1. Crop Area and Production Estimates Issues in collection ... Files/2... · 97 1. Crop Area and...

97

1. Crop Area and Production Estimates – Issues in collection compilation, and processing of data and remedies *

Introduction:

The state of has 29406 villages with 190.50 lakh hectares of land, out of which 130.62 lakh

hectares land (69%) was Gross cultivated area during 2010-11. The net area sown during the year was

105.23 lakh hectares, thus cropping intensity in the state was 124 only. Out of the 130.62 lakh hectares

Gross area cultivated only 42.79 lakh hectares of land (33%) was gross irrigated area. Out of the 105.23

lakh hectares of net area sown only 34.90 lakh hectares (33%) was net irrigated area and the thus irrigation

intensity in the state was only 123. The contribution of agriculture sector to GSDP is 15.4% in the year

2010-11 in the state.

Crop area and production statistics includes (i) Crop Area Statistics (ii) Land use statistics (iii)

Production statistics (iv) Horticulture statistics (v) Irrigation Statistics (vi) Crop forecasts and (vii)

Agriculture census (viii) Rainfall Statistics

The Directorate of Economics and Statistics is considered as State Agricultural Statistical

Authority (SASA) by government of India.

Economic Importance of Agriculture Statistics

1) To furnish Area & Production details of various crops.

2) To formulate policies for import and export for food and non-food agricultural crops, public

distribution system, minimum support prices.

3) To calculate Gross Domestic Product, State income and per capita income and to find growth rate.

4) Yield rates obtained from Crop Cutting Experiments are used to find the extent of crop loss for

National Agricultural Insurance Scheme.

5) To know the ups and downs in Agricultural Crops.

6) Agriculture Statistics are very important to prepare plans and policies for government and to

undertake various educational studies.

7) To decide on the compensation to be given in case of land acquisition

Procedure of collection of Crop Area & Yield Statistics:

A. Crop area statistics:

The Karnataka state belongs to the category of temporarily settled states. The crop area statistics are

originated by the Village Accountant on the basis of complete enumeration of the field. The Village Accountant

* Sri K.V.Subramaniam, Joint Director, AGS Division Sri S.V.Hegde & Smt. H.N. Sathyavani Assistant

Director, AGS Division

98

has to visit each and every sub survey number of the village in each crop season, i.e. Kharif (early Kharif, late

Kharif) rabi and summer, and record information such as area under different crops, land use categories and its

status in RTC (Record of Rights, Tenancy and Crop). Most of the geographical areas are cadastrally surveyed

and detailed hissa maps and village maps are available with village accountant. The stipulated period for field

inspection and writing of RTC by the Village Accountant is as follows.

1st July to 31st July – Early Kharif

1st September to 30th September – Late Kharif

1st January to 31st January – Rabi

1st April to 30th April – Summer

Then village accountant aggregates crop area village wise. The figures are further consolidated at

taluk and district level by the Taluk and District Offices as depicted below as per notification issued by

Revenue Department on 06.05.2005.

RTC

Village Abstract

Taluk Consolidation (Réconciliation Committee)

District Consolidation (Réconciliation Committee)

State Consolidation

Ministry of Agriculture and Co-operation, Government of India.

Annual State Report is sent to Ministry of Agriculture, Government of India, which contains crop

area statistics of each season with land use statistics and irrigation statistics. This report is known as

Annual Season and Crop Statistics Report (ASCR). ASCR is the only authenticated and exhaustive

document available on land use particulars in the state. In the ASCR of the year 2010-11, 161 crops are

covered with 115 horticultural crops. Besides land use particulars, it provides very useful data relating to

different sources of irrigation and the extent of area irrigated through various sources, seasonwise cropped

area etc. Information regarding nine fold classification of land is available in ASCR. In addition to this,

the area of crops under encroached Government land and forest is also compiled through reconciled

procedures.

99

B. Yield Statistics:

Estimates of crop production are obtained by multiplying the area under crop and the yield rate.

The estimates of yield rate are based on scientifically designed crop cutting experiments conducted under

Crop Estimation Survey (CES).

(i) GCES: The DES is implementing GCES since 1945-46. Its objective is to work out the average

yield per hectare of important food and non food crops. Yield rates are used to arrive at crop production.

The main responsibility of the Directorate of Economics and Statistics is to plan the crop cutting

experiments, arrange for the training and supervision of field work, data analysis and working out the

average yield estimates. Under Crop Insurance Scheme, the crops and unit of insurance are decided by the

Government of Karnataka (Department of Agriculture and Horticulture). Based on Government

Notification, experiments are planned by the Directorate of Economics and Statistics and communicated to

all the District Statistical officers who in turn allocate experiments to the primary workers drawn from

Revenue, Agriculture, Horticulture, Rural Development and Panchayat Raj (RDPR), Command Area

Development Authority (CADA) and Watershed Development Departments. Officers of National Sample

Survey Organisation also participate in the training programme and undertake supervision of crop cutting

experiments in selected samples.

The Statistical design adopted is “Stratified Multistage Random Sampling” which is scientific and

time tested design, with taluks as the strata, villages within a taluk as primary units, fields within the

selected village as secondary units and experimental plots of specified size within the selected field as ultimate

sampling units. The size of the experimental plot for all crops except cotton, castor, tur, tobacco and

sunflower is 5m X 5m area, for the latter, it is 10m X 5m area.

(ii) Crop Estimation Survey on Fruits, Vegetables and Minor Crops: The scheme aims at estimation

of area, production and yield of 7 fruits i.e., Mango, Grapes, Guava, Banana, Sapota, Pomegranate and

Lemon, 4 Vegetables i.e, Tomato, Beans, Brinjal and Cabbage and one Minor crop i.e, Turmeric. The

sampling design adopted for area enumeration is a stratified two stage random sampling with taluk as

strata, villages within the selected taluks as primary units of sampling and orchards/sub survey number as

the second stage sampling units. For yield estimation, the stratified three stage random sampling is

adopted. The taluks in the districts formed strata. Villages within the taluk formed primary sampling units,

orchards/sub survey numbers are the second stage sampling and the clusters of trees/plants within the

selected orchards/randomly selected experimental plots are the ultimate sampling units.

Directorate of Economics and Statistics is estimating yield rates of 60 crops. Under Crop

Estimation Survey (CES) yield rates of 28 crops are estimated. Under the scheme of Survey on Estimation

of Fruits, Vegetables and Minor crops yield rates of 12 crops are estimated. Thus for 40 crops DES is able

to obtain yield rates through statistically valid crop cutting experiments. However DES has to furnish

production estimates of about 61 major crops of the State. For these remaining 21 crops which are not

covered under Crop Estimation Surveys, DES used to use traditional methods to have yield rates earlier to

2007-08. Large variations were observed to that of package of practice in the yield information obtained

through these traditional methods which affected the production estimates. Therefore, to overcome this

100

problem, an oral enquiry of farmers approach within the reach of DES is initiated from the year 2007-08 to

obtain the yield rates by interviewing the farmers, on random basis with sufficient number of samples.

Oral Enquiry Method:

Under oral enquiry method, districts with largest area of the respective crop are selected. Out of

each district 2 taluks atleast with a minimum area of 25 hectares are selected. In each taluk, 2 villages are

randomly selected (in case of greater area taluks 4 villages are selected) and thus effort is made to make the

samples more representative and from each village 10 farmers are randomly selected and information is

elicited as per prescribed schedules so as to get yield rates of that crop.

Production of the crop for the district is obtained by multiplying the estimated average yield of the crop of

the district (Y) with the corresponding area under the crop after applying 5% bund correction to the area

under CES crops.

i.e., P = Y(A 5%A) ;

Where P=Production, A=Area under the crop in the district, Y = District average yield of the crop.

Production estimates for other crops is obtained by using the following formula where 1% bund

correction is considered.

P = Y(A 1%A) ;

Growth over a Decade:

Comparison of Land use classification over the decade (2001-2011) in the State

Classification 1960-61 2000-01 2010-11 %

Variation

over 50 years

%

Variation over

the decade

Total Geographical Area 187.80 190.50 190.50 1.4 0.0

1. Forest 27.09 30.68 30.72 13.4 0.1

2.Not available for cultivation

a.Land put to non agriculture use 8.12 13.12 14.30 76.1 9.0

b Barren and uncultivable land 9.22 7.94 7.87 -14.6 -0.9

3.Cultivable waste 6.56 4.27 4.14 -36.9 -3.0

4.Uncultivated land excluding fallow land

a.Permanent Pasteurs and other grazing land 17.39 9.59 9.12 -47.6 -4.9

b.Miscellaneous tree crops, groves not

included under net area sown 3.66 3.03 2.86 -21.9 -5.6

5.Fallow land

a.Current fallow 8.35 13.67 12.00 43.7 -12.2

b.Other fallow 5.13 4.09 4.26 -17.0 4.2

101

6.Net area sown 102.28 104.10 105.23 2.9 1.1

7.Total cropped Area 105.88 122.84 130.62 23.4 6.3

8. Cropping Intensity 104 118 124 19.9 5.2

Area in Lakh Hectares

Area in Lakh Hectares

Production in Lakh Tonns

Yield in Kgs/Hectare

The above table depicts the growth status of our Agriculture Sector where in area under food grains

is increased by only 3%, and increase over yield rate is 13%.

Comparison of Area, Production and Yield of Important crops in Karnataka

over the triennium ending 2000-01 & 2010-11

Crop

Area

%

Variation

Production

%

Variation

Yield %

Variatio

n

Average

triennium

ending

2000-01

Average

triennium

ending 2010-

11

Average

triennium

ending

2000-01

Average

triennium

ending 2010-

11

Average

triennium

ending

2000-01

Average

triennium

ending

2010-11

Rice 14.53 15.13 4 37.40 40.68 9 2709.00 2828.67 4

Jowar 18.85 13.31 -29 16.60 14.16 -15 926.67 1123.00 21

Ragi 9.90 7.98 -19 16.57 13.39 -19 1757.00 1770.67 1

Maize 5.96 11.98 101 18.03 34.42 91 3192.67 3009.33 -6

Bajra 4.34 2.93 -32 2.92 2.15 -26 707.00 769.00 9

Wheat 2.65 2.69 1 2.29 2.67 16 908.00 1047.00 15

MinorMillets 0.76 0.28 -63 0.39 0.13 -66 545.33 509.00 -7

Total Cereals 56.99 54.31 -5 94.21 107.60 14 1743.33 2085.41 20

Tur 5.22 6.97 34 2.58 3.74 45 522.00 555.33 6

Bengalgram

(Gram) 3.48 8.86

155 2.05 5.28 157 620.00 623.67 1

Horsegram 3.25 2.22 -32 1.47 1.12 -24 505.33 530.33 5

Blackgram 1.40 1.19 -15 0.46 0.30 -36 374.00 262.33 -30

Greengram 3.90 3.52 -10 1.04 0.66 -37 337.00 191.00 -43

Avare 0.84 0.79 -6 0.35 0.62 80 224.00 830.33 271

Other Pulses 1.20 0.10 -91 0.24 0.04 -83 286.67 368.00 28

Total Pulses 19.29 24.53 27 8.50 12.14 43 463.00 516.75 12

Total

Foodgrains 76.29 78.84

3 102.72 119.74 17 1417.00 1595.84 13

Groundnut 11.38 8.39 -26 10.14 5.32 -48 937.33 667.68 -29

Sesamum 1.06 0.73 -31 0.46 0.37 -20 457.67 526.67 15

Sunflower 6.02 7.35 22 2.32 3.06 32 423.33 474.00 12

Castor 0.28 0.19 -31 0.27 0.16 -43 1019.00 854.81 -16

Nigerseed 0.43 0.25 -43 0.08 0.09 8 191.33 351.00 83

Mustard 0.07 0.05 -30 0.02 0.02 20 271.33 365.00 35

Soyabean 0.64 1.62 152 0.65 1.07 66 1064.67 707.00 -34

Safflower 0.97 0.65 -33 0.69 0.53 -23 745.00 845.67 14

Linseed 0.19 0.12 -36 0.06 0.04 -32 361.67 355.33 -2

Total Oilseeds 21.04 19.34 -8 14.70 10.66 -27 738.00 597.73 -19

Commercial

Crops:

Cotton*(prod.170

kg / lint) 5.78 4.71

-18 8.33 9.16 10 256.67 344.98 34

Sugarcane 3.76 4.16 11 384.20 320.85 -16 107.33 96.93 -10

Tobacco 0.77 1.17 53 0.52 0.93 79 714.00 824.67 15

102

i) Yield of total food grains: (Kgs/Hect.)

