita water availability of about 1,066 m3 has placed it in the “high water stress
category” (Govt. of Pakistan, 2009). Water resources in Pakistan are under extreme
pressure from massive population growth, rapid industrial and domestic demands and
climate change (Archer et al., 2010, Barnett et al., 2005, Laghari et al., 2012, Sharma et
al., 2010). Archer et al. (2010) appraised various per capita water availabilities for
different population projections in Pakistan. They estimated a per capita water
availability of 725 m3 for 2025 and 415 m3 for 2050 if population continues to increase
under the current population growth rate. Indeed, these estimates indicate severe water
scarcity by any standard2 (Gleick, 1992, Postel, 1999).
Due to the arid and semi-arid climate, agriculture in Pakistan heavily relies on irrigation
water from both canal water supplies and groundwater withdrawals. Nevertheless,
surface water resources are deficient and are unevenly distributed across the Indus basin
of Pakistan. Inadequate and uneven canal water supplies have led farmers to meet their
irrigation water requirements through massive groundwater extractions. Over the past
half century, groundwater-fed irrigation has become the mainstay of irrigated
agriculture in Pakistan. Currently, more than 50% of the country’s irrigation
requirements are being met through groundwater extractions (Archer et al., 2010,
Qureshi et al., 2009).
2Water scarcity refers to a situation where there is insufficient water to satisfy normal human water needs for food, feed, drinking and other uses, implying an excess of water demand over available supply. It is a relative concept, therefore, difficult to capture in single indices.
Introduction
3
The remarkable increase in groundwater use commenced after the 1960s with the
installation of large capacity (0.084 to0.14 m3 sec-1) tube-wells3. During the first half of
19th century, the introduction of gravity based large scale irrigation system without
proper drainage system resulted in rising groundwater tables and, consequently, turned
large tracts of irrigated lands into waterlogged soils. In 1960, the government of
Pakistan initiated numerous schemes to overcome waterlogging and salinity problems
including mega salinity control and reclamation programme (SCARP) to lower
groundwater tables through vertical drainage in the form of thousands of shallow tube-
wells. The SCAPR tube-wells not only helped reclaiming waterlogged and saline soils
but also augmented irrigation water supplies by many times. The successful
demonstration of the benefits of SCARP tube-wells set the scene for rapid development
of private tube-wells in coming years. The government encouraged the adoption of
private tube-wells on a large scale with the aim of controlling the rising groundwater
tables in the waterlogged areas and encouraging agricultural production in fresh
groundwater areas (Steenbergen and Oliemans, 2002). The adoption of tube-well
technology was largely aided by government support policies such as rural
electrification, subsidization of electricity, diesel and drilling services, provision of free
pump sets and soft long-term loans (Falcon and Gotsch, 1968, Papanek, 1968, van
Steenbergen and Oliemans, 2002, Johnson, 1989 ). Later, higher crop yields and greater
economic returns from groundwater use (Meinzen-Dick, 1996, Byrelle and Siddiq,
1994) encouraged farmers to adopt tube-well technology even without government’s
support (Muhammad, 1964, Muhammad, 1965, Falcon and Gotsch, 1968, Nulty, 1972).
Subsequently, with the continued increase in demand for irrigation water in the face of
dwindling surface water supplies, more and more irrigation water supplies were met
through groundwater abstractions (Shiva, 1991, Ahmad et al., 2004b, Rodel et al.,
2009).
Although the number of tube-wells has gone beyond one million, many small-scale
farmers do not own tube-wells. These smallholder farmers and tenants purchase
groundwater from their neighbouring tube-well owners under informally developed
3A tube-well is a type of water well, drilled to extract subsurface water through a pump. In Pakistan, tub-wells of 5-7 inch diameter are usually drilled to extract groundwater. These tube-wells are mounted with either 15-25 horsepower diesel engine or 15-30 horsepower electrical motor depending upon the depth of water table.
Introduction
4
groundwater markets. Informal groundwater markets4 are reported throughout Pakistan
but are more common in Punjab and Balochistan provinces (Khair et al., 2012,
Meinzen-Dick, 1996). These markets generally function under social settings and are
greatly influenced by the social ties between tube-well owners and water buyers
(Meinzen-Dick, 1996, Rinaudo et al., 1997b, Khair et al., 2012). Markets for
groundwater involve the informal sale of groundwater from private tube-wells without
involving the exchange of permanent water rights. Tube-well owners receive payments
for groundwater that allows buyers to access the opportunity to increase agricultural
productivity (Manjunatha et al., 2011, Meinzen-Dick, 1996, Shiferaw et al., 2008).
Nevertheless, in the absence of groundwater entitlements and any formal regulatory
mechanism, sometimes tube-well owners prefer certain water buyers due to social ties
with them, thus discriminating on whom to sell water to (Shah, 1993, Jacoby et al.,
2004, Khanna, 2007). Although, such informal groundwater markets improve the equity
of access to groundwater resources and play an important role in addressing water
scarcity (Khair et al., 2012), these may not fully convey the scarcity value of water and
so do not prevent over-exploitation (Meinzen-Dick, 1996).
Over the past three decades, massive pumping of groundwater aquifers to meet
increasing irrigation water demands has started lowering groundwater tables rapidly in
different parts of the country (Kijne, 1999b, Shah et al., 2000, Khan et al., 2008a,
Qureshi et al., 2009). Besides lowering groundwater tables, the unimpeded tube-well
growth has led to many negative environmental externalities such as salt water intrusion
and secondary salinity which portend serious repercussions to the sustainability of
irrigated agriculture in the region (Kijne, 1999b, Shah et al., 2000, Khan et al., 2008a,
Qureshi et al., 2009). Khan et al. (2008b) projects that in the next 25 years there will be
a 10 to 20 metres decline in groundwater levels in the upper and lower regions of the
Rachna Doab5 in North-East Pakistan if it continues to be depleted at the current rate. In
many parts of the country, rapid depletion of the water table is not only pushing farmers
to re-install tube-wells at greater depths but also impacting their future decisions to
adopt tube-well technology. Despite a wide array of studies on hydrological assessment
4In South Asia (Pakistan, India, Bangladesh and Nepal) irrigation is highly dependent on groundwater supplies through tube-well pumping. 5The word “Doab" means land of two rivers. The Rachna Doab is one of the main agricultural regions of the Punjab. The Rachna Doab lies between 30° 35' and 32° 50' N. and 71° 50' and 75° 3'E., between the Chenab and Ravi rivers.
Introduction
5
of escalating groundwater exploitation, there remains a vast gap in the literature on
farmer’s tube-well adoption decisions, irrigation water use performance and irrigation
water demand patterns given the current state of rapid depletion of groundwater
resources.
1.3 Conceptual Framework
Figure 1.1 provides a conceptual framework that will be used to guide this study .The
conceptual framework links together different study objectives in the form of challenges
and opportunities for sustainable groundwater management. The conceptual framework
shows that ineffective groundwater management policies and inefficient utilization of
groundwater resources through unimpeded tube-well growth has led to groundwater
resource depletion. As a sequence of multiple steps, an ex post analysis of the factors
which have contributed to groundwater depletion is an important step to address the
groundwater depletion problem through policy instruments and appropriate practice
change.
Figure 1.1: Conceptual framework for the study
Introduction
6
As indicted by the conceptual framework, the rapid lowering of groundwater tables is as
a result of farmers’ decisions to adopt tube-well technology for groundwater extraction.
Assuming that farmers are risk-averse and want to maximize their farm profits, it
becomes important to analyse their decisions regarding the adoption of tube-wells when
groundwater resources are under rapid decline. It is unknown whether the decision to
adopt the tube well technology is influenced by farmers’ expectation about farm returns,
its variance, and downside risk. Because tube-well installation requires a large up-front
investment and is not a portable technology (i.e., a potentially stranded asset), lowering
groundwater tables may influence farmer’s investment decisions. Moreover, tube-well
installation entails a sunk cost if the tube-well goes unproductive. In such scenarios,
only financially secure farmers with enough capacity to bear sunk costs would decide to
invest in the tube-well technology. .
Because groundwater management requires multidimensional actions and policies,
assessing irrigation water efficiency and estimating irrigation water demand are
complimentary water management policy objectives. As the conceptual framework
shows, any value generated in terms of improving irrigation water use efficiency and
optimising irrigation water demands by both adopters and non-adopters is directly
linked to sustainable groundwater management. Whilst, irrigation efficiency analysis
helps identifying opportunities for sustainable groundwater management by
benchmarking inefficient water users against the efficient users, economic instruments
like water pricing foster efficient water allocation and induce allocation from lower to
higher values. The combined effect of improving water use efficiency and optimal water
allocations holds the key for sustainable groundwater management practice.
1.4 Research Objectives
The purpose of this doctoral study is to explore the economics of groundwater use for
irrigation in the Indus river basin of Pakistan. Specific objectives of the research are to:
1) identify causes and consequences of groundwater overdrafting and draws
attention about groundwater resource management issues;
2) analyse farmer’s decisions to adopt tube-well technology under the risk of
groundwater depletion and associated production uncertainties;
3) estimate efficiency of groundwater use in irrigation at for different crops and;
4) estimate the derived demand of groundwater use in irrigation by different
groundwater users i.e., water sellers and water buyers.
Introduction
7
1.5 Data Description and Research Design
The data used in this study come from a rural household survey conducted during the
2010 to 2011cropping year. Detailed farm level data was collected from 200
groundwater users randomly selected from two different cropping regions in the semi-
arid plains of the Punjab province of Pakistan: the cotton-wheat region and the mixed-
cropping region.
The study is conducted in the Jhang and the Lodhran districts of the Punjab province of
Pakistan. The study districts are show in Figure 1.2. In both study districts, agriculture
heavily relies on groundwater for irrigation purposes due to the arid and semi-arid
climate. The selected farms solely depend on groundwater for irrigation purposes in the
Jhang district while partly on canal water in the Lodhran district. Besides limited canal
water supplies, both districts receive very little rainfall. The average precipitation rate in
the Lodhran district is 71mm-1 while it is 180mm-1 in the Jhang district. Therefore,
majority of the irrigation water comes from groundwater which is extracted mainly
through deep tube-wells. The two study districts have large variation in the depths of
installed tube-wells. In the Lodhran district, the variation was observed to be between
60 to 99 meters compared to the Jhang district where it was between 33 to 57 meters.
As a result of deep groundwater tables and the high installation cost, tube-well
population is relatively less dense in the Northern part of the Jhang and Southern part of
the Lodhran district. Therefore, farmers generally engage in informal groundwater
trading. A multi-stage sampling technique was used in data collection. In the first stage,
one tehsil6 was selected purposively from the Lodhran and the Jhang district. In the next
stage, 10 villages were selected at random from each purposively selected tehsil. Then,
from each village 10 groundwater users (5 tube-well owners and 5 water buyers) were
selected randomly. A village is usually comprised of 60-70 farming households in the
study areas. Finally, we collected farm level data from 200 groundwater-fed agricultural
farms. The dataset used in this study is relatively small and is collected from one tehsil
in two districts. However, the sample farms reflect the typical situation of groundwater
irrigated farms in the study districts in particular and in the rural areas of the Punjab in
general.
6Tehsil is an administrative unit. A district usually comprise of 5-6 tehsils (sub-districts). Lodhran district is comprised of three tehsils i.e., Dunyapur, Kahror Pakka and Lodhran while Jhang district is comprised of four tehsils i.e. Athara Hazari, Shorkot, Ahmad Pur Sial and Jhang.
Introduction
8
Figure 1.2: Map showing study districts (Jhang and Lodhran) in red colour
1.6 Contribution to Scholarship and Originality
This study has carefully reviewed the existing literature on groundwater use for
irrigation in the Punjab, Pakistan. We find empirical studies that explore the economics
of groundwater use for irrigation to be rare. We did not find any study that focused on
analysing adoption of tube-well technology under the water scarcity risk, irrigation
water use efficiency and the groundwater derived demand for irrigated agriculture for
different water users under the traditional groundwater use rights and informal markets.
Although, some studies address challenges and prospectus of groundwater use and the
functioning of groundwater markets in Pakistan (Renfro and Sparling, 1986, Rinaudo et
al., 1997a, Meinzen-Dick, 1996, Khair et al., 2012), they have not addressed the above
mentioned research challenges.
Introduction
9
This study contributes to the existing literature on groundwater resource economics in
Pakistan by assessing irrigation water use efficiency for different crop enterprises and
optimal water allocation under water constraint. Overall, the results from this study
provide insights for policy makers and practitioners on how to ensure sustainable
irrigation using groundwater resources.
1.7 Thesis Organisation
In total this dissertation consists of five chapters with each empirical chapter as a stand-
alone research paper. A brief introduction of each chapter is discussed below.
Chapter 2 is a review paper on the current groundwater situation in Pakistan. It
provides the backdrop for this study. An overview of the current groundwater situation,
causes and consequences of groundwater overdrafting and groundwater policy
framework in Pakistan is discussed. The chapter highlights the challenges the
groundwater sector is facing in Pakistan.
Chapter 3 analyses farmers’ decisions to adopt tube-well technology under the
depleting groundwater resources and associated production uncertainties in the irrigated
semi-arid plains of the Punjab Province, Pakistan. Knowledge of the impact of adoption
of new technology is crucial to understanding how policy interventions can help to
overcome the effects of growing water scarcity and production uncertainties. In this
chapter, moments of the profit distribution are modelled as key determinants of farmer’s
decision regarding adoption of tube-well technology.
Chapter 4 estimates the technical and irrigation groundwater efficiency of irrigated
agriculture within the context of declining groundwater tables. Both non-parametric and
parametric input-specific technical efficiency approaches are used to evaluate the
irrigation water efficiency in wheat, cotton and rice cultivation. A meta-frontier data
envelopment analysis is used to investigate technology gap ratios while the sub-vector
DEA is used to estimate irrigation water use efficiency in wheat farming. A restricted
stochastic production frontier is used to estimate technical and irrigation water
efficiency in cotton cultivation. Finally, the sub-vector and slack-based DEA models are
used to estimate irrigation water efficiency along with production efficiencies in rice
farming.
Chapter 5 employs the Positive Mathematical Programming (PMP) approach to
estimate the derived demand for groundwater for irrigation among water sellers and
water buyers i.e., tube-well owners and water buyers. The shadow price of water is
Introduction
10
estimated to represent farmers’ willingness to pay when groundwater resources become
constrained at different levels.
Chapter 6 summarizes the main findings of this research. Innovative aspects of this
study that add to the existing knowledge of groundwater management, irrigation water
efficiency and the derived demand for groundwater and implications for groundwater
pricing are highlighted and discussed.
11
CHAPTER 2
2. Imperatives and Repercussions of Groundwater Depletion7
Abstract
The sustainability of agricultural growth has been greatly influenced by the massive use
of groundwater in Pakistan for the last few decades. However, the groundwater
economy of Pakistan is at a critical juncture. Concomitant with massive pumping of
groundwater aquifers through unrestricted expansion of tube-wells, groundwater
exploitation has led to many negative environmental, economic and spatial externalities
and serious threats to the sustainability of irrigated agriculture in the region. The
spectacular increase in the groundwater development during the last half-century has
manifested as a kind of “silent revolution” carried out by thousands of farmers in the
pursuit of reliable irrigation water supplies. The groundwater revolution in the Indus
basin has been a result of a succession of factors –each of which has exacerbated the
groundwater crises in the subsequent periods. Initially, groundwater extraction was
started to overcome waterlogging and salinity which was blown up by large scale
surface water developments in coming years.
Within this backdrop, this article attempts to identify the causes and consequences of
groundwater overdrafting in Pakistan and draws attention to groundwater resource
management issues. In this article, we discuss how the rigidity of the surface water
allocation system (Warabandi), the Green Revolution, the Indus Water Treaty, soaring
population and the ineffective groundwater management policies have led to the
“groundwater revolution”. Major environmental externalities identified include soil
salinization, salt water and sea water intrusions, land subsidence and drying up of lakes
and vegetation in different parts of the country. Various pecuniary externalities such as
increasing pumping costs and decreasing land values are also very prominent. Migration
and prospective social conflicts are the potential spatial externalities. We find that
decreasing surface water supplies, unimpeded extraction of groundwater through
7This chapter is accepted for publication as “Groundwater depletion in the Indus Plains of Pakistan: Imperatives, repercussions and management issues” in the Journal of International River Basin Management.
Imperatives and Repercussions of Groundwater Depletion
12
aquifers in the absence of groundwater entitlements and institutional impediments are
major bottlenecks to the sustainable management of groundwater in Pakistan.
2.1 Introduction
In many parts of the world, an increased demand for irrigation water, coupled with
uncertain surface water supplies, have led to an expanded reliance on groundwater as a
resource for irrigation systems (Morris et al., 2003 ). At global scale, out of 300 million
hectares of irrigated area, 113 million hectares (38%) relies on groundwater for
irrigation (Siebert et al., 2010a). Since the 1960s agriculture in many countries has
become increasingly dependent on groundwater. An approximate doubling of
groundwater use occurred between 1960 and 2000, bringing into question whether such
a high rate of withdrawal is suitable (Werner and Tom, 2012). Many studies have
estimated groundwater depletion rates (Döll et al., 2012, Wada et al., 2010, Schwartz
and Ibaraki, 2011, Konikow, 2011) and have indicated that depletion of groundwater
aquifers is a reality in many regions (Döll et al., 2012, Döll and Fiedler, 2008).
Agriculture in Pakistan highly dependent on irrigation water supplies both from canal
and groundwater sources due to its arid and semi-arid climate. Due to the changing
climate, surface water supplies have decreased by 15% over the past decade. The Indus
basin is now considered to be one of the most depleted river basins in the world
(Sharma et al., 2010). It is estimated that water demand in Pakistan is growing at an
annual rate of 10% whereas water resources are under rapid decline. Water resources
are under extreme pressure from domestic and industrial demands due to the rapid
population growth (Sharma et al., 2010, Archer et al., 2010, Laghari et al., 2012). As a
result, there has been a substantial increase in the use of groundwater to sustain irrigated
agriculture in the Indus basin of Pakistan.
Pakistan meets more than 50% of its overall irrigation requirements through
groundwater extraction (Qureshi et al., 2010). Over the past decade, groundwater
extraction rates have increased to 60 km3 , exceeding the annual recharge rate of 55 km3
(FAO, 2012a). Such prolonged overuse has raised concerns about the sustainability of
groundwater resources in the region. Using scenario analysis, Khan et al. (2008b) show
that if the dry conditions persist, there will be a 10 to 20 metre decline in groundwater
levels over the next 25 years in the upper and lower regions of Rachna Doab in North-
East Pakistan. Wada et al. (2010) identified several hot spots of groundwater depletion
Imperatives and Repercussions of Groundwater Depletion
13
in different regions of the world, with the highest depletion rates being observed in
North-East Pakistan and North-West India.
As a result of excessive extraction, groundwater tables are lowering rapidly. Numerous
tube-wells in the Punjab and Sindh regions have run dry and much of the Karez8 tunnel
system in Balochistan has left the tunnels dry and susceptible to collapse. The Karez
system has been replaced by dug-wells in many valleys of Baluchistan. The quest for
water has driven many farmers to invest in submersible electrical pumps where even the
dug well have also run dry. In some areas of Quetta, such as in the Kuchlak valley, ever
deeper tube-wells have now hit bedrock (Steenbergen, 2002). Declining water tables are
not only making irrigation water supplies expensive and unreliable (Banerji et al., 2006)
but are also creating many environmental concerns with serious repercussions to the
sustainability of irrigated agriculture in affected regions (Kijne, 1999b, Shah et al.,
2000, Kelleners and Chaudhry, 1998, Kahlown and Azam, 2002, Khan et al., 2008b,
Qureshi et al., 2009).
The recent shortages of surface water, combined with declining water tables, have led to
policy debates about the sustainable management of groundwater resources. This paper
traces the key trends and factors that have led to the current situation of over-
exploitation of groundwater resources and highlights policy and government failure.
The paper shows that the sustainable use of groundwater resources for food production
is not a technical challenge but rather an economic and political challenge. Noting
recent experience, it is noted that Pakistan’s capacity to appropriately and quickly
implement desired organisational and policy reforms appear to be limited.
In the next section, we identify the factors which increased reliance on groundwater and
provide a synthesis of different environmental, economic and spatial externalities
related to groundwater overuse in the Indus basin. In the last section we discuss some of
the groundwater management problems and finally provide some conclusions.
2.2 Groundwater from Menace to Mainstay
The utilisation of groundwater resources has played a key role in Pakistan’s agricultural
sector. A spectacular increase in the number of tube-wells began in the 1960s.
8Karez is an underground tunnel that is constructed to collect subsoil water through the gravitational pull at the foot of hills. This water is then diverted towards fields or villages either for irrigation or domestic needs.
Imperatives and Repercussions of Groundwater Depletion
14
Introduction of gravity based large scale irrigation systems without proper drainage
resulted in rising groundwater tables and consequently created waterlogging and salinity
problems in many areas. In order to overcome these problems a massive groundwater
extraction Salinity Control and Reclamation Programme (SCARP) was commenced to
prevent waterlogging. Initially, some 16,700 large capacity (0.084–0.14 m3 sec-1) tube-
wells were installed. Most of these tube-wells were operated on low speed crude oil
engines. The rural electricity grid expansion in 1970s made it possible to transition to
electricity operated pumps in tube-wells. Installation of tube-wells not only helped to
lower water tables but also supplemented canal supplies. As a result further irrigation
was possible (Ahmad et al., 2004b, Kazmi et al., 2012) causing groundwater to become
an important source of water for irrigation as traditional canal water.
Figure 2.1: Historical development of tube-wells and share of groundwater in irrigated agriculture in Pakistan
The SCARP programme was the beginning of the groundwater boom in the Indus basin
of Pakistan. However, irrigation area expansion supported by tube-well expansion and
increased groundwater extraction eventually caused a widening gap in water supplies to
crop water requirements due to spatio-temporal inflexible surface water allocations, loss
0
200
400
600
800
1000
1200
1965 1970 1975 1680 1985 1990 1995 2000 2005 2010
No.
of t
ube-
wel
ls (0
00)
a) tube-well development
PunjabRest of countryPakistan
0
10
20
30
40
50
60
70
1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Bill
ion
cubi
c m
eter
s
b) increase in gorundwater share for irrigation
Groundwater Share (billion m3)% Contribution of Surface Water
Imperatives and Repercussions of Groundwater Depletion
15
of near eight million cusecs of water under the Indus Water Treaty and increased
cropping intensities during the Green Revolution. Extraction of groundwater aquifers
continued through rapid and unrestricted construction of thousands of small capacity
(0.028 m3 sec-1 or less) private tube-wells. Until the 1960s the number of tube-wells was
limited to less than thirty thousand, yet currently there are over one million.
Figure 2.1shows the historical development of tub-wells and increase in the share of
groundwater use in irrigation. After an exponential growth during the 1990 to 2005
period, there is however, a decreasing trend in the number of tube-wells. This indicates
that besides a decreasing trend in adoption of tube-well, the number of previously
installed tube-wells is also decreasing. Similarly, the share of groundwater in irrigation
decreased slightly over the same period (Figure 2.1b).
2.3 Warabandi- A Rigid Irrigation Delivery System
Warabandi 9emerged as a protective10 irrigation system in the Indus-Ganges basin
during the British rule over the Indian sub-continent in the 19th century. The protective
irrigation was based on “scarcity by design” as its objective was to maximize the
irrigated area through canal water supplies and to overcome crop failures (Bandaragoda
and Badruddin, 1992, Gilmartin, 1994, Jurriens and Mollinga, 1996). Warabandi aimed
to distribute available water supplies equitably as a fixed weekly rotation and in
proportion to farm size (Bandaragoda and Rehman, 1995, Jurriens and Mollinga, 1996,
Rinaudo et al., 1997b, Bandaragoda, 1998 ). It was inflexible, however, and could not
be adapted to the differing temporal and spatial water requirements of various crops
(Kahlown et al., 2007, Zardari and Cordery, 2009). The rigidity of this system has
caused an increased dependence on groundwater for irrigation in the Indus basin
(Zardari and Cordery, 2009).
Moreover, under the Warabandi system water allocations occurred when water was not
scarce and cropping intensity was low. The Warabandi system discharged a meagre
allowance of 0.085 l/s per acre for a cropping intensity of 75% at that time
(Bandaragoda, 1998 ). However, during the Green Revolution cropping intensity
9Warabandi system distributes the canal water supplies equitably proportional to the farm size as a fixed weekly rotation. 10The notion “protective irrigation” means to design and operate an irrigation system based on the principle that the available water should be spread equitably in order to cover as many farmers as possible without taking into consideration the full crop water requirements (Jurriens et al., 1996).
Imperatives and Repercussions of Groundwater Depletion
16
increased by almost twofold which widened the gap between crop water requirement
and water allocations. .
2.4 The Green Revolution
Pakistan was amongst the early adopters of the new agricultural technology that
characterized the Green Revolution (Byrelle and Siddiq, 1994). The most intriguing
aspect of the Green Revolution, perhaps, in Pakistan was the groundwater revolution.
Groundwater irrigation was protective irrigation until the Green Revolution. Adoption
of high yielding and water sensitive crop varieties during the Green Revolution changed
the demand for irrigation (Mukherji and Shah, 2005). Improved modern crop varieties
increased yields two to three times those of conventional varieties and crop water
requirements increased about three times (Shiva, 1991, Ahmad et al., 2004b, Rodel et
al., 2009).
The widespread adoption of semi-dwarf varieties of wheat and rice was stimulated and
was made possible by the rapid expansion in irrigation water supplies. In the presence
of a vast irrigation network and a favourable environment, Pakistan was considered an
ideal place to take advantage of the new technology. New crop varieties, improved
inputs aided by government policies and improved water supplies played a key role in
increasing agricultural productivity.
Irrigation water supplies expanded in the Punjab province during the 1967 to86 period.
Over this period, total water supplies increased more than twice. The completion of
Tarbela and Mangla dams almost doubled canal water supplies and the installation of
private tube-wells expanded the groundwater supplies up to 8%. The growth in water
supply turned previously rain-fed lands into irrigated lands on a vast scale. By 1986
canal water supplies were fairly constant yet the irrigated area continued to grow
through greater reliance on groundwater extractions.
By 1960 groundwater contributed only 8% to total irrigation water supplies at the farm
gate in the Punjab Province but 25 years later this share had increased to 40%. By 1986,
groundwater contributed 59% to the total Rabi11 water requirements, compared to 36%
only two decades earlier (Byrelle and Siddiq, 1994). Later on canal water supplies
11 There are two cropping seasons in Pakistan, Kharif and Rabi. Kharif starts from June, July and goes to October, November, while the Rabi season starts from September, October and continues to April, May. However, cropping time varies geographically across the country.
Imperatives and Repercussions of Groundwater Depletion
17
began to decrease, causing an increased dependence on groundwater resources. The
inflexibility of canal water allocations and increased cropping intensities during the
Green Revolution placed great pressure on groundwater resources. Exacerbating water
management in Pakistan led to political wrangles between India and Pakistan over the
river water resources that flowed through both countries. The final signing of the Indus
Water Treaty provided clarity over access rights to river water but also triggered further
exploitation of groundwater resources in Pakistan.
2.5 The Indus Water Treaty and Beyond the Indus Water Treaty
The partition of the Indian subcontinent divided the Indus river basin and its irrigation
system between India and Pakistan. It was proposed to declare the regional river basins
as boundaries for the new countries; however, this did not receive much support
(Schwartzberg, 1990). Later, new boundaries were declared using canal head-works
near the Ferozpur and Gurdaspur districts in India and the Lahore district in Pakistan.
By that time, much of the construction work was completed on the eastern rivers which
enter into Pakistan after passing through India. After division, most of the head works
and canals on these rivers were left on the Indian side of the border. India claimed
sovereign rights over the waters passing through its territory and on April 1, 1948 India
completely diverted water supplies irrigating Pakistan’s fields (Wescoat et al., 2000).
Although canals supplying Pakistan's plains were eventually reopened, India’s claim
over river flows became the basis for wider transboundary conflicts between the two
countries (Michel, 1967, Barrett, 1994, Alam, 2002). Pakistan proposed to settle the
dispute through arbitration but India refused Pakistan’s proposal. Soon after the
partition, the severity of the dispute led to a war threat between the two countries
(Barrett, 1994).
The World Bank realised the situation and offered mediation to resolve the conflict and
both countries agreed. In 1952 negotiations were started between the three parties i.e.
India, Pakistan and the World Bank. However, the sovereignty concerns by India made
the World Bank’s first proposal of joint use and development of the Indus basin
unacceptable. In 1954, the World Bank proposed the division of the tributaries of the
Indus River. In 1960 India and Pakistan mutually agreed to sign the Indus Water Treaty.
The Indus Water Treaty allocated the three eastern rivers (Sutlej, Bias and Ravi) to
India and the three western rivers (Jehlum, Chenab and Indus) to Pakistan. Although
compensations were made to construct link canals and dams on western rivers to ensure
Imperatives and Repercussions of Groundwater Depletion
18
adequate water supplies in eastern rivers, the loss of access to the three eastern rivers
deprived Pakistan of nearly 8 million cusecs of surface water (Amir, 2006). At that time
the effect of diminished supplies from the eastern rivers on groundwater recharge was
not discussed nor covered under the Indus Water Treaty. As a result of diminishing
water flows into the eastern rivers groundwater recharge has reduced significantly
because groundwater aquifers in Indus basin by more than 60% are recharged through
river and canal flows (Amin, 2004). It is expected that transboundary conflict may go
beyond the surface water resources as it is highlighted recently by the National
Aeronautics and Space Administration (NASA) that groundwater depletion in India is
likely to impact on the aquifer on Pakistan’s side of the border (Figure 2.2).
Figure 2.2: The red depression highlights groundwater depletion rates with expanding sphere towards Pakistan side
2.6 Myopic Groundwater Policies
Management of groundwater resources in Pakistan continues to face many policy
challenges. The groundwater policy is historically was centred on two key requirements.
The first was to install public tube-wells to control rising water tables in waterlogged
areas and the second was to encourage agricultural production in areas with good
quality groundwater reserves through private tube-well adoption (Steenbergen and
Oliemans, 2002). However, the success of these policies led to a dramatic increase in
groundwater extraction that has become a policy challenge. Declining groundwater
resource now threatens the sustainability of irrigated agriculture in Pakistan.
Imperatives and Repercussions of Groundwater Depletion
19
Exacerbating the overuse of groundwater was the massive transfer of water from the
western rivers to the eastern rivers that formed part of the Indus Water Treaty (Michel,
1967, Biswas, 1992). A network of link canals was constructed to supplement the ‘lost
water’ of eastern rivers identified as part of the Treaty negotiations. However, use of
this large scale irrigation infrastructure led to waterlogging and secondary salinity in
many areas. To combat these problems a comprehensive public groundwater
development programme was initiated with the installation of 16,700 tube-wells during
the 1960s under the first Salinity Control and Reclamation Project (Bhutta and
Smedema, 2007). Over the next two decades, the government’s policy focused on
aiding the adoption of tube-well technology (Falcon and Gotsch, 1968, Papanek, 1998,
Johnson, 1989) through subsidies on electricity, diesel and drilling services, free pump
sets and easy and long term loans (Johnson, 1989).
Later, higher yields and greater economic returns to groundwater users (Meinzen-Dick,
1996) encouraged farmers to adopt the tube-well technology even without government
aid (Muhammad, 1964, Muhammad, 1965, Falcon and Gotsch, 1968, Nulty, 1972). In
the 1980s, a 227% increase in the number of electric tube-wells made the government
reconsider its support policies such as subsidies on electricity (Qureshi et al., 2003).
In1980s, the government introduced a new flat rate tariff on electricity for electric tube-
wells that was increased by 126% between 1989 and 1993 to slow down the pace of
tube-well adoption. Removing subsidies and increasing the tariff rate on electricity use
only made electric tube-wells less attractive and so farmers started switching to diesel
operated engines. The low installation costs for diesel tube-wells and their lesser energy
cost incentivized farmers to install greater numbers of diesel tube-wells. Moreover,
introduction of locally made diesel engines provided additional impetus for the
construction of diesel tube-wells. Between1990 to 1995 a twofold increase was
observed in the number of diesel tube-wells (Qureshi et al., 2010).
The momentum for adoption of tube-well technology during the 1960s and 1970s was
difficult to arrest, even when subsidies were lifted and electricity prices were increased
significantly. The flat rate tariff on electricity use largely proved to be electricity
conserving policy rather than an effective groundwater conservation policy.
Neither customary laws nor government policies and actions so far have adequately
dealt with finding the balance between groundwater recharge and extraction.
Imperatives and Repercussions of Groundwater Depletion
20
Figure 2.3: Groundwater use trends in Pakistan
2.7 Population Growth and Water Demands
Pakistan is the sixth largest country by population. Pakistan’s population grew from 40
million in 1950 to 173 million in 2012 and is expected to be 237 million by 2025 (FAO,
2012a). As the population grows, demand for water for all aspects of life also increases.
It is estimated that the agricultural sector will have to grow more than 4% per year and
water supplies to grow by almost 10% to meet the country’s growing population’s food
requirements (FAO, 2012a). By 2025 irrigation water demands in Pakistan would reach
to 349.2 km3 under the business as usual scenario. This represents a 48.3% increase in
current water availability (David et al., 1998).
Although the demand for irrigation water is projected to increase, the supply of
irrigation water is unlikely to increase, particularly as there is a limited potential for
surface water developments. Current per capita water availability has already hugely
decreased from 5260 m3 in the early 1950s to 1040 m3 in 2010; a per capita decline of
400%. Archer et al. (2010) estimates per capita water availability to be 725 m3 by 2025
and only 415 m3 by 2050 if population continues to grow at the current rate
(Figure 2.4).
Imperatives and Repercussions of Groundwater Depletion
21
Figure 2.4: Different water shortage versus population growth projections
2.8 Lack of Surface Water Developments
Since the completion of Tarbela and Mangla dams in the early 1970s, Pakistan has not
considered developing further surface water resources. Rather, groundwater resources
have been exploited. Yet the capacity of Tarbela and Mangla has been declining due to
heavy loads of sedimentation that has caused a storage loss of 3.67 billion cubic metres
in the gross initial capacity of the Tarbela and 1.21billion cubic metres of the Mangla
reservoir in 2009-10 (Pakistan, 2009-10). Until new reservoirs are constructed there will
be gradual decline in the storage capacity of these existing dams which will decrease the
overall surface water storage and heighten demand for use of groundwater resources
(Archer et al., 2010). Surface water reservoirs not only provide a substitute for
groundwater but they also play role in aquifer recharge.
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Imperatives and Repercussions of Groundwater Depletion
22
Figure 2.5: Storage loss in the capacity of different dams in Pakistan
2.9 Groundwater Overuse Externalities
Ostrom (1990) considers groundwater a typical example of a common-pool resource. It
is non-excludable in the sense that exclusion of multiple users is not easy and it is
subtractible in the sense that pumping beyond optimum limits leads to aquifer depletion.
Yet it is a rival good insofar as users compete over the resource and their use of the
resource leads to its depletion.
Non-excludability makes it difficult to regulate and rivalry leads to depletion of the
aquifer which may cause problems in fragile environments (Steenbergen, 1995, Reddy,
2005). Besides aquifer depletion, overdrafting of groundwater can have many
environmental, economic and social impacts on surrounding ecosystems (Skurray and
Pannell, 2012, Danielopol et al., 2003, Zektser et al., 2005, Harou and Lund, 2008).
Pakistan is among those countries whose groundwater withdrawals are greater than their
rate of renewal. The rapid decline in groundwater tables has also raised many
environmental, economic and social concerns in Pakistan. The environmental impacts
of groundwater overdrafting include: soil salinization, salt water and sea water
intrusions, land subsidence, drying up of lakes and loss in vegetation in different parts
of the country. In addition to these environmental problems, declining groundwater
tables have led to uneconomic pumping conditions, increased irrigation costs, and have
imposed population migrations due to the temporal and spatial impacts surrounding the
cost and availability of irrigation water.
Any cost-benefit analysis of groundwater pumping would need to take explicit account
of the wider impacts of the environmental, economic and social changes that
accompany groundwater use. However, there is a dearth of research that would facilitate
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Imperatives and Repercussions of Groundwater Depletion
23
the estimation of these costs of groundwater depletion in the Indus basin of Pakistan. In
the following section, we identify and outline the nature and possible magnitude of
some of these costs.
2.10 Environmental Externalities
2.10.1 Soil Salinization
Soil salinization is one of the main negative impacts of groundwater overuse for
irrigation in the Indus basin of Pakistan (Kijne, 1999b, Shah et al., 2000, Kahlown and
Azam, 2002, Ahmad et al., 2002a, Aslam and Prathapar, 2006, Khan et al., 2008b,
Bhutta and Smedema, 2007, Qureshi et al., 2009). Salinization refers to the gradual
accumulation of salts on soil surface. Salinization can be caused by excessive irrigation
or by using poor quality water for irrigation or rising groundwater tables. During the
Indus basin development, introduction of large-scale irrigation network without
adequate drainage systems changed the hydrological balance in the basin. Continuous
seepage from the newly constructed canals caused 20-30 meters deep groundwater
tables to rise up to the level of soil surface, turning millions of acres of land into
waterlogged and saline soils. Installing tube wells as a vertical drainage was proposed
as a potential solution. Following their installation, waterlogged soils were reclaimed to
a great extent. By reducing water logging through groundwater pumping, it was hoped
that the problem would be solved. But, this did not prove to be the case. Reclaiming
waterlogged soils created another problem. Firstly, large quantities of salts were already
present in the soils and pumping brought those salts to the surface. Secondly, the
groundwater contained far more salts than canal water. Hence, large quantities of salts
were left behind in the root zone after the water evaporated (Kijne, 1999b).
Salinity remains a major threat to the sustainability of irrigated agriculture in Pakistan
(Kahlown and Azam, 2002, Bhutta and Smedema, 2007) as there are currently 6.3
million hectares of land affected by different types and levels of salinity. Of the 25% of
irrigated land affected by salinity, about 1.4 million hectares of agricultural land is not
cultivable. In addition to rendering millions of acres of land unproductive, the losses
attributable to salinity’s adverse impacts on existing crops amounts to Rs. 55 billion
(US$1.5 billion) per annum. The costs of salinity were estimated to equate to 0.6% of
Pakistan’s GDP in 2004(Corbishley and Pearce, 2007).
Figure 2.6 shows province wise soil salinity status during 2001-04. Sindh has more area
affected by salinity than any other province while Khyber Pakhtunkhwa (KPK) has the
Imperatives and Repercussions of Groundwater Depletion
24
least affected area by salinity. Punjab has 1.6% of its cultivated area strongly affected
by salinity while Sindh has 20% area affected by strong salinity.
Figure 2.6: Province wise soil salinity status for the period 2001-04
2.10.2 Land Subsidence
Land subsidence is a well-known consequence of intense groundwater pumping
(Domenico and Schwartz, 1997, Zektser et al., 2005). Land subsidence occurs when
large amounts of groundwater are withdrawn from an aquifer. Excessive pumping
reduces the hydraulic head in an aquifer that in turn reduces soil pore pressure. The
draining of pore space reduces the pore space which in turn causes land subsidence. The
reduction in pore space can lead to a permanent diminution in the storage capacity of an
aquifer and subsidence can damage vital infrastructure such as roads and buildings. In
many parts of Pakistan, excessive pumping of groundwater aquifers has raised severe
concerns about land subsidence. Khan et al. (2013) reported a subsidence rate of 10
cm/year during the mid-2006 to the early 2009 in the Quetta valley of Balochistan.
2.10.3 Seawater Intrusion
The Indus River basin has a large underground water resource formation. However,
almost the entire aquifer is underlain by saline water. In most of the Indus basin, fresh
water is a narrow strip over the saline water where excessive pumping can cause mixing
of the deep mineralized water resources with freshwater resources (Sufi et al., 1998,
Saeed and Bruen, 2004, Qureshi et al., 2010, Chandio et al., 2012). In Punjab province
23% of groundwater is saline whereas in the Sindh Province about 78% groundwater is
of poor quality (Haider, 2000). Similar to get contaminated by the underlying saline
water layers, groundwater resources are seriously confronted by seawater intrusion in
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Imperatives and Repercussions of Groundwater Depletion
25
coastal areas (Sufi et al., 1998). In the coastal areas of Sindh Province, groundwater
overdrafting has led to sea water intrusion inland up to 100 km north of the Arabian
Sea, seriously impacting10 million acre feet of land in the Indus delta (Shah et al., 2000,
Qureshi et al., 2009).
2.10.4 Drying of Wetlands and Vegetation
Declining groundwater tables can severely affect wetlands and sparse vegetation cover
in arid and semi-arid plains. Phreatophytes are the plants most commonly affected by
declining groundwater tables. A classic example is date palm (Phoenix dactylifera)
which in many regions has died due to decreasing groundwater tables. Similarly, lower
groundwater tables compounded with prolonged droughts have affected wetlands and
their adjacent ecosystems. There are many examples of drying up or shrinking of
wetlands in Pakistan. In Balochistan, Spin Karez reservoir is one example. In 1975 it
covered an area of 1.04 Km2. By 1989 it covered an area of only 0.69 Km2 and by 2001
this lake had totally dried up (Khan et al., 2013).
2.11 Economic Externalities
Most groundwater pumping is for economic purposes. Groundwater is preferred for
irrigation due to its ready access, minimal infrastructure requirements and generally
more continuous water supply. However, the ubiquity of groundwater and the wide
applicability of pumping technology also encourage overdrafting. In an overdraft
regime, groundwater extraction continues until the marginal cost of groundwater
extraction begins to exceed the value of the pumped water for economic production. At
this point, farmers can either reduce crop production or implement more efficient
irrigation water use technologies or switch to higher value crops. Contrary to
hydrological concepts where aquifer exploitation is defined by its sub-optimality,
economic over-exploitation is not wise only if the net return on the least profitable crop
is less than the present value of all future pumping cost savings (Harou and Lund,
2008).
Declining water tables have greatly increased tube-well installation and groundwater
pumping costs in many parts of Pakistan. The cost of lowering a tube-well to a depth of
24 metres is seven times more than that of a tube-well drilled to a depth of 6 metres
(Qureshi et al., 2003). Similar to drilling costs, extraction costs and irrigation costs have
also greatly increased with lowering of groundwater tables. Watto and Mugera (2013)
found that irrigation costs per acre of cotton were 23.5% more in Lodhran district
Imperatives and Repercussions of Groundwater Depletion
26
compared to irrigation costs in Jhang district for tube-well owners. In addition, for non-
owners (who buy water from tube-well owners) irrigation costs were estimated at 30.1%
higher in Lodhran district compared to water buyers in Jhang district. The implications
of these spatial cost differences are that some small farmers or tenants are leaving
farming either selling their lands or leasing to other farmers that have better or cheaper
access to irrigation water. Farm size is already shrinking in Pakistan and the number of
landless farmers is increasing (Mustafa et al., 2013).
2.12 Spatial Externalities
Being a common-pool resource, groundwater is not usually regulated by well-defined
property rights, especially in a country like Pakistan where a large proportion of
population depends on it for domestic, industrial and agricultural purposes. However,
even under clearly defined property right scenarios, the fugitive nature of groundwater
can impose many spatial externalities. The ubiquitous nature of groundwater means that
a farmer’s water resource can be simultaneously accessed by other equally entitled
users. Due to this non-excludible nature of access to groundwater and the fact that few
limits on access apply, there is a little incentive for a farmer to forego his current use in
return for access at some future time. As a consequence, there is a rush to extract
causing a more rapid depletion of the groundwater resource (Reddy, 2005, Pfeiffer and
Lin, 2012). In many cases excessive extractions by one tube-well owner can cause
declining yields of adjacent tube-wells (Reddy, 2005). Such interference can be gradual
or sudden depending upon the hydrological conditions of the aquifer and rates of
extraction. Pfeiffer and Lin (2012) documented that 100 acre-feet of pumping can lower
the static level of groundwater table at one’s own well by 0.31 to 0.48 feet, and a
pumping of 1000 acre-feet by a neighbouring tube-well within about a two-mile radius
can reduce the static level at one’s well by 0.8 to 1.5 feet. In such situations individuals
with greater access to capital can strategically capture all the available groundwater and
deprive others (Reddy, 2005).
In Punjab and Sindh province many tube-wells have gone out of production due to rapid
falling of groundwater tables (Qureshi et al., 2003). Similarly, in Balochistan province
lowering groundwater tables have collapsed much of the traditional Karez based
irrigation system. Seawater intrusion miles up into the Indus delta have caused land
inundation and saline intrusion of aquifers. Consequently thousands of farmers and
fishermen have migrated to Karachi and other neighbouring cities. Sea-water intrusion
Imperatives and Repercussions of Groundwater Depletion
27
has forced 0.35 million people in the Badin and Thatta districts to migrate to some other
cities in search of livelihood (Gitanjali and Sahiba, 2011). Similar to sea-water
intrusion, drying up of tube-wells has also caused migrations from the tail ends of canal
systems.
As already reported, much of the groundwater recharge in the Indus basin occurs
through rivers and canals. The recharge to groundwater varies across the canal system,
being more at the head and far less at the tail ends of canals. Figure 2.7 shows the
recharge trend to groundwater along the Lower Bari Doab (LBD) canal in five different
districts. As the canal water supplies decrease towards the tail ends, recharge to
groundwater also decreases. In response to limited canal water supplies tube-well
densities are higher in canal command tail areas compared to the head areas.
Figure 2.7: Variability of the groundwater recharge from head to tail of the LBD canal command
In Balochistan many of the shareholders have lost their water shares because of the
drying of the Karezes. Karez is not only a source of livelihood but also the symbol of
social status in the Baloach community. Losing a water right simply means loss of
social status. Once they lose their water right, they have to either migrate to nearby
cities in search of livelihoods or work as farm labourers with tube-well owners. The
collapse of the Karez system has resulted in the breakdown of social cohesion in the
community. It is highly likely that social disintegration based on the Karez collapse may
create long-lasting social conflicts and enmities among the shareholders and non-
shareholders (Mustafa and Usman Qazi, 2008).
Imperatives and Repercussions of Groundwater Depletion
28
2.13 Groundwater Management Problems
The diminution of canal water supplies has increased the importance of groundwater in
irrigation. Yet groundwater use is poorly monitored and tube-well construction is not
regulated. Comprehensive information on groundwater withdrawals, water use and
groundwater quality are absent.
Water management policies have centred on canal water management whilst
groundwater management has been largely neglected. Some indirect groundwater
management strategies have been tried in Pakistan in recent years, but these strategies
have not proved very effective (Steenbergen and Oliemans, 2002, Qureshi et al., 2010).
The major reasons for the poor management of groundwater resources are as follows:
2.13.1 Institutional Impediments
Groundwater resource management faces a common-set of policy and institutional
challenges in Pakistan (Wescoat et al., 2000). The legislative foundation for water
management in Pakistan was the Irrigation and Drainage Act of 1873, which became
the basis for provincial irrigation and power departments. The Soil Reclamation Act of
1952 came much later. The Act empowered the Soil Reclamation Board to combat
waterlogging and salinity using tube-wells as means of vertical drainage. The Board
worked in designated land reclamation areas, but also issued permits to install private
tube-wells outside the reclamation areas. Later, the Board was merged with the
provincial irrigation departments. In 1958 the Water and Power Development Authority
(WAPDA) was formed. It served until 1970, after which the WAPDA became a large
federal agency having roles in resource allocation for irrigation, power development,
planning and executing of all major development interventions in the rural sector
(Bandaragoda and Badruddin, 1992). The Water and Power Development Authority Act
gave the WAPDA authority to control Pakistan’s groundwater resources and issue
official area-specific rules such as issuance of licenses for further installation of tube-
wells. However, the licensing rules framed in 1965 under the Reclamation Act and later
under the WAPDA Act have never been enforced. Hence, in spite of the formation of
agencies with regulatory powers those powers have not been exercised, indicating
widespread government failure. To illustrate this failure; in response to the rapid falling
of water tables, the government of Balochistan outlined a Groundwater Rights
Administration Ordinance in 1978. The Ordinance established an area-specific
procedure to issue licences for tube-well installation. Local District Water Committees
Imperatives and Repercussions of Groundwater Depletion
29
were supposed to be the sanctioning bodies with the possibility of appeal to the
Provincial Water Board. One of the special features of the Ordinance was that future
licensing would be based on area-specific guidelines. However, such area-specific
guidelines were ever formulated. Instead, the Ordinance was hardly ever implemented,
despite a dramatic fall in groundwater tables in the province. Similarly, the provincially
administered groundwater regulatory framework under the Provincial Irrigation and
Drainage Authority Act (PIDA) of 1990-2000, the Canal Act of 2006, and the National
Groundwater Management Rules are still awaited to be implemented, indicating the
consistent pervasiveness of government failure at a range of levels.
Hence, Pakistan’s water crises are mostly attributable to government failure whereby
institutions supposedly empowered to prevent unsustainable practices have been
muzzled or made ineffectual and thereby failed to prevent the exploitation of
groundwater resources. Government apathy towards empowering or reforming
ineffective institutions, policies and practices is very evident (Mustafa et al., 2013).
Although, 10 public sector institutions, 28 national organizations, and 19 academic and
research institutions cover the water sector their combined efforts amount to little
regarding effective regulation, penalty impositions, conservation guidelines and laws to
govern water distribution and use. Pakistan continues to have no single national
regulatory framework dealing with the use of groundwater (kamal, 2009).
2.13.2 Lack of Entitlements and Informal Groundwater Marketing
The open access nature of groundwater resource may lead to sub-optimal and
potentially wasteful uses. Anyone can extract groundwater and inflict social and
environmental costs on surrounding users and ecosystems in Pakistan.
Unlike the surface water, groundwater entitlements are not defined in Pakistan. Access
to groundwater for irrigation is open and is generally tied to land ownership. Similar to
access, the right to extract groundwater is not defined nor confined. There is neither any
restriction in terms of policy nor governance regarding groundwater use and allocation.
A tube-well owner has exclusive rights to use groundwater. He can extract and even sell
groundwater without any interference (Meinzen-Dick, 1996, Hussain, 2002, Qureshi et
al., 2010). Such informal groundwater transactions occur through locally governed
groundwater markets (Meinzen-Dick, 1996, Thobani, 1997). These informal markets
offer opportunities for tube-well owners to increase economic benefits and non-owners
to increase agricultural productivity (Shiferaw et al., 2008, Manjunatha et al., 2011).
Imperatives and Repercussions of Groundwater Depletion
30
However, the groundwater transactions under informal water markets do not consider
the shadow price of groundwater (Meinzen-Dick, 1996, Banerji et al., 2006).
Recently, increasing water shortages and energy crises have changed the nature and
functioning of informal groundwater markets which have become more complicated. A
water buyer has to pay water charges in advance and let the tube-well owner know
about his water demands well in advance in order to purchase water. In some areas
tube-well owners have developed a schedule for water buyers through a mutual
consensus based on their farm location relative to the tube-well owner’s farm. The
closer is the water buyer’s farm; the more immediate will be his access to the water.
Hence, after irrigating his fields the tube-well owner sells water to his immediate
neighbour and then to a more distant neighbour and so on. Sometimes a water buyer
seeking to defer payment is replaced by another buyer prepared to offer an advance
payment. In areas with a limited density of tube-wells but high dependency on
groundwater, water buyers have no or limited choice to choose among a number of
sellers, which lets tube-well owners exercise monopoly power in groundwater trading.
Unimpeded access has allowed tube-well owners in certain areas to enter into water
markets in such a way that they rent out their land and hold a piece of land where tube
well is installed. They offer their tube-well water as a discounted price conditional on
the water buyer using their own tractor or diesel engine to extract the water. This
practice leads to over-exploitation of groundwater and hampers efficiency of water use
in irrigation. The operation of these water markets also raises equity concerns. Social
ties among water sellers and water buyers can cause social discrimination (Shah, 1993,
Jacoby et al., 2004, Khanna, 2007).
2.13.3 Irrigation Efficiencies and Water Productivity
The cropping system in the Indus basin is dominated by wheat, rice, cotton, maize and
sugarcane. Most of these crops require water applications by flood or furrow irrigation
methods which are among the least efficient irrigation methods. By illustration, on-farm
water application efficiencies of these methods range between 23% and 70% depending
upon the crop type, farm characteristics and cropping region etc. (Kahlown et al., 1998).
Compounding the low application efficiencies of flood irrigation are actions of farmers
who apply more water than is actually required by their crops due to the farmers’ lack
of knowledge about crop water requirements and poor land levelling. Despite running
short of water, over-irrigation is one of the major limitations to crop production and
Imperatives and Repercussions of Groundwater Depletion
31
crop productivity in many parts of the Indus basin (Kahlown and Kemper, 2004). Rice
growers in particular over-irrigate their fields. Rice growers in Pakistan apply 13–18 cm
water per irrigation event, which is considerably higher than the consumptive use of
approximately 8 cm (Kahlown et al., 2001). Similarly, in Balochistan, where
groundwater is under rapid decline, irrigation to apple orchards sometimes exceeds
100% of requirements. Sarwar and Perry (2002) demonstrated that under water scarce
conditions, deficit irrigation practices can increase water productivity by almost 50% if
irrigation supplies are restricted to 80% of the total crop water requirements. Similarly,
Kahlown et al. (2007) demonstrated that using sprinkler irrigation allows rice yields to
increase by 18% and water savings of up to 35% are possible.
Pakistan is rapidly consuming its available water resources whilst generating, by
international comparison, very low productivities of water use. Water productivity for
wheat in Pakistan (0.76 kg/m3) is 24% less than the global averages of ~1.0 kg/m3 while
the water productivity of rice (0.45 kg/m3) is 55% lower than the average value of ~1.0
kg/m3 for rice in Asia (Water Watch, 2003). Another study, however, reports a higher
average of 0.69 kg of rice productivity per m3 of water in the Indus basin of Pakistan
(Cai et al., 2010). Similarly, water productivity for cereal crops in Pakistan is 0.13
kg/m3 which is very low compared to India’s 0.39 kg/m3 and China’s 0.82 kg/m3
(Kumar, 2003).
Figure 2.8: Water productivity as a ratio of total GDP to the total annual water withdrawals
Figure 2.8 gives another measure of overall water productivity for the 5 major wheat,
cotton, rice and sugarcane producing countries calculated as the ratio of total GDP
(2005 in US$) to the total water withdrawals. Using this measure, amongst the selected
countries, water productivity is the lowest in Pakistan. Although water productivity is
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Imperatives and Repercussions of Groundwater Depletion
32
very low, nonetheless Pakistan is able to produce large volumes of agricultural
products. Pakistan is ranked at number 8th in global wheat production, 4th in cotton, 6th
in sugarcane and 11th in rice. However, per hectare yields of all these crops in Pakistan
is much lower when compared to 5 major producing countries of these crops (see
Figure 2.9). Although, wheat yield has increased from 822 kg/ha in 1965 to 2713 kg/ha
in 2013, the average wheat yield is 17% and 65 % lower than its neighbouring countries
India and China, respectively. In case of cotton crop the average yield (485kg/ha) is,
however, 44% higher than India but still remains 64% lower than China. Average per
hectare sugarcane yield in Pakistan is also 40% and 42% lower than India and China
respectively. Pakistan and India respectively produce 2490 kg/ha and 2415 kg/ ha rice
on average. The average per hectare yield of Basmati rice which is more dominated
variety in Pakistan is, however, about 1500 kg/ha.
Figure 2.9: Yield (in kg/ha) in the selected countries
2.14 Conclusions
Over-extraction of groundwater for irrigation has raised concerns about the
sustainability of irrigated agriculture in the Indus basin of Pakistan. Besides threatening
the sustainability of irrigated agriculture, rapidly declining groundwater tables are
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Imperatives and Repercussions of Groundwater Depletion
33
imposing many negative environmental, economic and social costs on Pakistan. As the
dependence on groundwater is increasing, understanding groundwater availability,
allocation mechanisms, and future demand is increasingly critical if water use and
irrigation agriculture is to be sustained.
The purpose of this paper is to outline the nature, causes and impacts of groundwater
overuse in Pakistan. This paper has reviewed the causes and history of the unsustainable
exploitation of groundwater. Groundwater pumping which was started in the 1960s with
the objective of overcoming waterlogging has continued and expanded so rapidly that it
is now central to irrigation rather than being a supplementary source. Nowadays more
than 50% of the irrigation requirements are met through groundwater extractions. Over
the last decade the extraction rate has risen to 60 km3 which is 5 km3 more than the
renewability requirement of groundwater aquifers. This unsustainable extraction of
groundwater is rapidly lowering groundwater. Further, negative externalities associated
with over pumping of groundwater are arising, including salinization and land-surface
subsidence. Unchecked drilling of groundwater aquifers is also triggering the upward
flow of salts into freshwater aquifers.
Managing groundwater resources typically requires multidimensional actions,
management strategies and coordination activities across a range of institutions,
jurisdictions and stakeholders. Despite the need for such management and its associated
policy design and implementation activity, unfortunately it is not evident in Pakistan.
Unfortunately Pakistan’s groundwater management is characterised by government and
institutional failure. Moreover, there seems to be little political appetite to deal with this
pressing issue of how to sustainably manage Pakistan’s water resources to meet its
rising food demand. The front line challenge is to ensure sustainable groundwater
extraction. The future groundwater management strategies should not be based on the
conventional wisdom “where it exists----is used to meet growing water demands” but
rather on supply and demand management strategies under consideration of efficiency
objectives and resource sustainability concerns.
35
CHAPTER 3
3. The Groundwater Depletion Risk and Tube-well Technology Adoption12
Abstract
We employ a moment-based approach to empirically analyse farmer’s decisions to
adopt tube-well technology under the depleting groundwater resources and associated
production uncertainties. We use cross-sectional farm level data from 200 farming
households comprised of 100 adopters and 100 non-adopters. Risk is found to play an
important role in the adoption process. The results indicate that the higher the expected
profit the greater the probability of adoption. Similarly, with increasing variance of
profit the probability of adopting a tube-well increases significantly. The third moment
is statistically non-significant suggesting that farmers generally do not consider
downside profit risk when deciding to adopt tube-well technology whereas the highly
significant fourth moment suggests that extreme events decrease adoption significantly.
In addition, we show that land tenureship, extension services, access to different sources
of information and off-farm income play a significant role in the adoption process.
3.1 Introduction
It is an a priori expectation that irrigation increases agricultural production, decrease
variability of production, and hence the variability of farm income (Foudi and
Erdlenbruch, 2012). Irrigation consumes about 80 per cent of global freshwater
resources worldwide (Jury and Vaux, 2005). However, the limited potential for surface
water developments and declining groundwater aquifers is causing water shortages for
irrigated agriculture in many regions of the world (Tilman et al., 2002, Karagiannis et
al., 2003, Hanjra and Gichuki, 2008). Moreover, increasing inter-sectoral water demand
is limiting water allocations for irrigation purposes. In particular, the growing awareness
of environmental and in-stream water values has added new impetus on water
reallocations from low-value to high-value uses and the need to achieve greater
efficiencies in irrigation water applications (Seo et al., 2008, Hussain and Hanjra, 2004,
12This chapter was accepted and selected for presentation at the 11th Hydroinformatics Conference, New York, USA (August 17-21, 2014) as “groundwater depletion risk and the adoption of tube-well technology: some farm level evidences from Pakistan”.
The Groundwater Depletion Risk and Tube-well Technology Adoption
36
Malano et al., 2004, Wichelns, 2002). It is anticipated that that one third of the
population in developing countries will face severe water shortages by 2025 (Molden et
al., 2001).
Adoption of modern irrigation technology has been proposed as one of the major
solutions to overcome water scarcity in many agricultural countries (Caswell and
Zilberman, 1986, Dinar and Zilberman, 1991, Dinar et al., 1992, Shah et al., 1995,
Pereira et al., 2002, Karami, 2006, Bjornlund et al., 2009). Modern irrigation
technologies such as sprinkler and drip irrigation have been playing an increasingly
important role in agricultural production and water conservation (Caswell and
Zilberman, 1986). However, the adoption of modern irrigation technologies, such as
sprinkler and drip irrigation, is not yet common in many developing countries. This is
partly because of the high cost of installation and implementation of new irrigation
technology. Another reason is the perceived riskiness of the technology and the
uncertain outcome of adoption due to lack of knowledge and information about that
technology (Just and Zilberman, 1983, Abadi Ghadim and Pannell, 1999, Koundouri et
al., 2006). Many studies argue that risk is one of the major factors determining the rate
of adoption of new technology (Feder and Umali, 1993, Carey and Zilberman, 2002,
Marra et al., 2003, Lindner et al., 1982). However, the link between farmers’ adoption
decisions about irrigation technology and production risk under uncertain water
availabilities is rarely addressed in the empirical literature. A notable exception is the
work by Koundouri et al. (2006), who argue that farmers adopt irrigation technology as
a risk-reducing strategy under uncertain water availabilities.
Empirical evidences suggest that most decision makers are risk-averse and that
technology adoption contributes to reducing the exposure to risk, especially downside
risk (Chavas and Holt, 1996, Kim and Chavas, 2003). However, the perceived riskiness
associated with technological adoption and uncertainties related to future farm
production can also make low-income and risk-averse farmers reluctant to adopt new
technologies. Only financially secure farmers with enough capacity to cope with
downside risk would decide to make investment in new technology (Juma et al., 2009).
Intuitively, downside risk aversion means that farmers do not like to be exposed to
unexpectedly low returns. However, in the case of arid and semi-arid regions where
water availabilities are not secured, expected farm output and, therefore, farm profit is
random because it is dependent on exogenous climatic conditions. Therefore, risk-
The Groundwater Depletion Risk and Tube-well Technology Adoption
37
averse farmers might consider adopting water saving irrigation technologies or securing
irrigation supplies (Koundouri et al., 2006).
As in many other parts of the world, the agriculture sector is facing severe water
shortage in Pakistan. The Asian Development Bank has warned in its Asian
Development Outlook 2013 report that Pakistan is close to being classified as a ‘water
scarce’ country (ADB, 2013). The risk of vulnerability to water resources, climate
change and human growth and development is likely to affect the sustainability of
irrigated agriculture. Evidences suggest that the Indus basin is one of the most depleted
river basins in the world. It is expected that recent climate change trends and continued
population growth would substantially increase pressure on water resources in near
future. Existing surface water resources are not only deficient but are also highly
skewed in time and space throughout the Indus basin. Due to spatiotemporal variations
in surface runoffs, agricultural intensification in the pursuit of reliable irrigation
supplies have led to the expansion of a large scale groundwater-fed irrigation system in
the Indus basin.Since, 1960 groundwater contribution to irrigation sector has been
increased by more than 40 per cent (Byrelle and Siddiq, 1994, Qureshi et al., 2009).
Consequently, groundwater extraction rates have increased to 60 km3 y-1 which exceed
the recharge rate of 55 km3 y-1 (Giordano, 2009). Although, the utilisation of
groundwater resources has played a key role in agricultural development, the ongoing
excessive use of groundwater aquifers for irrigation is causing groundwater tables to
lower at alarming rates (Kijne, 1999b, Shah et al., 2000, Khan et al., 2008a, Qureshi et
al., 2009). Khan et al. (2008b) using scenario analysis project that in the next 25 years
there will be a 10 to 20 metres decline in groundwater levels in the upper and lower
regions of the Rachna Doab in North-East Pakistan. Rapidly declining groundwater
tables are not only making irrigation water supplies economically unviable (Banerji et
al., 2006) but are also creating many environmental concerns with serious repercussions
to the sustainability of irrigated agriculture in the region (Kijne, 1999b, Shah et al.,
2000, Kelleners and Chaudhry, 1998, Kahlown and Azam, 2002, Khan et al., 2008b,
Qureshi et al., 2009). Despite the continued depletion of groundwater resources, the
number of tube-wells has kept on increasing until recently. As of 2010, there were more
than one million tube-wells in the country (Pakistan, 2009-10). However, over the last
decade there has been a decline in the number of tube-well adoption as shown in Figure
2.1of Chapter 2.
The Groundwater Depletion Risk and Tube-well Technology Adoption
38
The purpose of this chapter is to investigate farmers’ decisions to adopt tube-well
technology under the risk of falling groundwater tables and associated production
uncertainty using the Punjab province of Pakistan as a case study. We follow the
approach by Koundouri et al. (2006) but analyse the adoption of tube-well technology
under depleting groundwater resources. Unlike sprinkler and drip irrigation
technologies, the adoption of tube-well technology does not necessarily increase the
efficiency of irrigation water use. However, tube-well ownership ensures more
promising irrigation water supplies and hence lessens production uncertainties during
irregular canal water supplies or uncertainties involved in purchasing groundwater13.
In this chapter, we use a rural household survey data of 200 farms from the irrigated
semi-arid plains of Punjab to analyse whether farmers facing high profit variability and,
hence a higher exposure to risk, adopt tube-well technology as a means to hedge against
production risks due to uncertain water availabilities. We also investigate whether
farmers consider downside risk and extreme events when they decide to adopt tube-well
technology (Antle, 1983, Antle, 1987, Antle and Goodger, 1984, Koundouri et al.,
2006).
The rest of the paper is organised as follows. Section 2 presents literature reviews on
irrigation technology adoption and considers the case of tube-well technology adoption
in Pakistan. Section 3 describes the theoretical framework used to analyse farmers’
decision to adopt tube-well technology under production uncertainties. Section 4
describes the data used in the estimation of the empirical model. The results are
presented in Section 5 and Section 6 provides conclusions and policy implications.
3.2 Literature Review on Irrigation Technology Adoption
Many researchers have empirically investigated adoption and diffusion of technological
innovations and proposed the use of irrigation efficient technology as a potential
solution to overcome water scarcity (Caswell and Zilberman, 1986, Dinar and
Zilberman, 1991, Dinar et al., 1992, Shah et al., 1995, Pereira et al., 2002, Karami,
13In South Asia (Pakistan, India and Bangladesh) irrigation is highly dependent on groundwater supplies through tube-well pumping. In response to diminishing canal water supplies, informal groundwater markets have spontaneously evolved over the time through trading surplus pumping capacities between the tube-well owners and non-owners (Meinzen-Dick, 1996) Such markets offer opportunities to non-owners to overcome crop failures through purchasing groundwater from their nearby tube-well owners (Manjunatha et al., 2011, Meinzen-Dick, 1996, Shiferaw et al., 2008). However, such markets do not guarantee access over spatial and temporal water requirements of different crops (Jacoby et al., 2004).
The Groundwater Depletion Risk and Tube-well Technology Adoption
39
2006, Bjornlund et al., 2009, Schuck et al., 2005) . However, despite the various
economic and environmental benefits of the new technology, farm-specific irrigation
technological adoption is still lagging around the world (Isik, 2004). Shrestha and
Gopalakrishnan (1993) argue that technological adoption is a result of profit
maximizing behaviour of a heterogeneous population. Besides water conservation, some
incentives (at least some increase in yield and some decrease in production costs) are
necessary in order to make farmers to be willing to adopt a water conserving technology
(Arabiyat et al., 2001, Wichelns, 1991).
Much of the literature on irrigation technology adoption does not provide a link between
the dynamic nature of adoption, including the effect of uncertainty and irreversibility,
and the option to wait on a farm’s investment strategy (Caswell et al., 1990, Abadi
Ghadim and Pannell, 1999). Carey and Zilberman (2002) conclude that the adoption of
modern irrigation technology takes place when water becomes increasingly scarce and
the expected investment return exceeds the cost of investment. Many farmers adopt
irrigation technologies under conditions of uncertainty in order to hedge against
production risk (Koundouri et al., 2006). Zilberman et al. (1995) outline that adoption of
drip irrigation increased dramatically during the drought period of 1987 to 1991 in
California. Dridi and Khanna (2005) show that adverse selection induces less
technology adoption than full information but that even under adverse selection,
bilateral water trading among farmers can reduce the distortion created in the allocation
of water quotas relative to the situation with full information. Besides technological
solutions, it is believed that tradable rights in water and the development of markets in
these rights can lead to efficiency increases in irrigated production. Markets for water
are considered as the favoured solution to water allocation by economists. These
markets play an important role in supporting some farmers’ adoption of modern water
conserving technologies (Rosegrant and Binswanger, 1994, Carey and Zilberman,
2002). Water markets may induce some farms to adopt while others to delay the
adoption of irrigation technology (Carey and Zilberman, 2002).
Caswell and Zilberman (1986) report the effect of farm characteristics and irrigation
technology characteristics on a farmer’s decision to adopt irrigation technology. They
argued that to understand the adoption process of irrigation technology, one must
consider land quality and variation in well depth. Modern irrigation technologies are
more likely to be adopted in locations with relatively deep water tables (Dinar et al.,
1992). Realistic but economically viable water costs are necessary to substitute for
The Groundwater Depletion Risk and Tube-well Technology Adoption
40
open-ditch furrow irrigation by surge-flow irrigation technology (Coupal and Wilson,
1990). Adoption of modern irrigation technology can help in reducing environmental
and agronomic impacts on profitability. Green et al. (1996) assess the effect of
economic variables, environmental characteristics, and institutional variables on
irrigation technology choices. They concluded that water pricing is not the most
important factor governing irrigation technology adoption. Rather, environmental
considerations are a major incentive for adoption of these technologies, such as drip and
sprinkler irrigation methods. In general, adoption is found to more likely occur among
growers having lower quality land, higher value crops, a high purchase price for water
or greater depth to groundwater, and more severe drainage problems.
A major focus in the recent literature has been the investigation of the decision-making
process in technological adoption process over time. Many researchers have empirically
investigated the impact of risk factors, credit and information availability and farm size
on adoption behaviour of farmers (Feder and Umali, 1993). However, except for
Koundouri et al. (2006) and Torkamani and Shajari (2008) empirical studies that
investigate irrigation technology adoption under agricultural production risk are rare.
The extant literature on tube-well technology adoption argues that neither the
indivisibility of the technology nor the risk behaviour of adopters has restricted the
adoption of the tube-well technology in Pakistan (Chaudhry, 1990). The very few
studies on the subject have merely documented the extent of adoption among farmers or
the economic returns to the tube-well owners and non-owners (Chaudhry, 1990,
Meinzen-Dick, 1996). Empirical literature that provide a link between the tube well
adoption decisions and variability in farm performance is still sparse.
3.2.1 Adoption of Tube-well Technology in Pakistan
Pakistan was amongst the early adopters of the new agricultural technology during the
Green Revolution (Byerlee and Siddiq, 1994). The most important aspect of the Green
Revolution in Pakistan was the groundwater revolution14. The role of groundwater
14In the early 1960s, massive groundwater extractions were initiated to combat the problem of water logging and salinity in some areas and to meet rising irrigation demands due to growing cropping intensities. Higher yields and economic returns to groundwater users, in subsequent periods, encouraged the farmers to adopt tube-well technology to a great extent.
The Groundwater Depletion Risk and Tube-well Technology Adoption
41
irrigation was protective15 irrigation until the Green Revolution. However, the adoption
of high yielding and water sensitive crop varieties during the Green Revolution changed
crop management. Modern crop varieties increased yield two to three times more than
the traditional varieties but their crop water requirements increased about three times
(Shiva, 1991, Ahmad et al., 2004a). The adoption of new crop varieties led to a rapid
increase in irrigation water demands. Consequently, irrigation water supplies doubled
from 1967 to 1976. With the completion of two major reservoirs Mangla16and Tarbela,
canal water supplies nearly doubled. The installation of private tube-wells expanded the
groundwater supplies by about 8 % during the same period. The overall growth in water
supply helped to turn rain-fed lands into irrigated lands on a vast scale. In the next
decade, 1976 to 1986, canal water supplies remained unchanged while the irrigated area
continued to increase. As a result, increasing irrigation water demands were met
through groundwater extractions. By 1986, pumped water contributed to about 59% of
the total Rabi water requirements (Byerlee and Siddiq, 1994).
In early 1960s, the adoption of tube-well technology was facilitated by government
support policies such as rural electrification, subsidization of electricity, diesel and
drilling services, free pump sets and low interest long-term loans (Falcon and Gotsch,
1968, Papanek, 1968, van Steenbergen and Oliemans, 2002, Johnson, 1989 ). The
objective of these policies was to control waterlogging in high water table areas and to
encourage agricultural production in areas with limited canal water supplies
(Steenbergen and Oliemans, 2002). Later, higher yields and greater economic returns
(Meinzen-Dick, 1996) encouraged farmers to adopt tube-well technology and transition
into growing water intensive crops such as sugarcane and rice (Muhammad, 1964,
Muhammad, 1965, Falcon and Gotsch, 1968, Nulty, 1972). Consequently, the number
of tube-wells which was limited to less than 30 thousand in the early 1960s now has
exceeded over one million. Owing to the recent and rapid declining of groundwater
tables and the number of tube-wells, ensuring sustainable groundwater extraction has
become a policy imperative in Pakistan. Knowing farmer’s adoption decisions about
15The notion “protective irrigation” means to design and operate an irrigation system based on the principle that the available water should be spread equitably in order to cover as many farmers as possible without taking into consideration the full crop water requirements (Jurriens et al., 1996) 16The Mangla Reservoir is located on the Jehlum River. It was completed in 1967. The Tarbela Dam is situated on the Indus River and is the largest earth filled dam in the World. It was completed in 1976.
The Groundwater Depletion Risk and Tube-well Technology Adoption
42
tube-well technology under the prevailing water shortage scenarios may help in
informing policy makers in designing appropriate groundwater management policies.
3.3 Theoretical Framework
It is known that farmers’ attitude to risk will affect their adoption of some technologies.
Empirical evidences suggest that most farmers are risk-averse (Antle, 1987, Saha et al.,
1994, Dercon, 2004). In particular, farming households in low-income countries are
generally more risk-averse and adopt different strategies to minimise risk impacts. Risk-
averse decision makers may experience welfare losses due to variability (as measured
by the variance) in consumption or production (Dercon, 2004, Kim and Chavas, 2003).
However, variance may not completely capture the degree of risk exposure nor identify
unexpected extreme events (Di Falco and Chavas, 2006). In this study, we go beyond
the mean-variance framework to test whether farmers also consider downside profit risk
and outlier activity such as extreme events when making decisions about the adoption of
new irrigation technology i.e., tube-well technology.
Since farming households are risk-averse and face water scarcity, we employ an
expected utility maximization framework based on Koundouri et al. (2006) to represent
adoption decisions under depleting groundwater resources. We conjecture that the farm
household j is risk averse and uses a vector of conventional inputs jx together with
applied irrigation water jwx to produce a single output q and profit j through a
technology described by a well-behaved (i.e., continuous and twice differentiable)
production function f . Let jp denote output price and jr the corresponding vector of
input prices for the household j . The farm is assumed to incur production risk as crop
yield might be affected by the climatic conditions. This risk is represented by a random
variable j , whose distribution G is exogenous to the farmer’s decisions. This is the
only source of risk we consider as jp and jr are assumed to be non-random (i.e., farmers
are assumed to be price takers in both output and input markets).