State 2007-08 2008-09 2009-10

A. Pradesh 2613 2744 2294

Karnataka 1548 1511 1377

T. Nadu 2125 2225 2477

ii) Production of total food grains: (Million tonns)

State 2007-08 2008-09 2009-10

A. Pradesh 19.30 20.42 15.30

Karnataka 12.19 11.28 10.96

T. Nadu 6.58 7.10 7.51

i) Yield of Pulses: (Kgs/Hect.)

State 2007-08 2008-09 2009-10

A. Pradesh 803 818 740

Karnataka 531 466 451

T. Nadu 303 307 382

iii) Production of Pulses: (Million tonns)

State 2007-08 2008-09 2009-10

A. Pradesh 1.70 1.45 1.43

Karnataka 1.27 0.97 1.12

T. Nadu 0.19 0.16 0.20

iv) Yield of Oil Seeds: (Kgs/Hect.)

State 2007-08 2008-09 2009-10

A. Pradesh 1276 842 724

Karnataka 681 556 502

T. Nadu 1739 1782 1898

v) Production of Oil Seeds: (Million tonns)

State 2007-08 2008-09 2009-10

A. Pradesh 3.39 2.19 1.50

Karnataka 1.55 1.21 1.01

T. Nadu 1.15 1.04 0.94

Crop Intensity:

State 2005-06 2006-07 2007-08 A. Pradesh 124.4 126.3 126.1 Karnataka 124.0 123.1 123.7 T. Nadu 115.0 114.0 114.9

103

The yield rates & Production of food grains, pulses and oil seeds of State shown above along with

Andhra Pradesh and Tamil Nadu clearly shows that there is much scope for Agriculture Sector to grow in

our State.

In our country during 11th Five Year Plan, as against the target of 5.4%, in agriculture sector,

5.5% progress is achieved.

During 9th five year plan (1997-2001), average production of total food grains is 95.1 lakh tonnes,

during 10th five year plan (2002-2006), average is 89.16 lakh tonnes, registering a decline of 6.24%. During

11th five year plan (2007-2011), (of 4 years) average production of total food grains is 119.9 lakh tonnes,

registering an increase of 34.47%.

Similarly, during 9th plan period average yield is 1345 kgs and during 10th plan period it is 1277.6

kgs, showing a decline by 5%. During 11th plan period, average yield is 1600 kgs, (of 4 years) registering an

increase by 27.25%.

Different Schemes implemented by DES on Estimating

Area & Yield Statistics

1. Timely Reporting Scheme: TRS is launched in the state during 1969-70, with the principal

objective of reducing the time lag in making available the area statistics of 15 major crops in addition to

providing the sample frame for selection of fields for crop cutting experiments, in the selected 20% of

villages through sample survey.

Seasonwise crops covered under this scheme are:

Kharif:1.Paddy 2.Jowar 3.Maize 4.Ragi 5.Bajra 6.Tur 7.Groundnut 8.Seasmum 9.Sunflower

Rabi: 1. Paddy 2. Jowar 3.Maize 4.Ragi 5.Wheat 6.Cotton 7.Gram 8.Sugarcane 9.Sunflower 10.Safflower

11.Linseed

Summer: 1.Paddy 2.Maize 3.Ragi 4.Groundnut 6.Sunflower

2. Improvement of Crop Statistics: The main objective of the scheme is to locate the deficiencies

in the collection of primary data during the course of area enumeration and conduct of crop cutting

experiments in the state through sample checks in order to bring improvement in the quality of primary data

collected.

Programme of work under the scheme is as follows:

a) Carrying out sample checks by inspecting field/sub survey numbers randomly selected on area

enumeration in 300 villages in Kharif, Rabi and Summer seasons for correctness of the crops and

area written in RTC.

b) Check on page totaling of Record of Rights, Tenancy and Crops (RTC) done by the Village

Accountants in the said 300 villages in each season.

c) Conducting supervision on 900 crop cutting experiments on 13 selected crops in all the three

seasons. These thirteen crops are Paddy, Jowar, Bajra, Ragi, Tur, Groundnut, Sugarcane, Cotton,

Maize, Sunflower, Greengram, Wheat and Bengal Gram.

104

3. Crop Estimation Survey on Fruits, Vegetables and Minor Crops: The scheme aims at

estimation of area, production and yield of 7 fruits i.e., Mango, Grapes, Guava, Banana, Sapota,

Pomegranate and Lemon, 4 Vegetables i.e, Tomato, Beans, Brinjal and Cabbage and Minor Crop i.e,

Turmeric.

4. General Crop Estimation Survey: GCES was intended for the specific objective of estimating

the yield. After the introduction of crop insurance scheme, the dimensions of the programme have achieved a

greater significance as the results of crop cutting experiments are being used to assess the extent of crop

loss.

5. National Agricultural Insurance Scheme (Rashtreeya Krishi Bima Yojana) The crop

insurance scheme, popularly known as Rashtriya Krishi Bima Yojana (RKBY) was implemented in

Karnataka from kharif 2000. The main objective of this scheme is to provide insurance coverage and

financial support to the farmers in the event of failure of any of the notified crops as a result of natural

calamities, pests and diseases. The special feature of this scheme lies in its wide range of implementation as

it covers both loanee and non-loanee farmers.

6. Agriculture Census: The objective of the Agricultural Census is to know the structure and

characteristics of agricultural holdings operated by cultivators. Besides, data on land use, sources of

irrigation, cropping pattern and dispersal of operated area were also collected on sampling basis. As a

follow up of agricultural census, the input survey is also conducted on sampling basis with the main

objective of collecting data related to number of parcels with multiple cropping, land use pattern, use of

chemical fertilizers, organic manures and pesticides, agricultural implements, livestock details, certified

seeds used and agricultural credit availed by the cultivators.

Discrepancies Observed in the system of collection of

Area & Yield Statistics in the State

There is deterioration in the reporting of area and yield statistics over the years. Discrepancies

observed in last few years raise the question about the quality of the Agriculture Statistics.

i) Area Statistics of the District:

In the Nine Fold Classification of land use, “land put to non-agricultural use” remains

constant, since last 6 years, in few districts.

From 2005-06 to 2010-11 area under nonagricultural use reported in Annual Season Crop

Report in case of following districts is same for the above 6 years. Bagalkote 28,832 hect, Bidar

22,006 hect, Chitradurga 51,243 hect, Gadag 10,481 hect, Mandya 60,906 hect, Raichur 20,563

hect.

Net irrigated area is more than net area sown, which is unrealistic.

In 2010-11 Annual Season Crop Report in some cases net area irrigated shown is more

than net cropped area for example in Belgaum district in Gokak Taluk net area irrigated is shown

as 108553 hect where as net cropped area is shown as 90,567 hect.

105

Irrigated area is more than total area of the crop.

In Mandya District K.R.Pet Taluk area irrigated more than once is shown as 3334 hect while area

sown more than once is 3010 hect.

In Shimoga District Sorab Taluk area irrigated more than once is shown as 3946 hect while area

sown more than once is reported as 3846 hect. In Mysore District Hunasur Taluk total irrigated area is

shown as 27 hect but the total area is only 20 hect. In Ramanagar District, Magadi Taluk irrigated area

reported under Astar crop is 127 hects but the total area reported for that crop is nil.

Above examples clearly shows that village accountants are not giving importance for crop

enumeration and RTC writing work.

Discrepancies observed in writing RTCs

1) Only Kharif Season entries are made in RTCs

2) Rainfed and irrigation details are not written

3) In most of the RTCs the entries for horticulture crops are not seen

4) Mixed crops are not properly recorded.

5) In almost all RTCs no entries are found in column 15 and 16 (for relay and plantation crop).

6) 9 fold classification are not properly given

Crop cutting experiments conducted but the area are not reported in case of following crops in

ASCR 2010-11

District Crop Taluk Variety Irrigation Season No of experiments

conducted

Dharwad Jowar Navalgund HYV Reainfed Kharif 12

Belgaum Maize Belgaum HYV Rainfed Kharif 12

Wheat Belgaum Local Rainfed Rabi 8

Chitradurga Bajra Hiriyuru HYV Rainfed Kharif 4

Gadag

Bajra Ron HYV Rainfed Kharif 24

Cotton Gadag WDV Irrigated Kharif 2

Shirahatti WDV Irrigated Kharif 2

Mudaragi WDV Irrigated kharif 4

Common errors observed in conducting Crop Cutting Experiments:

Mixed Proportion of experimental crop are not given properly

Without conducting Crop Cutting Experiments yields are given

Dates of sowing and harvesting are not tallying with season and duration of crops

Under crop cutting experiments, sometimes, very less yield rates are furnished for insurance claims.

In some cases CCE reports are submitted without conducting crop cutting experiments.

106

Part A reconciliation area are not tallying with ASCR 2010-11 in following cases

District

Crop

Table 3

(Irrigated area)

Table 4

(Irrigated + Un-Irrigated)

ASCR RECON ASCR RECON

Mandya Paddy (Kharif) 60775 61175 60845 61245

Ragi (Kharif) 5041 4941 - -

Bellary Groundnut (Summer) 8357 6321 8357 6321

Ramanagar Gram (Kharif) - 52 - -

Hasan Groundnut (Rabi/Sum) 523 475 - -

Chikkaballapur Jowar (Kharif) 218 - 2287 -

Discrepancies observed in TRS Scheme

In some cases area estimated through TRS scheme (by enumerating 20% of villages) are more than

ASCR figures (100% of villages enumeration figures).

Large variations observed in case of following TRS estimations for 2010-11 as compared to same

year reconciliation reports.

District Crop As per TRS

estimation

As per Reconciliation

Report

Belgaum Cotton 126 37666

Bellary Cotton 0 41426

Gram 44992 19251

Sugarcane 0 7184

Sunflower 14732 5575

Yadgiri Paddy 2979 9510

Difference in DES & Agriculture Department, even after reconciliation are given below for the year 2011-12.

area in hectares

Sl.

No District

JOWAR

Des Agri % Diff

Highest Difference

1 Bagalkot 6439 3300 48.75

2 Belgaum 26501 22805 13.95

Lowest Difference

1 Tumkur 195 2051 -951.79

2 Shimoga 149 284 -90.60

3 Yadagiri 278 355 -27.70

107

area in hectares

Sl.