Unlike Koundouri et al. (2006), in this study we deal with the adoption of tube-well
technology which does not necessarily increase efficiency of irrigation water like
sprinkler and drip irrigation. By contrast, tube-well ownership ensures more promising
irrigation water supplies and hence lessens production uncertainties (Qureshi et al.,
2009). Allowing for risk aversion, the farmer’s problem is to maximize the expected
utility of profit such as:
The Groundwater Depletion Risk and Tube-well Technology Adoption
43
j j jw jw jw j jj
j jw j jwx ,x x ,x3.1 Max U Max U p f ,x ,x r x r x dG
where U is the von Neumann-Morgenstern utility function. Assuming that jp and jwr
are non-random, the first order condition for groundwater irrigation water input can be
rewritten as follows:
j jw j
jw j
jw
f ,x ,x3.2 E r U E p U
x
j jw j jwj jw jjw
j
j jw
cov U ; f ,x , / xf ,x ,r3.3 E p Up x E[U ]
xx
where
U U . In the case of a risk-neutral farmer, the first term of Equation 3.3 i.e.,
the ratio of input price to output price jw jr p is equal to the expected marginal product
of irrigation water. However, for a risk-averse farmer the second term in the right hand
side of the relation Equation 3.3 is different from zero and measures deviations from the
risk-neutrality case. More precisely, this term is proportional and is opposite in sign to
the marginal risk premium with respect to the irrigation water input (Koundouri et al.,
2006).
Let us now incorporate into the above general model, the farmer’s decision whether or
not to adopt tube-well technology. This decision can be modelled using a binary choice
model, where a farmer can choose to adopt (A=1) or not (A=0). Suppose the farmer is
fully aware of the use and future costs and benefits of the tube-well technology at the
time of adoption, adopting the new technology implies a fixed cost 1 0I 0 and I 0 and
might change the marginal cost of water 1 0jw jwr r . Denote 1 0
j jx x the optimal input
use by the adopters and non-adopters. A farmer will decide to install a tube-well if the
expected utility with adoption 1E U is greater than the expected utility without
adoption 0E U such that:
1 03.4 E U E U 0
where
j j1 1 1 1j jw j jw
j j j0 0 0 0j jw j jw
1 1 1 1 1 1 1jw j jw jw j
,x ,x
0 0 0 0 0 0 0jw j jw jw j
,x ,x
3.5 Max U Max U p f x , r x I dG
and
3.6 Max U Max U p f ,x , r x I dG
x x
x x
x r x
x r x
The Groundwater Depletion Risk and Tube-well Technology Adoption
44
Both Equation 3.5 and Equation 3.6 are the expected utility for an adopter and non-
adopter respectively. For the risk-averse farmer, the first order condition for water input
corresponding to the case of adoption and non-adoption is given by Equation 3.7 and
Equation 3.8 respectively:
jj
j
j
1 1 11 11jw j jwjw jjw
1jw
cov U ; f ,x , / xf ,x ,r3.7 E p Up x E[U ]
xx
jj
j
j
0 0 00 00jw j jwj jjw
0j
cov U ; f , x , / xf , x ,r3.8 E p Up x E[U ]
x x
As the tube-well technology ensures more reliable access to irrigation water and offers
farmers more promising supplies and better control over spatio-temporal irrigation
water requirements, the option to invest in tube-well technology is likely to be valuable.
Now, assume that future profit flows after adopting tube-well technology are known
with certainty as a result of more reliable access to irrigation water. Installing a tube-
well entails a sunk cost due to either uncertainty associated with falling groundwater
tables or deteriorating groundwater qualities. The magnitude of the sunk cost depends
on tube-well construction/installation costs, the rate of obscelence of the technology and
the rapidity with which groundwater tables fall to render the investment less effective.
The magnitude of these sunk costs is likely to affect adoption of the technology and is
an uncertain cost conditional on the farmer’s perceptions about groundwater quality,
declining rates of water tables and the perceived impacts of water shortage on future
cropping patterns, production and grain prices. Farmers may delay adoption to acquire
more knowledge about groundwater level or quality rather than rush to adopt. The
farmers will decide to adopt if:
1 03.9 E U E U VI
where VI 0 represents the value of information diffusion for the farmers who might be
willing to adopt the technology and this should depend on the fixed cost of investment,
level of uncertainty related to future outcomes of the technology and the farmer’s own
characteristics.
3.3.1 Empirical Estimation Procedure
At the time of the survey, some of the farmers were found to be adopters whilst others
were non-adopters, and hence we cannot estimate the structural Equation 3.9. Rather we
The Groundwater Depletion Risk and Tube-well Technology Adoption
45
estimate a reduced form of this equation and focus on the impact of risk to explain the
adoption decisions. First, in order to avoid specifying a functional form for the
probability function of profit , the distribution of risk G , and farmer’s risk
preferences i.e., utility function U , we use a moment based approach which allows a
flexible representation of risk (Antle, 1983, Antle, 1987, Koundouri et al., 2006).
Production risk and thus profit uncertainty are accounted for in the adoption model by
using sample moments of the profit distribution as explanatory variables. As we
explained earlier, another source of risk associated with adoption could be due to falling
groundwater tables and increasing salinity levels in groundwater supplies. This cost of
uncertainty is represented by a premium (VI ) in Equation 3.9 which indicates the value
of seeking information either to confirm their perceptions or misperceptions about the
pros and cons before investing in the technology. In the empirical model, we use the
farmer’s education, access to different sources of information e.g., radio, television and
newspaper etc., and access to agricultural extension services as a measure of human
capital.
The econometric estimation procedure to analyse the impact of production risk on the
adoption process is a two stage procedure. Firstly, we compute the four sample
moments of profit distribution of each farm i.e., the coefficients of the mean, variance,
skewness and kurtosis. Many empirical studies have focused on estimating the first two
central moments i.e., mean and variance e.g., Just and Pope (1979) and Traxler et al.
(1995). However, we can go beyond the mean and variance and can get consistent
estimates of all relevant central moments econometrically i.e., skewness (Antle, 1983,
Kim and Chavas, 2003) and kurtosis (Koundouri et al., 2006). In this study we derive
the first four central moments of the profit distribution following (Kim and Chavas,
2003, Koundouri et al., 2006). In the second stage we incorporate the estimated central
moments of the profit distribution along with other explanatory variables and farm
characteristics into a probability model in order to analyse how production risk affects
the decision to adopt the tube-well technology.
Firstly, profit is regressed on the farm level input variables to estimate the “mean”
effect. The model takes the following general form:
j jw j j j3.10 f x , , ; u x z β
where j is the value of crop production i.e. profit of a household jwith j 1,...., N
denoting individual farms in the sample, jx is the vector of inputs (seed, labour,
The Groundwater Depletion Risk and Tube-well Technology Adoption
46
chemical, fertilizer and farm machinery, etc.), jz is the vector of extra shifters including
farmer’s characteristics (farmer’s age, education and off-farm income and other farm-
specific characteristics), ju is the usual identically independently distributed error term
which captures unobserved variations in crop production and production shocks while
β is the vector of parameters to be estimated. The Ordinary Least Squares (OLS)
estimation of Equation 3.10 gives consistent estimates of the parameter vectorβ . Then
the thj central moment of profit j 2,...,m is defined as:
j 1
j3.11 E
where 1 represents the mean or first moment of profit. The estimated errors from the
mean effect regression j j jw j ju f x , , ; x z β are estimates of the first moment of
profit distribution. The estimated errors ju are then squared and regressed on the same
set of explanatory variables:
jw j j j2j3.12 u g x , , ; u x z δ
The Ordinary Least Square (OLS) estimates of Equation 3.12 provide consistent
estimates of the parameterδ . The predicted values 2ju from Equation 3.12 are consistent
estimates of the second central moment i.e., variance of the profit distribution (Antle,
1983). We estimate the third and fourth moment i.e., skewness and kurtosis of profit
distribution by raising the estimated errors from the mean regression model to the power
of three and four. The four estimated moments are then incorporated into a discrete
model of technology adoption along with farmer’s structural and demographic
characteristics.
Given the expected utility maximization assumption and the additional value of
information (VI ), a farmer will only choose to adopt tube-well technology if:
* 1 0j3.13 Y E U E U VI 0
*jY is an unobservable random index for each farmer that defines their propensity to
adopt tube-well technology.
We assume that a farmer’s decision to make investments in installing a tube-well is also
based on their perceptions about groundwater characteristics i.e., groundwater table
falling rates, salinity levels and the level of uncertainty related to the groundwater
The Groundwater Depletion Risk and Tube-well Technology Adoption
47
characteristics etc. Moreover, farmers are likely to seek information according to their
perceptions. In order to capture the impact of the farmer’s perceptions on adoption of
tube-wells, we incorporate three dummy variables i.e., salinity perception (1=yes,
0=no), water table decline (1=yes, 0=no) and the farmer’s perceptions of the impact of
these changes on future cropping patterns (1=yes, 0=no) in the probability model.
The estimation of the indirect utility (per year) of farmer j if he is a non-adopter is
denoted as:
0 j 0 j 0 0 j 0 j 0 jm k0 0 ,3.14 Y z α m α k α
The indirect utility (per year) of farmer j if he is an adopter is given as:
1 j 1 j 1 1 j 1 j 1 jm k1 1 ,3.15 Y z α m α k α
where jz is a vector of regressors including all structural and demographic
characteristics , jm is the vector of four profit moments that introduce uncertainty into
the model, jk is the vector of farmer’s perception , is a vector of parameters to be
estimated and j is the error term.
From Equation 3.14 and Equation 3.15, the probability of farmer j adopting tube-well
technology is represented by the following model:
r j 0 j 1j3.16 P Y 1 = Y Y ,
jPr[ ] [ ]where
j j j jjm k m k
j z α m α k α , z α m α k α
j 0j 1j j 0j 1j j 0j 1j j 0j 1j 1 0m m m k k k
1 0 1 0ν =ν -ν ,z =z -z ,m =m -m ,k =k -k ,α=α -α ,α =α -α and
The binary choice model in Equation 3.16 is estimated using a probit model, i.e.,
assuming that j is 2N 0, and that is a cumulative normal distribution. Since we
incorporate estimated profit moments in the probit model, we use a bootstrapping
procedure to obtain consistent estimates of the corresponding standard errors (Politis
and Romano, 1994).
3.4 Data Descriptions
3.4.1 Salient Features of Study Districts
The study area is a part of the mixed-cropping zone and cotton-wheat zone of the
Punjab province of Pakistan. The mixed-cropping region is the alluvial plain between
The Groundwater Depletion Risk and Tube-well Technology Adoption
48
the rivers Ravi and Chenab while the cotton-wheat region lies between the rivers Ravi
and Sutlej. Both regions have arid to semi-arid continental subtropical climate with long
hot summers and cool winters. The mean annual rainfall is also very low with 360 mm
in the mixed-cropping zone and 120 mm in the cotton wheat-zone. Due to the arid and
semi-arid climate, agriculture in these regions is highly dependent on irrigation water.
However, due to scant canal water supplies agriculture heavily relies on groundwater as
a major source of irrigation water in some districts within these regions. The study area
in the Jhang district is solely groundwater irrigated while in the Lodhran district some
irrigation via canal water is available. In Lodhran, canals are used to supply water
during the Kharif season only. For instance, the canal water contribution during the
Kharif season of 2010 was observed to be between 20-44 percent of the total irrigation
requirement across different farms. The shortfall of the irrigation water comes through
groundwater.
The study areas in both districts are characterised by deep groundwater tables which
require high installation costs for tube-wells. The installation costs for a 24 metre tube-
well are seven times those for a 6 metre tube-well (Qureshi et al., 2003). The variation
in the bore depth was observed to be between 60 metres to 99 metres in Lodhran and
between 33 metres to 57 metres in Jhang district. Due to limited tube-well ownership in
some parts of these districts, farmers who do not own a tube-well opt to buy water from
their surrounding tube-well owners to meet their crop water requirements. Such
groundwater transactions occur as a result of social contract under informal
groundwater markets.
Farm size usually plays an important role in informal trading of groundwater in the
Indo-Pak region. Large farms are often involved in selling groundwater (Meinzen-Dick,
1996, Shah et al., 2008). However, due to electricity shortage and growing cropping
intensities, large farms now have less surplus water. Currently, only medium sized
farms or large farms having more than one tube-well are mostly involved in selling
groundwater. Since water buyers (non-adopters) get water after the tube-well owners
have irrigated their own fields, they face more uncertainties in getting irrigation water
and are more prone to crop failures than the tube-well owners (adopters). Moreover,
The Groundwater Depletion Risk and Tube-well Technology Adoption
49
variable higher prices for water paid by buyers (3-4 times more17) also add into
uncertainties for groundwater purchasers.
3.4.2 Data Descriptions
The data used in this study is collected from two districts i.e., Lodhran, a cotton-wheat
region18 and Jhang, a mixed-cropping region of the Punjab province, Pakistan. The data
were collected using a detailed survey during the Kharif season in 2010. Based on a
multi-stage sampling technique, a cross-sectional sample size of 200 farms was
randomly selected. In the first stage, one tehsil19 was selected purposively from each
district. In the next stage, 10 villages were selected at random from each selected tehsil.
A village usually comprises of between 70-80 farming households in the study districts.
Finally, from each village 10 groundwater users (5 adopters of tube-well technology and
5 non-adopters) were selected randomly, thus having 50% adopters and 50% non-
adopters in the study.
The survey provides detailed farm level information about production patterns, input
use, and output produced, gross revenues, structural characteristics and the number of
farms that either adopted tube-well technology or did not. Various inputs are measured
such as: (1) seed and fertilizer in kg/acre; (2) pesticide and farm operations as cost of
each application/acre; (3) labour in hours/acre; and (5) groundwater use in cubic
metres/acre. Output is measured in kg/acre as well. In order to calculate profit, total
crop revenue and different inputs and output costs were collected in Pakistani Rupees20.
The estimation was done at a farm level rather than at a household level.
Table 3.1compares selected variables used in the estimation. Overall, we see that the
average farm size for adopters is larger than that of non-adopters. On average, adopting
farms have 10.05 acres under cotton cultivation and for non-adopters it is 5.5 acres.
Similarly, adopting farms have 12.08 acres under wheat cultivation compared to 6.02
17Tube-well owners pay only extraction cost for groundwater while water buyers have to pay wear and tear charges (in other words some profit as well to the tube-well owner) along with the extraction costs. 18Due to climatic variations and the nature of cropping patterns, the Punjab province is classified into five cropping regions; barani region, mixed cropping region, rice-wheat region, cotton-wheat region and pulses-wheat region. 19Tehsil is an administrative unit. A district usually comprise of 5-6 tehsils (sub-districts) in Pakistan. 20Average exchange rate at the time of data collection (June-November 2010) was Rs.85.25/US$.
The Groundwater Depletion Risk and Tube-well Technology Adoption
50
acres for non-adopters. There is no statistical significant difference in the use of
different inputs between adopters and non-adopters except for the chemical use in
cotton cultivation and fertilizer use in wheat cultivation. However, adopting farms
generate, on average, more profit on per acre basis for cotton and wheat compared to
non-adopters.
Table 3.1: Summary statistics of the variables for cotton and wheat crops
Economic Data Adopters Non-adopters Mean Std. Dev. Mean Std. Dev. Cotton Farm size (acres) 10.05*** 6.78 5.47 4.33 Farm production (kg/acre) 838.25 177.08 821.47 181.70 Seed quantity (in kg/acre) 8.31 1.29 8.31 1.35 Labour (hours/acre) 326.84 55.63 328.34 51.68 Fertilizer (kg/acre) 215.00 63.07 200.82 56.60 Chemical input (Rs./acre) 4480.89* 1157.37 4219.75 1361.01 Machinery cost (Rs./acre) 3962.37 757.59 4050.60 898.98 Irrigation water (m3/acre) 2277.67** 424.80 2130.28 362.30 Total cost (Rs. /acre) 34011.10 4494.16 36729.10*** 4989.10 Total revenue (Rs. /acre) 74250.97 17541.13 71239.74 16914.02 Profit (Rs. /acre) 40239.88** 15899.22 34510.65 16069.19 Wheat Farm size (acres) 12.08*** 5.50 6.02 3.74 Farm production (kg/acre) 1556.58** 205.94 1475.59 224.07 Seed quantity (in kg/acre) 57.38 5.77 58.75 6.83 Labour (hours/acre) 56.48 22.97 61.70 37.58 Fertilizer (kg/acre) 201.00** 41.73 181.85 41.88 Chemical input (Rs./acre) 1324.76 458.36 1324.27 467.46 Machinery cost (Rs./acre) 4024.37 738.06 4374.60** 994.32 Irrigation water (m3/acre) 783.35 318.00 775.62 338.67 Total cost (Rs. /acre) 18475.24 2467.10 19894.46*** 2963.09 Total revenue (Rs. /acre) 35427.74*** 5047.03 33337.13 5197.05 Profit (Rs. /acre) 16952.50*** 4987.89 13442.67 4897.39
Note: The null hypothesis is that the mean difference between two subsamples (i.e., adopters and non-adopters) is not statistically significant. Double asterisks indicate statistically significant difference at 5%, triple asterisks indicate significance at 1% in the respective variables between two subsamples.
On average, adopters generate Rs. 40,239 profit from one acre cotton cultivation, or
16% more profit than non-adopters who generate on average Rs. 34,510.
Both adopters and non-adopters, on average, use similar seed rate (approximately 8
kg/acre) for cotton cultivation. There is also not a significant difference in the number
of hours worked on cotton farms, with adopters working on average 326 hours per acre
The Groundwater Depletion Risk and Tube-well Technology Adoption
51
compared to non-adopters with 328 hours. Adopters and non-adopters do not use
significantly different amounts of fertilizer in cotton cultivation. However, there are
statistically significant differences in chemical applications between adopters and non-
adopters with adopters being greater users. The average irrigation water use for adopters
is 2,277 m3 in contrast to 2,130 m3 for non-adopters. Finally, the average per acre cotton
yield is 838 kg for adopters versus 821kg for non-adopters.
Table 3.2: Summary statistics of the variables used in the probability model
Adopters Non-adopters Variable Mean Std.
Dev. Mean Std.
Dev. Farm Characteristics Farmer’s Age (years) 43 9 44 8 Land tenureship (1=owner, 0=tenant) 0.99 0.10 0.65 0.48 Off-farm income in Rs. 91,220 2,20,090 50,236 84,489 Farm debt in Rs. 32,000 68,854 41,333 60,207 Access to credit services (0=no, 1=yes) 0.25 0.435 0.47 0.502 Farmer’s education (years of schooling) 5.87 4.47 3.67 3.62 Access to extension services (1=yes, 0=no) 0.51 0.50 0.09 0.29 Access to information sources (0=no, 1=yes) 0.52 0.502 0.131 0.339 Salinity perception (1=yes, 0=no) 0.26 0.44 0.26 0.44 Water table decline perception (0=no, 1=yes) 0.54 0.50 0.58 0.50 Effect on future cropping pattern (1=yes, 0=no)
0.73 0.45 0.75 0.44
We find a similar difference between adopters and non-adopters for wheat cultivation in
the use of different farm inputs. Adopters on average work 56 hours versus 61 hours for
non-adopters. Both adopting and non-adopting farms slightly differ in the seed rate,
with an average of 57 kg/acre for adopters and 58 kg/acre for non-adopters. In contrast
to cotton cultivation, adopters use significantly higher amounts of fertilizer compared to
non-adopters. However, both adopters and non-adopters do not significantly differ in
chemical use in wheat cultivation. The average irrigation water use for adopters is
783m3 and 775 m3 for non-adopters. The average wheat yield is 1,556kg/acre for
adopters versus 1,475kg/acre for non-adopters. Adopters generate Rs. 16,952 profit
from one acre wheat cultivation, or 21% more profit than non-adopters who generate on
average Rs. 14,442.
Table 3.2 presents information on the socio-economic characteristics of the surveyed
farms. It is evident that age is not a decisive factor in adopting a tube-well technology
because both adopters and non-adopters on average are very similar in age, 43 years and
The Groundwater Depletion Risk and Tube-well Technology Adoption
52
44 years respectively. The majority of adopters (99%) cultivate their own land,
indicating that land owners are more likely to have a tube-well compared to tenants. The
difference in off-farm income shows that adopters, on average, generate 45% more from
off-farm business compared to non-adopters suggesting that off-farm income may play
an important role in adopting a tube-well. Off-farm income provides adopters with
additional financial resources and, perhaps indicative of their greater financial strength,
they owe 23% less farm debt compared to non-adopters. Information on access to credit
services indicates that higher proportion of non-adopters generally apply for credits
from different crediting agencies. There are statistically significant differences
regarding access to extension advice and to other information sources e.g., radio,
television and newspapers, etc. The average education level of adopters is 6 years of
schooling whereas for non-adopters the average education is 4 years of schooling.
Statistically there is a significant difference in education attainment between adopters
and non-adopters education at 0.05% level of significance. Non-adopters spend 12.5%
more on irrigation related expenditures compared to the adopters. There is little
difference in perceptions of adopters and non-adopters about the salinity level, rate of
decline in groundwater tables and their impact on future cropping patterns. The lack of
difference in perceptions about the salinity contents in groundwater between adopters
and non-adopters could be due to the reason that they both use water from the same
tube-well.
3.5 Results and Discussion
Estimation results of the bootstrapped probit model are presented in Table 3.3.The
statistical significance of the impact of three (first, second and fourth) out of four
distributional moments suggests that decision makers are not risk-neutral. Even though
most distribution functions are well approximated by their first three moments, the
estimation of the fourth moment helps to understand decision makers’ responses under
extreme events (Koundouri et al., 2006, Antle, 1983). Since moments of profit
distributions are assumed to be exogenous to a farmer’s adoption decision, their signs in
the probit model indicate that farmers who are more risk averse are more likely to install
a tube-well. The majority of the farmer’s characteristics are highly significant regarding
the choice of adopting tube-well technology. The age and education of the farmer do not
play a significant role in the adoption process. However, the statistically significant
association between tube-well adoption and land tenureship suggests that land owners
are more likely to adopt a tube-well than tenants or non-land holders.
The Groundwater Depletion Risk and Tube-well Technology Adoption
53
Table 3.3: Estimation of the results for the probability of adopting a tube-well
Variable Estimate Bootstrapped Std. Error
t-Ratio
Household and farm characteristics Age 0.008 0.011 (0.73) Land tenure status (0=tenants, 1=owners) 2.298*** 0.395 (5.83) Percentage of farm income spent on irrigation -0.681*** 0.213 (-3.20) Off-farm income Rs. 0.904*** 0.190 (4.76) Farm debt in Rs. -0.015 0.061 (-0.24) Access to credit services (0=no, 1=yes) 0.033 0.012 (0.84) Education (years of schooling) 0.007 0.028 (0.23) Access to extension services (0=no, 1=yes) 1.253*** 0.246 (5.10) Access to sources of information (0=no, 1=yes) 1.015*** 0.213 (4.76) Water scarcity perceptions Salinity perception (0=no, 1=yes) -0.288 0.235 (-1.23) Water table decline perception (0=no, 1=yes) -0.014 0.114 (-0.13) Change in cropping pattern perception (0=no, 1=yes)
-0.165 0.218 (-0.76)
Profit moments First moment 0.485* 0.278 (1.74) Second moment 7.858*** 1.746 (4.50) Third moment 0.634 1.017 (0.62) Fourth moment -2.683*** 0.846 (-3.17) Constant -3.617*** 0.676 (-5.35) Valid chi2 97.79 McFadden's R2 0.518
Note: *, **, *** indicate significance at 10%, 5% and 1% levels respectively. Number of
bootstraps=2000
In our study sample, 82% of respondents are land owners while the remaining 18% are
tenants or they have rented in land for farming. Because tube-well installation requires a
large up-front investment and is not a portable technology (i.e. a potentially stranded
asset), tenants put a much lower value on adopting a tube-well. Moreover, the presence
of water markets where tenants have the option to buy water does not make it necessary
to have their own tube-well. By illustration, Carey and Zilberman (2002) found that
water markets may induce farmers to delay the adoption of irrigation technology.
The estimated results indicate that off-farm income significantly increases tube-well
adoption. Since off-farm income helps to bear unexpected farming outcomes and
ensures a consistent income, this financial security may allow some farmers to invest in
installing tube-well. In other cases, the off-farm income may actually be one
The Groundwater Depletion Risk and Tube-well Technology Adoption
54
ramification of the farmer investing in a tube-well, whereby greater profits from using
the tube-well fund off-farm investments.
Similar to farm debts, access to credit services do not have significant impact on tube-
well adoption. A possible explanation is that usually small holder farmers get small
amounts of credit that is not sufficient to install a tube-well. Access to extension
services and different sources of information both have statistically significant positive
impacts on tube-well adoption. These findings suggest a positive value on waiting for
better information before deciding to adopt. Also adopters may inherently seek contact
with agricultural extension staff and use different other sources of information not just
to facilitate decisions over tube-well adoption but for many other agricultural decisions.
All the three explanatory variables representing farmer’s perceptions about groundwater
resource i.e., perception about salinity, groundwater table decline and potential impact
on future cropping patterns, do not seem to significantly impact the tube-well adoption.
The role of risk in a farmer’s adoption decision is highlighted through the significance
of the sample moments of the profit distribution. The first and the second moments,
which approximate mean profit and profit variance, are highly significant while the
fourth moment (kurtosis) is marginally significant. The third moment, i.e. skewness is
not statistically significant. The results indicate that the higher the expected profit the
greater the probability that a farmer decides to adopt a tube-well technology. In other
words as the mean or expected profit increases, the affordability to install a tube-well
also increases. Similarly, in case of variance, we see that with increasing variance the
probability of adopting tube-well increases significantly.
More generally, the higher is the variance of profit (and greater the probability of facing
extreme profit values), the greater is the probability to adopt tube-well. Based on these
results we can infer that: 1) since tube-well installation requires a large up-front
investment, farmers prefer to reduce production risks in order to get consistent and
reliable profits; 2) under uncertain water supplies for irrigation, farmers generally tend
to install tube-well to improve the reliability of their water supplies, especially to hedge
against crop failures. However, non-significant third moment (skewness) indicates that
downside yield risk does not have significant impact on tube-well adoption. However,
highly significant fourth moment may indicate the propensity to adopt will decrease
significantly as a result of extreme events. We estimate the marginal effects of each
explanatory variable by calculating their derivatives at their means. These derivatives
The Groundwater Depletion Risk and Tube-well Technology Adoption
55
are reported in Table 3.4 and represent the marginal effect of each regressor,
approximating the change in the probability of adoption at the regressor’s mean.
As shown in Table 3.4, the sample moments of profit distribution, in particular mean,
variance and kurtosis affect a farmer’s decision to adopt tube-well technology and
confirm that farmers are not risk-neutral but rather are likely to be risk-averse.
Table 3.4: Marginal effects of the explanatory variables
Variable Estimate Bootstrapped Std. Error
t-Ratio
Household and farm characteristics Age 0.003 0.004 (0.72) Land tenure status (0=tenants, 1=owners) 0.649*** 0.048 (13.56) Percentage of farm income spent on irrigation
-0.265*** 0.095 (-2.78)
Off-farm income in Rs. 0.361*** 0.076 (4.75) Farm debt in Rs. -0.005 0.024 (-0.23) Access to credit services (0=no, 1=yes) 0.013 0.012 (0.64) Education (years of schooling) 0.002 0.011 (0.21) Access to extension services (0=no, 1=yes) 0.4454*** 0.071 (6.29) Access to sources of information (0=no, 1=yes)
0.378*** 0.069 (5.41)
Water scarcity perceptions Salinity perception (0=no, 1=yes) -0.115 0.093 (-1.24) Water table decline perception (0=no, 1=yes) -0.014 0.114 (-0.13) Change in cropping pattern perception (0=no, 1=yes)
-0.066 0.086 (-0.77)
Profit moments First moment 0.192* 0.110 (1.74) Second moment 3.130*** 0.692 (4.52) Third moment 0.253 0.405 (0.63) Fourth moment -1.068*** 0.336 (-3.18)
Note: *, **, *** indicate significance at 10%, 5% and 1% levels respectively.
The highest marginal effect arises from the second moment (i.e. profit variance)
followed by land tenure status, access to extension services and access to different other
sources of information. A 1% increase in the value of these variables, ceteris paribus,
results in an increase in the probability of adoption by 3.13%, 0.65%, 0.45% and 0.38%,
respectively. The statistically significant relationships between access to agricultural
extension services, and access to different other sources of information and likelihood
of adoption suggests that quasi-option value (value of waiting to get more information)
may play an important role in adoption decisions.
The Groundwater Depletion Risk and Tube-well Technology Adoption
56
3.6 Conclusions
In this article we employed a moment-based approach to analyse farmers’ decisions to
adopt tube-well technology to hedge against production risks associated with
diminishing irrigation water supplies due to declining groundwater tables. We find that
farmers are not risk-neutral and that uncertain water supplies add to future uncertainty
relating to crop production and consequently profits.
We estimated an adoption model using a randomly selected sample of 200 farming
households located in two different districts of the Punjab province in Pakistan. The
estimation procedure followed two stages. In the first stage, we estimated the first four
sample moments of the profit distribution, namely the mean, variance, skewness and
kurtosis coefficients. In the second stage, we incorporated the estimated moments along
with other explanatory variables into a probit model to analyse how production risk
affects the decision to adopt tube-well technology.
We find that the sample moments of the profit distribution affect the farmers’ adoption
decisions. The first, second and fourth sample moments of profit (mean, variance and
kurtosis) are significantly associated with the probability to adopt tube-well technology,
thus confirming that farmers are not risk-neutral. Estimates show that the higher the
expected profit the greater the probability that a farmer decides to adopt a tube-well
technology. We also find that the probability of adopting tube-well increases
significantly with increasing variance of profit. These results imply that the farmers
adopt tube-well technology in pursuit of greater expected profits and more reliable
profits, generated by more reliable access to water resources that provide a hedge
against production risks.
However, as a result of production risks due to crop failures farmers face profit
uncertainties and some farmers, due to low or inconsistent profits, may not have
sufficient means to invest in tube-well technology. Most of the farmer’s own
characteristics are also highly significant in the choice of adopting tube-well
technology. Farmers with higher off-farm income, better access to agricultural
extension services and different sources of information, and those who cultivate their
own lands are found to be more likely to be tube-well owners. Farmers’ perceptions
about the magnitude and reliability of groundwater supplies are not an important
indicator of tube-well adoption. The statistically non-significance of the variables
representing farmers’ perceptions about groundwater levels and quality indicate that
farmers neither consider lowering groundwater tables nor increasing salinity levels
The Groundwater Depletion Risk and Tube-well Technology Adoption
57
when make decisions to adopt tube-wells. Moreover, farmers’ perceptions about the
potential impact of declining groundwater tables on future cropping patterns do not
significantly affect tube-well adoption.
The results have important policy implications. First, encouraging the adoption of tube-
well technology can be a pathway to increasing farm profitability and facilitating
production risk management. However, within the context of declining groundwater
tables sustainable extraction of groundwater aquifers should be encouraged to ensure
the longevity of groundwater resources. As tube-wells serve only to increase access to
irrigation water but do not improve irrigation water use efficiency nor conserve the
groundwater resource, policy interventions that encourage adoption of tube-wells needs
to be accompanied by other policies that require efficient use of the water resource (e.g.
joint adoption of sprinkler or drip irrigation technologies) and that limit extraction in
order to ensure sustainable use of groundwater resources. To establish these multi-
dimensional policies requires assessing the merits of policies that give incentives for
tube-well adoption (e.g., subsidies, long-term loans or provision of adoption related
information) within a larger cost-benefit framework that accounts for groundwater
resource management both in terms of short-term gains (i.e. farm profits) and long-term
future social benefits from water resource management. Balancing the income and food
needs of the current generation with the need for sustainable use of groundwater
resources required to serve the needs of future generations.
59
Chapter 4 4. The Efficiency of Irrigation Water Use and its Determinants21
Chapter 4 estimates the technical and irrigation water use efficiency of groundwater-fed
irrigated agriculture within the context of declining groundwater tables. We apply both
non-parametric and parametric approaches to estimate irrigation water use efficiency in
wheat, cotton and rice cultivation. The irrigation water requirements considerably vary
for these crops and even climatic variability can change irrigation water requirements
for the same crop cultivated indifferent geographic localities. Moreover, farmers may
grow different combinations of different crop such as wheat, rice, cotton, sugarcane,
maize etc., so farm-level estimates of irrigation water use efficiency may not be
rationally generalized at broader level due to heterogeneous crop enterprise choices.
Beyond these points, irrigation water use efficiency estimates derived based on
economic principles are directly comparable to technical efficiency which involves
measurement of managerial capability of the irrigators. Such irrigation water use
efficiency measure is defined as the ratio of minimum feasible to observed use of
irrigation water, conditional on observed levels of the desirable output and conventional
inputs. Hence, dealing with irrigation water use efficiency at a crop level is likely to be
much more useful in guiding irrigation decisions on farms. Hence, we estimate the
irrigation water use efficiency at crop level for two reasons: (i) to guide policy makers
and extension agents who may advise farmers about irrigation water use; and (ii) to
avoid potential aggregation bias of output that arises from inclusion of different annual
and biannual crops such as wheat, cotton, rice and sugarcane which also have different
production technologies, cropping seasons and irrigation water requirements.This
chapter comprises of three sub-chapters , each focusing on the analysis of irrigation
water efficiency of different crops i.e., wheat, cotton and rice.
21This Chapter is based on:
4.1Technical and irrigation efficiency of wheat farms in Pakistan: A nonparametric meta-frontier approach
4.2Econometric approach to estimating technical and irrigation efficiency in cotton farming in Pakistan
4.3Measuring production and irrigation efficiencies of rice farms: evidence from the Punjab, Pakistan
The Efficiency of Irrigation Water Use and its Determinants
61
4.1 Technical and Irrigation Efficiency of Wheat Farms in Pakistan: A
Nonparametric Meta-frontier Approach
(Accepted for publication in International Transactions in Operational Research)
The Efficiency of Irrigation Water Use and its Determinants
63
Abstract
Given the importance of water in agriculture, this study examines the level of, and
factors affecting technical and irrigation water use efficiency of irrigated wheat farms in
the Punjab, a province of Pakistan. We employ a non-parametric meta-frontier approach
to investigate both technical and irrigation water use efficiency for a randomly selected
sample of 200 groundwater-fed wheat farms in two different cropping zones i.e., a
cotton-wheat region and a mixed-cropping region. The mean technical efficiency (TE)
of wheat farming differs slightly under the metafrontier and groupfrontier estimations.
On average, water sellers (tube-well owners) are found to be more efficient under the
metafrontier and groupfrontier estimates (91% and 94%) compared to water buyers with
the mean TE (90% and 93%). The mean irrigation water use efficiency suggests
substantial inefficiencies among water sellers and water buyers. The metafrontier results
indicate average irrigation water use efficiency (IWE) estimates of 66% and 65% while
the groupfrontiers indicate 71% and 67%. Amongst the most influential factors
affecting TE and IWE are the farmer’s education level, improved seed variety and the
farmers’ perceptions about groundwater resource quality and availability.
4.1.1 Introduction
Agriculture heavily relies on groundwater for irrigation in Pakistan. Over the last half
century, groundwater contribution to overall irrigation water supplies has increased by
almost 50 percent (Byrelle and Siddiq, 1994, Qureshi et al., 2009). The rapid increase in
groundwater use has evolved as a “silent revolution” carried out by thousands of
farmers in quest of reliable irrigation water supplies. During early 1960s, the adoption
of tube-well technology was encouraged by government support policies such as rural
electrification, subsidization of electricity, diesel and drilling services, free pump sets
and soft long-term loans (Falcon and Gotsch, 1968, Papanek, 1968, van Steenbergen
and Oliemans, 2002, Johnson, 1989 ). However, the higher yields and greater economic
returns from the cultivation of high yielding but water intensive crop varieties
(Meinzen-Dick, 1996, Byrelle and Siddiq, 1994) encouraged farmers to adopt tube-well
technology even without government support in subsequent years (Muhammad, 1964,
Muhammad, 1965, Falcon and Gotsch, 1968, Nulty, 1972). As a result of the continued
increase in demand for irrigation water, more and more water supplies were rendered
with groundwater extractions (Shiva, 1991, Ahmad et al., 2004b, Rodel et al., 2009).
Limited to less than 10% in 1960s, the groundwater contribution to total irrigation
The Efficiency of Irrigation Water Use and its Determinants
64
supplies reached to 40% in the next 25 years (Byrelle and Siddiq, 1994). Having more
than one million tube-wells installed across the country, Pakistan irrigates 5.2 million
hectares of land area through groundwater extractions (Siebert et al., 2010b). As the
result of unrestricted expansion of tube-wells the excessive overdrafting of groundwater
aquifers has led to many negative externalities such as rapid declining groundwater
tables, salt water intrusions and secondary salinity etc. (Kijne, 1999a, Shah et al., 2000,
Khan et al., 2008a, Qureshi et al., 2009).
Given the rapid depletion of groundwater resources and various spatial and temporal
negative externalities, ensuring the sustainable use of groundwater resources has
become a policy imperative rather than a choice. Improving irrigation water use
efficiency is being considered as the best solution towards the sustainable use of
groundwater resources. The objective of this paper is to examine technical efficiency
and the extent of irrigation water use efficiency of groundwater irrigated agricultural
farms in Pakistan. To meet this objective, we use a randomly selected dataset of 200
groundwater-irrigated wheat farms from the Punjab province of Pakistan, including 100
water sellers (tube-well owners) and 100 water buyers (non-owners). We use the data
envelopment (DEA) metafrontier approach to estimate technical and irrigation water
use efficiencies of the selected wheat farms. It is well documented that tube-well
ownership ensures more promising irrigation water supplies and hence lessens
production uncertainties during irregular canal water supplies or uncertainties involved
in purchasing groundwater. The uncertain and delayed water application can have
serious impacts on crop growth and may decrease the marginal product of other inputs
such as fertilizer, labour and chemical inputs. Given that tube-well ownership ensures
reliable access to irrigation water over the spatio-temporal crop water requirements and
not having a tube-well adds into irrigation water uncertainties, we assume that tube-well
owners and water buyers operate under different states of technology. Hence, we
estimate a separate frontier for each group to reveal the difference between the
technology and efficiency levels.
The rest of the paper is organised as follows. The next section provides the background
about informal groundwater markets and the wheat farming system in Pakistan. Section
3 explains methods and Section 4 describes the data and principle features of the study
areas. The results are presented in Section 5. The final section draws conclusions and
provides some policy implications.
The Efficiency of Irrigation Water Use and its Determinants
65
4.1.1.1 Nature of Groundwater Markets
Although tube-wells ownership has been on the increase, thousands of smallholder
farmers (usually subsistent farmers and tenants) still do not own tube-wells. Those who
do not own tube-well, irrigate their lands by buying surplus pumped water from their
nearby tube-well owners (Meinzen-Dick, 1996, Qureshi et al., 2009). Such informal
groundwater marketing offer economic benefits to tube-well owners and opportunities
to non-owners to increase agricultural productivity by increasing access to irrigation
water (Manjunatha et al., 2011, Meinzen-Dick, 1996, Shiferaw et al., 2008). Informal
groundwater markets are reported throughout Pakistan but are more common in the
Punjab and Balochistan provinces (Khair et al., 2012, Meinzen-Dick, 1996). Markets
for groundwater generally function under social settings and are greatly influenced by
the social ties between the tube-well owners and water buyers. These markets involve
informally selling groundwater from the private tube-wells without involving the
exchange of permanent water rights (Meinzen-Dick, 1996, Rinaudo et al., 1997b, Khair
et al., 2012).
Access to groundwater resources in Pakistan is open and generally tied to land-
ownership. In the absence of permanent groundwater entitlements, farmers who have
capacity to invest in tube-well technology have exclusive control over groundwater
resources. The right to extract groundwater is not defined and confined. A tube-well
owner can extract and sell groundwater without any interference either under the
customary law or the local social setting (Meinzen-Dick, 1996, van Steenbergen and
Oliemans, 2002). Since these markets are not formally regulated, sometimes tube-well
owners prefer certain water buyers due to social ties with them, thus discriminating to
whom to sell water (Shah, 1993, Jacoby et al., 2004, Khanna, 2007).
It is believed that informal groundwater markets improve the equity of access to
groundwater and are playing an important role in addressing water scarcity (Khair et al.,
2012). However, informal groundwater markets may not fully convey the scarcity value
of water, and can encourage over extraction of groundwater resources (Meinzen-Dick,
1996). Hence, within the context of rapidly declining groundwater tables it is important
to assess the extent of groundwater use efficiency in irrigation under such informal
groundwater market structure.
The Efficiency of Irrigation Water Use and its Determinants
66
4.1.1.2 The Wheat Farming System in Pakistan
Wheat is the most important crop in Pakistan due to its importance in the national food
security and economic development. It is grown under different agro-climatic and
geographic environments but nearly all the wheat crop is cultivated on irrigated lands.
Wheat holds an important position in the agricultural and national economy, accounting
for 14.4 percent of the value added in agriculture and 3.1 percent of the country’s gross
domestic product (GOP, 2010-11). Being the staple food, wheat occupies the largest
portion of farmland, with 9.13 million hectares being devoted to wheat production each
year. In 2010/11 period, Pakistan produced 23.3 million tonnes of wheat, and was
ranked 7th among the world's wheat producing nations (FAO, 2012b, GOP, 2010-11).
Despite having such an important role in the national economy, wheat production has
been facing widespread stagnation in per hectare yields for more than a decade.
Inefficient management practices at the farm level and uncertain water supplies are
considered some of the major reasons for low wheat productivity (Ahmad et al., 2002b).
Pakistan was among one of the early adopters of the Green Revolution (1966-76)
technologies. The diffusion and adoption of semi-dwarf wheat varieties and associated
inputs accelerated wheat growth to 5.1% during the 1970s (Byrelle and Siddiq, 1994).
However, this growth in wheat yields could not be sustained. In the Post-Green
Revolution period the growth rate in wheat yields fell to 2.7% (Farooq and Iqbal, 2002).
Later, as a result of various policies and considerable expansion in the irrigated area,
Pakistan achieved self-sufficiency in wheat production at the beginning of the 2nd
millennia. However, due to the scarcity of new arable land and increasing water
shortage, area expansion is no longer a viable strategy to increase wheat production.
Given the water shortage backdrop, the widespread stagnation in wheat yields has
prompted research efforts to improve efficiency and productivity of wheat farming and
to make efficient use of dwindling water resources in Pakistan.
4.1.2 Methodological Framework
4.1.2.1 The Basic Analytical Metafrontier Framework
The analytical framework is used to assess the technical efficiency using the data
envelopment analysis method. This entails estimating separate frontiers for each group
and a metafrontier is then estimated by pooling all observations together for all groups
(O’Donnell et al., 2008).
The Efficiency of Irrigation Water Use and its Determinants
67
Let y and x denote non-negative output and input vectors of dimensions N 1 and M 1
respectively. We consider the case of a group of K farms ( where k 1 ) where each
farm operates under a specific technology kT k 1,2,....., k .
The technology set contains the set of all feasible output and input vectors.
p q4.1.1 T x, y R | x can produce y
We can define the input and output sets associated with the production technology set T,
which provides an equivalent representation of the production technology. The input set
for a specific output vector y is the set of all input vectors x which can produce y:
4.1.2 X y x : x, y T
The output set for a specific vector of input x is the set of all output vectors y that can
be produced using x:
4.1.3 P x y : x, y T
In a production process, the boundary of the output set is the production possibility
frontier and it represents technically efficient farms. This boundary envelope the set of
all technically efficient farms and can be regarded as the output metafrontier. For
instance, a particular output y can be produced using input vector x in one of the groups,
then (x, y) are considered as part of the metatechnologyT , which is defined by
O’Donnell et al. (2008) as:
*
1 2 k
such that x can produce y in at
least one of the production technologie
x,
s
y : x 0 and y 0, 4.1.4
i.e.,T
,
T ,......,T T
By defining the metatechnology as the convex hull of the union of group-specific
technologies, metatechnology ensures the convexity property as:
* 1 2 k4.1.5 T Covex Hull T T ...... T
Let *iD x, y denote the input distance function, the input distance function in terms of
production technology set *T can be defined as follows:
* *i4.1.6 D x, y sup 0 : x / , y T
The input distance function for a groupk can equivalently be defined in term of the
input sets as follows:
The Efficiency of Irrigation Water Use and its Determinants
68
k ki4.1.7 D x, y sup 0 : x / P x
It indicates the maximum degree to which a given input vector can be radially
contracted and yet producing the same output. As input distance function is defined
with respect to the input set, an input oriented measure of technical efficiency can be
defined in terms of input distance function as:
* *i i4.1.8 TE 1 D x, y
where *iD x,y is the input metadistance function. For groupk , an input oriented
technical efficiency in terms of the input distance function can be represented as:
k ki i4.1.9 TE 1 D x, y
where kiD x,y is the input distance function for groupk . In view of the fact that the
metafrontier envelops every groupfrontier production possibility set, the input distance
function *iD x, y should satisfy that for any given groupk :
k *i i4.1.10 D x, y D x, y , where k=1,2,....,k
Since k *i iD x, y D x, y , there exist technology gap22 between the groupfrontiers and
the metafrontier. An input oriented technology gap ratio (TGR) of each firm in group kis defined as follows:
k *
k i ii * k
i i
D (x, y) TE (x, y)4.1.11 TGR (x, y)D (x, y) TE (x, y)
where *iTE is the technical
efficiency with respect to the metafrontier, and kiTE is the technical efficiency with
respect to the groupk . The estimated TGR takes a value between zero and one and
measures the ratio of the output for the groupfrontier for each of the k group relative to
the metafrontier (Battese et al., 2004). The technical efficiency relative to the
metafrontier is always less than the technical efficiency relative to the groupfrontiers,
thus bounding the TGR value between 0 and 1. If the TGR value is close to 1, this
indicates that a group specific production frontier is close to the metafrontier, indicating
a more advanced technology level. In contrast, the closer the TGR is to 0, the further the
22Battese et al. (2004) refer to this measure as the “technology gap ratio”. However, an increase in the (technology gap) ratio suggests a decrease in the gap between the group-frontier and meta-frontier. To avoid this confusion, O’Donnell et al. (2008) used the term “metatechnology ratio”.
The Efficiency of Irrigation Water Use and its Determinants
69
group frontier is from the metafrontier, indicating a less developed production
technology level (O’Donnell et al., 2008).
4.1.3 Methodological Framework
We can estimate technical efficiencies and technology gap ratio or metatechnology ratio
either by using the data envelopment analysis or a stochastic frontier approach. We opt
to employ DEA in this study because : 1) DEA does not assume any a priori functional
relationship between the inputs and outputs in the production function and the error
term distribution; thus, potential misspecifications that could occur when using
stochastic frontiers are avoided (Latruffe et al., 2012) ; 2) multiple inputs and outputs
can be handled using DEA without input and output aggregation bias and; 3) DEA is
more appropriate approach to estimate efficiency when sample size is small. Banker et
al. (1989) propose that the sample size should be three times greater than the sum of the
number of inputs and outputs.
4.1.3.1 Metafrontier DEA Efficiency Estimation
We can construct a convex groupfrontier for thek group by applying the DEA method
to all the observed inputs and outputs of farms or decision making units (DMUs) ink
group. Let the groupk consist of data on kL decision making units (farms), the VRS
input-orientated DEA model for jDMU can then be formulated as follows:
j j j4.1.12 Min
Subject to:
j
j
j
j
j
j
j
j
j 1
n
j 1
1
n
n
j
y 0,
0,
1,
0
x X
Y
I
where jy is the output quantity for the jDMU; jx is the vector of input quantities used by
the jDMU ; jY is kL 1 vector of all output quantities for all kL DMUs; jX is kN L matrix
of input quantities for all kL DMUs; I is kL 1 vector of ones; j is vector of weights; and
The Efficiency of Irrigation Water Use and its Determinants
70
j is scalar. The equation njj 1
I 1 is a convexity constraint to compute technical
efficiency under a VRS specification.
Similarly, a convex metafrontier can be estimated by applying another DEA model to
the inputs and outputs of all kkL L DMUs. The structure of this linear programming
(LP) is identical to that of Equation 4.1.12 except that jX is of dimension N L , and jY
and j are L 1 . By solving this metafrontier LP separately for each DMU in the sample,
we get the efficiency estimates with respect to the metafrontier. The value of j that
solves the group k problem should not be greater than that the value of j that solves the
metafrontier problem. In other words, farms will not be more technically efficient when
they are evaluated under the metafrontier than the groupfrontiers, and the metafrontier
will never lie below any of the groupfrontiers (O’Donnell et al., 2008). Once, we have
estimated technical efficiencies with respect to the metafrontier and groupfrontiers, it is
straightforward to measure the technology gap ratio at observed input and output levels
using the expression in Equation 4.1.11.
4.1.3.2 The DEA Sub-vector Model
The DEA sub-vector approach is used to measure the input-specific technical
efficiency. The sub-vector efficiency considers the possible reduction of a sub-set of
inputs while keeping other inputs and output constant. In the literature, the sub-vector
efficiency concept has been widely used for measuring input-specific technical
efficiencies. We use the sub-vector concept following Speelman et al. (2008) to
estimate the possible reduction in irrigation water use. This “possible reduction” in the
case of irrigation water can be referred as the “irrigation water use efficiency”. We use
Figure 4.1.1 to illustrate the concept of technical efficiency and the sub-vector input-
specific technical efficiency.
Let us consider six farms using two inputs, irrigation water and fertiliser, to produce a
single output. Based on the efficiency concept, farms B, C, D, E and F are the best
performers because they are located on the frontier. A linear combination of their input
use defines a production frontier that envelops all of the other observed farms. Farm A
is inefficient because it is not located on the frontier. The radial contraction of inputs 1x
and 2x (irrigation water and fertiliser) produces a projected point A on the frontier,
which is a linear combination of all the observed data points. The technical efficiency of
The Efficiency of Irrigation Water Use and its Determinants
71
farm A with respect to farms B, C, D, E and F can be measured by the ratio
A OA ATE / O .
Figure 4.1.1: Graphical representation of the technical and sub-vector irrigation water use efficiency
The technical efficiency concept involves radial contraction of all inputs. However, the
sub-vector approach involves non-radial contraction of a particular sub-set of inputs or
an individual input while keeping output and other inputs constant (Fare et al., 1994). In
terms of Figure 4.1.1, the sub-vector efficiency of farm A for input 1x (here irrigation
water) could be measured by reducing 1x to a point 'A while keeping 2x and the output
constant. Hence, the sub-vector efficiency of input 1x (irrigation water) for farm A can
be given by the ratio ' ' 'IWE OA / OA .We solve the following LP to estimate the sub-
vector efficiency (irrigation water efficiency w ) of a particular jDMUfollowing
Speelman et al. (2008):
w
j j
j m w ,n j
j w ,n
j
j
j j
(λ , )
j 1
n
j 1
nw
j 1
n
j 1
w
n
4.1.13 Min
y 0,
x X 0,
x X 0,
I
Subject
1,
to:
Y
0.
The Efficiency of Irrigation Water Use and its Determinants
72
Similar to LP 1in Equation 4.1.12 jy is the output quantity for the jDMU
; jY is kL 1
vector of all output quantities for all kL DMUs ; I is kL 1 vector of ones; j is vector of
weights; and w is a scalar. However, in the second constraint, the input"w"column is
excluded, whereas the third constraint includes only the"w" input. Here, w is scalar and
has a score between 0 and 1, where a score of 1 for a given farm indicates that the farm
is using irrigation water efficiently. A value of less than 1 for a farm indicates that
irrigation water use inefficiency exists, meaning that there is some potential to reduce
irrigation water applications.
4.1.3.3 Truncated Regression
Tobit regression is the most commonly used approach to investigate the determinants of
DEA efficiency measures (Dhungana et al., 2004, Frija et al., 2009, Speelman et al.,
2008, Wadud and White, 2000). The use of tobit regression in a second stage has been
justified by the argument that because efficiency scores vary between zero and one, they
are censored values. However, McDonald (2009) argued that efficiency scores are not
censored but are actually fractional values. Alternatively, McDonald (2009) and Banker
and Natarajan (2008) proposed that Ordinary Least Squares (OLS) in a second stage
yields more consistent results than the tobit regression. However, the use of OLS is
consistent only under very peculiar and unusual assumptions of the data-generating
process (Simar and Wilson, 2011).
In an earlier paper, Simar and Wilson (2007) noted that conventional approaches to
inference in two-stage efficiency are invalid due to the complex and unknown serial
correlation among estimated efficiencies and the lack of description about the data-
generating process. They proved that in the second stage, single bootstrap truncated
regression yields more consistent results. We, thus, chose a single bootstrap truncated
regression to identify the determinants of technical and irrigation water use efficiency.
The estimated specification for the regression model takes the following general form:
n
j i i i ii 1
4.1.14 y z 0;
for i 1,....,N and 2i N(0, )
where jy
is either technical or irrigation water use efficiency, jZ is the set of explanatory
variables and i is the error term.
The Efficiency of Irrigation Water Use and its Determinants
73
4.1.4 Study Areas, Data and Variable Definitions
The data used in this study is based on a detailed survey conducted during the Kharif23
season in two districts - Lodhran, from the cotton-wheat region, and Jhang, from the
mixed-cropping region - of the Punjab province, Pakistan.
Rural households heavily rely on groundwater as their major source of irrigation water
in both districts. The study areas in both districts have deep groundwater tables that
require high tube-well installation costs. The variation in the bore depth was observed to
range between 60 metres and 99 metres in Lodhran and from 33 metres to 57 metres in
Jhang. Due to low groundwater tables and the high installation costs, the tube-well
population is relatively less dense in Lodhran and in parts of the Jhang district.
Therefore, water trading is more common among tube-well owners and non-owners in
Lodhran and Jhang compared to other districts having shallow water tables.
A multi-stage sampling technique was used in data collection. At the first stage, one
tehsil was purposively selected from each district. In the next stage, 10 villages were
selected at random from each selected tehsil. In the study areas, a village usually
comprise of 70 to 80 household farms. The information about tube-well owners and
water buyers was collected with the help of extension field staff and key informants in
the selected villages. Finally, from each village, 10 groundwater users (5 tube-well
owners and 5 water buyers) were selected randomly to obtain the differential impact of
tube-well ownership and to reveal the difference in water applications and grain yield
for tube-well owners (water sellers) and non-owners (water buyers), thus making a total
sample size of 200 groundwater users, i.e., 100 tube-well owners and 100 water buyers.
The data was collected using an interview schedule. During the interview, we collected
farm-level information on various inputs and output quantities. The inputs measured
were: (1) seed and fertiliser in kg/acre; (2) pesticide and farm operations as number of
applications/acre; (3) total labour, consisting of hired (casual and permanent) and family
labour in hours/acre and (5) groundwater use in cubic metres/acre. Wheat yield (output)
was measured in kg/acre. Because, farmers generally follow recommended agronomic
practices for wheat crops, we do not observe much variation in per acre use of inputs.
Hence, we aggregated per acre inputs and output at a farm level before analysing the
23There are two cropping seasons in Pakistan, Kharif and Rabi. Wheat is a Rabi crop.
The Efficiency of Irrigation Water Use and its Determinants
74
data. The descriptive statistics of the variables used in the DEA model are presented in
Table 4.1.1.
In this study, we collected information about the number of irrigations for wheat crop
and the duration of irrigation water application per irrigation. We used an approximate
estimation model, as used by Eyhorn et al. (2005) and Srivastavaa et al. (2009) to
measure groundwater extraction in litres and then converted into m3:
2 2 4t 129574.1 BHP
[d (255.4.1.15
5998 BHP
) / d D )]Q
where Q represents the volume of water in litres, t is the total irrigation time, d is the
depth of bore, D is the diameter of the suction pipe, and BHP is the power of the engine.
Table 4.1.1 compares the selected variables used in the DEA analysis on per acre basis.
The average farm size is 12.08 acres for tube-well owners and 6.02 for water buyers.
The seed rate and labour usage per acre do not differ considerably for tube-well owners
and water buyers. However, there is a significant difference in the amount of fertilizer
with an average rate of 201 kg/acre for tube-well owners and 181kg/acre for water
buyers. We also do not find a significant difference in the use of groundwater for
irrigation with tube-well owners, on average, using 783m3/acre and compared to water
buyers using 775m3/acre. The average wheat yield is slightly above 1,550 kg/acre for
tube-well owners and 1,469 kg/acre for water buyers.
The average farmer’s age is 45 years for tube-well owners and 42 years for water
buyers. The statistics on education clearly reflect lack of education. In terms of years of
schooling, tube-well owners on average have 5 years of schooling while water buyers
have less than 4 years of average schooling.
Amongst the tube-well owners, 4% farms are categorised as tenant farms whereas
amongst the water buyers 65% farms are tenants. A small proportion of the farms with
34% tube-well owners and 22% water buyers have adopted different types of
agricultural innovations such as improved seed varieties and seed treatments. Because
farming is a major livelihood activity among rural communities, only a small proportion
(20%) of the tube-well owners and water buyers (13%) has off-farm income sources.
The statistics show that 47% of the water buyers whereas 22% of the tube-well owners
received credit from private banks or public agencies. Nearly 30% of the tube-well
owners and water buyers participated in agriculture related training programmes or
received advice from the agricultural extension field staff.
The Efficiency of Irrigation Water Use and its Determinants
75
Table 4.1.1: Descriptive statistics of the variables used in the DEA analysis
Tube-well owners
Water buyers
Variable Mean Std. Dev. Mean Std. Dev. Economic Data Wheat yield/acre (kg/acre) 1556 205 1475 224 Farm size (acres) 12 5 6 3 Seed rate (kg/acre) 57 5 58 6 Labour (hours/acre) 56 22 61 37 Fertilizer (kg/acre) 201 41 181 41 Chemical applications per acre 2.5 0.54 2.63 0.48 Farm operations per acre 6.8 1.65 5.7 1.29 Irrigation water (in m3/acre) 783 318 775 338 Farm Characteristics Farmer’s age (years) 45 8 42 8 Farmer’s education (years of schooling) 5 4 3 3 Proportion of farm characteristics 0 1 0 1 Off-farm income (0=no, 1=yes) 4 96 35 65 Land tenureship (0= tenants, 1=owners) 80 20 87 13 Seed (0=unimproved, 1=improved) 66 34 78 22 Access to credit services (0=no, 1=yes) 75 25 53 47 Access to extension advice (0=no, 1=yes) 69 31 68 32 Salinity perception (0=no, 1=yes) 83 17 74 26 Is the water table declining? (0=no, 1=yes) 59 41 49 51
More water buyers (26%) than tube-well owners (17%) perceived that salinity was
increasing in groundwater. Similarly, more water buyers (51%) than tube-well owners
(41%) think that groundwater tables are lowering in the study regions.
4.1.5 Empirical Results and Discussion
4.1.5.1 Technical Efficiency
Table 4.1.2 presents technical efficiency (TE) estimates for tube-well owners and water
buyers under both the metafrontier and groupfrontier specifications. The metafrontier
TE score for tube-well owners vary from a minimum of 65% to a maximum of 100%
with a mean score of 91% whereas for water buyers the TE scores vary from a
minimum of 64% to a maximum of 100% with a mean value of 90%. The groupfrontier
estimates of TE for tube-well owners vary from a minimum of 69% to a maximum of
100% with a mean score of 93% whereas water buyers’ TE scores vary from a
minimum of 67% to a maximum of 100% with a mean value of 94%.
The Efficiency of Irrigation Water Use and its Determinants
76
On average, both the tube-well owners and water buyers operate at fairly high
efficiency levels. Based on the mean estimates, the gains from improving technical
efficiency are small, although across all farms, only 29% of the tube-well owners and
water buyers were fully technically efficient (TE=1) when assessed under the
metafrontier settings. Similarly, under the groupfrontier estimates only 37% of the tube-
well owners and 53% of water buyers were completely technically efficient (TE=1).
These estimates suggest that a significant majority of the wheat growers including tube-
well owners and water buyers are operating with technical inefficiencies. We find that
average technical efficiency estimates for tube-well owners and water buyers slightly
vary under the metafrontier and groupfrontier estimations. The overall frequency
distribution, however, shows that metafrontier and groupfrontier technical efficiency
estimates vary considerably24. Similarly, the cumulative frequency distribution
(Figure 4.1.2) of technical efficiency clearly indicates that when assessed against the
groupfrontier wheat farms are more technically efficient than when compared against
the metafrontier. The results for the technology gap ratio (i.e. ratio of the metafrontier
technical efficiency and the groupfrontier technical efficiency) are presented in
Table 4.1.3.
Table 4.1.2: Metafrontier and groupfrontier technical efficiency frequency distribution
Metafrontier Groupfrontier Frequency (%) Tube-well
owners Water buyers Tube-well
owners Water buyers
<40 0 0 0 0 40-50 0 0 0 0 50-60 0 0 0 0 60-70 4 1 1 1 70-80 10 13 7 9 80-90 26 34 18 24 90-99 31 22 37 13 100 29 29 37 53 Mean 0.91 0.90 0.94 0.93 Std. Deviation 0.09 0.09 0.08 0.09 Minimum 0.65 0.64 0.69 0.67 Maximum 1 1 1 1
24A paired sample t-test is used to test that whether the mean difference between meta-frontier and group-frontier technical efficiency estimates is significantly different from zero or not? The t-statistics of 8.55 with a p-value of 0.000 reject the null hypothesis that the difference between the technical estimates from both estimations is equal to zero.
The Efficiency of Irrigation Water Use and its Determinants
77
The results show that tube-well owners are operating closer to the metafrontier than are
the water buyers. In other words, tube-well owners perform better in terms of exploiting
their productivity potential compared to the water buyers.
The productivity potential ratio (technology gap ratio) estimates for tube-well owners
range between a minimum of 0.81 and a maximum of 1.00 with a mean estimate of
0.97. With a slight difference, the productivity potential ratio estimates for water buyers
range between a minimum of 0.71 and a maximum of 1.00 with a mean estimate of
0.96. Not surprisingly, water buyers have lower productivity potential ratio compared to
tub-well owners.
Figure 4.1.2: Cumulative distribution of meta-frontier and group-frontier technical
efficiency
Table 4.1.3: Average groupfrontier and metafrontier technical efficiency scores and the technology gap ratio
Group TE Meta TE Technology gap ratio Tube-well owners Average 0.94 0.91 0.97 Minimum 0.64 0.65 0.81 Maximum 1 1 1.00 Water buyers Average 0.93 0.90 0.96 Minimum 0.67 0.69 0.71 Maximum 1 1 1.00
4.1.5.2 Irrigation Water Use Efficiency
The metafrontier and groupfrontier sub-vector estimates of irrigation water use
efficiency are presented in Table 4.1.4. The results show substantial inefficiencies in
irrigation water use among tube-well owners and water buyers. For tube-well owners
00.10.20.30.40.50.60.70.80.9
0 0.2 0.4 0.6 0.8 1
Perc
ent o
f far
ms
Technical efficiency distribution
Group-frontier TE estimatesMeta-frontier TE estimates
The Efficiency of Irrigation Water Use and its Determinants
78
and water buyers their mean sub-vector irrigation water use efficiency score under the
metafrontier setting are 66% and 65%, respectively whereas the groupfrontier estimates
are 71% and 67%, respectively. In other words, metafrontier results indicate that on
average tube-well owners and water buyers can save 34% and 35% of current irrigation
water usage in wheat farming without decreasing their existing output level. The mean
groupfrontier estimates suggest a 29% and 37% reduction in irrigation water use for
tube-well owners and water buyers, respectively.
As we observe in the case of technical efficiency, statistically significant difference25
between the metafrontier and groupfrontier sub-vector estimates suggest that combining
tube-well owners and water buyers in one sample, would lead to biased efficiency
estimates. The cumulative frequency distribution (Figure 4.1.3) of irrigation water use
efficiency clearly indicates that when assessed against the groupfrontier wheat farms are
more technically efficient than when assessed against the metafrontier.
Table 4.1.4: Frequency distribution of irrigation water use efficiency under the metafrontier and groupfrontiers
Metafrontier Groupfrontier Frequency (%) Tube-well
owners Water buyers Tube-well
owners Water buyers
<30 10 7 5 7 30-40 17 17 16 17 40-50 11 14 9 11 50-60 11 12 12 11 60-70 3 5 4 6 70-80 6 13 6 9 80-90 10 3 9 6 90-99 4 7 3 3 100 28 22 36 30 Mean 0.66 0.65 0.71 0.67 Std. Deviation 0.28 0.26 0.27 0.27 Minimum 0.23 0.24 0.26 0.24 Maximum 1 1 1 1
The sub-vector estimates under the groupfrontier specification imply a considerable
scope for reducing irrigation water use, with the observed values of other inputs and
maintaining the same output level. These results suggest that if the efficiency improves,
25A paired t-test statistic is 6.63 with P-value of 0.000. Hence the null hypothesis that mean difference between meta-frontier and group-frontier sub-vector estimates is equal to zero is rejected.
The Efficiency of Irrigation Water Use and its Determinants
79
it would be possible to reallocate groundwater to other usage without compromising
wheat production.
As we can see from the Table 4.1.4, water buyers are slightly less efficient in irrigation
water use than tube-well owners. This can be attributed to several reasons: (1) the
functioning of informal groundwater markets where water buyers usually need to be in
the queue to buy water; (2) the energy crises which has increased uncertainty for water
buyers to get water on time; (3) social ties between the tube-well owners and water
buyers which results to preference for certain users and discrimination against others
(Jacoby et al., 2004); and (4) lack of surplus water to sell by tube-well owners because
of large farm sizes or competing demand from water buyers. Therefore, in such
circumstances, the water buyer is highly likely to face delays in getting water for
irrigation, resulting in serious impacts on crop growth and ultimately on productivity.
Based on the correlation26 between output produced using per m3 of groundwater and
the per m3 price27 of groundwater, we can infer that water buyers produce more output
per m3 of groundwater. This result may be because water buyers pay a higher price for
groundwater than tube-well owners, which induces them to use water more efficiently.
These results also imply that water pricing can trigger improved groundwater use
efficiency in irrigation as argued by some authors (Gómez-Limón and Riesgo, 2004,
Johansson et al., 2002)28.
Table 4.1.5: Spearman’s rank correlation among technical efficiency and the sub-vector irrigation water use efficiencies
TE SV-IWE TE 1.000 SV- IWE 0.763* 1.000
Note: * indicates a 5% significance level
Irrigation water use inefficiencies are not uncommon in other parts of the world. A large
degree of irrigation water use inefficiency was also reported by Karagiannis et al.
(2003) for out-of-season vegetable farming. Similarly inefficiency in use of irrigation
26The Spearman’s correlation coefficients between wheat yield and price per m3 of groundwater are 0.78 and 0.81for tube-well owners and water buyers, respectively. 27Water buyers paid Rs. 6.4 per m3 of groundwater, while tube-well owners paid Rs. 3.4. 28It has been argued that at least some pricing is necessary to make farmers aware of the water scarcity and to induce them to adopt water-saving technologies. Therefore, “getting prices right” is considered an important tool to improve water use efficiency and to encourage its conservation.
The Efficiency of Irrigation Water Use and its Determinants
80
water has been reported by Lilienfeld and Asmild (2007) for irrigated agriculture in
Western Kansas, USA, Speelman et al. (2008) for small-scale irrigators in South Africa
and Frija et al. (2009) for small-scale greenhouse farmers in Tunisia.
Figure 4.1.3: Cumulative distribution of metafrontier and groupfrontier irrigation water use efficiency
As shown in Table 4.1.5, technical efficiency is highly correlated with the irrigation
water use efficiency. The correlation between technical efficiency and irrigation water
use efficiency suggest that if irrigation water use efficiency increases, it will also
improve overall technical efficiency of wheat farming.
Similar to technical efficiency, the cumulative frequency distribution (Figure 4.1.3) of
irrigation water use efficiency clearly indicates that when assessed against the
groupfrontier wheat farms are more technically efficient than when compared against
the metafrontier.
4.1.5.3 Explaining Efficiency Differentials
The results of the determinants of technical and water use efficiency are presented in
Table 4.1.6.
The farmer’s age is not found to be significantly associated either with the technical or
irrigation water use efficiency. The level of education has positive and significant
impact on technical and irrigation water use efficiency. In the literature, we find mixed
results for the efficiency and education relationship, e.g., Karagiannis et al. (2003)
found that the degree of technical and irrigation water use efficiency is positively
affected by the level of education. However, Speelman et al. (2008) found that
education does not significantly affect technical and irrigation water use efficiency. The
00.10.20.30.40.50.60.70.8
0 0.2 0.4 0.6 0.8 1
Perc
ent o
f far
ms
Irrigation water efficiency distribution
Group-frontier IWE estimatesMeta-frontier IWE estimates
The Efficiency of Irrigation Water Use and its Determinants
81
mixed results of the impact of education in the literature suggest that when interpreting
the impact of education on efficiency levels researchers should consider the relevance of
a farmer’s education to his farming business. We find that land ownership is positively
and significantly associated with technical efficiency which is intuitive. Many other
studies have found that that land owners are more efficient than tenants (Frija et al.,
2009, Speelman et al., 2008). The results for seed quality show a statistically significant
and positive association between the seed quality and technical and irrigation water use
efficiency.
We find that off-farm income significantly affects farmer’s technical efficiency,
implying that with alternative income resources, farmers have a better edge to purchase
inputs and therefore use an optimal mix of inputs. Hence, these farmers tend to be more
technically efficient. Likewise, those farmers who opted to get credit are more
technically efficient than those who did not. The findings of Karagiannis et al. (2003)
and Haji (2007) also confirm the positive impact of off-farm income and credit in
improving farmer’s technical efficiency.
Table 4.1.6: Bootstrap truncated estimates of the determinants of technical and irrigation water use efficiency
Explanatory variables Technical efficiency
Irrigation water use efficiency
Coefficient
Std. Dev.
Coefficient Std. Dev.
Farmer’s age (years) 0.005 0.001 0.007 0.002 Education (years of schooling) 0.025** 0.009 0.118*** 0.023 Land tenureship (0= tenants, 1=owners) 0.027** 0.013 0.017 0.034 Seed (0=unimproved, 1=improved) 0.053*** 0.012 0.265*** 0.036 Off-farm income (0=no, 1=yes) 0.027* 0.014 0.023 0.038 Access to credit services (0=no, 1=yes) 0.033** 0.012 0.048 0.030 Access to extension advice (0=no, 1=yes)
0.032** 0.013 -0.019 0.033
Salinity perception (0=no, 1=yes) 0.025* 0.013 0.137*** 0.034 Is water table declining? (0=no, 1=yes) 0.011 0.010 0.070*** 0.026 Constant 0.804** 0.029 0.365*** 0.077 Log-likelihood 255.20 63.49
Note: *, **, *** indicate significance at 10%, 5% and 1%, respectively. Number of
bootstraps=4,000
The positive significant impact of extension advice on technical efficiency confirms the
belief that the farmers who tend to seek more extension advice are technically more
efficient than those who have less or no contact with the agricultural extension staff
The Efficiency of Irrigation Water Use and its Determinants
82
(Parikh and Shah, 1994). In contrast, the impact of extension services is non-significant
on irrigation water use efficiency. The significant impact of extension advice in
improving technical efficiency suggests that extension advice could also have
significant impact on rationalising irrigation water use (Frija et al., 2009, Karagiannis et
al., 2003).
Amongst the explanatory variables representing farmers’ perceptions about
groundwater resource, perception about salinity is positively and significantly
associated with technical efficiency and irrigation water use efficiency while perception
about the decline in groundwater tables suggest that farmers seriously consider the
declining groundwater tables.
4.1.6 Conclusion
The objective of this study was to estimate technical efficiency (TE) and irrigation
water use efficiency (IWE) of groundwater-fed wheat farms. We employed the data
envelopment analysis method to compute TE and IWE using a cross-sectional dataset of
200 wheat growing farms from Punjab, Pakistan. We estimated TE efficiency using
DEA metafrontier framework and irrigation water use efficiency using the DEA sub-
vector model.
The mean TE estimates suggest that wheat farms are operating at fairly high technical
efficiencies. The mean technical efficiency scores for the metafrontier and groupfrontier
estimates only suggest little scope for improving technical efficiency among tube-well
owners and water buyers. However, there is a substantial scope for improving irrigation
water use efficiency in wheat farming. In the case of irrigation water use efficiency,
metafrontier estimates suggest a 34% and 35% potential saving of groundwater for
tube-well owners and water buyers, respectively. However, the groupfrontier estimates
suggest slightly lower reductions in irrigation water use with 29% reductions for tube-
well owners and 33% for water buyers. In terms of total groundwater volumes, based on
the sub-vector estimates, we calculated that the tube-well owners and water buyers in
our sample could save a total volume of 0.48 million m3 of groundwater, with 0.32
million m3 reductions for tube-well owners and 0.15 million m3 for water buyers. Put in
monetary terms, tube-well owners and water buyers could save up to Rs. 0.79 million
and Rs. 0.83 million from irrigation costs during the wheat cropping season.
The Efficiency of Irrigation Water Use and its Determinants
83
Whilst this study has policy implications for improving technical efficiency in wheat
farming, the study also suggest possible reductions in the current irrigation water
application to wheat crop. The study results indicate that irrigation water use
inefficiencies are more pronounced than the technical inefficiencies, implying that that
access to technology is not the major factor constraining efficiency improvements, but
rather inefficient use of irrigation water. The bottlenecks to the inefficient use of
irrigation water arise perhaps due to the lack of information about the crop water
requirement and groundwater resource availability. We suggest that educating farmers
about the actual crop water requirement either by extending the extension advice from
crop management to groundwater management or creating a separate water extension
wing can be important for the sustainable use of groundwater resources. Greater
provision of advice may have spin-off benefits such as encouraging greater use of
improved seed and providing information to facilitate decision-making by elderly risk-
averse farmers.
We suggest that any policy intervention for sustainable groundwater management
should also consider regulating informal groundwater markets to improve the security
of water allocation and to improve equity of access to non-tube-well owners.
The Efficiency of Irrigation Water Use and its Determinants
85
4.2 Econometric Approach to Estimating Technical and Irrigation
Efficiency in Cotton Farming in Pakistan
(Journal of Hydrology: Regional Studies. doi:10.1016/j.ejrh.2014.11.001)
The Efficiency of Irrigation Water Use and its Determinants
87
Abstract
Massive pumping of groundwater aquifers in pursuit of reliable irrigation water supplies
is lowering groundwater tables in Pakistan. Consequently, depletion of groundwater
resources has raised concerns to examine more closely the level of, and the factors
affecting, technical efficiency and irrigation water use efficiency. We employ a
stochastic production frontier method to estimate the technical and irrigation water use
efficiency of 173 randomly selected groundwater irrigated cotton farms in the Punjab
province of Pakistan. The mean technical efficiency results suggest considerable scope
for improvements in technical efficiency, with water buyers being more inefficient than
the tube-well owners. Irrigation water use inefficiency is even more pronounced than
the technical inefficiency. Results on the determinants of efficiency indicate that
improved seed, consultation with extension field staff and farmers’ perceptions about
the future state of groundwater resources are positively associated with efficiency.
4.2.1 Introduction
Groundwater irrigation contributes significantly to agricultural production in many parts
of South Asian countries (Shah, 2007). In Pakistan, dwindling surface water supplies
have increased reliance on groundwater resources more than in many other Asian
countries. Evidences suggest that existing surface water resources are not only deficient
in Pakistan but are also highly skewed in time and space. The spatio-temporal variations
in surface runoffs have led to the development of a large scale groundwater-fed
irrigation system in the Indus basin of Pakistan. The spectacular increase in
groundwater use over the last half-century has emerged as a “silent revolution” carried
out by thousands of farmers in the pursuit of reliable irrigation water supplies. Since
1960 groundwater contribution to the total irrigation water supply has increased by
more than 50% in Pakistan (Byrelle and Siddiq, 1994, Qureshi et al., 2009).