N0. DISTRICT

RAGI

Des Agri % Diff

Highest Difference

1 Shimoga 1003 261 73.98

2 Kodagu 229 90 60.70

3 Haveri 604 316 47.68

Lowest Difference

1 Tumkur 150746 178600 -18.48

2 Hassan 58620 66950 -14.21

area in hectares

Sl.

N0. DISTRICT

MAIZE

Des Agri % Diff

Highest Difference

1 Dharwad 40717 35595 12.58

2 Mandya 4498 4002 11.03

Lowest Difference

1 Yadagiri 241 3627 -1404.98

2 Bidar 943 2277 -141.46

3 Shimoga 42950 62743 -46.08

4 Chickballapur 49740 58155 -16.92

5 Bangalore(R) 14355 15827 -10.25

area in hectares

Sl.

N0. DISTRICT

BAJRA

Des Agri % Diff

Highest Difference

1 Davangere 894 540 39.60

2 Yadagiri 17401 11959 31.27

108

area in hectares

Sl.

N0. DISTRICT

TUR

Des Agri % Diff

Highest Difference

1 Bidar 76010 66976 11.89

2 Yadagiri 77524 69216 10.72

3 Bijapur 102093 92511 9.39

4 Chickballapur 8165 7579 7.18

Lowest Difference

1 Shimoga 444 1004 -126.13

2 Bangalore (U) 1013 1626 -60.51

3 Ramanagaram 3210 4328 -34.83

4 Bangalore (R) 1650 2095 -26.97

5 Davangere 8352 10292 -23.23

area in hectares

Sl.

N0. DISTRICT

HORSE- GRAM

Des Agri % Diff

Highest Difference

1 Davangere 724 8 98.90

2 Chickballapur 1136 285 74.91

3 Bangalore (R) 838 242 71.12

Lowest Difference

1 Yadagiri 63 476 -655.56

2 Kolar 135 628 -365.19

3 Bagalkot 730 1850 -153.42

4 Chickmagalur 960 1900 -97.92

5 Gadag 232 357 -53.88

area in hectares

Sl.

N0. DISTRICT

BLACK- GRAM

Des Agri % Diff

Lowest Difference

1 Yadagiri 673 1015 -50.82

2 Haveri 114 165 -44.74

109

area in hectares

Sl.

N0. DISTRICT

GREEN-GRAM

Des Agri % Diff

Highest Difference

1 Yadagiri 30697 23316 24.04

2 Bagalkot 42815 32850 23.27

Lowest Difference

1 Shimoga 23 98 -326.09

2 Bellary 273 1025 -275.46

3 Davangere 807 1425 -76.58

4 Haveri 1704 2281 -33.86

5 Belgaum 16954 19562 -15.38

Discrepancies under ICS: Discrepancies observed by Central & State agencies while conducting sample checks under ICS

scheme for crop area enumeration and conduct of crop cutting experiments

Discrepancies in recording of crops and crop area during 2008-2009

Year/Season/

Agency

No. of Survey/ Sub-survey numbers

Total

Where crop area

tallied

Where crop area not

tallied

No. % No. %

Kharif

Central 7837 3192 40.70 4645 59.30

State 7190 5725 79.60 1465 20.40

Pooled 15027 8917 59.30 6110 40.70

Year/Season/

Agency


Total

Where crop area

tallied

Where crop area not

tallied

No. % No. %

Rabi

Central 1917 703 36.70 1214 63.30

State 2515 2219 88.20 296 11.80

Pooled 4432 2922 65.90 1510 34.10

Summer

Central 275 200 72.70 75 27.30

State 584 546 93.50 38 6.50

Pooled 859 746 86.80 113 13.20

110


Year/

Season/

Agency


Total

Where crop area

tallied

Where crop area not

tallied

No. % No. %

Kharif

Central 9001 3284 36.50 5717 63.50

State 7592 6732 88.70 860 11.30

Pooled 16593 10016 60.40 6577 39.60

Rabi

Central 3329 1078 32.40 2251 67.60

State 3510 3245 92.50 265 7.50

Pooled 6839 4323 63.20 2516 36.80

Summer

Central 1136 454 40.00 682 60.00

State 493 485 98.40 8 1.60

Pooled 1629 939 57.60 690 42.40


Year/

Season/

Agency


Total

Where crop area

tallied

Where crop area

not tallied

No. % No. %

Kharif

Central 9109 3549 39.00 5560 61.00

State 8807 7479 84.90 1328 15.10

Pooled 17916 11028 61.60 6888 38.40

Supply of equipments for the conduct of crop cutting experiments

During 2008-2009

Year/

Season/

Agency

No. & (%) of experiments for which

the primary workers were found not supplied with

Tape Balance Weight

No. % No. % No. %

Kharif

Central 340 57.2 402 67.7 398 67.0

State 106 18.3 122 21.0 120 20.7

Pooled 446 38.0 524 44.6 518 44.1

111

Year/

Season/

Agency



Tape Balance Weight

No. % No. % No. %

Rabi

Central 126 68.5 152 82.6 152 82.6

State 66 34.0 66 34.0 66 34.0

Pooled 192 50.8 218 57.7 218 57.7

Summer

Central 30 46.2 37 56.9 37 56.9

State 20 25.0 20 25.0 20 25.0

Pooled 50 34.5 57 39.3 57 39.3


During 2009-10

Year/ Season/

Agency



Tape Balance Weight

No. % No. % No. %

Kharif

Central 340 57.2 402 67.7 398 67

State 106 18.3 122 21 120 20.7

Pooled 446 38.0 524 44.6 518 44.1

Rabi

Central 126 67.7 148 79.6 154 82.8

State 84 42.9 82 41.8 88 44.9

Pooled 210 55.0 230 60.2 242 63.4

Summer

Central 42 55.3 50 65.8 50 55.8

State 14 18.4 14 18.4 14 18.4

Pooled 56 36.8 64 42.1 64 42.1

112


During 2010-11

Year/

Season/

Agency



Tape Balance Weight

No. % No. % No. %

Kharif

Central 352 58.9 440 73.6 439 73.4

State 133 21.9 139 22.9 143 23.5

Pooled 485 40.2 579 48.0 582 48.3

Use of equipments for the conduct of crop cutting experiments

During 2008-2009

Year/ Season/

Agency


the primary workers were found not using the

supplied equipments

Tape Balance Weight

No. % No. % No. %

Kharif

Central 101 17.0 82 13.8 84 14.1

State 136 23.4 136 23.4 148 25.5

Pooled 237 20.2 218 18.6 232 19.8

Rabi

Central 22 12.0 18 9.8 18 9.8

State 60 30.9 62 32.0 62 32.0

Pooled 82 21.7 80 21.2 80 21.2

Summer

Central 10 15.4 7 10.8 7 10.8

State 10 12.5 10 12.5 10 12.5

Pooled 20 13.8 17 11.7 17 11.7

113


During 2009-10

Year/ Season /

Agency



supplied equipments

Tape Balance Weight

No. % No. % No. %

Kharif

Central 174 29.0 143 23.9 139 23.2

State 130 21.7 130 21.7 138 23.1

Pooled 304 25.4 273 22.8 277 23.1

Rabi

Central 36 19.4 24 12.9 24 12.9

State 48 24.5 50 25.5 46 23.5

Pooled 84 22.0 74 19.4 70 18.3

Summer

Central 13 17.1 12 15.8 12 15.8

State 14 18.4 14 18.4 14 18.4

Pooled 27 17.8 26 17.1 26 17.1


During 2010-11

Year/

Season/

Agency



supplied equipments

Tape Balance Weight

No. % No. % No. %

Kharif

Central 107 17.9 75 12.5 71 11.9

State 156 25.7 150 24.7 148 24.3

Pooled 263 21.8 225 18.7 219 18.2

Variation of 1 Kg in a plot of 5x5 sq.mts will vary hectare yield by 4 quintals

What do reports say?

1) Accurate, timely and reliable agriculture statistics are not emanating because primary reporting

agency i.e., Village Accountant with multiple functions and answerable only to the Revenue

Department, is not recording the crop details on priority basis.

114

a) In the “Hand Book on Methods of Collection of Agricultural Statistics in India, Ministry of

Agriculture”, Government of India and the Statistical Adviser, Institute of Agricultural Research

Statistics, Indian Council of Agricultural Research, 1959, it is opined that “all though statistics of

acreage are collected as part of the land records in the temporarily settled states, the data, at

present, collected are not often reliable on account of defects of the primary reporting agencies. One

of the chief causes of inaccuracy in the reporting of the primary reporting agencies is perhaps the

large increase in the work of the patwari who has a heavy burden to bear. Being the only the

Government official in the village, he has to undertake multifarious duties connected with various

departmental and developmental schemes. As such, he has little time to devote to the proper

compilation of agricultural statistics”.

b) The National Statistics Commission (2001), in its conclusions and recommendations pointed out

that, “It is seen that a major reason for the poor quality of area statistics is the failure of the

patwari agency to devote adequate time and attention to the girdwari. The fact that the patwari

agency is over burdened with multifarious functions and has to cope with a large geographical

jurisdiction, typically four to five villages and in some States extending over more than 10 villages

(Bihar, Himachal Pradesh, Orissa and Uttaranchal) has long been acknowledged”.

c) Report of the Expert Committee on Agricultural Statistics, headed by Prof. Vaidyanathan (2011),

observed that “the central problem is the deterioration of the system of maintaining village land use

and crop records – the basic source of primary data. Village level revenue staffs are increasingly

over burdened with multiple functions; maintenance of accurate and complete agricultural data is

given a low priority. Supervision of crop related records is increasingly rare and far too perfunctory

to ensure their completeness and accuracy”.

In all the above three reports it has been said as follows:

“Being the only the Government Official in the village, he has to undertake multifarious duties

connected with various departmental and developmental schemes. As such, he has little time to devote to

the proper compilation of Agricultural Statistics”

Recommendations of National Statistical Commission:

The following recommendations in the report of National Statistical Commission August 2001 on

Crop Area and Production Estimates

Crop Area Statistics:

1. As the data from a 20 percent sample is large enough to estimate crop area with a sufficient degree

of precision at the all-India, State and district levels, crop area forecasts and final area estimates

issued by the Ministry of Agriculture should be based on the results of the 20 percent Timely

Reporting Scheme (TRS) villages in the temporarily settled States and Establishment of an Agency

for Reporting Agricultural Statistics (EARAS) scheme villages in the permanently settled states. In

the case of the North-Eastern States, Remote sensing methodology should be used for this purpose

after testing its viability.

115

2. The patwari and the supervisors above him should be mandated to accord the highest priority to the

work of the girdawari and the pawari be spared, if necessary, from other duties during the period of

girdawari.

3. The patwari and the primary staff employed in Establishment of an Agency for Reporting

Agricultural Statistics (EARAS) should be imparted systematic and periodic training and the

fieldwork should be subjected to intensive supervision by the higher-level revenue officials as well

as by the technical staff.

4. For proper and timely conduct of the girdawari, the concerned supervisory staff should be made

accountable.

5. Timely Reporting Scheme (TRS) and Establishment of an Agency for Reporting Agricultural

Statistics (EARAS) scheme should be regarded as programme of national importance and the

Government of India at the highest level should prevail upon the State Government to give due

priority to them, deploy adequate resources for the purpose and ensure proper conduct of field

operations in time.

Crop Production:

6. In view of the importance of reliable estimates of crop production, the States should take all

necessary measures to ensure that the crop cutting surveys under the General Crop Estimation

Survey (GCES) are carried out strictly according to the prescribed programme.