In contrast to the uncertain canal water supplies, on-demand availability and reliability
of groundwater resources has helped farmers to hedge against low and uncertain crop
production. Presently, more than one million farmers have invested in installing tube-
wells across the country. Earlier government policies such as rural electrification,
subsidization of electricity, diesel and drilling services, free pump sets and easy access
to long-term loans have encouraged the adoption of tube-well technology while higher
yields and greater economic returns from groundwater use have encouraged farmers to
adopt tube-wells in subsequent periods (Falcon and Gotsch, 1968, Papanek, 1968, van
The Efficiency of Irrigation Water Use and its Determinants
88
Steenbergen and Oliemans, 2002, Johnson, 1989 ). Although, the number of tube-wells
has increased manyfold, thousands of smallholder farmers still do not own tube-wells.
Many of them irrigate their lands by informally buying surplus pumped water from their
neighbouring tube-well owners (Meinzen-Dick, 1996, Qureshi et al., 2009). Such
informal groundwater transactions offer economic benefits to tube-well owners and
offer non-owners opportunities to hedge against water scarcity risk and to increase farm
production (Manjunatha et al., 2011, Meinzen-Dick, 1996, Shiferaw et al., 2008).
Although groundwater resources have played a key role in agricultural production,
overdrafting of groundwater resources is at a critical juncture (Kijne, 1999b, Shah et al.,
2000, Khan et al., 2008a, Qureshi et al., 2009). Massive groundwater extractions of up
to 60 km3 y-1 have exceeded the recharge rate of 55 km3 y-1, resulting in substantial
depletion of groundwater aquifers (Giordano, 2009). Wada et al. (2010) mapped various
hot spots of groundwater depletion in different regions of the world and noted that the
highest depletion rates were in north-east Pakistan. Rapidly depleting groundwater
resources are not only making relative accessibility of groundwater resources
economically unviable, but are also creating many environmental concerns with serious
repercussions to the sustainability of the agrarian economy of Pakistan (Kijne, 1999b,
Shah et al., 2000, Kelleners and Chaudhry, 1998, Kahlown and Azam, 2002, Khan et
al., 2008b, Qureshi et al., 2009).
Whilst the agrarian economy of Pakistan is mainly dominated by wheat, cotton, rice and
sugarcane crops, cotton production remains the most important agricultural commodity
due to its export value. It holds an important position in the national economy;
accounting for 6.9% of the value added in agriculture and 1.4% of the country’s gross
domestic production (GDP). Pakistan remained the 4th largest cotton producer with
9.80% share in global cotton production during the period 2011/12. Over the same
period, Pakistan’s yarn and apparel exports were 26% and 14% of the global market
shares. At a national level, cotton exports account for 46% of the country’s entire
exports and the cotton sector employs 35% of the total industrial labour force (Pakistan,
2011-12, FAO, 2012b). In brief and by any measure, cotton production and processing
are the most important economic sectors of Pakistan’s economy. Therefore, national
economic growth is greatly influenced by the volume and value of cotton production
and its by-products. As a result, cotton production has always been under agricultural
policy limelight. Due to different policy supports, the area under cotton cultivation and
production has grown by 33% and 163% respectively since 1980, while domestic
The Efficiency of Irrigation Water Use and its Determinants
89
consumption for cotton has increased by almost 400% over the same period (Pakistan,
2011-12, USDA, 2012). However, cotton production has been facing widespread
stagnation in per hectare yields. Based on per hectare yield estimates, Pakistan is ranked
at number 20th in world cotton production, and has enormous potential for improving
cotton productivity. Figure 4.2.1 shows historical trends in cotton production and
consumption in Pakistan.
Figure 4.2.1: Historical trends in cotton production and consumption in Pakistan
Pakistan Central Cotton Committee (PCCC) aims to increase cotton production by 40%
to 60% as a national strategy to achieve the 19.1 million bales target by 2015. Major
components of this strategy include: 1) to increase the area under cotton cultivation; 2 )
to encourage adoption of genetically modified cotton varieties; 3) to improve production
technology; 4) to subsidize fertilizers; and 5) to apply integrated pest management
(PCCC, 2008). However, despite widespread policy efforts and other encouraging
incentives, the planned outcomes may not be realized. On-going water stress may
undermine the potential of this policy. Water availability unfortunately has not been
taken into consideration under this policy. Besides its environmental and ecological
footprints that result from excessive chemical use, cotton production is also associated
with excessive water applications, in spite of water being the key limiting factor in
agricultural production. Evidences suggest that inefficient irrigation water application is
one of the major reasons for low water productivity. Owing to the poor irrigation water
0
2000
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ales
ProductionDomestic Consumption
The Efficiency of Irrigation Water Use and its Determinants
90
management, there is a considerable scope for improving current water productivity29 of
0.22 kg m-3 which is far below the productivity of other major cotton producing
countries (Shabbir et al., 2012). The severe water stress facing cotton producers and the
widespread stagnation in cotton yields has thus prompted research efforts to improve
efficiency and productivity of cotton farming and to make more efficient use of
dwindling water resources.
The objective of this paper is to estimate farm level technical efficiency and irrigation
water use efficiency for groundwater irrigated cotton farms in Pakistan. We apply a
stochastic production frontier to 173 randomly selected cotton farms from the Punjab
province of Pakistan to estimate the extent of farm level technical and irrigation water
use efficiencies.
From a theoretical and methodological perspective, micro-economic theory postulates
that production functions should increase monotonically in all inputs (Henningsen and
Henning, 2009). A production function which is not increasing monotonically inhibits
the reasonable interpretation of the efficiency estimates. Satisfying monotonicity
conditions becomes more important when considering the efficiency of a particular
input (e.g., fertilizer or water). Therefore, this study advances the frontier of existing
input-specific technical efficiency (in this study irrigation water use efficiency) by using
restricted translog model along with the traditional unrestricted translog model. This is
one of the few studies which have focused on irrigation water use efficiency in
agricultural production and is the only study to use parametric methods to estimate
irrigation water use efficiency in irrigated cotton farming in Pakistan.
The rest of the chapter is organised as follows. The next section describes the stochastic
production frontier used to estimate technical and input-specific (irrigation water use
efficiency) technical efficiency. Section 3 describes the data and principal features of
the study areas. The results are presented in Section 4. The final section draws
conclusions and provides some policy implications.
29Water productivity (kg/m3) is defined as crop yield (kg) per accumulated actual evapotranspiration for the growing season (m3):
a,seasonal
Crop yieldWPEt
Increasing water productivity means that producing more output per unit of water application while irrigation efficiency is a measure to suggest possible reductions in the current use of irrigation water.
The Efficiency of Irrigation Water Use and its Determinants
91
4.2.2 Conceptual and Methodological Framework
4.2.2.1 Definition and Measurement of Technical and Irrigation Water Use
Efficiency
In production economics, technical efficiency is defined as the ability of a decision
making unit (DMU) to produce the maximum possible output within the available set of
inputs, under the given technology (Coelli et al., 2002). However, irrigation water use
efficiency is defined as the ratio of minimum feasible water use to observed use of
irrigation water, conditional on observed levels of the desirable output and conventional
inputs. More generally, irrigation water use efficiency is an input-oriented, single factor
measure of technical efficiency (Karagiannis et al., 2003). The standard technical
efficiency involves radial contraction of all inputs which does not allow estimation of
the efficiency of individual input use. However, input-specific technical efficiency,
which is a non-radial measure, can also be used to estimate the efficiency of individual
inputs. The idea of input-specific efficiency or irrigation water use efficiency is
illustrated in Figure 4.2.2.
Figure 4.2.2: Graphical representation of irrigation water use efficiency
Source: Karagiannis et al. (2003)
The Efficiency of Irrigation Water Use and its Determinants
92
Let us consider three farms A, B and C using two different inputs ( 1x = irrigation water
and 2x = fertiliser) to produce a single output oY . Farm A is inefficient because it is not
located on the frontier. Let the inefficient farm A produce an output oY using 2Ax amount
of fertilizer and 1w units of irrigation water. The radial contraction 1x and 2x produces a
projected point oA on the frontier which is technically efficient. The technical efficiency
of farm A is given by the ratio A OA ATE / O and the irrigation water use efficiency is
given by the ratio 2 12C 2Ax / xI wW wE / . The proposed irrigation water use
efficiency measure determines both the minimum feasible water use 2w and the
maximum possible reduction in water use 1 2w w without compromising the existing
output level oY .
Figure 4.2.2 shows that in order to make thi farm technically efficient, the maximum
possible reduction required in water use 1 3w w is lower than the reduction 1 2w w
required making the thi farm efficient in irrigation water use. Hence, the maximum
possible reduction in water use , i.e., irrigation water use efficiency, is an upper bound
(Akridge, 1989).
4.2.2.2 The Estimation of Technical and Irrigation Water Use Efficiency
Let technology be described by the stochastic production function as follows:
i i i ii4.2.1 y f x ,w ; exp v u where iy denotes the amount of crop output
for farm i (i = 1,…,N); ix represents the vector of conventional inputs; iw is the volume
of groundwater for irrigation; is a vector of parameters to be estimated; i is a
composed error term and iv is a symmetric and normally distributed error term,
independently and identically distributed as 2vN 0, , intended to capture the exogenous
random forces which are beyond the control of the farmers; iu 0 is a non-negative
random error term independently and identically distributed as 2uN 0, , that captures
the shortfall of output from the production frontier.
The stochastic version of the output- oriented technical efficiency for the thi farm is
expressed as:
i i i i i4.2.2 TE y f x ,w ; exp v
i i4.2.3 TE exp u
The Efficiency of Irrigation Water Use and its Determinants
93
Since iu 0 and i0 exp u 1 , technical inefficiency has to be separated from
statistical noise in the composed error term. Battese and Coelli (1992) proposed the
technical efficiency estimator as:
i i i4.2.4 TE E exp u |
The outlined measure of efficiency does not estimate the efficient use of individual
inputs which in our case is irrigation water. Conceptually, irrigation water use
efficiency measurement requires an estimate of the quantity 2w which is not observed as
illustrated by Figure 4.2.2. However, using i 2 1IWE w w , we can easily observe that
2 1 iw w IWE . By substituting this into Equation 4.2.1 output can be expressed as:
i iE
i i4.2.5 y f x ,w ; exp v where 2Eiw w (Reinhard et al., 1999). A
measure of iIWE can be obtained by equating Equation 4.2.1 with Equation 4.2.5 and
by using the estimated parameters .
4.2.2.3 Empirical Model
The unknown production frontier (Equation 4.2.1) is specified by the following translog
specification:
j ji jk jii i
w
0 ki
ji i
wj
j j
1 j 1 k 1
2ww i jw i i
j
j
j
1
1lnx lnx lnx lnw2
1 lnw lnx ln
4.2.6 lny
x v u2
where iy denotes the level of production; ix represents the vector of conventional inputs
as described in Equation 4.2.1; iw represents the amount of irrigation water; is a vector
of parameters to be estimated; iv is a random error term, independently and identically
distributed as 2vN 0, , and iu is a non-negative random error term independently and
identically distributed as 2uN 0, . To separate the stochastic and inefficiency effects in
the model we need to impose a distributional assumption. In this study, inefficiency is
modelled explicitly as a function of known characteristics and exogenous effects, such
that:
i ij
j
0 j ij 1
4.2.7 u z
The Efficiency of Irrigation Water Use and its Determinants
94
where iz is a vector of variables which explain efficiency differentials among farmers;
is the vector of parameters, and i is a random variable defined by the truncation of
the normal distribution with mean zero and variance 2 where the point of truncation is
iz such that i iz (Battese and Coelli, 1995).
The translog production function is widely used in efficiency estimation models.
However, there are several concerns about its flexibility and theoretical consistency
when used in efficiency estimation (Sauer, 2006). Since micro-economic theory
requires that a production function should be monotonically increasing in all inputs
(Henningsen and Henning, 2009) and quasi-concave (Lau, 1978), it is necessary to test
the estimated production frontier for theoretical consistency and, if necessary, to impose
conditions for consistency. The monotonicity restrictions require holding
i iy x 0 i, x, for all observations (Coelli et al., 2005, Perelman and Santin, 2011).
However, imposing global convexity restrictions to ensure local quasi-concavity of the
production function greatly restricts the flexibility of the functional form (Lau, 1978,
Sauer, 2006). . Henningsen and Henning (2009) argue that when estimating a
production function under the assumption of output maximization, quasi-concavity is
not essential. Monotonicity can be imposed by using Bayesian techniques (O’Donnell
and Coelli, 2005, Pascoe et al., 2010) or a non-parametric approach (Grosskopf et al.,
1995) or a multistage process as proposed by Henningsen and Henning (2009).
In this study, we follow Henningsen and Henning (2009) three step procedure to adjust
the model. First, we estimate the translog frontier and extract the unrestricted
parameters and their covariance matrix. Second, we estimate the restricted parameters
through a minimum distance approach as follows:
10 0 0ˆ ˆ ˆ ˆ ˆ4.2.8 arg min
Subject to :
0f x,4.2.9 0 i, x
x
Then, Equation 4.2.8 is solved using quadratic programming to get the revised set of
coefficients that ensure the monotonicity assumption holds. These restricted parameters
0 are asymptotically equivalent to the restricted parameters of a one-stage maximum
likelihood (ML) estimation model (Koebel et al., 2003). Finally, the stochastic frontier
model (adjusted-restricted) is re-estimated as:
The Efficiency of Irrigation Water Use and its Determinants
95
i 0 1 i i4.2.10 ln y ln y v
where i0ˆy f x, . That is, the only input is the estimated frontier output based on the
restricted parameters. The parameters 0 and 1 represent final adjustments to the
parameter estimates. The advantage of the three step approach is that the parameter
values estimated in the first stage provide appropriate starting values where the
variance-covariance matrix limits the degree to which these parameters are altered when
imposing monotonicity in the non-parametric component (Gedara et al., 2012b).
Since the above mentioned measure of technical efficiency is incapable of identifying
the efficient use of individual inputs such as fertilizer or irrigation water etc. In the next
step, we drive the efficiency of individual input i.e., irrigation water using the Equation
4.2.11 following Reinhard et al. (1999) who proposed this model to estimate
environmental efficiency. Later, this same approach was adopted by Karagiannis et al.
(2003) to estimate irrigation water use efficiency. We can write the equation for the
translog specification as follows:
i i ww2i ww i4.2.11 IWE exp 2 u /
where
ii w jw ji ww i
i
j
j 1
ln y ln x ln wln w
where iw represents the irrigation water variable input. Assuming weak monotonicity, a
technically efficient farm should also be efficient in its irrigation water use, although
this may not be necessarily true (Karagiannis et al., 2003).
As micro-economic theory requires that a production function is increasing
monotonically in all inputs, satisfying the monotonicity assumptions in estimating
single-input efficiency is also important. A technically efficient farm is supposed to use
all inputs efficiently; however, a technically inefficient farm could be efficient in the
use of at least one input whilst being inefficient in the use of the other inputs. Hence, if
we are concerned about any particular input, we must ensure that monotonicity
assumption holds for that input. So, we estimate input-specific technical efficiency in
two ways. We extract the estimated coefficients from the unrestricted and restricted
stochastic frontier models and use Equation 4.2.11 to get the irrigation water use
efficiency estimates.
The Efficiency of Irrigation Water Use and its Determinants
96
4.2.3 Study Area and Data
4.2.3.1 Characteristics of Study Areas
This study is conducted in the Jhang and the Lodhran districts of the Punjab province of
Pakistan. In these districts, cotton farming heavily relies on groundwater for irrigation
purposes. However, farmers in the study area solely depend on groundwater for
irrigation purposes in the Jhang district and partly on canal water in the study area of the
Lodhran district. In the Lodhran district, canals supply water only during the Kharif30
season. The canal water’s contribution during the Kharif season of 2010/11 was
observed to range between 20 to 44 percent of the total irrigation requirements.
Therefore, the majority of the irrigation water comes from groundwater which is
pumped through mainly electricity operated tube-wells.
Tube-well installation costs are very high due to deep groundwater tables and are
further projected to increase due to the rapid falling of groundwater tables. The cost of
lowering a tube-well to a depth of 24 metres is seven times that for a tube-well of 6
metres (Qureshi et al., 2003). For this study, the variation in the bore depth was
observed to range between 60 metres and 99 metres in the Lodhran district and 33
metres and 57 metres in the Jhang district during the 2010/11 field survey.
We find that due to low groundwater tables and the high installation cost, tube-well
populations are relatively less dense in the northern parts of the Jhang and southern part
of the Lodhran district. As a result, farmers generally engage in informal groundwater
trading. Such informal groundwater transactions have increased access to irrigation
water for tenants and smallholder farmers who do not own tube-wells. However, water
buyers have equity concerns under such informal market settings (Jacoby et al., 2004).
Despite the fact that the cost of buying groundwater is 3 to 4 times more than that of
pumping groundwater, sometimes water buyers face delays in getting water for
irrigation (Shah, 1993, Jacoby et al., 2004, Khanna, 2007).
4.2.3.2 Data Collection and Variable Definition
A multi-stage sampling technique was used in data collection. In the first stage, one
tehsil was selected purposively from the Lodhran and the Jhang districts. In the next
stage, 10 villages were selected at random from each selected tehsil. Finally, from each
30Cotton is a Kharif crop.
The Efficiency of Irrigation Water Use and its Determinants
97
village 10 groundwater users (5 tube-well owners and 5 water buyers) were selected
randomly to assess the difference in the amount of irrigation water applied and yield for
tube-well owners and water buyers. Data was collected from a total sample of 200
farming households. However, out of the selected 200 farming households, only 92
tube-well owners and 81 water buyers cultivated cotton crop during the cropping season
of 2010-11.
The data was collected using an interview schedule. During the interview, we collected
information on various inputs and output quantities. The inputs are measured as (1) seed
and fertiliser in kg/acre, (2) total labour, consisting of hired (casual and permanent) and
family labour in hours/acre, (3) farm operations as number of applications/acre, and (5)
groundwater use in cubic metres/acre. Cotton yield (output) is measured in kg/acre as
well. Various studies have used different approaches to compute the volume of
irrigation water. However, they do not give actual estimates of irrigation water used.
For example, Gedara et al. (2012a) measured the quantity of water used which was
related to the proportion of total land owned by the farmer and total quantity of water
released, assuming that this was distributed evenly across the irrigated area. Sharma et
al. (2001) measured irrigation water by the number of times water was released to the
farm from the main water source. In contrast to the surface water volumes, groundwater
use estimates are more realistic and reliable. In this study, we collected information
about the number of irrigations to cotton crop and the duration of water application per
irrigation event. We estimated groundwater extractions using an approximate estimation
model, as used by Eyhorn et al. (2005) and Srivastavaa et al. (2009) as follows:
2 2 4t 129574.1 BHP
[d4.2.12
(255.5998 BHP ) / d D ) Q
]
where Q represents the volume of water in litres, t is the total irrigation time, d is the
depth of the bore, D is the diameter of the suction pipe, and BHP is the power of the
engine. The descriptive statistics of the variables used in the estimation model are
presented in Table 4.2.1.
Table 4.2.1 compares selected variables for both the tube-well owners and water buyers
used in the analysis. It is evident from the descriptive statistics that on average there is
little variation in the use of farm inputs on per acre basis for seed, labour and fertilizer
application. Similarly, output produced by the tube-well owners and water buyers does
not vary considerably. In contrast, there is some variation in the number of farm
operations and irrigation water applied by the tube-well owners and water buyers. On
The Efficiency of Irrigation Water Use and its Determinants
98
average, tube-well owners use 7% more groundwater for irrigation compared to the
water buyers. The average cotton yield is 838 kg/acre, with a maximum of 1400 kg/acre
for tube-well owners and 821 kg/acre, with a maximum of 1200 kg/acre for water
buyers.
Table 4.2.1: Summary statistics of the variables used in the empirical model
Tube-well owners Water buyers Variable Mean Std.
Dev. Mean Std. Dev.
Economic Data Farm production (kg/acre) 838 177 821 181 Seed quantity (kg/acre) 8.31 1.29 8.31 1.35 Labour (hours/acre) 326 55 328 51 Fertilizer (kg/acre) 215 63 200 56 Machinery cost (Rs. /acre) 3962 757 4050 898 Irrigation water (m3/acre) 2277 424 2130 362 Farmer’s age (years) 45 8 42 8 Land tenureship (1=owners, 0=tenants) 0.815 0.390 0.813 0.393 Off-farm income in Rs. 0.174 0.381 0.113 0.318 Seed (1=improved, 0=not-improved) 0.261 0.442 0.263 0.443 Farmer’s education (years of schooling) 5.674 4.401 3.750 3.733 Access to extension services (1=yes, 0=no) 0.337 0.475 0.263 0.443 Salinity perception (1=yes, 0=no) 0.261 0.442 0.213 0.412 Is water table declining? (1=yes, 0=no) 0.750 0.435 0.238 0.428 Effect on cropping pattern (1=yes, 0=no) 0.543 0.501 0.288 0.455
The average farmer’s age is 42 years, ranging from 27 to 60 years. The rural sociology
of the study districts is dominated by the joint family system. Among the sampled
farmers, approximately 68% are living as joint families. The statistics on education
clearly reflect lack of education. The average education level of tube-well owners is
slightly above 5 years of schooling whereas for water buyers the average education is
slightly less than 4 years of schooling. A significant proportion of the surveyed farmers
cultivate their own land. Only 12% of the farmers are tenants. Because farming is a
major livelihood activity among rural communities, only a small proportion (13%) of
the farmers has an off-farm income source. Similarly, one third (33%) of the farmers
participate in agricultural training programmes or obtained advice from the agricultural
extension field staff.
The Efficiency of Irrigation Water Use and its Determinants
99
4.2.4 Estimation Results
The parameter estimates of the stochastic frontier model are presented in Table 4.2.2
and the estimates of the inefficiency model are presented in Table 4.2.3. The estimated
parameters of the unrestricted and the restricted models show clear differences;
however, these differences are less than standard errors of two.
The initial maximum likelihood estimates of the production frontier indicate that none
of the variables fully satisfy monotonicity conditions for all observations (Table 4.2.4).
Irrigation water which we are particularly concerned about satisfies monotonicity
conditions for only 78% of the total observations. Similarly, quasi-concavity is satisfied
for only 29% of the total observations in the initial model. The monotonicity condition
is fully satisfied for all observations and all variables in the adjusted model. Similarly,
quasi-concavity is also improved in the final adjusted model where 95% of the
observations satisfy the conditions.
The Efficiency of Irrigation Water Use and its Determinants
100
Table 4.2.2: Restricted and unrestricted model parameter estimates
Parameters MLE Estimates Minimum Distance Estimates Final SFA Estimates Estimate SE Coefficient Difference Diff/SE Estimate SE Constant -13.523* 7.351 -13.236 0.287 0.039 -13.534 Ln Seed (kg) 1.108 1.946 0.962 -0.146 -0.075 0.944 Ln Labour (hours) -1.990* 1.145 0.059 2.049 1.790 0.023 Ln Fertilizer (kg) 0.480 1.615 0.524 0.044 0.027 0.497 Ln Machinery (no. of farm operation) 1.936 2.186 1.344 -0.592 -0.271 1.334 Ln Water (m3) 1.071 1.023 0.345 -0.727 -0.711 0.315 Ln Seed × Seed -0.549 0.427 -0.538 0.012 0.027 -0.585 Ln Seed × Labour -0.545** 0.195 -0.128 0.417 2.138 -0.167 Ln Seed × Fertilizer 0.423 0.286 0.342 -0.081 -0.283 0.312 Ln Seed × Machinery 0.004 0.295 0.014 0.010 0.035 -0.022 Ln Seed × Water 0.348 0.219 0.120 -0.228 -1.041 0.086 Ln Labour × Labour -0.218 0.220 -0.004 0.215 0.975 -0.040 Ln Labour × Fertilizer 0.370** 0.172 0.094 -0.276 -1.607 0.059 Ln Labour × Machinery 0.561** 0.272 0.030 -0.530 -1.948 -0.006 Ln Labour × Water -0.195 0.168 -0.016 0.179 1.063 -0.053 Ln Fertilizer × Fertilizer -0.604** 0.257 -0.444 0.160 0.623 -0.490 Ln Fertilizer × Machinery 0.011 0.265 0.037 0.027 0.101 0.001 Ln Fertilizer × Machinery -0.102 0.188 -0.020 0.082 0.436 -0.057 Ln Machinery × Machinery -0.332 0.361 -0.110 0.222 0.614 -0.149 Ln Machinery × Water -0.194 0.194 -0.050 0.144 0.742 -0.088 Ln Water × Water 0.088 0.252 -0.030 -0.118 -0.470 -0.068 Model Variance 2 2 2
u v 0.055*** (0.011) 0.058*** (0.012) Variance Ratio 2 2 2
u u v/ 0.833*** (0.107) 0.827*** (0.100) Intercept -0.036 (0.541) IcFitted 1.000*** (0.234)
The Efficiency of Irrigation Water Use and its Determinants
101
Table 4.2.3: Inefficiency model estimates
Initial Estimates (MLE) Final Estimates (adjusted model) Parameter Coefficient Estimate Std. Error Coefficient Estimate Std. Error
AGE 0.007*** (0.002) 0.007*** (0.002) EDC -0.003 (0.007) -0.001 (0.007) OFIN -0.131 (0.102) -0.168 (0.112) LTS 0.173** (0.088) 0.149* (0.099) SDQ -0.221** (0.088) -0.176** (0.088) EXT -0.282*** (0.097) -0.294*** (0.102) WTD 0.001 (0.065) 0.003 (0.065) SPER -0.048 (0.074) -0.030 (0.073) GWSH -0.254*** (0.092) -0.262*** (0.100)
Note: **, *** indicate statistical significance at 10% and 5% levels respectively. AGE: is the farmer’s age in years, EDC: is a dummy variable indicating farmer’s education level, OFIN: is a dummy variable indicating farmer’s participation in off-farm business activities, LTS: is a dummy variable representing land tenure status, SDQ: is a dummy variable for seed quality, EXT: is a dummy variable representing access to extension services, WTD: is a dummy variable indicating farmer’s perception about decline in groundwater table, SPER: is a dummy variable indicating farmer’s perception about salinity perception.
Table 4.2.4: Proportion of farms satisfying the monotonicity and quasi-concavity conditions
Variables Maximum likelihood model Final adjusted model Monotonicity Seed 93.1% 100% Labour 67.7% 100% Fertilizer 94.2% 100% Farm machinery 97.7% 100% Irrigation water 78.0% 100% Quasi-concavity 28.9% 95.4%
We can interpret this scenario as, for the remaining 5% of observations that are not
quasi-concave, the individual inefficiency score may be either over or under estimated
(Sauer, 2006). Since the standard micro-economic theory requires satisfying quasi-
concavity under the profit-maximizing assumption, Henningsen and Henning (2009)
argue that the technical efficiency concept assumes that producers tend to maximize
their output given their input quantities rather than to maximize profit. Thus, in contrast
to the monotonicity condition, there is no technical rationale for satisfying the quasi-
concavity assumption. The intercept term in the final step is not significantly different
from zero, while the scaling coefficient is not significantly different from 1. From these
The Efficiency of Irrigation Water Use and its Determinants
102
results we can infer that the three-step procedure has not introduced substantial bias in
the model (Gedara et al., 2012b).
The partial production elasticities with respect to all inputs are reported in Table 4.2.5.
These results indicate that production is inelastic with respect to each of the inputs
included in the model. The elasticities at the sample mean are almost identically ranked
under both estimations. The seed variable exhibits the largest partial production
elasticity while labour displays the smallest. The elasticities relating to seed and labour
are slightly lower in the initial estimate compared to the final estimate. Irrigation water,
with an elasticity of 0.079, is ranked 4th out of the five variables included in the model.
Similar results were reported by Karagiannis et al. (2003) in his study for out-of-season
vegetable farms in Greek. Regardless of the measurement units, cotton production is
highly responsive to type and quality of seed (0.41) while it is least responsive to labour
(0.039) and irrigation water (0.079), respectively.
The returns to scale (derived from the sum of input elasticities) was estimated to be
1.174, suggesting that cotton farms on average were operating under increasing return
to scale. The cross-product of the input elasticities are relatively small, suggesting that
there is limited opportunity for input substitution.
Table 4.2.5: Partial production elasticities for the sample mean from the unrestricted and restricted models
Variables Maximum likelihood model Final adjusted model Seed 0.409 0.455 Labour 0.039 0.079 Fertilizer 0.288 0.273 Farm machinery 0.359 0.323 Irrigation water 0.079 0.079
As far as estimates of the inefficiency model (Table 4.2.3) are concerned, the estimated
coefficients and standard errors of the unrestricted and restricted models differ slightly
in some cases. However, the difference is not statistically significant. We see that
farmer’s education and off-farm business activities do not significantly affect technical
efficiency. As expected, old farmers and tenants have slightly lower technical efficiency
levels than their counterparts. We find that improved seeds and extension services play
a significant role in improving technical efficiency. The results for farmers’ perceptions
indicate that farmers who perceive that over-extraction of groundwater resources may
deteriorate its quality and availability, are generally more efficient than the farmers who
think oppositely.
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103
Table 4.2.6 and Table 4.2.7 present the technical efficiency and irrigation water use
efficiency estimates derived from both the unrestricted and the restricted models.
Table 4.2.6: Frequency distribution of technical and irrigation water use efficiency for tube-well owners from the unrestricted and the restricted models
Efficiency Range
TE (unrestricted)
TE (restricted)
IWE (unrestricted)
IWE (restricted)
<30 0 0 9 14 30-40 0 0 7 11 40-50 1 2 16 11 50-60 7 5 12 17 60-70 12 12 7 21 70-80 16 15 15 18 80-90 28 31 18 0 90-100 28 27 8 0 Mean 0.810 0.810 0.614 0.558 Std. Dev. 0.133 0.131 0.225 0.223 Minimum 0.405 0.412 0.079 0.124 Maximum 0.966 0.967 0.943 0.893
Table 4.2.7: Frequency distribution of technical and irrigation water use efficiency for water buyers from the unrestricted and the restricted models
Efficiency Range
TE (unrestricted)
TE (restricted)
IWE (unrestricted)
IWE (restricted)
<30 0 0 20 24 30-40 0 0 18 16 40-50 7 7 10 9 50-60 10 9 9 9 60-70 19 19 6 8 70-80 14 20 13 8 80-90 19 14 4 7 90-100 12 12 1 0 Mean 0.729 0.725 0.471 0.459 Std. Dev. 0.146 0.146 0.219 0.221 Minimum 0.405 0.413 0.041 0.111 Maximum 0.962 0.959 0.932 0.895
The unrestricted and the restricted TE estimates for tube-well owners have average
scores of 81% while the average IWE scores are 61% and 56% for the unrestricted and
the restricted models respectively. For water buyers, the average TE scores are 71%
while the average IWE scores are 47% and 46% for the unrestricted and the restricted
models. The equality of means test (t-test) for the unrestricted and the restricted TE
estimates cannot be rejected at the 1% significance level. However, we reject the null
The Efficiency of Irrigation Water Use and its Determinants
104
hypothesis that mean IWE estimates derived from the unrestricted and the restricted
models are not significantly different from zero.
Figure 4.2.3: Technical efficiency estimates from the restricted and the unrestricted models
Figure 4.2.4: Irrigation water use efficiency estimates from the restricted and the unrestricted models
Figure 4.2.3 and Figure 4.2.4 also illustrate that estimates of TE based on the
unrestricted and the restricted models are highly correlated; the coefficient of
correlation for TE is 0.99 and that for IWE is 0.80.
4.2.5 Discussion and Conclusions
This study has estimated the level of, and factors affecting, technical efficiency and
irrigation water use efficiency among groundwater-fed cotton farms in Pakistan. The
.4.5
.6.7
.8.9
1
TE e
stim
ates
from
the
rest
ricte
d m
odel
.4 .5 .6 .7 .8 .9 1TE estimates from the unrestricted model
.1.3
.5.7
.9
IE e
stim
ates
from
the
rest
ricte
d m
odel
.1 .3 .5 .7 .9IE estimates from the unrestricted model
The Efficiency of Irrigation Water Use and its Determinants
105
results obtained from a cross-sectional data of 173 cotton growers; including 92 tube-
well owners and 81 water buyers, indicate that considerable technical and irrigation
water use inefficiencies exist. Despite the severe water shortage in Punjab, the IWE
estimates reflect poor irrigation water management practices.
We find that, on average, tube-well owners are technically more efficient than water
buyers. Tube-well owners and water buyers can potentially increase cotton production
by 19% and 28%, respectively without increasing their existing input levels. Meinzen-
Dick (1996) found that tube-well owners were better-off in terms of farm productivity
compared to water buyers, presumably as a result of greater control over groundwater
access and supplies. Nevertheless, water buyers are prone to delayed irrigation water
supplies. As groundwater trading is informal, it is highly influenced by the social ties
between tube-well owners and water buyers. Hence, the absence of formal contracts
sometimes leads to inequities in water allocation and distribution among the buyers
(Jacoby et al., 2004, Rinaudo et al., 1997a). Moreover, on-going energy crises have
further added to uncertainty to water trading. Consequently, water buyers face delays in
obtaining water for irrigation. It is highly likely that delayed water application may
decrease the marginal product of other inputs such as fertilizer, labour and chemical
inputs.
Tube-well owners also irrigate their cotton fields more efficiently than the water buyers.
However, their irrigation water use inefficiencies are more pronounced than their
technical inefficiencies. The mean irrigation water use inefficiency estimates suggest
that a 46% and 54% reduction in current water applications is feasible for tube-well
owners and water buyers respectively. Our estimated IWE scores are generally lower
than those reported in some other studies on irrigation water use efficiency in many
other water stressed regions. Higher IWE estimates are reported by Speelman et al.
(2008) for small-scale irrigators in South Africa, Frija et al. (2009) for small-scale
greenhouse farmers in Tunisia and Manjunatha et al. (2011) for irrigated agriculture in
India. However, the average scores are higher than those of Karagiannis et al. (2003) for
out-of season vegetable farming in Greece. In contrast to our work, however, these
other studies have included multiple crops in their analyses.
The mean IWE estimates suggest that considerable gains in groundwater conservation
can be achieved by improving IWE across all farms. We calculated that cotton growers
on average produce 0.67 kg m-3. Although these estimates are fairly higher than the
previous estimate of 0.22 kg m-3 (Shabbir et al., 2012), there is considerable scope for
The Efficiency of Irrigation Water Use and its Determinants
106
improving water productivity and efficiency. We estimated that the 173 cotton farms
can save a total of 1.06 million m3 of water if they achieve 100% efficiency in irrigation
water use. Besides its impact on the sustainability of groundwater resources, such
savings in water use would make it possible to reallocate water to other sectors.
Most of the estimated coefficients in the inefficiency model conform to a priori
expectations about their impact on efficiency levels. Our estimates indicate that
farmer’s age significantly impacts the level of technical efficiency. A number of other
studies suggest that old farmers are more sceptical about adopting new farming
techniques and technologies and hence their agricultural production can lag (Villano
and Fleming, 2006, Speelman et al., 2008).The coefficient of land tenure status
indicates that non-owners are more efficient than the land owners. These results
contradict the common intuition that, ceteris paribus, land owners usually invest more
in recent production technologies and, consequently, increase their expected returns
(Frija et al., 2009, Speelman et al., 2008, Gebremedhin and Swinton, 2003). However,
some studies have also reported a negative impact of land ownership on farm efficiency
(Byiringiro and Reardon, 1996). Nonetheless, our results support the notion that farmers
who rent land will also devote extra effort in management oversight to generate returns
above what they pay for rent. Hence, they are more efficient. As expected, education
and extension services have positive impacts on efficiency and support the premise that
increases in human capital enables farmers to improve resource utilisation and thus
achieve higher efficiency. In the literature, we find mixed results for the efficiency and
education relationship, e.g., Karagiannis et al. (2003) and Solı´s et al. (2009) found the
impact of education significant while Haji (2007) and Speelman et al. (2008) found
education’s impact to be non-significant. These mixed results indicate that using general
years of schooling is not being a substitute for specialized education e.g., agricultural
education has different requirements compared to the social sciences. The impact of
agricultural extension services on efficiency is consistent with the commonly
established assumption that the farmers who tend to seek more extension advice and get
involved in training programmes are technically more efficient than those who have less
or no contact with agricultural extension staff (Parikh and Shah, 1994, Frija et al.,
2009).
The results for seed quality show a statistically significant positive association between
seed quality and technical efficiency. We find that off-farm income is positively
associated with technical efficiency, suggesting that with alternative income resources,
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107
farmers may have a better edge to purchase and use an optimal input mix which in turn
results in higher efficiency gains (Karagiannis et al., 2003).
Amongst the explanatory variables representing farmers’ perceptions about the
groundwater resource, perception about salinity and the potential impact on future
cropping patterns are positively associated with technical efficiency, while perception
about decline in groundwater tables is negatively associated with technical efficiency.
These results suggest that farmers do not worry about the declining water tables until
the groundwater quality starts deteriorating, as increasing salinity levels decrease crop
yields.
Whilst this study has policy implications for improving technical efficiency of cotton
growers, it also identifies the need to improve irrigation water use efficiency in cotton
production. Cotton production could be potentially increased through greater technical
efficiency; there is also considerable scope for improving irrigation water use efficiency
in cotton production. Both types of cotton producers i.e., tube-well owners and water
buyers, can reduce their current rates of irrigation application by 46% and 54%. The
main limitations to improving irrigation water use efficiency, however, arise mostly
from the lack of information about future viability of groundwater resources and lack of
information about water requirements of different crops at different times. We suggest
that educating farmers about crop water requirements and changing their perceptions
about groundwater resource availability may help achieve greater irrigation water use
efficiency.
Water buyers are generally down the water supply chain and they face more water
uncertainties that lead to reduced efficiency in the use of water. We suggest that policy
interventions are required to improve allocation security and equity of access for water
buyers whilst also providing information of the state and quality of groundwater
resources.