7. Efforts should be made to reduce the diversity of agencies involved in the fieldwork of crop cutting

experiments and use as far as possible agricultural and Statistical personnel for better control of

field operations.

8. A statistical study be carried out to explore the feasibility of using the Improvement of Crop

Statistics (ICS) data for working out a correction or adjustment factor to be applied to official

statistics of crop area to generate alternative estimates of the same. Given the past experience of the

Land Utilization Surveys of the NSS and the controversies they created, the Commission is of the

view that the objective of redesigning of the ICS, at present, should be restricted to working out a

correction factor.

9. The two series of experiments conducted under the National Agricultural Insurance Scheme (NAIS)

and the General Crop Estimation Survey (GCES) should not be combined for deriving estimates of

production as the objectives of the two series are different land their merger will affect the quality

of general crop estimates.

10. Crop estimates below the level of district are required to meet several needs including those of the

National Agricultural Insurance Scheme (NAIS). Special studies should be taken up by the

National Statistical Office to develop appropriate “small area estimation `` techniques for this

purpose.

116

Crop Forecasts:

11. The Ministry of Agricultural and the National Crop Forecasting Centre (NCFC) should soon put in

place an objective method of forecasting the production of crops.

12. The National crop Forecasting Centre (NCFC) should be adequately strengthened with professional

statisticians and experts in other related fields.

13. The programme of Forecasting Agricultural output using Space, Agro- meteorology and Land based

observations (FASAL) is experimenting the approach of Remote Sensing to estimate the area under

principal crops should be actively pursued.

14. The State should be assisted by the centre in adopting the objective techniques to be developed by

the National Crop Forecasting Centre (NCFC).

Production of Horticultural Crops:

15. The methodology adopted in the pilot scheme of “Crop Estimation Survey on Fruits and Vegetables

`` should be reviewed and an alternative methodology for estimating the production of horticultural

crops should be developed taking into account information flowing from all sources including

market arrivals, exports land growers associations. Special studies required to establish the

feasibility of such a methodology should be taken up by a team comprising representatives from

Indian Agricultural Statistics Research Institute (IASRI), Directorate of Economics and Statistics,

Ministry of Agriculture (DESMOA), Field operations Division of National Sample Survey

Organization (NSSO(FOD) and from one or two major States growing horticultural crops. The

alternative methodology should be tries out on a pilot basis before actually implementing it on a

large scale.

16. A suitable methodology for estimating the production of crops such as mushroom, herbs and

floriculture needs to be developed and this should be entrusted to expert team comprising

representatives from Indian Agricultural Statistics Research Institute (IASRI), Directorate of

Economics and Statistics, Ministry of Agriculture (DESMOA), Field operations Division of

National Sample Survey Organization (NSSO(FOD) and from one or two major States growing

these crops.

Land Use:

17. The nine-fold classification of land use should be slightly enlarges to cover two or three more

categories such as social forestry, marshy and water logged land, and land under still waters, which

are of common interest to the centre and States, and which can easily be identified by the patwari

through visual observation.

18. State Governments should ensure that computerization of land records is completed expeditiously.

Irrigation Statistics:

117

19. In view of wide variation between the irrigated area generated by the Ministry of Agriculture and

the Ministry of Water Resources, the State Governments should make an attempt to explain and

reduce the divergence, to the extent possible, through mutual consultation between the two agencies

engaged in the data collection at the local level.

20. The State Directorates of Economics land Statistics (DESs) should be made the nodal agencies in

respect of irrigation statistics and they should establish direct links with the State and Central

Agencies concerned to secure speedy data flow.

21. Statistical monitoring the evaluation cells with trained statistical personal should be created in the

field offices of the Central Water Commission (CWC) in order to generated a variety of statistics

relating to water use.

22. The Central Statistical Organization (CSO) should designate a senior level officer to interact with

the central and State irrigation authorities in order to promote an efficient system of water

resources statistics and oversee its activities.

Strengths Weakness Opportunity & Threats (SWOT) ANALYSIS of the Agricultural

Statistical System of the State

Strengths:

1. The Directorate of Economics and Statistics is the State Agricultural Statistics Authority (SASA).

2. Usage of well established methodology.

3. Nodal Agency for state statistical activities.

4. Good demand for agricultural data from academicians and researchers, especially from researchers of Agriculture University, NGO`s and agriculture based entrepreneurs.

5. Only agency compiling seasonwise and cropwise area of all crops (land use statistics and irrigation statistics) area, production and productivity of 60 crops.

6. Reconciliation of area statistics, seasonwise,/cropwise at Taluk/District level by Agriculture, Horticulture, Irrigation and Revenue departments.

7. Yield rates of crops under crop insurance are in great demand by farmers and insurance company.

8. Computation of GSDP estimates on the basis of Area and production estimates.

9. The results of crop cutting experiments are considered to decide on crops for minimum support price.

10. Correction factor used for adjustment of area under field ridges and bund.

11. Supervision by DES and line departments

12. Imparting regular/refresher training classes.

13. Computerisation of land records.

14. Four times crop enumeration in a year.

15. Presence of statistical personnel at different levels of hierarchy in line departments.

118

16. Inbuilt checks and balances in the form of Reconciliation and ICS.

17. Availability of skilled personnel.

18. Land use details are available since 1955.

Weaknesses :

1. Lack of knowledge of methodology and importance of area and production estimates in field staff

and supervisory staff.

2. No Information Education and Communication (IEC) activity.

3. Absence of primary statistical personnel at the Gram Panchayat level.

4. Problems observed under ICS Scheme.

a) Not writing of RTC in time

b) Partial writing of RTC.

c) Late submission of TRS abstracts.

d) Crop and crop area reported by Village Account and Supervisor differ.

e) Differences in reporting irrigation/seed variety.

5. Work load of Village Accountant.

a) Inadequate time and attention to the writing up of RTC.

b) Large geographical jurisdiction with 4 to 5 villages on an average.

c) Due priority has not been assigned to the writing of RTC in working schedule.

d) Vibrant horticultural statistics especially short duration crops are not recorded in RTC.

e) Little time to spare for area enumeration is left because of miscellaneous work such as

identification of BPL families, issue of ration cards, election drought etc., which is a never

ending process.

6. Non existence of legislation to solicit cooperation from public.

7. Timeliness and accuracy, not maintained by Village Accountants

8. Lack of skill by primary and supervisory staff for area enumeration and crop cutting experiments.

9. Lack of well defined training for recording of RTC for mixed crops.

10. Methodology to estimate yield of Horticulture crops to be improved.

11. Lack of interest in owning the statistical information by the concerned departments.

12. Lack of horizontal and vertical co-ordination among the line departments.

13. Irrigation sourcewise, variety wise, cropwise enumeration details are not forthcoming from

Bhoomi. Auto aggregation is not available on area.

14. Maintenance of different data sets by line departments.

15. Non availability of hissa maps (atlas).

16. Reconciliation, not done in its true spirit for Horticulture crops, unauthorized cultivation area.

17. Vacancy of statistical staff at primary and supervisory level.

119

18. Inadequate I.T. facility and infrastructure.

19. Posts to be filled up at village level by line departments.

20. Unscientific methodology of collecting of area, yield and production statistics in line departments

for estimates.

21. Lack in service training facility.

22. Usage of inaccurate CES equipment by primary workers.

23. Increase in work load of primary workers.

24. Due to insurance claim, primary worker has pressure to under estimate crop cutting experiments.

Opportunities:

1. The system of complete crop enumeration stood the test of time and proved to be cost effective and

efficient in generating crop and land use statistics. To improve the crop statistics-

a) Declaration of Crop enumeration period as a programme of high priority

b) Sparing Village Accountant of all other duties during crop enumeration period.

c) Enumeration of TRS sample villages to be done on priority.

2. Advance I.T. infrastructure to be made available.

3. Manpower skills for collection and analysis of data be improved through training.

4. Periodical sample surveys on various crops not covered under CES for estimate of area and yield.

5. IEC activities by outsourcing.

6. Periodical workshops in association with Research institutes like IASRI, ISI, KSRSA, on

technologies like Remote Sensing, Aerial Photography etc.

7. Necessity of Statistical act with the immunity from litigation.

8. Village level agricultural statistical information shall be published seasonwise, relating to land

use irrigation and crops grown.

9. Creation of a statistical cell at Gram Panchayath level for various coordination activities.

10. Increase in supervision of area enumeration and crop cutting experiments to be mandatory up to

the level of Additional Director of Agriculture, Horticulture, Revenue, RD&PR, Watershed,

Irrigation and Directorate of Economics and Statistics departments. This should be reviewed at

State level in KDP by Development Commissioner.

11. Methodologies to be devised for cross checking of data of market arrivals in markets.

12. A methodology to be devised to arrive at area and yield figures at appropriate level by using

Improvement of Crop Statistics and Timely Reporting Scheme and results of H schedules of

Agriculture Census.

120

Threats:

1. Village Accountant does not know the importance of proper writing of RTC.

2. Lack of knowledge about the importance of agricultural statistical data in all levels.

3. Negligence towards statistical work by line departments.

4. Lack of sufficient statistical personnel in Line departments

5. Least priority to filling up of vacant posts by Government.

6. External pressure affects the yield rates in crop insurance scheme.

7. Downsizing of staff under economic measures always affects statistical staff of line departments.

8. No budgetary support for special studies/surveys.

9. Low priority to statistical work by Revenue Department.

ROAD MAP TO IMPROVE CROP AREA AND PRODUCTION STATISTICS

Goals and Strategies:

Goal: 1 Creating awareness among public.

Creating awareness among farmers and public will increase the accuracy of the ground level

information, by receiving crop sown and yield rate particulars given voluntarily to administration.

Strategy:1 This can be achieved by Information, Education and Communication (IEC) Activity.

IEC involves workshops, seminars to farmers, canvassing activities related to agriculture statistics and its

importance in Grama sabhas, through pamplets and other publicity.

NGOs may be involved to propagate the importance of agriculture statistics among the public by media,

pamplets, handbooks etc.

Goal: 2 Increase the accuracy and reliability of statistics.

More accurate and reliable agriculture statistics is very much necessary as this statistics gives the

totality of the effect of implementation of the development schemes.

Strategy: 1. A government enactment for field inspection and writing of RTC in scheduled period.

A government order to declare the writing of RTC as a programme of high priority and the Village

Accountants and Revenue Inspectors be mandated to carryout the crop inspection according to the

prescribed time schedule by sparing him from other duties during crop enumeration period through necessary

amendment to Karnataka Land Records Act and Rules.

Strategy: 2. A methodology to be derived to augment results of ICS and TRS with that of final area

and production estimates. A correction factor may be derived from the observation in area enumeration of

ICS.

121

Strategy: 3. Exploring the possibility of Arial Photography and Remote Sensing. The programme of

forecasting of agricultural output using Space, Argometerology and land based observation (FASAL) which

is experimenting the approach of remote sensing to estimate the area under principle crops is to be used for

cross checking the accuracy.

Goal: 3 Increase of accountability.

Strategy: 1. A statistical act for good response and accountability.

Strategy: 2. Lapse due to negligence in conducting of crop cutting experiments and non-writing of RTC are

to be made punishable.

Strategy: 3. Failure in supervision is to be made punishable.

Goal: 4 A scientific, simpler methodology to cover more crops for accurate estimates.

Strategy: 1. A simplest methodology to include more fruits and vegetable crops under area and yield

estimation survey on fruits and vegetables. The methodology adopted in the pilot scheme of crop estimation

survey on fruits and vegetables should be reviewed and an alternative methodology for estimating

production of horticulture crops like flowers, orchids etc. should be developed taking into account

information flowing from all sources including market arrivals, exports and growers association.

Strategy: 2. A systematic method of oral enquiry to estimate yield. Oral enquiry method may be

launched on a pilot basis for one or two crops, to start with. In this method, farmers growing the crop

under survey will be questioned directly regarding area, production and productivity in the village. This

gives first-hand information

Goal 5: Development of skill and capability of the staff.

Strategy: 1. Intensive training to field staff and supervisory staff.

Strategy: 2. An inbuilt model of training in all line departments in their in-service training.

Strategy: 3. To have a continuous in-service training for capacity building, an institute for training is to be

established for DES, which has to be an institute to guide and conduct surveys also.

Goal: 6 Improve the credibility of the system.

Strategy:1. Follow up supervisions in ICS for corrective actions. Under ICS, follow up supervision

should be undertaken in the defaulting villages in the succeeding year.

Strategy:2. Improve the credibility by co-ordinating among the line departments. Inter

departmental committees starting from grass root levels to the apex level should be formed. The frequency of

meetings of these committees should be increased. A committee has been formed to narrow down the

differences in horticulture statistics with members from both the departments.

Strategy:3. Introduction of supervision by state level officers of the concerned departments. To

have more credible and accurate statistics the supervision structure has to include state level officers up to

the level of additional directors of Revenue, RD&PR, Agriculture, Horticulture, Irrigation and Statistics

departments.

Strategy:4. Monitoring of conduct of area enumeration and crop cutting experiments by concerned

departments in Karnataka Development Programmes meeting. To improve the quality of area and

production estimates and to make the primary worker aware of the seriousness of area enumeration and

122

crop cutting experiments, this may be added in KDP formats as separate points. These should be discussed

threadbare in the KDP meetings and serious action should be taken against defaulters.

Goal 7: Improve the timeliness.

Strategy:1.Use of latest technology for onward transmission of information. Field personnel may be

provided with mobiles and simputers for transparent and speedy dissemination of data

Strategy:2. Computerisation of RTC. Bhoomi in coordination with NIC has to make the formats

more comprehensive to include all types of crop area details.

Strategy:3. Timely publication of the periodicals. The five publications of schemes on area and

production estimates are to be brought out with in cut off date.

Goal 8: Development of better sustainable system to have reliable, credible and timely area and

production statistics.

Strategy:1. There is large number of vacant posts in taluk and below level of DES, Agriculture,

Horticulture, Revenue and RD&PR departments.

These posts are to be filled up at least in a phased manner with in three years to have better field work,

supervision and monitoring. If all the posts are filled up, the percentage of supervision may be increased for

accuracy.

Strategy:2.Committed staff only for collection of statistics will increase reliability, credibility and

timeliness. Creation of a separate statistical cell at Gram panchayath level for doing agriculture statistics

work, surveys, census and collection of basic statistics etc will increase the reliability, credibility and

timeliness of statistical information.

Type studies recommended on Crop Area & Production Statistics

1. Oral Enquiry method for horticulture crops:

In order to reduce the divergence of horticulture figures by DES and Horticulture department, oral

enquiry method may be adopted to estimate area and production of horticulture crops, by interviewing the

horticultural farmers, on random basis with sufficient number of samples.

2. Study on the ICS villages of the preceding year.

A study may be conducted in defaulting ICS villages of preceding year to verify whether corrective

measures are taken by the concerned officials/officers.

3. Study on verification of agricultural Statistics figures by outsourcing:

A study may be entrusted to a non-governmental agency to cross check the figures of area

enumeration and crop cutting experiments of the department on sample basis for one or two major crops in

123

two or three districts. Since it is an independent study from non-governmental agency, an impartial and

comparative view on present system of area enumeration and production estimates may be obtained, with

its merits and demerits is helpful in increasing the accuracy.

4. Study of bund/ridge correction:

A sample survey to arrive at percentage of bund correction/ridge correction to be applied in

production estimates.

Efforts by Directorate of Economics and Statistics under KSSSP:

1) Creation of awareness:

a. Training: Every Year, training is given to village accountants, on updation of RTC and on

procedures of crop cutting experiments

b. Special Drive: Special Drive is taken up for season-wise complete area enumeration in TRS villages

in a systematic manner from 2009-10 and supervised by the officers of the stake holder

departments.

c. Creation of Awareness: State level training to trainers to create awareness for entry of crop details

in pahani was conducted. These trainers, in turn have imparted training to the taluk level trainees

at the district level, who in turn acted as resource personnel at the hobli level to create awareness to

elected panchayath raj members. A Hand Book on enumeration of crop details and conduct of CCEs

is prepared and distributed by the DES. This awareness campaign was conducted in 2011-12 and

continued in 2012-13.

d. Updation of Bhoomi: 12th and 13th Columns of RTC which deal with season-wise, irrigated/un

irrigated status- wise, variety-wise, crop details and land use, have been neglected by the present

system. Besides having correspondence with the Bhoomi, to update the crop details, irrigated details

and 9 fold classification, in time, in Bhoomi software, the importance of these columns have been

highlighted during the above mentioned workshops and trainings were imparted to all the

tahasildars and Assistant Commissioners in 2011-12.

e. Using of Remote Sensing: As per the recommendation of Prof. Vaidyanathan Committee‟s Report,

GOI has taken up comparative study in 15 villages, out of which four villages are from Karnataka

State. The conclusion is that “the pilot study in selected villages to explore the use of RS to track

land use and cropping at the village level shows the limited capacity of LISS III for this purpose.

Much more work is essential to understand its potentials and limitations before RS can be put to

effective use”.

124

2) Correction factor: A methodology is sought from IASRI to compare the results of ICS and TRS

with that of final figures, by devising a correction factor. Expert Committee on Agricultural

Statistics was set up by GOI and one of its terms of reference was to review TRS, EARAS and ICS

schemes to arrive at a correction factor.

The Committee in its reports stated that the desired convergence of various schemes like TRS,

EARAS and ICS has not happened and the recommendations of various expert groups including the

National Statistical Commission couldn't make any visible impact on the present system.

Instead it recommended the establishment of NCSC (National Crop Statistics Center) for producing

reliable and timely data on land use, crop area and crop yield.

3) Web Updation: KSSSP is preparing the software for crop abstract updation through web with the

help of NIC.

4) Timely Publication: Efforts are being made to bring out the publications on time

Further steps taken by the department are as follows:

1) Under KSSSP, a “Special Drive” has been initiated to involve horticulture staff both at area

enumeration and supervision levels. Accordingly in TRS villages, village accountant, agriculture

assistant, horticulture assistant and work inspector should conduct crop inspection before the village

accountant enters the crop details in the RTC. Supervision should be done at the rate of 25% each, by

the officers of Agriculture, Horticulture, Irrigation and Directorate of Economics and Statistics

Departments.

2) A baseline survey for major horticulture crops in few taluks in order to identify the areas of

permanent and semi-permanent crops and to find the actual departure in area statistics given by DES

and Horticulture Department. This survey of conducting base line horticulture census in 11 Districts

is initiated with a proposal of Rs. 6.15 crores (1.15 crores under 13th Fin. & 5 crores under State

Budget)

3) Vacant Statistical Posts of DES in the field are provided by outsourcing from KSSDA.

4) Follow up supervision is being carried out by State Level in case of ICS State Sample villages covered

under crop area enumeration sample check where more mistakes were found.

5) Counter check for the Special Drive supervision by the line departments is also being carried out by

State Level officers of DES.

6) Proposal to GOI

125

To redress the problems arising out of the current dependence on functionary of Revenue

Department at the village level for collecting and maintaining records of land use and conducted of crop

cutting experiments, it was proposed to Ministry of Agriculture and Co-operation, GOI, to provide fund of

Rs.58.82 crores to have personnel by outsourcing, who are accountable to DES.

************

126

1a. Issues and solutions in Horticulture Statistics *

Introduction:

Horticulture is an important Sub-sector of agriculture and is contributing in an increasing

manner to the national and state economy. It is necessary to have accurate statistics with regard to both area and productivity for purpose of planning and also for assessing the Gross State Domestic Product (GSDP). However, there are considerable discrepancies in the figures relating to horticulture furnished by National Horticulture Board (NHB) and the Directorate of Economics &

Statistics.

Horticulture Statistics are collected by Horticulture Department and Directorate of Economics and Statistics. The latter collects it as a part of Agriculture Statistics. Many discrepancies are found in the horticulture figures reported by these two agencies, each claiming the authenticity of figure.

Area and Yield

Methodology adopted by Horticulture Department

The department of Horticulture is publishing “Horticultural crops Statistics of Karnataka State at a glance” every year. The department determines the area covered under different horticulture crops on the basis of the seeds and horticulture plants distributed in Taluks and Districts. The production figures under different horticulture crops are obtained by considering the yield of the individual crop based on the package of practices recommended by the University of Agriculture Sciences, Bangalore and Dharwad under normal conditions.

Methodology adopted by Directorate of Economics & Statistics.

The Directorate of Economics and Statistics collects the area information from the Record of

Rights, Tenancy and Crops written by the village accountants for all the villages in all the seasons viz. Kharif, Rabi and summer.

The entire edifice of agriculture statistics in Directorate of Economics & Statistics stands on Record of Rights, Tenancy and Crops (RTC). At the Taluk Level, reconciliation meetings are chaired by the Tahasildar. The members of the Co-ordination Committee are Senior Assistant Director of Horticulture, Asst. Director of Agriculture and Asst. Executive Engineer of Water Resources Department. After getting approval at the taluk level, the same information is sent to the Dist. Statistical Office to get the approval at District Level Committee. These meetings take place for all the three seasons. In the Dist. Statistical Office all the taluk information is consolidated. This Report is approved in the District Level Reconciliation Committee meeting headed by the District Commissioner. The members are Joint Director of Agriculture, Deputy Director of Horticulture, Executive Engineer of Water Resources Department and Dist. Statistics Officer. Thus the data will flow from village to taluk, to district and district to state. * Sri K.V.Subramaniam, Joint Director, AGS Division & Smt.Jyothi, ASO, Smt.Hemalatha, ASO, F&V

Section, Directorate of Economic & Statistics

127

Crop Estimation Survey on Fruits, Vegetable & Minor Crops:

A Scheme to enumerate selected fruits, vegetables and minor crop is implemented in

Directorate of Economics and Statistics under “Crop Estimation Survey on fruit, vegetable and minor crops”

The survey includes banana, grapes, mango, guava, pomogranate, sapota and lemon, under fruits, beans, tomato, brinjal and cabbage under vegetables and turmeric under minor crops.

It aims at estimating area, yield and production with an acceptable degree of precision. For area estimation, two stage random sampling design is adopted which is depicted below.

Taluks (stata)

Villages (1st stage units)

Orchards/sub survey numbers (2nd stage units)

For yield estimation three stage random sampling is followed.

Taluks (strata)

Village (1st stage units)

Orchards (2nd stage units)

Cluster of trees/Plants (3 rd stage units)

Yield Estimation by DES:

Directorate of Economics and Statistics follows the time tested and scientifically proved

crop cutting experiments under General Crop Estimation Survey (SCES) to arrive at yield rates of onion, potato, dry chillies and crops included in sample survey of fruits, vegetables and minor crops.