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4.3 Measuring Production and Irrigation Efficiencies of Rice Farms:
Evidence from Punjab, Pakistan
(Asian Economic Journal, Vol. 28 (3): 301–322)
The Efficiency of Irrigation Water Use and its Determinants
111
Abstract
We employ a non-parametric approach, data envelopment analysis, to estimate
production and irrigation water use efficiency of rice farms in Punjab, Pakistan. We use
a cross-sectional dataset of 80 rice growers comprising of 45 tube-well owners and 35
water buyers. The mean technical efficiency scores show that tube-well owners and
water buyers are operating at fairly high efficiency levels, indicating that access to
technology is not a major constraint for rice farms. However, irrigation water use
efficiency estimates suggest considerable inefficiencies with water buyers being more
inefficient than tube-well owners. A bootstrap truncated regression is used to investigate
the determinants of technical and irrigation water use efficiency. We suggest that
groundwater management policies should be designed to address efficiency enhancing
factors such as knowledge of crop water consumption requirement, better credit
opportunities, outreach extension services and training programs.
4.3.1 Introduction
Rice is a staple food crop in many parts of the world, and especially in Asian countries.
About 90% of the world’s rice is grown in Asia under different agro-climatic conditions
and geographic locations. Irrigated lowland rice, covering an estimated 80 million
hectares of cultivated land, contributes to approximately 75% of the global rice
production and consumes approximately 40% of the world’s irrigation water.
Approximately 60 million hectares of rainfed lowland rice meet 20% of the world’s rice
demand. The 14 million rainfed upland rice contributes only 4% to the world’s total rice
production (IRRI, 2013).
As in many Asian countries, rice is a major agricultural export commodity for Pakistan.
It holds an important position in the national economy, accounting for 1.4% of the
country’s gross domestic production (GDP). Pakistan is the world’s 5th largest rice
exporter, with the country exporting 3 million tonnes of rice in 2012 (Pakistan, 2009-
10). The two main varieties of rice produced and exported are Basmati 370 and IRRI-
631. However, the country is facing highly fluctuating trends in rice exports due to
increasing water scarcity and widespread stagnation in yields. In 2012, Pakistan’s rice
31 Basmati 370 is a high quality long grained aromatic variety and IRRI-6 is a medium-long grained variety. Prior to 1990s, Pakistan almost monopolised in international trade in Basmati. (Efferson, 1985)
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exports declined by 13% compared to 2011 exports. The agricultural economy of
Pakistan is operating at its water limits and rice water productivity (0.45 kg/m3) remains
55% lower than the average estimates of 1.0 kg/m3 in Asia (Watch, 2003). Evidences
suggest that inefficient irrigation water application is one of the major reasons for low
rice water productivity. Rice growers generally apply water to uneven bunded fields,
resulting in long irrigation events, poor water uniformity and over-irrigation (Kahlown
et al., 2007, Kahlown and Kemper, 2004). Due to the inefficient irrigation practices,
severe water stress, and low rice productivity, improving rice productivity is a policy
imperative rather than a choice. Besides severe water stress, rising water demands for
the domestic and industrial sectors have added further impetus to improve irrigation
water use efficiencies (Archer et al., 2010, Laghari et al., 2012, Sharma et al., 2010,
Hussain and Hanjra, 2004). In particular, rice production is being overly pointed out for
inefficient irrigation water applications in Pakistan (Kahlown et al., 2007).
Rising water scarcity is one of the key factors limiting agricultural production in
Pakistan. Surface water availability is deficient and unevenly distributed. Whilst the
demand for irrigation water continues to increase, the supply of surface water is
unlikely to increase due to limited potential in surface water developments (Archer et
al., 2010, Laghari et al., 2012, Sharma et al., 2010). As a result, farmers have started
augmenting their irrigation water supplies through groundwater abstractions.
Groundwater use now constitutes more than 50% of the total irrigation water supplies in
Pakistan (Qureshi et al., 2009). Groundwater utilisation has played a key role in
propelling agricultural development in the country but massive groundwater
abstractions have led to the rapid depletion of groundwater aquifers with serious
repercussions to the sustainability of irrigated agriculture over the last few decades
(Kijne, 1999b, Shah et al., 2000, Khan et al., 2008a, Qureshi et al., 2009). Within this
context, pressure is increasing to improve rice productivity and irrigation water use
efficiency.
The objective of this chapter is to investigate the production and irrigation water use
efficiency of rice farms and the factors which affect technical and irrigation water use
efficiency. We employ a non-parametric approach, data envelopment analysis (DEA), to
estimate production and irrigation water use efficiencies using a cross-sectional dataset
of 80 rice growers in the Punjab Province of Pakistan. A second-stage truncated
regression is used to identify the factors influencing technical and irrigation water use
efficiencies. Besides investigating production efficiencies of rice farms, this study
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113
contributes to the literature on rice production economics in several ways. First, it is the
only attempt to measure irrigation water use efficiency among rice growers. Second, to
check the robustness of our results, two methods, the data envelopment sub-vector
model and slack-based model, are used to measure irrigation water use efficiency.
The following section provides some literature review. Section 3 provides the
methodological framework for efficiency measurements. Section 4 describes the data
and principle features of the study area. The results are presented in the Section 5, and
Section 6 provides conclusions and policy implication.
4.3.2 Review of Literature
Due to its importance in food security and economic development, the efficiency of rice
production has been extensively investigated in developing countries32 , especially in
Asian countries (Villano and Fleming, 2006, Smith et al., 2011, Battese and Coelli,
1992, Abedullah et al., 2007) . Much of the empirical research suggests improving
production efficiency among rice growers both in the developed and developing
countries. One of the key finding in Asian countries is that low rice productivity is
attributed to disparities in technical efficiency (Thibbotuwawa et al., 2013). Much of the
published empirical work has estimated and explained technical inefficiency based on
either: i) socio-economic characteristics such as farm size, age, education, farming
experience, land tenureship status, family size, off-farm income; ii) institutional factors
such as extension services, farm organization membership; or iii) input use
characteristics such as soil and seed type. Methodologically, both parametric (Villano
and Fleming, 2006, Gedara et al., 2012a) and non-parametric (Dhungana et al., 2004,
Balcombe et al., 2008) approaches have been applied to estimate production efficiency
of rice farming, with some studies comparing the results from both approaches (Linh,
2012, Wadud and White, 2000).
Amongst the different socio-economic characteristics, various studies show a positive
and significant relationship between farm size and technical efficiency (Balcombe et al.,
2008, Coelli et al., 2002, Wadud and White, 2000, Rahman and Rahman, 2009) while
others find a negative relationship (Rahman et al., 2009). Age of the head of household
is one of the most influential characteristics in many studies. Older farmers are found to
32Bravo-Ureta and Pinheiro (1993) and Thiam et al. (2001) provide an excellent literature review on efficiency measurements in developing countries.
The Efficiency of Irrigation Water Use and its Determinants
114
be more technically efficient than younger ones (Rahman, 2010, Tan et al., 2010, Khan
et al., 2010, Villano and Fleming, 2006). Similar to age, the impact of farming
experience on rice grower’s efficiency level is mixed and inconclusive. Mariano et al.
(2011) and Khan et al. (2010) found experience is positively related with efficiency
while Coelli et al. (2002) found a negative relationship. Many studies confirm to a
priori expectations about the impact of education on efficiency levels (Villano and
Fleming, 2006, Tan et al., 2010). Nonetheless, numerous studies do not find a
significant relationship between education and efficiency improvements (Gedara et al.,
2012a, Rahman and Rahman, 2009). Some studies even suggested a negative
relationship between education and efficiency, presumably due to the reason that formal
education does not focus on rice farming practices (Coelli et al., 2002, Tian and Wan,
2000, Rahman, 2003).There is also mixed results in regard to the impact of different
institutional factors such as extension services and participation in farmer organizations
on rice production efficiency (Rahman, 2003, Gedara et al., 2012a, Rahman and Hasan,
2008).
Abedullah et al. (2007) used a stochastic production frontier (SFA) model to determine
technical efficiency of 200 rice growers in the Punjab province of Pakistan. They
reported a fairly high average technical efficiency (91%) compared to the study by
Ahmad et al. (1999) in the same province who reported a mean technical efficiency
score of 85%. Amongst the other Asian countries Gedara et al. (2012a) used a stochastic
production frontier to estimate technical efficiency of 460 irrigated rice farms in Sri
Lanka. They estimated a mean technical efficiency of 72%, suggesting significant scope
for improving existing efficiency levels. In Vietnam, Huynh Viet Khai (2011) reported
a mean technical efficiency of 82% for a dataset of 3,733 rice growing households. In
Thailand, Rahman and Rahman (2009) reported substantial technical inefficiencies in
Jasmine rice production. Results from the stochastic frontier model suggest that on
average 59% of the rice productivity is lost due to technical inefficiency.Trewin et al.
(1995) employed SFA to estimate technical efficiency for two different data sets of rice
producers in Cimanuk River basin of West Jawa in Indonesia. Results from the panel
data report an average technical efficiency of 90% while those from the cross-sectional
data report an average score of 85% and 84% for the wet and dry season. Battese and
Coelli (1992) used SFA with time varying firm effects to estimate technical efficiency
for a panel data of rice growers from India. They found that technical efficiency is time
variant when year of observation was included in the model. They reported technical
efficiency ranging between 55% -86% for 1975-76 while 84%-96% in 1984-85. Tian
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and Wan (2000) used the SFA approach to estimate technical efficiency in grain
production including rice in China. They reported mean technical efficiency estimates
of 95%, 94% and 95% for the early, late and mid-season cultivations for Indica rice and
91% for Japonica rice. Most of the above reported studies have considered land, labour,
fertilizer and machinery as the major variables. Despite a wide array of empirical
studies on rice production efficiency analysis, there are few studies which have included
water as an input in the production function. Moreover, empirical studies with an
exclusive focus on irrigation water use efficiency in rice production are rare.
4.3.3 Methodological Framework
4.3.3.1 Data Envelopment Analysis and Efficiency Measures
The data envelopment analysis (DEA) method is a mathematical programming
approach for measuring efficiency of different decision making units (DMU) e.g., firms
or farms etc. The method was introduced by Charnes et al. (1978), who extended
Farrell’s (Farrell, 1957) idea of measuring technical efficiency relative to a production
frontier. The DEA model proposed by Charnes et al. (1978) assumed a constant returns
to scale (CRS) technology. Later, Banker et al. (1984) introduced a DEA model under
the variable returns to scale (VRS). We refer the readers to the text book by Cooper et
al. (2000) for the detailed description on DEA.
4.3.3.1.1 Estimation of Technical and Scale Efficiency
Let us consider n DMUs that produce an output Y using input X. X is i × n matrix of
inputs and Y represents a k × n output row vector. Following Banker et al. (1984),
technical efficiency under VRS for a test DMUp, can be computed by solving the
following standard linear programming problem:
(λ, )4.3.1 Min
Subject to:
j ij
j kj kp
ip
n
n
j 1
j 1
n
j 1
j
j
x 0,
y y 0,
1,
0
x
The Efficiency of Irrigation Water Use and its Determinants
116
where is a scalar and represents technical efficiency; j is a vector of j elements which
represents the influence of each farm in determining the technical efficiency of the
observed farm; p , ipx and kpy are the input and output vectors of farm p . The equation
njj 1
1 is a convexity constraint which specifies variable returns to scale in the
model. Technical efficiency can be further decomposed into scale efficiency which
helps adjusting the scale of operation for a DMU under observation. We impose a
restriction njj 1
1 in Equation 4.3.1 to calculate technical efficiency under CRS and
then we compute scale efficiency as follows:
4.3.2 SE TE(CRS) / TE(VRS)
Scale inefficiency can be either due to decreasing (supra optimal) or increasing (sub
optimal) returns to scales. However, the above measure of scale efficiency does not
indicate whether a DMU is operating in an area of increasing or decreasing returns to
scale. This problem can be resolved by solving an additional DEA model with a non-
increasing return to scale restriction njj 1
1 . The relationship (NIRTS) (VRTS)TE TE ,
(NIRTS) (VRTS)TE TE and (VRTS) (CRTS)TE TE indicates the existence of DRTS, IRTS and
CRTS (Coelli et al., 2005).
4.3.3.1.2 Estimation of Cost and Allocative Efficiency
The cost efficiency for a DMUp, is computed by solving the following DEA linear
programming with cost minimisation objective, where *px represents the cost
minimisation vector of input quantities and 'pw is the vector of input prices.
*p(λ,x
' *p) p4.3.3 wn xMi
Subject to:
ij
kj
n
jj 1
n
j pj 1
jj 1
j
*p
n
x 0,
y y 0,
x
1,
0.
The Efficiency of Irrigation Water Use and its Determinants
117
The total cost efficiency for DMUp is calculated as ' * 'p p p pCE w x w x . That is, CE is
the ratio of minimum cost to actual cost for the DMUp. The allocative efficiency is then
calculated as a ratio of CE and TE:
4.3.4 AE CE / TE
4.3.3.2 Irrigation Water Use Efficiency
In non-parametric research, two approaches are generally used to measure the efficiency
of a particular input: the DEA sub-vector efficiency method (SVM) and the slack-based
DEA method (SBM). The major difference between the two methods is that the SVM is
a non-radial efficiency measure that ignores possible non-zero slacks, while SBM
calculates efficiency scores together with the slack values.
We use Figure 4.3.1 to illustrate the concept of the sub-vector and the slack based DEA
models. Let us consider six farms using two inputs 1x and 2x (e.g., water and fertiliser)
to produce a single output.
Figure 4.3.1: Graphical representation of the sub-vector and the slack-based input oriented efficiency models
Based on the efficiency concept, farms B, C, D, E and F are the best performers because
they are located on the frontier. A linear combination of their input use defines a
production frontier that envelops all of the other observed farms. Farm A is inefficient
because it is not located on the frontier. The radial contraction of inputs 1x and 2x (water
and fertiliser) produces a projected point on the frontier A , which is a linear
combination of all the observed data points. The technical efficiency of farm A with
The Efficiency of Irrigation Water Use and its Determinants
118
respect to farms B, C, D, E and F can be measured as A OA ATE / O . The sub-vector
efficiency of farm A for input 1x can be measured by reducing 1x to a point 'A while
keeping 2x and the output constant. Hence, the sub-vector efficiency of input 1x for farm
A can be given by the ratio ' ' 'IWE OA / OA . In contrast to the technical efficiency
which involves radial contraction of all inputs, the sub-vector efficiency measure
contracts a particular sub-set of inputs or any individual input non-radially while
keeping other inputs and the output constant. Although the sub-vector measure allows
the estimation of the possible contraction of a particular sub-set of inputs, it does not
allow estimation of the possible slack value F E1 1ox - ox as illustrated in the Figure 4.3.1.
The slack values are useful in estimating excessive use of an input in a production
process.
4.3.3.2.1 The Sub-vector Efficiency Model
Following Speelman et al. (2008), the irrigation water use efficiency w for a given
pDMU can be estimated by solving the following DEA sub-vector model:
w(λ , )w4.3.5 Min
Subject to:
i w , j
w , j w ,
kj
p
p
kp
i
n
jj 1
n
jj 1
nw
jj 1
n
jj 1
j
x x
x x
1,
0
y y 0,
0,
0,
.
where w is the sub-vector efficiency of input"w" for a pDMU . The constraints (i), (iv)
and (v) are the same as in Equation 4.3.1. However, (ii) is the inputs constraint
excluding water while (iii) is the water input constraint.
4.3.3.2.2 The Slack-based Model
We estimate the slack-based DEA model following Cooper et al. (2011) to get the
difference between the optimal values and the observed values of inputs and output. The
The Efficiency of Irrigation Water Use and its Determinants
119
linear programming that represents the DEA model to calculate slacks under VRS is
formulated as follows:
m
i(λ, ,S , S
s
k)i 1 k 1
4.3.6 Min S S ,
Subject to:
j kj
j ij ip
kp
n
ij 1
n
j 1
n
jj 1
k
j
x S x ,
S y ,
1,
0.
y
where kiS , S 0 i and k. X is an i × n matrix of inputs, Y represents an k × n output row
vector, and kiS ,S represents the inputs and output slacks respectively. The symbol 𝜀 is a
non-Archimedean infinitesimal defined to be smaller than any positive real number. By
solving this programme we are able to interpret the results as follows:
If * 1 and all slacks ki** ,SS 0 , DMU is considered to be strongly efficient.
If * 1 and *iS 0 and/or *
kS 0 , DMU is considered to be weakly efficient.
We used the following equation following Chemak et al. (2010) to measure the
excessive use of water in irrigation:
w
w
Ve4.3.7 IWE TEVo
where TE is the technical efficiency estimated using Equation 4.3.1, wVe is the slack
value of the input w, and wVo is the observed quantity of the input w.
4.3.3.3 Efficiency Determinants
In the literature, we find tobit regression as the most commonly used approach to
investigate the determinants of DEA efficiency measures. Many studies have justified
the use of tobit regression based on the argument that DEA efficiency scores are
censored values (Dhungana et al., 2004, Frija et al., 2009, Speelman et al., 2008, Wadud
and White, 2000). However, McDonald (2009) argues that efficiency scores are not
censored but are actually fractional values and proposed that Ordinary Least Squares
(OLS) in a second stage yields even more consistent results than the tobit model.
The Efficiency of Irrigation Water Use and its Determinants
120
However, OLS is only consistent under certain assumptions of the data-generating
process (Simar and Wilson, 2011). In an earlier paper, Simar and Wilson (2007) proved
that in the second stage single bootstrap truncated regression performs better in terms of
estimating confidence intervals. Hence, in contrast to the general use of tobit, we use a
single bootstrap truncated regression to identify efficiency determinants as follows:
n
j j j j jj 1
4.3.8 Y z 0 ;
for j 1,...., N and 2j N(0, )
where jY is either technical or irrigation water use efficiency, jZ is the set of explanatory
variables for j 1,....,9 and j is the error term.
4.3.4 Study Area and Data
4.3.4.1 Study Area
This study was conducted in the northern agricultural territory of the Jhang district of
the Punjab province in Pakistan. In the study area, rural households heavily rely on
groundwater as a major source of irrigation. As a result of excessive pumping,
groundwater tables are in gradual decline. Declining water tables have increased
groundwater extraction costs many times over the last two decades. The variation in the
bore depth was observed to be between 33 and 57 metres during this field survey. We
find that due to low water tables and the high installation cost, tube-well populations are
relatively less dense in the northern parts of the Jhang district. As a result, farmers
generally engage in informal groundwater trading. Such informal groundwater
transactions have increased access to irrigation water for tenants and small farmers who
do not own tube-wells. However, for water buyers there are some equity concerns under
such informal market settings (Shah, 1993, Jacoby et al., 2004).
4.3.4.2 Data and Variable Definitions
The dataset for this study is based on a detailed survey conducted during the Kharif 33
season 2010-11 in the Jhang district of the Punjab province, Pakistan. A multi-stage
sampling technique was used in data collection. In the first stage, one tehsil was
selected purposively from the Jhang district. In the next stage, 10 villages34 were
33Rice is a Kharif crop. 34In the study area, a village usually contains of 70-80 household farms.
The Efficiency of Irrigation Water Use and its Determinants
121
selected at random from the selected tehsil. Finally, from each village 10 groundwater
users (5 tube-well owners and 5 water buyers) were selected randomly to obtain the
differential impact of tube-well ownership and to reveal the difference of amount of
irrigation water applied and production gains of tube-well owners and water buyers,
thus making a total sample size of 100 respondents. However, during the cropping
season of 2010/11, only 45 tube-well owners and 35 water buyers cultivated rice crop
out of total 100 farming households.
Table 4.3.1: Descriptive statistics of the variables used in the DEA analysis
Tube-well owners Water buyers Variable Mean Std.
Dev. Mean Std. Dev.
Economic Data Cropped area (acres) 4.11 1.84 1.57 0.83 Seed (kg/acer) 4.71 0.53 4.58 0.80 Seed cost(Rs./acre) 356 61 321 82 Total labour (hours/acre) 167 51 239 112 Total labour cost (Rs./acre) 6992 2146 9958 4677 Fertiliser (kg/acre) 207 35 173 40 Fertiliser cost (Rs./acre) 5667 1846 4329 1879 Number of chemical applications/acre 2.46 0.69 2.48 0.65 Chemical cost (Rs./acre) 1323 476 1809 603 Number of farm operations/acre 8.00 1.18 6.97 1.54 Machinery cost (Rs./acre) 4694 833 4419 1318 Irrigation cost (Rs./acre) 8320 1378 14671 2336 Groundwater (m3/acre) 5495 1006 6124 1189 Rice yield (100kg/acre) 1547 161 1419 133
Farm level data was collected using an interview schedule on various inputs and output
quantities. The inputs were measured as: (1) seed and fertilizer in kg/acre; (2) pesticide
and farm operations as number of applications/acre; (3) total labour, comprising hired
(casual and permanent) and family labour in hours/acre; and (5) groundwater use in
cubic metres/acre. Output (rice yield) was measured in kg/acre. For different inputs and
output quantities, information on their respective prices was also collected in Pakistani
Rupees. The descriptive statistics used in the DEA model are presented in Table 4.3.1.
We collected information on groundwater use by obtaining data regarding the number
of irrigations applied to rice crop and the duration of each irrigation. We measured
groundwater extraction in litres using the following formula followed by Eyhorn et al.
(2005) and Srivastavaa et al. (2009).
The Efficiency of Irrigation Water Use and its Determinants
122
2 2 4t 129574.1 BHP
[d (255.4.3.9
5998 BHP
) / d D )]Q
where Q represents the volume of water in litres, t is the total irrigation duration, d is the
depth of the bore, D is the diameter of the suction pipe, and BHP is the power of the
engine. Later, groundwater extraction was converted into m3. Table 4.3.1 compares
selected variables used in the DEA analysis. Descriptive statistics show considerable
variations in the use of the inputs and output produced by tube-well owners and water
buyers. The average farm size of the sample farms is 4 acres for the tube-well owners
and 1.5 for the water buyers. We see considerable variation in the number of hours
worked on farms by tube-well owners and water buyers. Overall, the average rice yield
is 1491 kg/acre with 1547 kg/acre for the tube-well owners and 1422 kg/acre for the
water buyers. There is also a significant difference in the seed rate with an average of
4.6 kg/acre ranging from a minimum of 2 kg/acre to a maximum of 7 kg/acre. Similarly,
there is great variability across the farms in fertiliser and chemical application. In the
case of irrigation water use, water buyers use an average of 10% more groundwater than
tube-well owners. The respective prices of the different inputs also show significant
variability. On average, labour, fertiliser, machinery, and irrigation cost constitute 94%
of the total production cost. The share of the irrigation cost to the total production cost
is observed to be 35% for the tube-well owners and 43% for the water buyers. The
explanatory variables used to explain the efficiency differentials are presented in
Table 4.3.2.
The average farmer’s age is 42 years, ranging from 27 to 60 years. Among the sampled
farmers, approximately 68% of the farming families are living as joint families. The
statistics on education clearly reflect lack of education with 39% of the surveyed
farmers having no formal education. Only 24% of the farmers have an education level
above matriculation. A significant proportion of the surveyed farmers cultivate their
own land. Only 12% of the farmers are tenants. Because farming is a major livelihood
activity among rural communities, only a small proportion (13%) of the farmers has off-
farm income business. Only 33% of the farmers managed to get credit from private
banks or public agencies. Similarly, only one third (33%) of the farmers participated in
agricultural training programmes or obtained advice from the agricultural extension
field staff.
The Efficiency of Irrigation Water Use and its Determinants
123
Table 4.3.2: Summary statistics of variables included in the truncated regression
Variable definition Continuous variables Proportion of farmers with dummy variables
Mean SD Min. Max. 0 1 2 Farmers’ age (years) 42 1 27 60 Area under rice cultivation (acres) 3 1.95 0.50 9 Family status (0= single family, 1=joint family)
32 68
Education (0=illiterate, 1=up to metric, 2=above metric)
38 37 24
Off-farm income (0=no, 1=yes) 86 14 Land tenure status (0=tenants, 1=owners)
13 87
Credit access (0=no, 1=yes) 66 34 Extension services (0=no, 1=yes) 66 34
4.3.5 Results and Discussion
4.3.5.1 Technical, Scale, Cost and Allocative Efficiencies
The DEA efficiency estimates under variable returns to scale are presented in
Table 4.3.3. The mean technical efficiency is found to be 96% and 94% for the tube-
well owners and water buyers, respectively. Based on the mean estimates, gains from
improving technical efficiency do not seem to be considerable. However, across all the
villages, only 57.8% of the overall tube-well owners and 51.4% of the total water
buyers were fully technically efficient (TE=1). This implies that a significant majority
of the tube-well owners and water buyers are operating with technical inefficiencies.
The mean scale efficiency values for the tube-well owners and water buyers are found
to be 90% and 89% with 35.5% tube-well owners and 34.2% water buyers being fully
scale efficient (SE=1).
The mean scale efficiency implies that the scale of operation did not differ considerably
for the tube-well owners and water buyers. However, the analysis was further
disaggregated into those farms that exhibit increasing returns to scale (IRTS) and
decreasing returns to scale (DRTS). From Table 4.3.3 we see that: (1) more tube-well
owners are operating at an optimal scale than the water buyers; (2) more water buyers
tend to operate at sub-optimal scale than the tube-well owners. Only a small proportion
of farmers, with 2% of tube-well owners and 3% of water buyers, are operating at
supra-optimal scale. The results on returns to scale imply that most of the rice farms
The Efficiency of Irrigation Water Use and its Determinants
124
should be larger than they presently are to produce efficiently under the given state of
technology.
Table 4.3.3: Frequency distribution of technical, scale, cost and allocative efficiencies
Tube well owners Water buyers Frequency (%) TE SE CE AE TE SE CE AE <30 0 0 0 0 0 0 0 0 30-40 0 0 0 0 0 0 0 0 40-50 0 0 4 4 0 0 4 3 50-60 0 3 5 2 0 0 11 8 60-70 0 1 15 11 0 4 9 8 70-80 0 5 10 16 1 5 4 6 80-90 5 9 3 4 9 4 3 5 90-99 14 11 3 3 7 10 1 2 100 26 16 5 5 18 12 3 3 Mean 0.97 0.90 0.72 0.74 0.94 0.89 0.67 0.71 Std. Deviation 0.05 0.13 0.16 0.16 0.07 0.12 0.16 0.16 Minimum 0.85 0.57 0.41 0.41 0.78 0.63 0.45 0.46 Maximum 1 1 1 1 1 1 1 1
Table 4.3.4: Distribution of returns to scale for tube wells owners and water buyers
Returns to scale Tube well owners Water buyers IRTS (%) 67 86 DRTS (%) 2 3 CRTS (%) 31 11
Note: IRTS, DRTS, CRTS, indicate increasing returns to scale, decreasing returns to scale and constant returns to scale
Rice farms appear to be having low cost and allocative efficiency. The mean cost
efficiency estimates are found to be 71% and 66% for the tube-well owners and water
buyers. Similarly, the mean allocative efficiency is found to be 73% and 70% for the
tube-well owners and water buyers. Only 11.1% of the tube-well owners and 8.5% of
water buyers are found to be fully cost and allocative efficient (CE=AE=1). On average,
allocative inefficiency accounts for 27% and 30% loss in income respectively for the
tube-well owners and water buyers, suggesting the lack of revenue maximizing
behaviour and scope for improving income by increasing allocative efficiency.
4.3.5.2 Efficiency of Irrigation Water Use
The sub-vector and slack-based irrigation water use efficiency estimates are presented
in Table 4.3.5. The results show large scale irrigation water use inefficiency among
The Efficiency of Irrigation Water Use and its Determinants
125
tube-well owners and water buyers. The mean sub-vector estimates indicate 20% and
22% irrigation water use inefficiency among tube-well owners and water buyers.
The slack-based model, however, indicates average irrigation water use inefficiency of
10% and 13% for tube-well owners and water buyers. These estimates indicate that
there is a considerable scope for reducing irrigation water use by using the observed
quantities of other inputs and maintaining the same output level. The results presented
in Table 4.3.5 indicate that water buyers are less efficient in irrigation water use than the
tube-well owners.
Table 4.3.5: Frequency distribution of sub-vector and slack-based water use efficiencies
Sub-vector IWE Slack based IWE Frequency (%) Tube-well
owners Water buyers Tube-well
owners Water buyers
<30 0 0 0 0 30-40 0 0 0 0 40-50 2 1 0 0 50-60 4 5 0 0 60-70 8 9 2 3 70-80 7 5 7 8 80-90 5 2 10 9 90-99 3 2 11 4 100 15 11 15 11 Mean 0.796 0.779 0.900 0.874 Std. Dev. 0.194 0.187 0.109 0.112 Minimum 0.404 0.442 0.649 0.665 Maximum 1 1 1 1
Several reasons can explain this: (1) the functioning of informal groundwater markets
where water buyers sometimes cannot buy water when their crop requires and; (2)
sometimes tube-well owners prefer certain water buyers due to social ties with them,
thus discriminating on whom to sell water to (Jacoby et al., 2004); and (3) the nearby
tube-well owner may not have enough surplus water to sell because of the large farms
and high water demands; therefore, water buyers have to buy water from a far-off tube-
well owner. Due to all these factors, it is highly likely that water buyers will face delays
in obtaining water for irrigation. Such delays in irrigation water applications can have
serious impacts on crop growth and may decrease the marginal product of other inputs
such as fertilizer, labour and chemical inputs etc.
The Efficiency of Irrigation Water Use and its Determinants
126
Table 4.3.6: Spearman’s rank correlation among technical efficiency and the sub-vector and slack-based irrigation water use efficiencies
TE Sub-vector IWE Slack-based-IWE TE 1.000 SV- IWE 0.899* 1.000 SB-IWE 0.874* 0.965* 1.000
Table 4.3.7: Paired samples t-test demonstrating the difference between technical and irrigation water use efficiencies
Mean difference Std. deviation t-statistics
TE-Sub-vector IWE .1389 .1322 9.399*** TE-Slack-based IWE .0387 .0697 4.966***
Ho: mean (diff) = 0 Ha: mean (diff) ≠0 Pr (|T| > |t|) =0.000
The results presented in Table 4.3.6 suggest that the sub-vector efficiency model
measures relatively lower degrees of irrigation water use efficiency compared to the
slack-based model; however, both estimates are highly correlated.
Figure 4.3.2: Cumulative distribution for technical, sub-vector and slack-based irrigation water use efficiencies
From, the cumulative frequency distribution of technical efficiency and the sub-vector
and slack-based irrigation water use efficiency (Figure 4.3.2), we can infer that
irrigation water use inefficiencies are more pronounced than the respective technical
inefficiency. A paired sample t-test35 was applied to analyse the equality of means for
technical and irrigation water use efficiency. We find significant difference between
35The t-statistics 9.628 with a p-value 0.000 show that there is a significant difference between the technical and the sub-vector irrigation efficiency. Similarly, t-statistics 16.423 with a p-value of 0.000 indicate that the means difference between the technical and the slack-based irrigation efficiency is also significant.
00.050.1
0.150.2
0.250.3
0.350.4
0.450.5
0 0.2 0.4 0.6 0.8 1
Shar
e of
farm
s
Technical efficiency (VRS)SV-WUESB-WUE
The Efficiency of Irrigation Water Use and its Determinants
127
technical and the irrigation water use efficiency. The test statistics show that irrigation
water use efficiency is significantly lower than the technical efficiency in rice farming.
4.3.5.3 Explaining Efficiency Differentials
The empirical findings concerning the sources of efficiency differentials among rice
farms are presented in Table 4.3.8. The truncated regression estimates indicate that the
exogenous variables have a significant impact on both technical and irrigation water use
efficiency. The impact of farmer’s age, family status and off-farm income do not seem
to significantly affect either technical or irrigation water use efficiency. However, the
level of education, land tenure status, credit and extension services have statistical
significant impact on technical efficiency while farm size is associated significantly
with the irrigation water use efficiency.
The level of education is shown to have a positive significant impact on technical
efficiency while non-significant on irrigation water use efficiency. Similar to our
findings, Karagiannis et al. (2003) found increased schooling years positively associated
with high levels of technical efficiency. However, Speelman et al. (2008) found that
education does not improve irrigation water use efficiency. This implies that more
educated farmers have the ability to utilize the optimal input bundle and the best
available technology when we consider their overall technical efficiency; however,
when we focus on irrigation water use, education does not seem to contribute towards
the optimal use of irrigation water. Similarly, land ownership has significant impact on
technical efficiency but is non-significant on irrigation water use efficiency. This may
be because land owners have greater access to inputs such as water, credit and extension
services compared to water buyers who are usually tenants or subsistent farmers.
We find that farm size is not associated significantly with technical efficiency; however,
irrigation water use efficiency decreases significantly with increasing size of the farm
suggesting that larger farms use irrigation water less efficiently. This may be due to
flood irrigation which increases the chances of over irrigation as farm size increases.
Many other studies have found a positive relationship between farm size and technical
efficiency (Balcombe et al., 2008, Wadud and White, 2000) and negative relationship
between farm size and irrigation water use efficiency (Speelman et al., 2008).
The Efficiency of Irrigation Water Use and its Determinants
128
Table 4.3.8: Bootstrap truncated estimates of the determinants of technical and irrigation water use efficiency
Explanatory Variables Technical efficiency Irrigation water use efficiency
Coefficient Sd. Dev.
Coefficient Sd. Dev.
Farmer’s ager (years) Age 0.006 0.007 0.0048 0.012 Age2 -0.000 0.000 -0.000 0.000 Family status (0= single, 1= joint family)
-0.021 0.017 0.023 0.026
Education dummy (0= illiterate, 1= up to matriculation, 2=above matriculation) Up to matriculation 0.034** 0.017 0.006 0.029 Above matriculation 0.033 0.027 0.002 0.038 Land tenure status dummy (0= tenants, 1=owners)
0.035* 0.021 0.043 0.039
Off-farm income dummy (0=no, 1=yes)
0.005 0.022 -0.004 0.049
Farm size (acres) 0.006 0.006 -0.019** 0.009 Credit dummy (0=no, 1=yes) 0.051** 0.023 -0.036 0.028 Extension services dummy (0=no, 1=yes)
0.034* 0.019 0.031 0.031
Constant 0.703*** 0.152 0.266 0.266 Log likelihood 141.1 73.72
Note: *, **, *** indicate significant at 10%, 5% and 1% respectively. Number of bootstraps=4000 We found that farmers who opted to get credit were more technically efficient than
those who did not. Many studies argue that access to credit enables farmers to purchase
and use better mix of inputs at the most appropriate time, hence it helps in improving
technical efficiency (Haji, 2007, Karagiannis et al., 2003). The impact of agricultural
extension services on technical efficiency level is consistent with the commonly
established assumption that the farmers who tend to seek more extension advice and get
involved in training programmes are technically more efficient than those who have less
or no contact with the agricultural extension staff (Parikh and Shah, 1994, Karagiannis
et al., 2003).We find technical and irrigation water use efficiencies are highly correlated
(Table 4.3.6). We infer that the statistically significant impact of agricultural extension
advice on technical efficiency could also have a significant impact on increasing
irrigation water use efficiency (Frija et al., 2009, Karagiannis et al., 2003).
The Efficiency of Irrigation Water Use and its Determinants
129
4.3.6 Conclusion
The objective of this study was to measure production efficiencies and irrigation water
use efficiency among rice growers in Punjab, Pakistan. We used data envelopment
analysis to compute technical, scale, cost and allocative efficiencies using a survey data
of 80 randomly selected rice growers. Irrigation water use efficiency is estimated using
the sub-vector and slack-based DEA models to estimate excessive use of irrigation
water.
The empirical results on technical efficiency indicate that on average rice growers
operate at fairly high efficiency levels. Likewise, the mean estimates of scale efficiency
show that scale inefficiencies are nearly absent among tube-well owners and water
buyers. However, the results on returns to scale suggest that farm efficiency can be
improved by expanding the scale of operation. The cost and allocative efficiency
estimates indicate that rice growers are not utilising optimal quantities of inputs given
their respective prices. This study finds that rice production could be potentially
increased without increasing current input levels; likewise there is a considerable scope
for improving irrigation water use efficiency by using less water than the current levels.
The average sub-vector irrigation water use efficiency estimates for the tube-well
owners and water buyers suggest 20% and 22% savings in their current use of irrigation
water. In terms of total groundwater volumes, based on the sub-vector estimates, we
calculated that the tube-well owners and water buyers can save a total of 0.28 million
m3 of groundwater, with 0.19 million m3 reductions for tube-well owners and 0.08
million m3 for water buyers. Put in monetary terms, tube-well owners and water buyers
can save up to Rs. 0.28 million and Rs. 0.22 million of their total irrigation costs during
one cropping season of rice.
A key finding of the present study is that access to technology is not a major constraint
in rice production. However, high cost of inputs does affect cost and allocative
efficiency. We suggest that to improve technical and irrigation efficiency in rice
production, efforts and development strategies should be directed towards educating
farmers, providing better credit opportunities and agricultural extension services. In
particular, rice growers would benefit from better education and extension advice about
crop water requirements. Moreover, because water buyers face uncertainty regarding
access to water, policy intervention in groundwater markets to improve water allocation
could improve equity of access and, presumably, irrigation water use efficiency among
water buyers.
131
CHAPTER 5
5. Derived Demand for Irrigation Water
Abstract
We employ the Positive Mathematical Programming (PMP) approach to estimate the
derived demand for groundwater for irrigation using a cross-sectional data of 200
households who predominately use groundwater to produce wheat, cotton, rice and
sugarcane crops in the Punjab province of Pakistan. First, we find that the optimal
solution uses less water than what is being extracted for irrigation among the sampled
households. Second, farmers do not allocate land to different crops based on their water
requirement but based on their profitability. We suggest that water pricing can facilitate
appropriate and efficient use of groundwater for irrigation sector. Our analysis indicate
that that introducing water pricing at Rs. 41/1000 m3 for water sellers and Rs.
36/1000m3 for water buyers can help to achieve a 2% reductions in irrigation water
demand. The high shadow price (marginal value) is due to the higher expected net
returns per hectare. We suggest that water pricing should be used alongside other
policies geared towards improving irrigation water use efficiency.