Under these crops yield rates and production of horticulture crops computed by Directorate of Economics and Statistics do not tally with that of Horticulture Department. It is paradoxical to note that the horticulture department does not take these yield rates inspite of the fact that the officers/officials have actively participated in the crop cutting experiments of these horticultural crops at Taluk level.

Oral Enquiry Method:

The horticulture crops included under oral enquiry method of yield estimation are Madaki,

Nigarseed, Mustard, Peas, Korle, Mesta, Papaya, coconut, Arecanut, Ginger, Cardamam, Garlic, Pepper, Coriander, Cashewnut, Sweet Potato, Tapico, in the year 2009-10. The procedure of oral enquiry method is as follows:

128

Districts with largest area of the respective crop are selected. Out of each district 2 taluks atleast with a minimum area of 25 hectares are selected. In each taluk, 2 villages are randomly selected (in case of greater area taluks 4 villages are selected) and thus effort is made to make the samples more representative and from each village 10 farmers are randomly selected and information is elicited as per prescribed schedules so as to get yield rates of that crop.

Comparison:

It may be observed that there is lot of variation in the two sets of figures arrived at by Directorate of Economics and Statistics and Horticulture Departments as given in annexure. As per Horticulture Department, the area and yield rates are higher side in respect of almost all crops than the estimates of Directorate Economics and Statistics (see Annexure).

CROPS

2009-10 (Area in Hectares) 2010-11 (Area in Hectares)

DES HORTICULTURE %

Variation

over DES

DES HORTICULTURE % Variation

over DES

I.VEGEGATABLES 1.Potato(CES) 37108 37909 2.16 40596 43451 7.03 2.Onion (CES) 191903 181387 -5.48 162587 172102 5.85 3.Tomato(F&V) 36944 48773 32.02 35774 54287 51.75 4.Sweet Potato(F&V) 2397 2554 6.55 5.Beans(F&V) 6394 11467 79.34 5461 12271 124.70 II FRUITS 1.Mango(F&V) 120018 153875 28.21 117331 162648 38.62 2.Banana(F&V) 63981 104436 63.23 63076 87238 38.31 3.Grapes(F&V) 13777 17356 25.98 13634 16286 19.45 4.Guava(F&V) 4523 7168 58.48 4804 6944 44.55 5.Sapota(F&V) 17707 29313 65.54 16923 28909 70.83 III Condiments and Spices

1.Dry Chillies(CES) 138711 121501 -12.41 113849 107475 -5.60 2.BlackPepper(NON CES) 19706 22136 12.33 21061 23616 12.13 3.Cardamom(NON CES) 19182 19899 3.74 19081 19816 3.85 4.Dry Ginger(NON CES) 44837 47631 6.23 46511 49614 6.67 IV Garden/Plantation Crops 1.Cashewnut(Rawnut) 63314 70404 11.20 2.Coconut(No. of nuts) 429860 487075 13.31

The above annexure provides comparison of area and yield of important Horticulture crops for the year 2009-10 and 2010-11 of DES with that of Horticulture Department.

It is observed that Horticulture Department area is more compared to DES in vegetables, Fruits, Condiments and spices, Garden/plantation crops during both the year, but for onion(-5.48%) among vegetables in 2009-10, Dry chillies(-12.41%) in 2009-10 and(-5.60%)in 2010-11 among condiments and spices. The percentage variation is high in Beans among vegetables, Sapota in fruits and Black

pepper in Condiments and spices and coconut in Garden crops.

129

CROPS

2009-10 (Yield in kgs/Hectare) 2010-11 (Yield in kgs/Hectare)

DES HORTICULTURE %

Variation

over DES

DES HORTICULTURE % Variation

over DES

I.VEGEGATABLES 1.Potato(CES) 8762 9980 13.90 9001 10650 18.32 2.Onion (CES) 3983 13610 241.70 6915 13840 100.14 3.Tomato(F&V) 10348 34300 231.47 11681 35130 200.74 4.Sweet Potato(F&V) 6637 12860 93.76 5.Beans(F&V) 7574 10890 43.78 8308 10700 28.79 II FRUITS 1.Mango(F&V) 4253 11010 158.88 4890 10840 121.68 2.Banana(F&V) 21993 20420 -7.15 23487 25670 9.29 3.Grapes(F&V) 31127 18300 -41.21 39926 17130 -57.10 4.Guava(F&V) 5200 19370 272.50 6883 19080 177.20 5.Sapota(F&V) 4530 12280 171.08 4888 12310 151.84 III Condiments and Spices

1.Dry Chillies(CES) 1048 990 -5.53 1191 1020 -14.36 2.BlackPepper(NON CES) 375 360 -4.00 610 540 -11.48 3.Cardamom(NON CES) 67 140 108.96 82 140 70.73 4.Dry Ginger(NON CES) 3181 11460 260.26 3655 10010 173.87 IV Garden/Plantation Crops 1.Cashewnut(Rawnut) 766 1480 93.21 2.Coconut(No. of nuts) 7182 11100 54.55

It is observed that Yield of vegetables given by Horticulture Department are more in all the crops compared to DES in both the year i.e.2009-10 and 2010-11. Except for Banana (-7.15%) in 2009-10 and Grapes(-41.21% and 57.10%) in both the years among fruits, drychillies(-5.53, -5.60) and black pepper (-4.00) among condiments and spices in 2009-10 and 2010-11 .

Interest of the Department:

Horticulture Department is the participating department in reconciliation process at taluk and district level. It is observed every year that the Horticulutre Department does not stick to the reconciled area, which they only have approved.

Following two situations shows us the interest by the Horticulture Department to minimise the discrepancies in area statistics.

1.For improving quality, reliability and timeliness of Agricultural Statistics and to reduce the time lag between the period of sowing and the availability of estimates of area sown, between completion of harvesting & availability of estimates of production in respect of important crops, the Directorate of Economics & Statistics, Ministry of Agriculture initiated TRS in 1969-70 in the land record states. The area of principal crops such as Paddy, Jowar, Bajra, Maize, Ragi, Wheat, Tur, Gram, Sugarcane, Cotton, Groundnut, Sesamum, Sunflower, Safflower and linseed are estimated under the scheme.

2. A special drive is taken up for complete crop area enumeration in TRS villages (20% of total village selected every year) in a systematic manner. This drive was taken up during 2009-10, is continued and completed during 2010-11 and 2011-12. Supervision work entrusted to the officers of Stake holder departments i.e., Revenue, Agriculture, Horticulture and Directorate of Economics

130

and Statistics at the rate of 25% each. It is observed that the percentage of supervision properly done is 78% by Revenue, 95% by Directorate of Economics and Statistics, 91% by Agriculture and 61% by Horticulture during 2010-11. In 2011-12 it is observed that the percentage of supervision properly done is 89% by Revenue, 95% by Directorate of Economics and Statistics, 83% by Agriculture and 50% by Horticulture. This shows that the interest of the department in updating RTC is minimum.

Problems: The agriculture statistics, generated in Directorate of Economics and Statistics, with many

checks and balances at different levels i.e. village, taluk and district levels are more authentic, whereas for horticulture crops following are the constraints.

1. Fruit trees, besides being grown in regular orchards, are also extensively grown on canal banks, field bunds, roadsides, backyard of houses and even as stray trees.

2. Horticulture crops which are of short duration are likely to be left out during area enumeration

3. The domain of horticultural statistics is rural and hence does not provide any scope of covering urban areas, nurseries and poly houses.

4. Intermediate growing of the horticulture crops between Kharif and Rabi & Rabi and summer are not reported by village accountant. Horticulture crops are grown with greater flexibility of sowing and harvesting.

5. The domain of horticulture crop is large covering fruits, vegetables, aromatic plants, medicinal and herbs, spices, nuts and flowers. The inadequate reporting of area in respect of these crops is due to their large number, their identification and flexibility of their cultivation.

6. The National Statistical Commission recommended that the scheme should be reviewed and an alternative methodology for estimating the production of horticultural crops should be developed taking into account information flowing from all source including market arrivals, exports and growers associations.

7. Horticulture crops are many times a mixed crop. At the time of writing of mixed crops area in RTC, its proportions are not properly calculated due to lack of knowledge of average plants that should be in an hectare

8. A clear cut methodology to estimate area of horticulture crops was not given either by Revenue Department in the Government order of 1961 or Horticulture Department in case of relay crops/mixed crops.

9. The yield rates and production of Horticulture crops computed by DES do not tally with that of Horticulture Department. It is paradoxical to note that the horticulture department does not take the yield rates inspite of the fact that the officers/officials have actively participated in the crop cutting experiments of horticulture crops at Taluk Level.

Solutions

1. Village accountants in Revenue Department are not updating the Pahani records. Hence

outsourcing staff may be recruited from DES for this purpose who should be accountable to DES.

131

2. Horticulture Department is participating in crop cutting experiments conducted by DES in Banana, Mango, Grapes, Guava, Sapota onion, pototao, Tomato, Beans, Drychillies, Cardamon, DryGinger and Dry chillies. Therefore they should consider yield of these crops in their publication.

3. To arrive at reality, a baseline survey or one time horticulture Census of crop area be conducted.

4. Intensive training to be given to village Accountants and statistical staff about writing of RTC.

5. „State Horticultural Statistics Authority‟ to be created.

6. Horticulture Department should maintain farmerwise, villagewise horticulture statistics.

***********

132

2. Crop Area, Yield and Production Statistics* AGRICULTURE IN INDIA

India is an agricultural country. About seventy percent of our population depends on

agriculture. One-third of our National income comes from agriculture. Our economy is based

on agriculture. The development of agriculture has much to do with the economic welfare of

our country.

Our agriculture remained under developed for a long time. We did not produce enough

food for our people. Our country had to buy food-grains from other countries, but the things

are changing now. India is producing more food-grains than its needs. Some food-grains are

being sent to other countries. Great improvements have been made in. agriculture through our

five year plans. Green Revolution has been brought about in the agricultural field. Now our

country is self-sufficient in food-grains. It is now in a position to export surplus food-grains

and some other agricultural products to other countries.

Now India ranks first in the world in the production of tea and groundnuts. It ranks

second in the world in the production of rice, sugarcane, jute and oil seeds. Till recent past

before independence our agriculture depended on rains. As a result our agriculture produce

was very small. In case the monsoons were good, we got a good harvest and in case the

monsoons were not good, the crops failed and there was famine in some parts of the country.

After the independence our Government made plans for the development of its agriculture.

AGRICULTURE STATISTICS IN INDIA

Agricultural statistics in India have a long tradition. ArthaShastra of Kautilya makes a

mention of their collection as a part of the administrative system. During the Moghul period

also some basic agricultural statistics were collected to meet the needs of revenue

administration. Ain-e-Akbari is most important document which throws great light on the

manner in which statistics were collected during the moghul period. After the Moghul period

British rule started in the country Ryotwari System was introduced during18th Century by the

East India Company to collect land revenue. The historical famine of 1860 emphasized the

need for more statistical information. In 1866, the British Government Initiated collection of

agricultural statistics mainly as a byproduct of revenue administration and these reflected the

then primary interest of the Government in the collection of land revenue. Subsequently, the

emphasis shifted to crop forecasts designed primarily to serve the British trade interests.

After the First World War significant improvements were made in the agricultural

statistics of the country. The Royal Commission of Agriculture was appointed in 1926 by

Government of India, to examine the conditions of agricultural and rural economy. The

Commission recommended the constitution of the Imperial Council of Agricultural Research

which was renamed after independence as the Indian Council of Agricultural Research

(ICAR).