5.1 Introduction
The agriculture sector is the largest consumer of global water withdrawals with
irrigation accounting for approximately 70% of the withdrawals (Siebert et al., 2010b,
Döll, 2009). The irrigated area comprise of less than 20% of the global cropland but
contributes to more than 40% of global food production (Döll and Siebert, 2002).
Irrigated agriculture heavily relies on groundwater resources in many regions.
Currently, groundwater contributes to about 42% of the global irrigation water supplies
(Döll et al., 2012, Siebert et al., 2010b, Rodell et al., 2009). The huge increase in
groundwater use over the past half-century is as a result of development of large-
capacity wells and wide water distribution technologies (Schwartz and Ibaraki, 2011,
Scanlon et al., 2012). A recent study reports an approximate 1500 km3y-1 use of tapped
water at global scale (Döll et al., 2012) and the area equipped for groundwater irrigation
is about 113 million hectares, which is about 38% of the global irrigated cropland
(Siebert et al., 2010b). With the growing dependence on groundwater and high
abstraction rates, groundwater resources are rapidly declining in many parts of the
world (Giordano, 2009, Werner and Tom, 2012, Schwartz and Ibaraki, 2011). Although
Derived Demand for Irrigation Water
132
global groundwater abstractions (1500 Km3 yr-1) are far lower than the global recharge
(12,600 Km3 yr-1), groundwater resources measured locally in different regions are in
gradual decline (Döll et al., 2012, Döll and Fiedler, 2008, Konikow, 2011, Scanlon et
al., 2012, Wada et al., 2010). A recent study in Nature Geoscience has re-mapped global
groundwater depletion. It shows the highest depletion rates in USA, Mexico, Saudi
Arabia, China, India and Pakistan (Aeschbach-Hertig and Gleeson, 2012).
It is widely recognised that Pakistan is amongst those countries where groundwater is a
depleting resource (Rodel et al., 2009, Wada et al., 2010, Khan et al., 2008b). Pakistan
is the 3rd largest groundwater consumer accounting for about 9% of the global
groundwater withdrawals (Giordano, 2009). Having 5.2 million hectares of land area
equipped with groundwater irrigation, Pakistan irrigates 4.6% of the global
groundwater-fed cropland (Siebert et al., 2010b). A sharp increase in groundwater use
in Pakistan was started after the 1960s’ Green Revolution. The continued expansion of
irrigated area and the introduction of high yielding but water intensive crop varieties
during the Green Revolution increased irrigation water demands by about three times
(Shiva, 1991, Ahmad et al., 2004b, Rodel et al., 2009). In 1960 groundwater
contribution to the total irrigation water supplies at the farm gate was about 8%, but 25
years later in 1985 this share had gone up to 40% (Byrelle and Siddiq, 1994). In recent
years, overall groundwater dependence has increased to more than 50% in different
parts of the country (Qureshi et al., 2010, Strosser and Rieu, 1997). Nevertheless,
renewable groundwater resources are not sufficient enough to meet the escalating
irrigation water demands. As a result, groundwater resources are under immense
pressure from overdrafting and this has led to many negative economic and
environmental externalities with serious repercussions to the sustainability of irrigated
agriculture (Kijne, 1999b, Shah et al., 2000, Khan et al., 2008a, Qureshi et al., 2009).
Given the well-documented spatial and temporal externalities associated with
groundwater overdrafting, some sort of regulatory mechanism is necessary to ensure its
sustainability. Many policy mechanisms propose water pricing to allocate irrigation
water more efficiently by regulating demand and supply (Tsur et al., 2004a, Tsur and
Dinar, 1997). It is considered that ‘getting the price right’ is important to improve water
allocation and conservation (Johansson et al., 2002). An effective water pricing policy
must consider the elasticity of demand for irrigation water and any collateral effects
which may come in the form of reduced agricultural production as a result of
constrained water supplies (Huang et al., 2010). Many studies have demonstrated that
Derived Demand for Irrigation Water
133
the demand for irrigation water is inelastic and hence water pricing would not stimulate
the intended changes in water re-allocation and conservation (Varela-Ortega et al.,
1998, Berbel and Gómez-Limón, 2000, Moore et al., 1994, Schoengold et al., 2006).
Generally, farmers are considered to be unresponsive to low water pricing36 and make
no on-farm investments in water conservation technologies to make efficient use of
water resources (Chaudhry and Young, 1989).
In the past, the Pakistan government tried various indirect groundwater management
strategies such increasing electricity tariffs to control groundwater withdrawal but
achieved limited success. The successful implementation of an effective water
management requires the assessment of the levels at which farmers' demand for
irrigation water becomes elastic and remains socio-economically acceptable. If
groundwater users in Pakistan are not responsive to existing water pricing (cost of
extraction and cost of buying water), imposing price37 on groundwater will not
significantly reduce demand. Therefore, it is necessary to understand how farmers
would respond to water pricing policy and determine the optimal water allocation
among the different uses (Storm et al., 2011).
The goal of this study is to estimate the derived demand for groundwater for irrigation.
We use the Positive Mathematical Programming (PMP) approach to assess how
different irrigators, i.e., tube-well owners (water sellers) and water buyers, value
groundwater resources. Several studies have discussed water pricing policies in Pakistan
36In Pakistan, some water charges are levied as a user charge (Abiana) for canal water distribution by the respective provincial irrigation departments while groundwater is a free resource. Over the last half a decade the Abiana charges are enforced on flat rate basis. These flat rates are different for different crops and very among different provinces. The above mentioned study was done more than two decades ago and at that time Pakistan was not facing the sever water crises. 37Markets for groundwater have been in operation for informal trading of surplus groundwater pumping capacities between the tube-well owners and water buyers without involving the exchange of permanent water rights (Khair et al., 2012, Meinzen-Dick, 1996). Basically, these markets offer a win-win situation to tube-well owners by offering economic benefits and to non-owners offering opportunities to increase agricultural productivity (Manjunatha et al., 2011, Meinzen-Dick, 1996, Shiferaw et al., 2008). Due to open access to groundwater resources farmers who have means to invest in tube-well technology, they can extract and even sell groundwater without any interference (Meinzen-Dick, 1996, van Steenbergen and Oliemans, 2002). The private tube-well owners bear only the energy and machinery costs to extract groundwater. Nonetheless, water buyers have to pay extra charges in terms of wear and tear charges besides paying pumping costs. Traditionally, price for groundwater is determined through a social consensus in the beginning of new cropping season or with increasing energy prices as an hourly flat rate basis or fixed share in crop production per unit of land. However, in many instances tube-well owners set the price first and then inform the water buyers. The price usually varies with the type of tube-well i.e., electric or diesel operated tube-well and based on the horse power of the engine etc.
Derived Demand for Irrigation Water
134
(Sahibzada, 2002, Chaudhry et al., 1993, Sufi, 2011, Farooqi et al., 2012). Nevertheless,
those studies explicitly focus on surface water by addressing gaps between water supply
costs and operation and maintenance costs. None of the studies have empirically
estimated the derived demand for groundwater for irrigation, especially in the context of
depleting groundwater resources. Therefore, this study is amongst the very few
quantitative studies to estimate the derived demand for groundwater for irrigation in
Pakistan.
The rest of the chapter is organised as follows. The next section provides literature
review within the context of irrigation water demand and pricing. Section 3, describes
the methodological frameworks to estimate irrigation water demand. The results are
presented in Section 4. The final section draws conclusions and provides some policy
implications.
5.2 Irrigation Water Pricing and Demand
The neoclassical economic literature argues that irrigation water pricing is necessary to
induce farmers to rationalize their irrigation water demands and adopt water saving
technologies (Berbel and Gómez-Limón, 2000, Frija et al., 2011). Moreover, due to
heavy investments in irrigation infrastructure and the resulting temporal and spatial
unintended consequences, some sort of regulation is required to create an equilibrium
between the supply and demand (Tsur et al., 2004a) and make farmers aware of the
value of water (Perry, 2001, Easter and Yang, 2007). Consequently, a lot of policy
mechanisms to allocate water and rationalize irrigation water demand have emerged
both at the managerial and the institutional levels. Amongst these many policy options,
some seek to regulate water allocations by water pricing (Tsur, 2004, Dinar, 1998, Tsur
and Dinar, 1997). However, there is no consensus on what is the right pricing
mechanism and how pricing should be implemented (Tsur, 2004). Many authors
propose that for the successful implementation of water pricing policy, it should be
accompanied by a set of complimentary policies that help to improve water productivity
and efficiency simultaneously (Gómez-Limón and Berbel, 2000, Gómez-Limón and
Riesgo, 2004, Liao et al., 2007, Molle et al., 2008).
Empirical studies from several developed countries have shown that demand for
irrigation water is inelastic (Berbel and Gómez-Limón, 2000, Salman and Al-Karablieh,
2004, Moore et al., 1994). These studies indicate that water pricing would not reduce
agricultural water consumption until prices negatively affect farm income. This implies
Derived Demand for Irrigation Water
135
that raising the price of water will not significantly reduce demand and will not be
effective because water users are not responsive to water pricing (Huang et al., 2010).
Berbel and Gómez-Limón (2000), in a study in Spain, demonstrated if water is priced,
farm incomes would decrease by 25% to 40% before water demand starts to decrease
significantly. Likewise, Salman and Al-Karablieh (2004) conducted a study in Jordan
and demonstrated that pricing water at 0.35$/m3 would not significantly reduce
irrigation water demand. Moore et al. (1994) investigated multi-cropping production
decisions in western United States and concluded that irrigation water demand is
inelastic for short-run water use decisions and is elastic for long term crop-choice and
land allocation decisions. In contrast, few other studies indicate more elastic demand
and show that the price of water is a strong determinant of water demand and an
important incentive for farmers to adjust their irrigation water requirements (Scheierling
et al., 2006)38.
Numerous methods to price and allocate irrigation water have been proposed in theory
and practice, some more efficient and some easier to implement than others. These
methods include: volumetric pricing, non-volumetric pricing, quotas and market-based
mechanisms (Johansson et al., 2002, Dinar, 1998). Amongst the pricing methods,
volumetric pricing is more effective in inducing efficient use of water. Yet volumetric
pricing is an exception worldwide due to its high implementation costs (Tsur, 2004,
Tsur and Dinar, 1997). On financial grounds, water pricing is considered a means to
recover the cost of supplying water and on economic grounds, a tool to signal water
scarcity (Dinar and Saleth, 2005). Nevertheless, the most commonly used marginal-cost
pricing (MCP) method which equate water price to marginal-cost of supply does not
cover the economic dimensions of water price such as capital depreciation and other
fixed costs (Tsur et al., 2004a, Huang et al., 2010). In the case of groundwater, the
marginal opportunity cost is associated with the unavailability of a unit of groundwater
that is over-extracted today (Koundouri, 2004). Hence, it is necessary to estimate the
social cost of groundwater over-exploitation (Huang et al., 2010). The social cost of
groundwater extraction arises as a result of over-exploitation of the resource by some
users that increases the cost for other users (Harou and Lund, 2008). Because of its non-
excludability nature, there is little incentive for a groundwater user to forego his current
38Scheierling et al. (2006) provide detailed description of irrigation water demand using meta-analysis.
Derived Demand for Irrigation Water
136
water needs for future needs, resulting in an increased rate of extraction and more rapid
resource depletion (Reddy, 2005, Pfeiffer and Lin, 2012). Generally, groundwater is not
regulated by well-defined property rights or by competitive water markets. Hence, both
the opportunity cost and social cost largely remain unrecognised (Huang et al., 2010,
Koundouri, 2004, Lynne, 1989).
5.3 Theoretical Framework
5.3.1 Approaches to Derive Demand for Irrigation Water
Irrigation water demand can be estimated by both statistical (econometrics) and
mathematical programming techniques. We explain the two techniques theoretically
following Tsur et al. (2004a) and Tsur et al. (2004b) . Consider the case of a farmer who
produces m crops using a single input i.e., irrigation water. Let j j jy f q represent a
yield-water response function for crop j , where jy is yield, jq is water input and j jf q is
an increasing and strictly concave production function and j 1,2,......,m . With jp
representing price of crop jand w for water price, a farmer’s operating profit is
represented by j j
mj jj 1
p f q wq and the necessary conditions for profit
maximization are j j j j'/ q f q w w p 0 which give rise to the individual crops
derived demand for water; j j' 1jq w f w p , j 1,2,......,m .Thus, the water demand can
be represented as:
j j j j
m m ' 1j 1 j 1
5.1 q w q w f w p
The above equation can easily be
extended to the general case of n farmers as:
i ij j
n n m' 1
i 1 i 1 j 1
5.2 q w q w f w p
Alternatively, the derived demand for
irrigation water can be obtained as follows. Let us consider that water is not priced but
is constrained at the level x . Here we are interested on how much farmers are willing to
pay to relax the water constraint represented by x units (i.e. x x ). Suppose that a
farmer uses water up to constraint x , the revenue generated is pf x and the additional
water ( x ) will generate additional revenue p f x x f x . For the additional
quantity of water demanded, the farmer is willing to pay at most p f x x f x
x
.
For small enough change in water constraint x , the marginal revenue is 'pf x . This
Derived Demand for Irrigation Water
137
represents the (maximal) price the farmer is willing to pay to relax the water constraint
by one unit; it is also called the shadow price of water. The shadow price varies at
different levels of water constraint and will only be positive when the constraint is
binding. This approach of computing the shadow price of irrigation water can be
extended to situations involving multiple inputs and additional constraints are easy to
impose.
Suppose production of crop j involves inputs j j jq ,z and b , where jq is water input, jz
represents k inputs like seed, fertilizer, pesticide and machinery that can be purchased at
unlimited quantity at the going market price 1 2 kr (r , r ,...r ) and jb denote primary inputs
e.g., land and family labour 1 2 Ls s ,s ,....,s that are available at limited quantities
1 2 Lb b ,b ,....b . Let the production function for crop j be denoted by j j j jf q ,z ,b , the
input output decisions problem of profit maximizing and price-taking is:
j j j j j k jk
m k
q,z,sj 1 k 1
5.3 x,b,p,r Max p f q ,z ,b r z
j
m
j 1 q
Subject to
x
:
jl l
m
j 1
b s ,
where jq 1,2,....,m, j j1 j2 jkz z ,z ,......,z , j j1 j2 jLb b ,b ,......,b , and L 1,2,....,L the non-
negativity constraints of some variables. This problem can be solved by forming the
Lagrangian: j j j j j k jk j l l jl
m k m L m
j 1 k 1 j 1 l 1 j 1
p f q ,z ,b r z x q s b
(other
constraints times their multipliers)
The multiplier, ,of the water constraint evaluated at the optimum level is the shadow
price of water, which when calculated for all feasible water levels x , constitute the
inverse derived demand for water.
5.3.2 Method of Analysis-Positive Mathematical Programming (PMP)
The Positive Mathematical Programming (PMP) approach emerged in the late 1980s as
means to analyse agricultural, environmental and land use policy decisions in
accordance with the economic behaviour. In this work, we apply the Positive
Mathematical Programming (PMP) approach formalized by Howitt (1995) as a method
to model economic behaviour where a concave profit maximization function is solved
Derived Demand for Irrigation Water
138
using the non-linear marginal cost (MC) parameters of a variable cost function. The
term “positive” implies that the parameters of the non-linear objective function are
derived from an economic behaviour and they are considered to be rational given the
observed and non-observed conditions that generate the observed activity level. Later,
Tsur et al. (2004b) advanced this model to estimate the derived demand for irrigation
water. The PMP calibration approach consists of three stages. Let n represent the
number of crops j 1,2,......, n, in the base year, jp price of crop j , jy yield/ha of crop j , jx
water requirement/ha for crop j , jL land allocation to crop j in the base year and jc
production cost/ha excluding water cost for crop j . In the first step, a linear
programming model is set to solve a constrained profit maximization problem with
calibration constraints on the total amount of water available to the system and the total
land available to cultivate j crops. We choose crop area allocation so as to maximize net
farm return subject to land and water constraint. The optimization model is represented
as:
j j j jj
n
Lj 1
5.4 Max p y c L
Subject to:
j j
n
j 1
x L W
(water constraint)
j
n
j 1
L L
(land constraint)
where is small positive perturbation and the usual non-negativity constraint holds.
By solving the constrained profit maximization problem, we generate shadow values
for different crop allocations and optimal allocation of crop area that is devoted to
various crops. In the second step, we use the estimated shadow values (dual values)
along with the data based average yield function to derive the calibration parameters
that represent the crop yield function parameters. Letting j represent the shadow price
derived in step1, we define the yield slope coefficient j as j j j jp L and the intercept
coefficient j as j j j j y L .
The third step involves specifying the PMP using the yield parameters ( and ) along
with the base-year data on all crops. The specified PMP involves reformatting the
constrained optimization as a quadratic programming model using the crop yield
function parameters and solving for the shadow value of water based on the water
availability constraint and the prices for crops. The specified PMP is solved using the
following optimization model:
Derived Demand for Irrigation Water
139
j j j j j j j
n
Lj 1
5.5 Max p L c L
Subject to:
j j
n
j 1
x L W
(total water constraint)
j
n
j 1
L L
(total land constraint)
The dual multiplier of the water constraint is the shadow price of water and constitutes
the marginal value of water which means that if an additional increment of water
resource x (which is constrained at certain level x ) becomes available, output would
increase by some amount * *y x ; in other words * is the marginal value of water
(Silberberg and Suen, 2001).
By changing the annual water constraint x in step 3 only and recording the shadow price
that corresponds to each x level, we obtain the correspondence between x and the
shadow price of water , which constitutes the (inverse) derived demand for irrigation
water.
5.4 Study Areas and Data Description
The data used in this study is based on a detailed farm survey conducted over two
cropping seasons i.e., Rabi and Kharif in Punjab, Pakistan, in the period 2010 to11. We
collected farm level data from 200 groundwater-fed agricultural farms located in two
districts, Jhang and Lodhran, in the cotton-wheat region and mixed cropping region.
The major crops in the cotton-wheat zone are wheat and cotton while wheat, rice, cotton
and sugarcane are major crops in the mixed cropping region. Due to limited canal water
supplies, irrigated agriculture in the study districts heavily rely on groundwater
resources. Despite having unreliable canal water supplies and deep groundwater tables,
both districts grow the most water intensive crops: sugarcane, rice and cotton. Detailed
input and output data on physical quantities and prices were collected for each crop
enterprise. Data was also collected for irrigation water used.
5.4.1 Nature of Irrigation Water Demand in the Central and South Punjab
As in many other districts of Punjab, agriculture relies heavily on groundwater in Jhang
and Lodhran districts due to the arid and semi-arid climate. Both districts receive very
little rainfall. The average precipitation rate in the Lodhran district is only 71 mm-1
Derived Demand for Irrigation Water
140
while it is 180 mm-1in the Jhang district. The potential irrigation water requirement for
cotton in the south of the Indus basin (Punjab) is 27% higher than in the northern part.
The high irrigation water requirement in the south is mainly due to high temperature
and sandy soils. Similarly, for rice and sugarcane, the potential irrigation requirements
are 20% and 25% higher in the southern part compared to the northern side of the Indus
basin. Our sample data show large variation in the depths of installed tube-wells. In the
Lodhran district, the variation was observed to be between 60 to 99 meters compared to
the Jhang district where it was between 33 to 57 meters. Low water tables not only
contribute to high groundwater extraction costs but also to high tube-well installation
costs. The total installation cost to bore a 24 meter deep tube-well is seven times higher
compared to boring a tube-well at a depth of 6 meters (Qureshi and Akhtar., 2003).
Table 5.1 provides the breakdown of the major crops cultivated in both districts by
water sellers and water buyers.
Table 5.1: Number of sample households that grew wheat and cotton in the Lodhran and Jhang districts during 2010-2011
Name of district and type of GW user Number of households that grew different crops Wheat Cotton Rice Sugarcane
Lodhran district total 100 100 0 0 Tube-well owners 50 50 0 0 Water buyers 50 50 0 0
Jhang district total 100 89 80 85 Tube-well owners 50 45 45 40 Water buyers 50 44 35 45
Table 5.2 shows area allocation, yield, irrigation water requirements and different crop
prices. Wheat covers the largest area and is cultivated by all the faming households in
the study districts. It is more popular than any other crop because it is a staple food.
Amongst the cultivated crops, wheat is the least water consumptive crop while
sugarcane is the most water consumptive crop. Column 4 of Table 5.2 gives the
standard irrigation water requirements of the cultivated crops in the regions. In
monetary terms, one cubic meter (m3) of groundwater utilized by wheat crop generates
an average of Rs. 54 while one cubic meter of irrigation water in cotton, sugarcane and
rice generates Rs. 34, Rs. 24 and Rs. 15, respectively. In terms of water productivity,
wheat is the most water productive while sugarcane is the least productive crop.
However, in terms of total returns per hectare, sugarcane is the most profitable crop
followed by rice and cotton.
Derived Demand for Irrigation Water
141
Table 5.2: Area allocation to different crops, yield, irrigation water requirements and crop prices
Groundwater Sellers Crop enterprise
Area jL
ha
Yield jy
metric tons/ha year-1
Irrigation requirement jx
m3 /ha year-1
Crop price jP
Rs./kg Wheat 489 3.8 2,870 23 Cotton 386 2.1 7,770 88 Rice 75 3.8 6,640 55 Sugarcane 97 75 1,5,650 5
Groundwater Buyers Wheat 244 3.6 2,870 23 Cotton 202 2 7,770 86 Rice 22 3.5 6,640 54 Sugarcane 23 70 1,5,650 5
Table 5.3 shows the input and farm operation cost for different crops. We see that there
is a large variation in the price39 for groundwater across different crops. On per hectare
basis, we observe that water buyers, on average, pay 52% more for groundwater for all
crops compared to water sellers. The total groundwater extraction cost, including
electricity and maintenance, is Rs. 2 m-3 for water sellers and Rs. 3.85 m-3 for water
buyers.
39 Groundwater is not priced in Pakistan. The above mentioned groundwater pricing refers to different costs associated with groundwater extraction such as energy costs. Under informal groundwater marketing, tube-well owners bear only the extraction costs (energy and machinery costs) whereas water buyers have to pay extra charges to cover wear and tear charges besides paying pumping costs. Traditionally, price for groundwater is determined through a social consensus in the beginning of new cropping season or with increasing energy prices as an hourly flat rate basis or fixed share in crop production per unit of land. However, in many instances tube-well owners set the price first and then inform the water buyers. The price usually varies with the type of tube-well i.e., electric or diesel operated tube-well and based on the horse power of the engine etc.
Derived Demand for Irrigation Water
142
Table 5.3: Input cost for different farm operations in Rs.ha-1
Groundwater Sellers Crops Wheat Cotton Rice Sugarcane Seed costs 3,583 5,964 881 n.a Labour costs 5,812 33,210 17,271 45,936 Fertilizer costs 17,419 13,506 13,998 16,689 Chemical costs 3,272 11,068 3,269 4,880 Farm operation costs 9,940 9,787 11,596 26,307 Groundwater irrigation costs 5,608 10,472 20,552 29,543 Total costs 45,634 84,007 67,567 1,23,355 Total cost, excluding groundwater cost
40,026 73,535 47,015 93,812
Groundwater Buyers Seed costs 3,361 5,485 795 n.a Labour costs 6,358 33,823 24,599 38,946 Fertilizer costs 14,874 12,449 10,695 12,821 Chemical costs 3,271 10,423 4,470 4,863 Farm operation costs 10,805 10,005 10,916 24,233 Groundwater irrigation costs 10,469 18,536 36,239 51,047 Total costs 49,139 90,721 87,713 1,31,910 Total cost, excluding groundwater cost
38,670 72,185 51,474 80,863
5.5 Results and Discussion
The first step PMP results for water sellers and water buyers are presented in Table 5.4
and Table 5.5, respectively. In the first step, we chose crop area allocation to maximize
net farm returns given the total amount of water and land available for the sample
farming households. The Step1constrained profit maximization model indicates that
given the annual water (total groundwater that was extracted for the observed cropping
season) constraint, total land constraint is binding. It means that area allocation to each
crop is equal to the total land available to the sample farms of both water sellers and
water buyers. However, the water constraint is not binding, indicating that the total
irrigation water requirement is lower than the amount of groundwater that is being
extracted for irrigation. We computed total groundwater extraction for water sellers and
water buyers which indicate an over-extraction of 1, 23,228 m3 for water sellers and
50,510 m3 for water buyers. By taking the total groundwater availability (total
extraction) as an annual water constraint, we calibrate the dual multipliers (shadow
price) for land allocation to each crop.
Derived Demand for Irrigation Water
143
Table 5.4: PMP step 1, water sellers
Crop Wheat Cotton Rice Sugarcane Constraint Level
Return per ha (excluding water cost)
47374 111265 161985 281188
Crops area 489 386 75 97 The (PMP chosen)
The Objective j j j jj
n
Lj 1
max p y c L
9.87E+07
Constraints Total land 1 1 1 1 1047 <= 1,047 Wheat 1 489 <= 489 Cotton 1 386 <= 386 Rice 1 75 <= 75 Sugarcane 1 97 <= 97 Groundwater 2780 7770 6640 15650 6.37E+06 <= 6.50E+06
Table 5.5: PMP step 1, water buyers
Crop Wheat Cotton Rice Sugarcane Constraint Level
Return per ha (excluding water cost)
43432 103004 137399 263932
Crops area 243 202 22 23 The (PMP chosen)
The Objective j j j jj
n
Lj 1
max p y c L
4.05E+07
Constraints Total land 1 1 1 1 490 <= 490 Wheat 1 243 <= 243 Cotton 1 202 <= 202 Rice 1 22 <= 22 Sugarcane 1 23 <= 23 Groundwater 2,780 7,770 6,640 15,650 2.75E+06 <= 2.80E+06
The dual multipliers (calibrated in Step 1) are then used to compute the optimization
function parameters i.e., yield slope coefficient and the intercept coefficient which are
presented in Table 5.6.
Derived Demand for Irrigation Water
144
Table 5.6: PMP Step 2, dual multipliers, yield slope coefficient and the intercept coefficient
Constraint/crop Duals j j j jp L
j j j jy L
Groundwater Sellers Total land 0.00 0.00 0.00 Wheat 47374 0.00 5.86 Cotton 111265 0.00 3.36 Rice 161985 0.04 6.75 Sugarcane 281188 0.58 131.24 Groundwater Buyers Total land 0.00 0.00 0.00 Wheat 43431.58 0.01 5.53 Cotton 103004.11 0.01 3.22 Rice 137398.92 0.12 6.06 Sugarcane 263932.00 2.23 120.95
Table 5.7 and Table 5.8 report the third Step PMP results under the available total land
and groundwater constraints. At this stage, total land constraint is binding while water
constraint is not binding for both the water sellers and water buyers. At regional level
PMP calibrations, total land constraint is binding because the cultivated land by all
farms cannot exceed the total agricultural land that is available. Moreover, farmers may
allocate land differently to various crops in different seasons. In contrast to the total
land available at regional level, groundwater is not a limited resource for short term
extractions. In particular, when there is no volumetric restriction on groundwater use, it
can be an expensive resource rather a limited resource. In this situation, some farmers
may extract more groundwater than others even to irrigate the same size of land with
same type of crop. Because groundwater extraction is not limited either at regional or
farm level, the water constraint may or may not be binding. We observe that
groundwater is not binding for both water sellers and water buyers, suggesting that that
the optimal solution uses less water than what is available. As we adjust the water
constraint level, farmers start re-allocating land to different crops in response to water
availability. We compute the shadow price of groundwater at each constraint level to
assess farmer’s responsiveness at different water constraint levels.
145
Table 5.7: PMP Step 3, water sellers
Crop Wheat Cotton Rice Sugarcane Constraint Level
Return per ha (excluding water cost) 47374 111265 161985 281188 Cropped area No. of ha (PMP chosen) 489 386 75 97 Price of crop ( jp ) 87400 184800 209000 375000 Production cost/ha excluding water cost( jc ) 40026 73535 47015 93812
j 0.00 0.00 0.04 0.58
j 5.86 3.36 6.75 131.24 Land allocation jL 489 386 75 97
The Objective j j j j j j
n
j 1p L c L
3.04E+09
j jL 2.06 1.26 2.95 56.24
j j jL 3.80 2.10 3.80 75.00 j j j jp L 3.32E+05 3.88E+05 7.94E+05 2.81E+07 j j j j jp L c 2.92E+05 3.15E+05 7.47E+05 2.80E+07
Quasi-rent/ha j j j j j jp L c L 1.43E+08 1.21E+08 5.60E+07 2.72E+09
Constraints Total land 1 1 1 1 1047 <= 1,047 Groundwater 2,780 7,770 6,640 15,650 6.37E+06 <= 6.50E+06
Derived Demand for Irrigation Water
146
Table 5.8: PMP step 3, water buyers
Crop Wheat Cotton Rice Sugarcane Constraint Level
Return per ha (excluding water cost) 43432 103004 137399 263932 Cropped area No. of ha (PMP chosen) 243 202 22 23 Price of crop ( jp ) 82102 175189 188873 357744 Production cost/ha excluding water cost( jc ) 38670 72185 51474 93812
j 0.01 0.01 0.12 2.23
j 5.53 3.22 6.06 120.95 Land allocation jL 243 202 22 23
The Objective j j j j j jnj 1
p L c L 7.02E+08
j jL 1.91E+00 1.19E+00 2.55E+00 5.13E+01 j j jL 3.62 2.03 3.51 69.60 j j j jp L 2.97E+05 3.56E+05 6.63E+05 2.49E+07 j j j j jp L c 2.59E+05 2.83E+05 6.11E+05 2.48E+07
Quasi-rent/ha j j j j j jp L c L 6.28E+07 5.73E+07 1.35E+07 5.71E+08
Constraints Total land 1 1 1 1 490.00 <= 490.00 Groundwater 2,780 7,770 6,640 15,650 2.75E+06 <= 2.80E+06
Derived Demand for Irrigation Water
147
The results for the derived demand for groundwater for irrigation for water sellers and
water buyers are presented in Figure 5.1 and Figure 5.2.
Figure 5.1: Derived demand for groundwater for irrigation for water sellers
Figure 5.2: Derived demand for groundwater for irrigation for water buyers
We find a high marginal value of water for both water sellers and buyers at low water
constraint; this is possibly due to the high profitability from cotton and sugarcane crops.
As in the Step 1model, land allocation to each crop cannot exceed the actual land
devoted to that crop. Therefore, when water constraint increases farmers start re-
allocating land to different crops. We observe that as the water constraint increases both
water sellers and buyers keep allocating their land to sugarcane crop and reduce
allocation to other crops. Sugarcane cultivation requires high irrigation water
applications but, higher land allocations to sugarcane are due to the higher per acre net
0.00200.00400.00600.00800.00
1000.001200.001400.001600.001800.002000.00
Wat
er p
rice
(Rs./
m3 )
Annual water constraint (m3)
0200400600800
10001200140016001800
Wat
er p
rice
(Rs./
m3 )
Annual water constraint (m3)
Derived Demand for Irrigation Water
148
returns for sugarcane compared to the other crops. The derived demand for water sellers
is almost inelastic when water is constrained between 4.98E+06 and 1.20E+05 m3; for
water buyers it is inelastic at constraint level between 2.45E+06 and 5.05E+04 m3. The
derived demand for water sellers is responsive to price changes when water is
constrained between 1.20E+05 and below 8.00E+04 m3. For water buyers, the demand
is responsive to price changes at when water is restricted between 5.05E+04 to
3.03E+04 m3.
Table 5.9: Percent change in water demand given and the percent change in shadow price
Water demand (1000 m3)
Shadow price (Rs./1000m3)
% change in consumption
% change in shadow price
Water Sellers 6.37E+06-3.30E+06 40.48-105.06 48% 160% 3.30E+06-2.00E+06 105.06-112.52 40% 7% 2.00E+06-1.52E+06 112.52-1791.13 24% 1499%
Water Buyers 2.75E+06-1.16E+06 36.47-92.088 58% 115% 1.16E+06-9.51E+05 92.088-93 18% 1% 9.51E+05-3.51E+05 93-1585 67% 1604%
Table 5.9 shows percentage changes in water demand corresponding to percentage
change in the shadow prices. For water sellers the groundwater availability of between
6.37E+06 to 3.30E+06 correspond to shadow price between Rs. 40-105/1000m3. It
indicates that the % change in water demand is lower than the % change in shadow
price. Similarly, for water buyers the groundwater availability is between 2.75E+06 to
3.30E+06, the shadow price of water for water buyers corresponds to a shadow price
range of Rs.36-92/1000m3, again with % change in water demand lower than the %
change in shadow price.
The study results suggest that water pricing can induce irrigators to optimise irrigation
water demand. We conjecture that a 2% reduction to the current groundwater volumes
would require that groundwater should be priced at Rs. 41/1000 m3 for water sellers and
Rs. 36/1000m3 for water buyers. At 2% reduction level, farm income does not change
significantly for both water sellers and water buyers. However, a 20% reduction in
irrigation water demand would decrease farm income by 18% and 16% for water sellers
and water buyers, respectively. We believe that imposing price on groundwater is a
complicated issue. The difficulties of implementing water pricing are well documented
in the literature (Dinar, 2000). The direct pricing (e.g., volumetric pricing) is
Derived Demand for Irrigation Water
149
complicated because groundwater extractions are not measured and farmers’ demand
for groundwater has been found to be unresponsive to small price changes, suggesting
that they do not assign any economic value to water. Indirect pricing, such as energy
taxation, is difficult because most of the tube-wells are operated by diesel and the use of
diesel for agricultural purposes and non-purposes are not clearly distinguished.
5.6 Conclusions
It is widely recognised that Pakistan is amongst the most water scarce countries and
dealing with growing water scarcity has become a policy imperative. Amongst the many
ongoing policy discussions to guarantee the sustainability of groundwater resources is
market based solution i.e., pricing water to optimise irrigation water requirements.
This study employed the Positive Mathematical Programming (PMP) approach to
estimate the derived demand for groundwater for irrigation among water sellers and
water buyers. We used a cross-sectional dataset of 200 households who predominately
use groundwater for irrigation in the Punjab province of Pakistan. We estimate the
shadow price of water which represents farmers’ willingness to pay when groundwater
resources become constrained at different levels.
Given the total annual water availability, the land constraint is binding as the total area
allocation to different crops is equal to the total land available for both water sellers and
water buyers. However, the water constraint is not binding suggesting that the optimal
solution uses less water than what is being extracted. We observe that as water
constraint increases, farmers start re-allocating land to crops taking water constraint into
consideration. Under the revised cropping plan, both water sellers and water buyers give
top priority to sugarcane. This is possibly because sugarcane cultivation generates the
highest net returns compared to other crops. This implies that farmers do not re-allocate
land to different crops based on their irrigation water requirements (because sugarcane
is the most water consumptive crop) but based on the expected returns per hectare. We
propose that introducing water pricing at Rs. 41/1000 m3 for water sellers and Rs.
36/1000m3 for water buyers can help achieving 2% reductions in irrigation water
demand.
We suggest that water pricing can facilitate appropriate and efficient use of groundwater
in irrigation sector. However, we suggest that rather solely relying on pricing, additional
policies are required that improves irrigation water use efficiency. First, policy makers
should set a groundwater saving target in tandem with water pricing (such as 2% initial
Derived Demand for Irrigation Water
150
annual water saving target). An introduction of Rs. 0.04/m3 would not decrease farm
income rather it would make farmers aware of the economic value of water. Second, all
subsidies on agricultural tube-wells estimated at Rs. 16.4 billion in 2012 should be
removed and farmers should be encouraged to adopt water conserving irrigation
technologies.
151
Chapter 6 6. Conclusions
6.1 Summary
Water is becoming an increasingly scarce resource for agricultural production in many
regions of the world. In the past, irrigation water policies largely focused on the
development of adequate infrastructure to guarantee water supply as the demand for
agriculture sector was increasing. However, these expansionary policies have resulted in
a massive use of irrigation water and physical scarcity in different parts of the world.
Now water scarcity has become an important economic and social concern for policy
makers on the international and national agendas. Effective management of water
resources raises the challenge of how to use available water resources more efficiently
and sustainably and find possible ways to address and manage water scarcity to meet
the competing inter-sectoral multiple water demands more equitably.
Irrigation water requirements are very high in the Indus basin of Pakistan due to the arid
and semi-arid climate. Existing surface water resources are not only deficient but are
also highly skewed in time and space throughout the Indus basin. Consequently, the
agriculture sector heavily relies on groundwater extraction to meet irrigation water
demands. However, over the last two decades groundwater exploitation has escalated
which has resulted into lowering of groundwater tables. The rapid decline of
groundwater resources and the escalating number of tube-wells has brought into greater
focus the challenge of how to control over-exploitation among policy makers.
Therefore, an understanding of how farmers make decisions to adopt tube-well
technology, their response to constrained water resources and pricing, and the extent of
groundwater-use efficiencies in irrigation is essential to designing or revising and
refining groundwater management policies.
This PhD study focused on the economics of groundwater use in irrigation in the Indus
basin of Pakistan. The specific objectives of the study were to: (1) identify causes and
consequences of groundwater depletion; (2) analyse farmers decision to adopt tube-well
technology under farm profit variability and production uncertainties related to
depleting groundwater resources; (3) investigate the extent of technical and irrigation
water use efficiency for different groundwater irrigated crops and factors that explain
efficiency variability across farms; and (4) estimate the derived demand for
Conclusions
152
groundwater for irrigation among water sellers (tube-well adopters) and water buyers
(non-adopters of tube-wells).
6.2 Methods
The thesis incorporates a number of innovate aspects that contribute to the overall
knowledge of groundwater economics in Pakistan. First, the moment based approach
was used to understand farmer’s decisions to adopt tube-well technology under
production risk and farm profit variability. Although, the moment based approach has
been used to analyse risk exposure (Antle, 1987, Juma et al., 2009, Kassie et al., 2008,
Kim and Chavas, 2003), and except for Koundouri et al. (2006), its application to
analyse irrigation technology adoption decisions under production risk due to water
scarcity issues is rare. In this thesis, the moments of profit distribution and farmer’s
perceptions regarding groundwater resources were simultaneously incorporated into the
technology adoption function.
Second, this study estimates technical and irrigation water use efficiency of different
agricultural crops using both parametric and non-parametric approaches. The DEA sub-
vector model has been used to measure irrigation water use efficiency (Speelman et al.,
2008, Frija et al., 2009, Manjunatha et al., 2011) but application of the DEA slack-based
model is rare (Chemak et al., 2010). The innovative feature of this study is that it used
both the DEA sub-vector and slack-based models to estimate irrigation water use
efficiency in rice farming. Similarly, this study applied the metafrontier framework to
estimate technically efficiency, irrigation water use efficiency, and technology gap
ratios in wheat farming. Many studies have applied the restricted stochastic frontier
model to estimate technical efficiency (Gedara et al., 2012b, Pascoe et al., 2012,
Tiedemann and Latacz-Lohmann, 2013), but no study was found that estimate input-
specific technical efficiency, i.e., irrigation water use efficiency using the restricted
stochastic frontier model. Therefore, this study advanced the input-specific technical
efficiency concept and estimated irrigation water use efficiency in cotton farming by
imposing monotonicity and quasi-concavity restrictions into an input-specific translog
stochastic frontier model.
The third chapter employed the Positive Mathematical Programming (PMP) approach to
estimate the derived demand for groundwater for irrigation. The farmers’
responsiveness to groundwater is assessed by estimating the shadow price of
groundwater at various constraints levels.
Conclusions
153
6.3 Main Results
Chapter 2 identified the causes and consequences of groundwater overdrafting and
draws attention to groundwater resource management issues. The major causes of
groundwater overdrafting were found to include the rigidity of the surface water
allocation system (Warabandi System), the Green Revolution, the Indus Water Treaty,
the increasing population and the ineffective groundwater management policies.
Consequently, massive pumping of groundwater aquifers to meet the increasing
irrigation water demands has started lowering groundwater tables rapidly in different
parts of the country. Besides lowering groundwater tables, overdrafting has led to many
negative environmental, pecuniary and spatial negative externalities which portend
serious repercussions to the sustainability of irrigated agriculture in the region. Major
environmental externalities identified include: soil salinization, salt water and sea water
intrusions, land subsidence and drying up of lakes and vegetation in different parts of
the country. Various pecuniary externalities such as increasing pumping costs and
decreasing land values are also identified. Migration and social conflicts are identified
as potential spatial externalities in the coming years.
Chapter 3 employed a moment-based approach to analyse farmer’s decisions to adopt
tube-well technology under farm profit variability and depleting groundwater resources.
It was found that the sample moments of the profit distribution affect farmers’ adoption
decisions. Estimates show that the higher the expected profit the greater the probability
that a farmer decides to adopt a tube-well technology. It was also found that the
probability of adopting tube-well increases significantly with increasing variance of
profit. These results imply that farmers adopt tube-well technology in the pursuit of
reliable access to irrigation water and hence greater farm profits. Having access to
irrigation water supplies provides a hedge against production risks associated with
unreliable and scarce water supplies. The non-significant skewness of profit distribution
indicates downside profit risk does not have a significant impact on tube-well adoption.
The highly significant kurtosis indicates that farmers’ adoption decreases in the
presence of extreme events like flooding and crop failure due to crop disease outbreak.
Farmers with higher off-farm income, better access to agricultural extension services
and other sources of information, and those who cultivate their own lands are found to
more likely to own a tube-well. The farmers’ perceptions about falling groundwater
tables and deteriorating groundwater quality suggest that they are either not fully aware
Conclusions
154
of the declining groundwater levels and water quality or they are aware but do not care
because of their growing dependence on groundwater resources.
Chapter 4 presented the estimated results of the technical efficiency (TE) and irrigation
water use efficiency (IWE), and the factors affecting TE and IWE of different crop
enterprises.
Section 4.1 employed the data envelopment analysis (DAE) metafrontier approach to
estimate TE and IWE of groundwater-irrigated wheat farms. It was observed that both
TE and IWE were slightly different under the metafrontier and groupfrontier
specifications. We found that the tube-well owners were more efficient than water
buyers. The mean TE estimates for tube-well owners and water buyers were found to be
91% and 90% when estimated under the metafrontier specification whereas the mean
scores were found to be 93% and 94% under the groupfrontier settings. The mean
irrigation water efficiency estimates for tube-well owners and water buyers under the
metafrontier specification were found to be 66% and 65% respectively whereas the
estimates were found to be 71% and 67% under the groupfrontier settings.
In wheat farming, farmers’ education, seed quality and farmer’s perceptions about
salinity levels and groundwater table depth significantly increase wheat growers’
technical and irrigation water use efficiency. However, land tenureship, off-farm
income, access to credit, and access to extension services were found to be significantly
associated only with the TE of wheat farmers.
Section 4.2 estimated a theoretically consistent translog production function and
computed the TE and IWE in cotton production. The mean TE estimates for tube-well
owners was not different (81%) under both the restricted and unrestricted models while
the mean IWE score was 61% and 56% under the unrestricted and restricted models,
respectively. Similarly, the mean TE score for water buyers was the same (71%) under
the both models while the mean IWE score was 47% and 46%. The equality of means
test (t-test) for the unrestricted and restricted TE estimates cannot be rejected at the 1%
significance level for both water sellers and water buyers. However, we reject the null
hypothesis that the mean IWE estimates derived from the unrestricted and restricted
models were not significantly different from zero.
The most important factors having significant impact on cotton growers’ technical and
irrigation efficiency are the seed quality, access to extension services and their
perceptions about groundwater shortage (groundwater table depth). Both age and land
Conclusions
155
tenure status is found to be negatively associated with the TE and IWE of cotton
growers.
Section 4.3 estimated the TE and IWE in rice farming by employing the sub-vector and
slack-based DEA models. It was shown that rice production could potentially be
increased without increasing current input levels. The high mean TE of 96% and 94%
for tube-well owners and water buyers suggest that access to technology is not a major
limiting factor. However, there is considerable scope for improving IWE by using less
water. The mean IWE estimates of 80% and 78% suggest considerable reductions in the
current water usage.
Farmers’ education, land tenureship and access to credit are the most important factors
that are positively associated with technical efficiency. Farm size is the only major
factor that is found to be significantly associated with IWE; with increasing rice farm
size, irrigation water use efficiency decreases significantly.
Chapter 5 used a Positive Mathematical Programming (PMP) approach to estimate the
derived demand for groundwater use in irrigation. We find that the actual crop water
requirement is lower than the amount of groundwater that is being extracted. Given the
land constraint, additional water supplies would not increase the representative farm’s
profit. Therefore, producers are unlikely to respond to any pricing policy at the current
rate of groundwater extraction. Producers would only respond to changes in
groundwater price if groundwater supplies are constrained relative to their demand. The
results indicate that water sellers would be willing to pay a higher price than water
buyers when irrigation water becomes constrained. We propose that introducing water
pricing at Rs. 0.04/ m3 for water sellers and Rs. 0.036/m3 for water buyers can help
achieving 2% reductions in irrigation water demand.
6.4 Synthesis of Main Findings
The overall synthesis of the study findings indicate that over-extraction of groundwater
resources has raised several concerns to the sustainability of groundwater resources and
irrigated agriculture in the Indus basin of Pakistan. Despite that hydrological
assessments indicate that groundwater extraction rates have exceeded the annual
recharge rates, the number of tube-wells has been on the increase since early 1960s.
Nevertheless, the depleting groundwater resources has not only impacted the adoption
of tube-wells but also has raised concerns to improve irrigation efficiency and increase
overall agricultural productivity. Besides suggesting improvements in irrigation water
Conclusions
156
applications, the on-going policy discussions also suggest market based solutions to
induce water resource conservation practices.
It is found that farmers’ profit distribution influences tube-well adoption. More
specifically, farmers who anticipate higher profits are more likely to adopt tube-wells
and invest in the technology in order to hedge against the risks of profit variability.
Among different socio-economic factors, land ownership, off-farm income, access to
extension services and different other sources of information make risk-averse farmers
better off and hence play decisive role in tube-well adoption. However, whilst there are
concerns about the increasing adoption of tube-wells and massive extraction of
groundwater resources, farmers’ perceptions about groundwater resource availability
and utilization indicate that they are not really concerned about the groundwater
resource depletion.
Analysis of irrigation water use efficiency analysis also indicate frivolous attitude
towards groundwater resource utilization. There are considerable inefficiencies in using
groundwater for irrigation purposes. Based on data from the 200 wheat farms in this
study, an over-use of 0.48 million m3 of groundwater can be saved by achieving 100%
irrigation water use efficiency. Similarly, an over-use of 1.06 million m3 from 173
cotton farms and 0.28 million m3 of 80 rice growing farms can be saved if they achieve
100% efficiency in irrigation water use. Extrapolating this to the entire farms in the
study region suggest that improving water use efficiency at the farm level can reduce
over-extraction of groundwater resources.
The impact of different farm and farmer’s characteristics on grower’s efficiency level
was found to be mixed and inconclusive on different crops. Many factors confirm to a
priori expectation about their impact on efficiency levels whereas numerous other
factors do not. These findings suggest that that there is a lot of heterogeneity across the
different farmers and farm characteristics that that eventually influence capacity to use
irrigation water more efficiently.
The results from the derived demand analysis are consistent with those from irrigation
water use efficiency; the optimal solution for water allocation suggests that groundwater
is not a limiting factor and less water can be used that what is being extracted for
irrigation purposes. The optimization results from the Positive Mathematical
Programming (PMP) model suggest that pricing water at Rs. 0.04/ m3 for tube-well
owners and Rs. 0.036/m3 for water buyers can help achieving 2% reductions in
irrigation water demand.
Conclusions
157
6.5 Policy Recommendations
Managing groundwater resources requires multidimensional actions, management
strategies and coordination activities across a range of institutions and stakeholders.
Despite the need for such management and its associated policy design and
implementation activity, this is not evident in Pakistan. The front line challenge to
control over-extraction is to develop and implement both supply and demand
management strategies that improve irrigation water use efficiency and sustainable use
of groundwater resources.
Results from this study have important policy implications covering. We found that
farmers adopt tube-well technology to overcome production risk and variability in farm
profits. However, tube-well adoption does not necessarily improve irrigation water use
efficiency nor conserve groundwater resources. Moreover, farmers’ do not perceive
groundwater as a finite resource and see not need to adjust their production practice to
conserve it. Therefore, further tube-well adoption must be accompanied by
complimentary policies that promote efficient use of groundwater for irrigation by
limiting groundwater extractions. The study results indicate that groundwater is being
over-used in agriculture sector. Thus, there is need for policies that educate farmers on
actual crop water requirements as a way to promote irrigation water use efficiency. This
may involve extending extension advice from crop management to groundwater
management or creating a separate water extension wing. Groundwater metering and
pricing can be explored as an option to induce farmers to reduce irrigation water
demands. Finally, additional policies are also required to improve water allocation,
security and equity of access for both water buyers and sellers. Water buyers are
generally down the water supply chain and face more irrigation water uncertainties than
water sellers. We note that the use of indirect pricing such as putting a tariff on energy
to control over extraction of groundwater is not a viable strategy because the use of
diesel for agricultural purposes and non-purposes is not clearly distinguished.
Therefore, a direct water pricing policy is required to make farmers aware of the
economic value of water. It is suggested that policy makers should set a groundwater
saving target in tandem with water pricing. An introduction of Rs. 0.04/m3 would not
decrease farm income but would decrease groundwater demand, suggesting that farmers
would start to be aware of the economic value of water. Second, current subsidies on
agricultural tube-wells - subsidized electricity and fuel costs - should be reallocated to
promote water conserving irrigation technologies.
Conclusions
158
6.6 Limitations and Future Research Needs
There are several caveats to this analysis. First, survey data was collected in only two
out of the five districts in the Punjab province due to time and financial resource
constraints. As a result, this study relies on a small dataset of only 200 farmers. Second,
there are potential problems related to sample selection and representation. There is no
systematic record of the actual number of tube-well adopters and non-adopters in the
study regions. , Although a multistage sample selection approach was used to select 100
adopters and 100 non-adopters of tube-well technology, the approach was not entirely
random at all stages and therefore sample selection bias cannot be ruled out. The choice
of 50% adopters and 50% non-adopters may not reflect the true proportion in the
population.
Second, like in many other developing countries, farmers in Pakistan generally do not
keep good records of various farm activities over the years. Due to non-availability of
panel data, the presented estimates do not give any indication of the year to year
variability in farm efficiency and productivity. Third, there could be potential errors in
the way groundwater use was computed. Farmers do not have installed meters to
monitor their exact groundwater use levels. The approximation formula used to measure
groundwater extractions is based on the assumptions that the lifting head is equal to the
depth of the tube-well. The formula did not account for variations in efficiency in
groundwater extraction across different types of water pumps. Hence, there is a
likelihood of over-estimation or under-estimation of groundwater extraction. .
Fourth, the demand analysis does not consider inter-seasonal variations in irrigation
water demand. It might be that during the cropping season that underpinned the survey
data irrigation requirements were higher due to climatic conditions. Thus, future studies
in irrigation water demand analyses should control for variability in inter-seasonal
demand for groundwater.
159
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8. Appendix
The Economics of Groundwater Irrigation in the Indus Basin, Pakistan: Production Efficiency, Water-use Efficiency and Optimal Allocation
This research survey is part of a PhD project which focuses on groundwater use efficiency,
production efficiency and optimal allocation of groundwater for sustainable management in Punjab,
Pakistan. The survey will collect data on different production inputs and outputs for small scale
irrigated agriculture at the farm level. It will also look at how the performance of groundwater
markets affects farmers’ (tube well owners and buyers) groundwater use efficiency.
Your participation is completely voluntary, and you can choose not to answer any questions you do
not want to. Your response will be kept confidential and will be used for research purposes only. The
interview will not take more than 2 hours to complete.
I hereby certify that this interview is being conducted for my academic research only (Muhammad Arif Watto).
Date of the interview ---------------------------------- Name of enumerator --------------------------------
A. Identifying Variables and Demographic Information of the Household Head A.1. What is the main source of irrigation water for the household?
A.1.1. Own tube well
A.1.2. Purchased tube well water A.1.3. Own tube well + canal water A.1.4. Purchased tube well water + canal water
A.2. Household ID -------------------------------------
A.3. Name of the cropping region -------------------------------------
A.4. Name of the district -------------------------------------
A.5. Name of the tehsil -------------------------------------
A.6. Name of the village -------------------------------------
A.7. Name of the respondent
(Please get household’s head name, if respondent is not the HH) ------------------------------------A.8. Age of the household head ------------------------------------
A.9. Farming experience of the household head -------------------------------------
A.10. Gender of the household head -------------------------------------
A.10.1. Male -------------------------------------
A.10.2. Female -------------------------------------
A.11. Household family structure
A.11.1. Single ---------------------------------------
A.11.2. Joint ---------------------------------------
A.12. Number of household members ---------------------------------------
A.12.1. Adults (Above 18 years) ---------------------------------------
A.12.2. Children (Under 18 years) ---------------------------------------
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A.13. Formal education of the household head
A.13.1. Illiterate -------------------- A.13.2. Primary ----------------------
A.13.3. Middle----------------------A.13.4. Metric -----------------------
A.13.5. Intermediate ---------------A.13.6.Graduate ---------------------
A.13.7. University --------------------
A.14. Informal education (Training related to water management)
A.14.1. Training by NGO (No=0, Yes=1) ----------------------------
A.14.2. Training by extension field staff (No=0, Yes=1) ----------------------------
A.15. Approximate off-farm annual income of the household during 2010
----------------------------------------------------------------------------------------------------------
B. Farm Characteristics and Production Details
B.1. What is the land tenure status of the household head?
B.1.1. Owner ----------------------------
B.1.2. Tenant ----------------------------
(If, the household head is tenant please do not ask questions B.4. and B.5.)
B.2. Total farm size (Acres) ---------------------------
B.3. Total area under cultivation (Acres) ---------------------------
B.4. Additional area rented in (Acres) ----------------------------
B.5. Area rented out (Acres) ----------------------------
B.6. Which major crops did you grow in the last 12 months among the following crops? (No=0, Yes=1)
B.8.1. Wheat Yes/No B.8.2. Cotton Yes/No B.8.3. Rice Yes/No B.8.4. Sugarcane Yes/No B.8.5. Maize Yes/No
B.7. Please tell me about the production of different crops that you sow in the last year.
Name of Crop B.7.1
Cropped area in acres B.7.2
Production in 100Kg per acre B.7.3
Total farm production in 100Kg B.7.4
Total produce sold in 100Kg B.7.5
Price fetched in Rs./ 100Kg B.7.6
Wheat Cotton Rice Sugarcane Maize
B.8. What are the major objectives of your farm production? Please rate their importance based on the following scale: 1= low importance; 2= No importance; and 3= High importance.
B.8.1. Production for local market 1 2 3
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B.8.2. Production for self-subsistence 1 2 3 B.8.3. Both 1&2 1 2 3
B.9. Is there any difference of land rent for a piece of land with and without tube well? (No=0, Yes=1)
9.1. Yes
9.2. No
If yes, then;
B.10. What is the amount land rent with a tube well per acre? ----------------------------
B.12. What is the amount of land rent without tube well per acre? ----------------------------
Questionnaire
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C. Input Details
C.1.Seed
Please tell about the seed rates you used for each of the following crops during the last cropping year.
Name of crops Details of seed use
Crop code 1=Wheat 2=Cotton 3=Rice 4=Sugarcane 5=Maize
Crop name C.1.1.
Seed type C.1.2
Seed quality C.1.3
Mode of purchase C.1.4 0=Cash 1=Borrowed
Source of purchase C.1.5 1=Private agency 2=Government agency 3=Local stockist 4=Farmers group 5=Neighbouring farmer 6=Friend/relative
Quantity of seed Kg/Acre C.1.6
Price of seed Rs./Kg C.1.7
Total seed cost for whole farm C.1.8 1=Retained
2=Purchased 3=Ratoon (If retained, please skip C.1.4-C.1.5)
0=Improved 1=Un-improved
Questionnaire
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C.2. Labour
C.2.1. Family Labour
Please provide the details about your family labour for different cropping activities during the last cropping year.
Name of different labour activities for different crops
Labour used in different cropping activities
Crop code 1=Wheat 2=Cotton 3=Rice 4=Sugarcane 5=Maize Activity type code 1=Sowing 2=Transplanting 3=Irrigation 4=Hoeing 5=Spraying 6=Harvesting 7=Threshing 8=Picking 9=Shelling
Adults Children Crop name C.2.1.1
Activity type C.2.1.2
No. of males C.2.1.3
Total days worked C.2.1.4
Total hours worked C.2.1.5
No. of females C.2.1.6
Total days worked C.2.1.7
Total hours worked C.2.1.8
No. of children C.2.1.9
Total days worked C.2.1.10
Total hours worked C.2.1.11
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C.2.2.Hired Labour
Please provide the details about the labour which you hired for different cropping activities during the last cropping year.
Code for different labour activities for different crops
Hired Labour Name of crop C.2.2.1
Activity type C.2.2.2
No. of persons hired C.2.2.3
No. of days worked each C.2.2.5
Total hours worked (average) C.2.2.4
Daily paid wages to labour If in cash, Rs./ person per day C.2.2.6
If in kind, Kg/ person per day C.2.2.7
Crop code 1=Wheat 2=Cotton 3=Rice 4=Sugarcane 5=Maize Activity type code 1=Sowing 2=Transplanting 3=Irrigation 4=Hoeing 5=Spraying 6=Harvesting 7=Threshing 8=Picking 9=Shelling
Questionnaire
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C.3. Fertilizer
Please provide the details about the different fertilizers which you applied to different crops during the last cropping year.
Crop code 1=Wheat 2=Cotton 3=Rice 4=Sugarcane 5=Maize Input codes 1=Urea 2=DAP 3=SSP 4=TSP 5=NPK 6=ZnSO4 7=NP 8=MOP 9=Other
Name of crop C.3.1
Fertilizer name C.3.2
Number of bags (50Kg)/Unit applied per acre C.3.3
Mode of purchase C.3.4 0=Cash 1=Borrowed cash
Source of purchase C.3.5 1=Private agency 2=Government agency 3=Local stockist 4=Farmers group 5=Neighbouring farmer 6=Friend/relative
Price per bag (50Kg)/Unit in Rs. C.3.6
Total cost for fertilizer used for whole farm C.3.7
Questionnaire
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C.4. Pesticide
Please provide the details about the different chemicals which you sprayed/applied to different crops during the last cropping year.
Crop code 1=Wheat 2=Cotton 3=Rice 4=Sugarcane 5=Maize Input codes 1=Pesticide 2=Herbicide 3=Fungicide 4=Other
Name of crop C.4.1
Chemical type C.4.2
Mode of purchase C.4.3 0=Cash 1=Borrowed
Source of purchase C.4.4 1=Private agency 2=Government agency 3=Local stockist 4=Farmers group 5=Neighbouring farmer 6=Friend/relative
Number of application of each chemical C.4.5
Cost per application per acre in Rs. C.4.6
Total cost of the chemical used for whole farm C.4.7
Questionnaire
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C.5. Farm Operations and Machinery Cost
Please provide me the details of your farm operations for the last cropping year.
Different farm operations for different crops Farm operations for different crops Crop code 1=Wheat 2=Cotton 3=Rice 4=Sugarcane 5=Maize Farm operations code 1=Ploughing 2=Harrowing 3=Seed broadcasting 4=Furrowing/ridging 5=Harvesting 6=Threshing 7=Transporting
Name of crop C.5.1
Type of farm operation C.5.2
No./time of operation C.5.3
Type of machinery used C.5.4 1=Cultivator 2=Disc plough 3=Drill 4=Seed bed planter 5=Boom sprayer 6=Ridger 7=Reaper 8=Combined harvester 9=Wheat thresher 10=Tractor trolley
Farm machinery Status C.5.5 0=Own 1=Hired
Cost of operation per acre C.5.6
Cost of operation for whole farm C.5.7
Questionnaire
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Section D. Irrigation Details
D.1.Volume of Irrigation Water
Please provide the irrigation details of your major crops during the last cropping year.
Crop code Name of crop D.1
Total no. of irrigations applied D.2
Time of each irrigation/Acre D.3
No. of tube well irrigations applied D.4
Amount of groundwater in m3 at farm level D.5
No. of canal irrigations applied D.6
Amount of canal water in m3at farm level D.7
Total amount of water in m3 at farm level D.8
1=Wheat 2=Cotton 3=Rice 4=Sugarcane 5=Maize
D.2. Energy Consumption and Cost Details of Tube Wells
Please provide the energy consumption and energy cost details of your tube well for the last cropping year.
Tube well type by mode of operation
Tube well type
Energy consumption by mode of operation
Energy cost/hour by mode of operation in Rs.
Extraction cost/hour
Lubrication cost/hour
Maintenance cost/hour
Total cost for one irrigation
D.2.1 Diesel L/hour D.2.2
Elec. units/hour D.2.3
Diesel cost/L D.2.4
Elec. cost/unit D.2.5
D.2.6 D.2.7 D.2.8 D.2.9
Tube well type code 1=Tractor 2=Peter engine 3=Electricity 4= Other
Questionnaire
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D.3.Irrigation water measurement
Please provide me the specifications of your tube well (both engine and bore).
Tube well type code
Type of tube well
Power of the engine (hp)
Depth of bore (m)
Diameter of suction pipe (in)
1=Tractor 2=Peter engine 3=Electricity 4= Other
D.3.1 D.3.2 D.3.3 D.3.4
D.4. Canal Water Irrigation Details
D.4.1. What is the total allocated time of your canal water turn (Warabandi) per acre?
--------------------------------------------------------------------------------------------------------------------
D.4.2. What is the share of canal water to total irrigation for each of the following crop?
Name of the crop D.4.2.1
Share of tube well irrigation in (%) D.4.2.2
Share of canal water irrigation in (%) D.4.2.3
1=Wheat 2=Cotton 3=Rice 4=Sugarcane 5=Maize
D.4.3. How much cost (Abiana) did you pay during the last year? ----------------------------
Note: Please skip section E (E.1. to E.13) if the respondent does not own a tube well and skip questions (E.14. to E.29) if the respondent owns a tube well.
E. Characteristics of Tube Wells and Groundwater Markets
E.1. Tube Well Owners/Groundwater sellers
E.1. How long have you been using groundwater? ----------------------------
E.2. What is the age of the engine/well operating machine? ----------------------------
E.3. Did you test your groundwater before the installation of tube well? (No=0, Yes=1)
E.3.1. Yes E.3.2. No
E.4. Is there any competition among water sellers in the area? (No=0, Yes=1)
E.4.1. Yes E.4.2. No
E.5.To whom do you usually sell water (TSW)?
E.5.1. Relative E.5.2. Friend E.5.3. Neighbour E.5.3. Anyone
E.6. Did you ever refuse to sell water to anyone? (No=0, Yes=1)
E.6.1. Yes
Questionnaire
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E.6.2. No E.7. If yes, what were the reasons not to sell water to that person? What are the three most important? (Mark as 1, 2, and 3where, 1= low importance; 2= No importance; and 3= High importance.)
E.7.1. Deferred payments by the purchaser 1 2 3 E.7.2. Tube well declined 1 2 3 E.7.3. Unavailability of diesel oil 1 2 3 E.7.4. Negotiation failed on price issue 1 2 3 E.7.5. Long queue of buyers 1 2 3
E.8. Is there any conflict in the region over groundwater extraction among different users? (No=0, Yes=1)
E.8.1. Yes E.8.2. No
E.9. If yes, please explain the nature of conflict?
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
E.10. Do you think that a nearby tube well may affect your tube well extraction? (No=0, Yes=1)
E.10.1. Yes E.10.2. No
E.11. Did it ever happen that a nearby tube well decreases the extraction rate of your tube well? (No=0, Yes=1)
E.11.1. Yes E.11.2. No
E.12. Are people willing to pay for groundwater at the price that you set? (No=0, Yes=1)
E.12.1. Yes E.12.2. No
E.13. If no, do you lower the price or not? (No=0, Yes=1) E.13.1. Yes E.13.2. No
E.2. Non-tube well owners /Water buyers E.14. What are the reasons not to install your own tube well? What are the three most important? (Mark as 1, 2, and 3where, 1= low importance; 2= No importance; and 3= High importance.)
E.14.1. Do not have own land (tenants) 1 2 3 E.14.2. Poor groundwater quality 1 2 3 E.14.3. Low groundwater table 1 2 3 E.14.4. High installation costs 1 2 3 E.14.5. Easy access to water due to water markets 1 2 3
E.15. How important are the factors are that you consider when buying groundwater for irrigation? (Please rank these factors as 1, 2, and 3where, 1= low importance; 2= No importance; and 3= High importance.)
E.15.1. Groundwater quality 1 2 3 E.15.2. Distance from the farm 1 2 3 E.15.3. Water course type 1 2 3 E.15.4. Reliability 1 2 3
E.16. Is there any competition among water buyers in the area? (No=0, Yes=1) E.16.1. Yes E.16.2. No
E.17. From whom do you usually purchase groundwater?
E.17.1. Relative E.17.2. Friend E.17.3. Neighbour E.17.4. Anyone
(Note: In case of 17.4. please skip E.18)
Questionnaire
189
E.18. What are the reasons to buy water from a particular well owner? What are the three most important? (Mark as 1, 2, and 3where, 1= low importance; 2= No importance; and 3= High importance.)
E.18.1. Flexible in terms of payment 1 2 3 E.18.2. Near to the farm 1 2 3 E.18.3. Lined water course 1 2 3 E.18.4. Type of tube well (tractor, peter, electric) 1 2 3 E.18.5. Low price 1 2 3
E.19. Did you ever decide not to buy water from a particular well owner? (No=0, Yes=1)
E.19.1. Yes E.19.2. No
E.20. If yes, what are the three most important reasons not to buy water from that well owner (RNB)? (Mark as 1, 2, and 3where, 1= low importance; 2= No importance; and 3= High importance.)
E.20.1. Rigid in terms of payments 1 2 3 E.20.2. Far from the farm 1 2 3 E.20.3. Unlined water course 1 2 3 E.20.4. Type of tube well (Tractor, Peter, Electric) 1 2 3 E.20.5. High price 1 2 3
E.21. Do you get sufficient groundwater for irrigation when required? (No=0, Yes=1)
E.21.1. Yes E.21.2. No
E.22. If no, what are the reasons, please explain?
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E.23. What is the reliability of water supply of your supplier?
E.23.1. Somewhat reliable E.23.2. Much reliable E.23.3. Not reliable
E.24. What is the reliability of groundwater quality of your water supplier?
E.24.1. Somewhat reliable E.24.2. Much reliable E.24.3. Not reliable
E.25. Do you face any fluctuations in groundwater price during the same cropping season? (No=0, Yes=1)
E.25.1. Yes E.25.2. No
E.26. What is the time/schedule of payment for groundwater purchase?
E.26.1. In advance E.26.2. Monthly E.26.3. After crop harvest E.26.4. Annually
E.27. What are the irrigation costs and payment terms and conditions for water buyer?
Codes for different payment conditions
Payment terms and conditions E.28.1
If flat charge, what is per/h cost of groundwater in Rs.E.28.2
If share in proportion, what is the share for each crop (monds) E.28.3
If share in irrigated plot, what is the share for each crop (kanals) E.28.4
1= Flat charge 2=Share in production 3=Share in irrigated area
a) Wheat a) Wheat b) Cotton b) Cotton c) Rice c) Rice
e) Maize e) Maize f) Sugarcane f) Sugarcane
Questionnaire
190
E.3. Both Well Owners and Water Buyers
E.28.Which irrigation method do you use for irrigating the following crops?
Irrigation method Name of crop Type of irrigation method 1=Flood irrigation 2=Furrow irrigation 3=Sprinkler irrigation 4=Drip irrigation
Wheat Cotton Rice Sugarcane Maize
E.29. What proportion of your farm income did you spend on tube well irrigation last year?
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E.30. In response to declining groundwater table, what mitigation measures are you adopting?
E.30.1. Changing cropping pattern E.30.2. Irrigation infrastructure E.30.3. Drought resistant varieties E.30.4. Increase irrigation intervals E.30.5. None of the above
E.31. What type of conservation techniques are you using to conserve groundwater?
E.31.1. Mulching E.31.2. Watercourse lining E.31.3. Irrigation technology applications E.31.4. Laser land levelling E.31.5. None of the above
E.32. Is there any investment in groundwater saving technology in the region? (No=0, Yes=1), 2= Don’t know)
E.32.1. Yes E.32.2. No E.32.3. Don’t know
E.33. If yes, then what are the major sources of investment?
E.33.1. Government E.33.2. Community groups E.33.3. International donor institutions E.33.4. National donor institutions E.33.5. Farmer himself
E.34. Who does establish/govern the prices for groundwater?
E.34.1. Tube well owners association E.34.2. Well owner and the buyer mutually E.34.3. Well owner individually E.34.4. Any other
E.35. When does the groundwater price change?
E.35.1. Beginning of cropping season E.35.2. Mid of cropping season E.35.3. As the change in prices for diesel and electricity E.35.4. As the change in price of crop
E.36. Do you think that social ties between well owner and water buyer have some effect on water pricing? (No=0, Yes=1)
E.36.1. Yes E.36.2. No
Questionnaire
191
E.37. If yes, please explain how?
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E.38. Is there any social institution that regulates groundwater extraction? (No=0, Yes=1), 2=Don’t know)
E.38.1. Yes E.38.2. No E.38.3. Don’t know
E.39. If yes, which is that institution?
E.39.1. Local community group (Punchayat) E.39.2. Farmer’s organization E.39.3. Cooperative association E.39.4. Community development council E.39.5. No. organization
E.40. Do you think that farmers overuse groundwater? (No=0, Yes=1)
E.40.1. Yes E.40.2. No
E.41. Is there any role of farm position relative to water course in groundwater transactions? (No=0, Yes=1)
E.41.1. Yes E.41.2. No
E.42. Is there any role of water course type (lined or unlined) in groundwater transactions? (No=0, Yes=1)
E.42.1. Yes E.42.2. No
E.43. Do you think that groundwater quality is changing in the region? (No=0, Yes=1, 2=Don’t know)
E.43.1. Yes E.43.2. No E.43.3. Don’t know
E.44. If yes, can you guess how groundwater quality has changed over the last 10 years?
E.44.1. Somewhat improved E.44.2. Much improved E.44.3. Somewhat declined E.44.4. Much declined
E.45. Do you think that land is degrading in the region due to tube well irrigation? (No=0, Yes=1, 2=Don’t know)
E.45.1. Yes E.45.2. No E.45.3. Don’t know
E.46. Do you think that groundwater irrigation is creating some environmental problems? (No=0, Yes=1, 2=Don’t know)
E.46.1. Yes E.46.2. No E.46.3. Don’t know
E.47. Do you think that groundwater table is lowering in the region? (No=0, Yes=1, 2=Don’t know)
E.47.1. Yes E.47.2. No E.47.3. Don’t know
E.48. If yes, can you guess how groundwater availability has changed over the last 10 years?
E.48.1. Somewhat improved E.48.2. Much improved E.48.3. Somewhat declined E.48.4. Much declined
Questionnaire
192
E.49. How do you rate the importance of preserving groundwater reserves?
E.49.1. Not very important E.49.2. Somewhat Important E.49.3. Very important E.49.4. Highly important E.49.5. Don’t know
E.50. Do you have some limitations to use groundwater in irrigation? If there are any, please specify:
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E.51. Do you have any comments or suggestions regarding sustainable groundwater use in irrigation? If there are any, please do specify:
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G. Perceptions about Water Rights and Water Scarcity G.1. Is there any government policy that affects how you use groundwater? (No=0, Yes=1, 2=Don’t know)
G.1.1.Yes G.1.2.No
G.1.3.Don’t know G.2. Do you think that written laws for the extraction of groundwater can help in controlling misuse of water? (No=0, Yes=1, 2=Don’t know)
G.2.1.Yes G.2.2.No G.2.3.Don’t know
G.3. If the government issues groundwater law are people going to adhere to it and apply to it? (No=0, Yes=1, 2=Don’t know)
G.3.1.Yes G.3.2.No G.3.3.Don’t know
G.4. To whom do you think groundwater belongs?
G.4.1. God G.4.2. State G.4.3. Public property G.4.4. Individual
G.5. Do you think that an individual should own a groundwater source? (No=0, Yes=1)
G.5.1.Yes G.5.2.No
G.6. If an individual has right to groundwater, do you think that after satisfying his demands he should be allowed to sell groundwater? (No=0, Yes=1)
G.6.1. Yes G.6.2. No
G.7. What is the extent of groundwater right according to people in the area? The owner of the water;
G.7.1. May use it any time G.7.2. May use when it upon need G.7.3. Has freedom to behave with water as he wanted G.7.4. Has secure right over water and cannot be confiscated any time
G.8. Do you know that groundwater resources are finite? (No=0, Yes=1)
G.8.1. Yes G.8.2. No
Questionnaire
193
G.9. Are you ready to get water allocated by the government or any agency for irrigation over the next 10 years? (No=0, Yes=1)
G.9.1. Yes G.9.2. No
G.10. Do you think that your region can face groundwater shortage? (No=0, Yes=1, 2=Don’t know)
G.10.1. Yes G.10.2. No G.10.3. Don’t know
G.11. If yes, what are the chances of facing groundwater shortage over the next 10 years in your region?
G.11.1. 10% G.11.2. 20% G.11.3. 30% G.11.4. 40% G.11.5. 50%
G.12. Do you think that groundwater situation may affect your future farming patterns? (No=0, Yes=1, 2=Don’t know)
G.12.1. Yes G.12.2. No G.12.3. Don’t know
F. Credit Details F.1. Do you think that lack of credit affect your use of inputs and ultimately production? (No=0, Yes=1)
F.1.1.Yes F.1.2.No
F.2. Did your household get any cash credit during the last cropping year? (No=0, Yes=1)
F.2.1.Yes F.2.2.No
F.3. If yes, please provide details about the purpose, source and amount of credit which you got. Purpose of the Credit
F.3 Source of the Credit
F.5 Amount of the Credit
F.6
1=Fertilizer (FER) Amount of the Credit in Rs.
2=Pesticide (PES) 1.Commercial bank (CBNK)
3=Diesel OR electricity (DOE) 2.Government bank (GBNK)
4=Seed (SEED) 3. Neighbour (NBR)
5=Land rent (if tenant) (LRNT) 4. Friend/relative (FRND)
6=Irrigation equipment (IREQ) 5.Shopkeeper/Aarhti (SHK)
7=Water purchase for irrigation (WPR)
8=Above all (ALL) H. Extension Services and Sources of Information H.1. Do you have access to different extension services related to different agricultural activities? (No=0, Yes=1)
H.1.1 Yes H.2.1. No
Questionnaire
194
H.2. Did you receive any benefit from the services provided by the extension field staff particularly related to groundwater management during the last year? (No=0, Yes=1)
H.2.1. Yes H.2.2. No
H.3. Did you get any recommendations regarding sustainable use of groundwater in irrigation by the extension field staff last year? (No=0, Yes=1)
H.2.1. Yes H.2.2. No
H.3. What are the main sources of information regarding sustainable groundwater use in irrigation and what was their frequency for the last 12 months?
Source of Information Never Occasionally Often
H.3.1. Newspaper -------- ----------------- ------- H.3.2. Radio -------- ----------------- ------- H.3.3. Television -------- ----------------- ------- H.3.4. Extension field staff -------- ----------------- ------- H.3.5. Private Agencies -------- ----------------- -------
H.4. Did you get any information about groundwater level and quality in your region through any of the following sources? (No=0, Yes=1)
H.4.1. Newspaper Yes/No H.4.2. Radio Yes/No H.4.3. Television Yes/No H.4.4. Extension field staff Yes/No H.3.5. Private Agencies Yes/No H.3.6. Directorate of soil and water Yes/No