*Sri. B.H. Narayanaswamy, JD, CIS, Directorate of Economics and Statistics

133

During the Second World War, when the attention of the Government was focused on

the critical food situation, the need for timely and reliable statistics of food production was

keenly felt for implementation of food policy and administration of controls. The initiation of

the crop-cutting experiments based on random sample surveys for estimation of yield rates of

principal crops for replacing the traditional eye-appraisal method was the direct result.

With the ushering in of the planning era in 1951-52 greater attention was paid to the

improvements in the collection of statistical data on a number of items and various schemes

for improvement of agricultural statistics were implemented by the Central and State

Governments as part of the successive five year plans.

Agriculture being a state subject and statistics falling in the concurrent list the

Agricultural Statistics System is a decentralized one with the state governments (The State

Agricultural Statistics Authorities, or SASAs, henceforth) playing a predominant role in

collection and compilation of agricultural statistics and more particularly the crop statistics.

The Directorate of Economics and Statistics (DES), Ministry of Agriculture at the Centre is

the pivotal agency for coordination and compilation of agricultural statistics at all India level.

Other principal agencies which collect data and conduct methodological studies on

agricultural statistics are the National Sample Survey Organisation (NSSO), the Indian

Agricultural Statistics Research Institute (IASRI), the State Directorate of Economics and

Statistics (State DESs),etc.

The present system of agricultural statistics generates valuable statistics on a vast

number of parameters. Some of the very important statistics are land-use statistics and area

under principal crops through the Timely Reporting Scheme (TRS) and also on complete

enumeration basis, yield and Production estimates through the General Crop Estimation

Surveys (GCES), cost of production estimates, agricultural wages, irrigation statistics etc.

Crop Area Statistics

The information on crop area statistics is backbone of Agricultural statistical system.

Reliable and timely information on crop area is of great importance to planners and policy

makers for efficient and timely agricultural development and making important decisions

with respect to procurement, storage, public distribution, export, import and other related

issues.

Karnataka possesses an excellent tradition and infrastructure in generating

comprehensive series of crop and land use statistics. Of late, the quality of area and crop

statistics is deteriorating. Most parts of our State the Land records are not being updated

properly. This is mainly due to lack of field knowledge, administrative apathy and inaction.

In Karnataka Area and Land use Statistics form a part of the Land records maintained

by the Revenue authority. Statistics of Crop area are compiled with the help of the Village

Accountant commonly known as Patwari Agent.

134

The crop area statistics collected by village accountant in the temporarily settled States

are on the basis of complete enumeration called girdawari. The village accountant is to visit

each and every field of the village in each crop season and record the information such as area

under different crops / land use categories and its status in standard forms called Khasra

register (Pahani).

The Directorate of Economics and Statistics is the State Agricultural Statistics

Authority (SASA) and has been designated as the Nodal Agency for state statistical activities.

Karnataka State has a well-established methodology of collection of agricultural

statistics with three time crop enumeration in a year. In addition there is successful effort in

the computerization of all land records and Land use details in the Bhoomi software are

available since 1955.

DES is collecting information on agricultural statistics in each season through Village

Accountants compilation of hobli wise crop area statistics and land use particulars are being

recorded at taluk level. Reconciliation of crop area statistics is being done at hobli/taluk level

with the assistance of Departments of Revenue, Agriculture, Horticulture, and Irrigation

regularly in each season.

Though, there is a well established system for collection of agricultural statistics in the

State, still lot of gaps have been identified and listed below for further improvement:

Revenue Department :

The Village Accountant working at the village panchayat level is the custodian of all

land records. It is the foremost duty of the VA to update land records regularly in each

season every year. The seasonwise crop statistics has to be recorded in the RTCs by

involving Agriculture, Horticulture and Irrigation Departments officials working at

field level. But in practice this is not taking place regularly. Therefore, the statistics

provided by the Departments of Agriculture, Horticulture , Irrigation and Sugarcane

Inspectors does not tally with the RTC details. During reconciliation all the department

should produce documentary evidence i.e. survey numberwise crop details for

incorporating in the RTC.

Gaps in Crop Area Statistics

(a) The VAs may not write RTC‟s for all the three seasons (Kharif, Rabi and Summer)

and may not prepare Survey number-wise Crop Abstract in each season.

(b) The details of nine fold Land use classification are not being updated regularly. It is

observed that the land put to non-agricultural use i.e., commercial buildings, roads,

schools, civic amenities and house buildings etc., are remains constant for 3 to 4 years

in some of the districts.

(c) The variety wise crop details i.e. HYV/HYB/Local in the irrigated and un-irrigated

area details may not be recording properly.

135

(d) It is observed that there is a lack of Proper coordination among the field agencies

namely Agriculture/Horticulture/Irrigation/Sugarcane Inspector etc. in the updation of

crop statistics and reconciliation in each season.

(e) The details of mixed crops grown in the field may not be recording uniformaly

throughout the State, due to lack of knowledge.

(f) The Irrigation Statistics may not be updating periodically by the VAs.

(g) The supervisors are not inspecting of RTCs regularly in each season.

(h) The checklist of RTC extracted from Bhoomi software was not provided in time to the

VAs for updation of crop statistics.

(i) The present Bhoomi software should take care of collection of crop area statistics in

each season.

(j) Short duration Crops like flowers, mushrooms, vegetables and commercial crops may

not get figured in the RTC.

(k) The coconut and fruit yielding trees like mango, sapota, jackfruit, banana, guava etc.,

which are grown in the urban limits / GP limits has been left out as there is no

provision to write in RTCs.

(l) The review meetings at taluk level/district level by the TLCC and DLCC may not be

taking place regularly in some of the districts.

(m) There is a lack of awareness among the farmers and local body official about the

writing of RTC and its importance.

Suggestion for improvement:

(a) The VAs should write RTC in each season by visiting the fields along with

Agriculture, Horticulture, Irrigation and Sugarcane department officials without

fail.

(b) The changes that took place in the irrigation and land use statistics should be

updated regularly by the VAs.

(c) While recording crop statistics in the RTC the VA should ascertain from the

farmers about variety of crops that are grown in their fields at the time of field visit

and incorporate the same in the RTC.

(d) The reconciliation committee should be strengthened properly at hobli level. The

departments involved in the writing of RTC should understand the importance of

crop statistics and work together harmoniously for the improvement of crop

statistics. The effective training programme should be undertaken regularly.

(e) The guidelines prescribed for writing of mixed crops should be explained in detail

in the training programmes regularly. The DES officers/Officials should undertake

136

inspection regularly, suitable suggestions may be given to the VAs for

improvement of crop statistics.

(f) As and when the farmers go for drilling of borewells for irrigation purpose and the

land brought under irrigation should be updated in the RTC regularly.

(g) The officers should undertake effective supervision in each season, the lapses

noticed at the time of supervision should be brought to notice of VAs and strict

instructions may be given to set right the lapses.

(h) The Thasildars should provide extract of RTC to VAs regularly for updation of

crop statistics.

(i) The Bhoomi software should be utilised properly to record crop statistics in each

season.

(j) Short duration crops like flowers, mushrooms, vegetables and commercial crops

should be recorded in the RTC.

(k) Provision should be made to record the trees like coconut, mango, sapota, jackfruit,

banana, guava etc, grown in the urban limits / GP limits in the RTC.

(l) The review meetings of TLCC and DLCC should take place regularly.

(m) Awareness among the farmers and local body official about the writing of RTC and

its importance should be made.

(n) An effort may be made to use the latest technology evolved in the IT sector i.e.

GPS, GPRS, Arial photography and remote sensing etc.to collect crop area

information, the mini laptops provided to the VAs should be utilised properly for

online submission of crop area statistics.

System of data collection for crop yield estimation

Estimates of crop production are obtained by multiplying the area under crop and the

yield rate. The estimates of yield rate are based on scientifically designed crop cutting

experiments conducted under General Crop Estimation Survey (GCES). The GCES covers

around 28 crops (25 food and 3 non-food) in the State.

GCES: The DES is implementing GCES since 1945-46.

Importance of GCES

Agriculture is the back bone of Indian economy.

Timely and reliable statistics on crop production is of vital importance and the back

drop of

1) National and State Incomes

2) Arriving at an accurate GSDP.

137

Over 95% of food grain production is estimated on the basis of yield rates obtained

from the results of CCEs.

The objective of the survey

To work out the average yield per hectare of important food and non-food crops. Yield

rates are used for crop production, which is a vital input for the agricultural policy both

at the state and national levels and also to estimate the Gross State Domestic Product

of the agricultural sector.

After the introduction of the National Agricultural Insurance Scheme (NAIS), the

results of the crop cutting experiments are being used to assess the crop loss also.

To collect useful ancillary information on crop management on the existing cultivation

practices, attack of pests and diseases etc.,

Crop production is used for the purposes of research, planning, policy formulation and

implementation.

The programme of the work under the survey consists of conduct of crop cutting

experiments variety-wise in irrigated and un-irrigated areas in each season in the selected

plots of the villages.

Sampling Design

The Statistical sampling design adopted for the survey is Multistage Stratified Random

Sampling, with taluks as strata, villages within a stratum as primary units of sampling, fields

within each selected village as sampling units at second stage and experimental plot of

specified size as the ultimate unit of sampling.

The process of CCE’s consists of

(a) Selection of Villages.

(b) Selection of Survey /Sub-Survey number.

(c) Selection of the field.

(d) Locating and Marking of experimental plot of specified size

in a field selected on the principle of random sampling.

(e) Harvesting and threshing of produce and

(f) Recording the weight of the produce in the prescribed forms.

Around 1,00,000 CC experiments are conducted every year with the help of field staff

drawn from Revenue, Agriculture, Horticulture, RD&PR, Watershed and CADA

departments.

138

Supervision:

Supervision of field work is an essential part of a sample survey for ensuring quality of

data collected at field level and thereby enhancing the reliability of estimates. The volume

(35%) of supervision to be done at harvest sage by the staff of different department is:

(a) A minimum of 20% each of the experiments allotted to Revenue, Agriculture,

Watershed, CADA, RDPR and Horticulture department is to be supervised by

the respective Supervisory Officers.

(b) A minimum of 15% of the experiments crop-wise is to be supervised by the

Statistical Department.

(c) About 900 experiments are entrusted to Central Agency (NSSO).

The Directorate of Economics and Statistics is responsible for planning and organizing

the Crop Estimation Surveys as also processing, analyzing the data and publication of the

results. The District Statistical Officer is responsible for organizing the Crop Estimation

Survey work at district level.

Karnataka State is participating in the crop cutting experiments since 1944-45. The

Directorate of Economics and Statistics is imparting training to the field and supervisory staff

every year during kharif season All the primary workers are well aware of the procedure to be

followed in conducting CCEs. The supervision of CCEs is entrusted to the field departments

according to their cadre strength. Sensitization of field departments was done through

awareness programme conducted with the assistance of KSSSP. Video conference with

Deputy Commissioners, Chief Executive Officers of ZP and Joint Directors of Agriculture

was done under the chairmanship of ACS and Development Commissioner along with

Economic Advisor to the CM, Principal Secretary Planning etc., to improve the quality of

data of area statistics and crop cutting experiments. The department officers are participating

in the workshops and video conferences conducted by the agricultural department. The

analysis of CCEs agencywise, divisionwise, cropwise and lapses noticed in the previous year

were brought to the notice of the senior officers who were present in the video conference and

workshop. Power point presentations were prepared and sent to all DSOs to make use these

ppts for imparting training at district/taluk level.

Karnataka State Strategic Statistical plan (KSSSP)

The State Government has set up a nodal agency called Karnataka Statistical System

Development Agency (KSSDA) for the formulation and implementation of the KSSSP vide

Government order dated 25.03.2009.

139

Objectives of Karnataka State Strategic Statistical plan

Strengthening of statistical system in the State for effective Planning, Monitoring

and Evaluation.

Putting in place capable and adequate manpower.

Capacity for complimentary research, training and support service.

Hardware and software technology for data collection, collation, analysis, storage,

dissemination and sharing.

Coherent policy and minimum standards on statistics, storage, use, disclosure,

sharing.

Reliable, Credible, Timely and adequate Statistical support.

Clarity on responsibility of line department and DES; Mutual support, synergies

and clear accountability

Bringing out of Reliable, Credible, Timely publications of the departments.

IT initiatives

The KSSDA has extended support by developing Web Based Modules for online

collection of information on Crop Area Statistics and Crop Cutting Experiments data.

Programme consultants have been recruited in the District Statistical Office to undertake

training programme to the field functionaries about the usage of Web Based Modules,

software and data entry at field level.

Hardware Assistance

The KSSDA has extended support by providing hardware items like Desktops,

Projectors, Laptops and Printers/Scanners to the District Statistical Office and Desktops and

Mini laptops to the Taluk Office for collection of data on Crop Area Statistics, CCEs, NSSO,

Vital Statistics, Local Body Statistics, Consumer Price Index, Retail and Wholesale prices

and State GSDP etc.,

Gaps in Yield Statistics and Crop Production

(a) Some of the Primary workers are not conducting CCE‟s in a scientific manner as

stipulated in the CCE‟s manual and recording the yield results without

conducting CCE‟s.

(b) The primary workers may do mistakes while recording the Mixed crop

proportion in the CCE‟s forms. This will have direct impact on area under each

crop and production analysis.

(c) The field staff may not use the weighing scales and measures and other

equipments provided to them to record the harvest produce.

140

(d) The green weight of the produce obtained while conducting CCE‟s should be

recorded in kilos and grams (up to 10 grams). But the primary workers may

record weight in terms of grams up to 250 to 500 grams. This will cause

reduction in yield results and production estimates at macro level.

(e) Sometimes the farmers may harvest the crops without intimating the primary

workers.

(f) It is observed that the Revenue and RDPR department personnel are giving least

priority for conducting of CCE‟s because they are overburdened with work load

of administrative and developmental activities, adhoc census, election work etc.,

(g) The primary workers may not inform the date of harvest to the supervisors well

in advance to undertake supervision. This will decrease the quality of data.

(h) If the selected crop is multi-picking then all pickings information is to be

recorded in the CCE‟ form, but some of the primary workers are recording only

two to three pickings information.

(i) The review meetings at taluk level/district level by the TLCC and DLCC are not

taking place regularly in all seasons.

(j) For the row crops like Tur, Castor, Cotton, Tobacco and Sunflower the

experimental size of the plot should be 10x5 meters, due to lack of knowledge

and training there are possibilities that the primary workers may conduct the

CCEs in 5x5metres only. This directly reduce average yield of the crop and

production there on.

(k) In order to get the benefit of the insurance scheme, it is possible that the

farmers/local leaders and politicians will pressurise on primary workers to record

low yield.

Suggestions for improvement:

(a) The primary workers should conduct the CCEs as per the guidelines stipulated in

the training manual without fail.

(b) While recording the mixed crops details in form-2, the crops selected for CCEs

and other crops should be apportioned as per RTC manual.

(c) The field staff should compulsory take weighing scales and other equtipments

provided to them to record the harvest produce.

(d) The green weight of the produce obtained while conducting CCE‟s should be

recorded in kilos and grams up to 10 grams.

(e) Primary workers should have regular contacts with the farmers at the time of

harvest.

141

(f) The Revenue and RDPR department personnel should be instructed to conduct

the CCEs by giving utmost importance.

(g) Primary workers should intimate the date of harvest to the supervisors well in

advance without fail.

(h) If the selected crop is multi-picking then all pickings information is to be

recorded in the CCE‟ form.

(i) The review meetings at taluk level/district level by the TLCC and DLCC are not

taking place regularly in all seasons.

(j) The review meetings at taluk level/district level by the TLCC and DLCC should

take place regularly in all seasons.

(k) For the row crops like Tur, Castor, Cotton, Tobacco and Sunflower the

experimental size of the plot should be 10x5 meters.

(l) The local leaders and politicians should inform the objective of crop insurance

scheme and is also not wise to pressurise on primary workers to record low yield.

(m) Bhoomi software should take care of crop updation in each season.

(n) Writing of RTC should be made mandatory for all the three seasons. Vigorous

supervision should be carried out to bring accuracy in area and land use statistics.

(o) Intensive training and awareness programmes on importance of agricultural

statistics and its collection to be carried out on regular intervals to the field

functionaries, supervisors, local body members and farmers.

(p) Usage of modern IT technology like Remote Sensing, GPRS and Arial

Photography etc., to facilitate asset mapping activity, online transmission of data

and speedy dissemination of data.

(q) The DES should be strengthened by creating posts at hobli level to take up crop

updation and crop cutting experiments effectively.

(r) To reduce the burden of work of VAs outsourcing of data collection at village

level should be thought off.

(s) Reconciliation of agricultural statistics should be done effectively by involving

the other field departments like Horticulture, Agriculture, Sericulture and

Irrigation departments at gross root level.

(t) Agriculture Department should supply weighing scales and measures and other

equipments to the field functionaries for recording of harvest produce properly.

(u) Disciplinary action against the defaulting staff should be initiated for having not

conducted CCEs.

142

(v) The review meetings at taluk level/district level by the TLCC and DLCC should

take place regularly in all the seasons.

(w) The primary workers should inform the date of harvest to the supervisors well in

advance.

(x) Reduction in the number of experiments allotted to Revenue and RDPR

Department officials as they are over burdened in their departmental work.

(y) Farmers should be educated about the importance of CCEs.

(z) Filling up of vacant post in Agriculture and Horticulture Departments.

(aa) CCEs contains two phases, Ist phase contains selection of Village/Survey

number/sub survey numbers i.e. CES form-1. IInd phase contains harvest and

recording experimental plot yields and other auxiliary information i.e. CEs-2.

As Village Accountants are the custodian of RTCs, they should be the work of filling the CEs

form -1 only. Agriculture and Horticulture staff are the technical personnel they should

conduct the experiment and submit the CEs-2 only. If the above exercises has been done

quality of yield data will be improved very much and there by improvement in agriculture

production.

*****

143

2a. INSURANCE SCHEME IN KARNATAKA*

Agriculture production and farm incomes in India are frequently affected by natural

disasters such as droughts, floods, cyclones, storms, landslides and earthquakes.

Susceptibility of agriculture to these disasters is compounded by the outbreak of epidemics

and man-made disasters such as fire, sale of spurious seeds, fertilizers and pesticides, price

crashes etc. All these events severely affect farmers through loss in production and farm

income, and they are beyond the control of the farmers. With the growing commercialization

of agriculture, the magnitude of loss due to unfavorable eventualities is increasing. The

question is how to protect farmers by minimizing such losses. For a section of farming

community, the minimum support prices for certain crops provide a measure of income

stability. But most of the crops and in most of the states MSP is not implemented. In recent

times, mechanisms like contract farming and future‟ s trading have been established which

are expected to provide some insurance against price fluctuations directly or indirectly. But,

agricultural insurance is considered an important mechanism to effectively address the risk to

output and income resulting from various natural and manmade events. Agricultural

Insurance is a means of protecting the agriculturist against financial losses due to

uncertainties that may arise agricultural losses arising from named or all unforeseen perils

beyond their control (AIC, 2008). Unfortunately, agricultural insurance in the country has not

made much headway even though the need to protect Indian farmers from agriculture

variability has been a continuing concern of agriculture policy. According to the National

Agriculture Policy 2000, “Despite technological and economic advancements, the condition

of farmers continues to be unstable due to natural calamities and price fluctuations”. In some

extreme cases, these unfavorable events become one of the factors leading to farmers‟

suicides which are now assuming serious proportions .

Agricultural insurance is one method by which farmers can stabilize farm income and

investment and guard against disastrous effect of losses due to natural hazards or low market

prices. Crop insurance not only stabilizes the farm income but also helps the farmers to

initiate production activity after a bad agricultural year. It cushions the shock of crop losses

by providing farmers with a minimum amount of protection. It spreads the crop losses over

space and time and helps farmers make more investments in agriculture. It forms an important

component of safety-net programmes. However, one need to keep in mind that crop insurance

should be part of overall risk management strategy. Insurance comes towards the end of risk

management process. Insurance is redistribution of cost of losses of few among many, and

cannot prevent economic loss.

There are two major categories of agricultural insurance: single and multi-peril

coverage. Single peril coverage offers protection from single hazard while multiple-peril

provides protection from several hazards. In India, multi-peril crop insurance programme is

being implemented, considering the overwhelming impact of nature on agricultural output

and its disastrous consequences on the society, in general, and farmers, in particular

*Sri. B.H. Narayanaswamy, JD, CIS, Directorate of Economics and Statistics

144

BACKGROUND OF INSURANCE SCHEME:

From 1972-73 to 1978-79, crop insurance schemes for crops such as cotton,

groundnut, potato etc, was implemented in selected places on “individual approach” basis.

During the period from 1979 to 1984-85, a pilot crop insurance scheme was implemented for

Food crops & Oilseeds on “Area approach” basis. Based on the experience of the pilot

scheme a Comprehensive Crop Insurance Scheme (CCIS) was implemented from Kharif

1985 till Kharif 1999. The present crop insurance scheme is National Agricultural Insurance

Scheme (NAIS) and Modified National Agricultural Insurance Scheme (MNAIS).

Evolution of Crop Insurance Program :

First ever scheme on „Individual‟ approach basis (1972-78)

Pilot Crop Insurance Scheme –PCIS (1979-1984)

Comprehensive Crop Insurance Scheme –CCIS (1985-1999)

Experimental Crop Insurance Scheme –ECIS (Rabi 1997-98)

National Agriculture Insurance Scheme – NAIS (1999……)

Farm Income Insurance Scheme – FIIS (Rabi 2003-04 season & Kharif 2004 season)

Rainfall based insurance (Kharif 2004...)

Weather based insurance products (Rabi 2005….)

Satellite Imagery based insurance products (Rabi 2005….)

Progress and Performance of Agricultural Insurance:

The question of introducing an agriculture insurance scheme was examined soon after

the Independence in 1947. Following an assurance given in this regard by the then Ministry of

Food and Agriculture (MOFA) in the Central Legislature to introduce crop and cattle

insurance, a special study was commissioned during 1947-48 to consider whether insurance

should follow an „Individual approach’ or a „Homogenous area approach‟ . The study

favoured „homogenous area approach‟ even as various agro-climatically homogenous areas

are treated as a single unit and the individual farmers in such cases pay the same rate of

premium and receive the same benefits, irrespective of their individual fortunes. In 1965, the

Government introduced a Crop Insurance Bill and circulated a model scheme of crop

insurance on a compulsory basis to State governments for their views. The bill provided for

the Central government to frame a reinsurance scheme to cover indemnity obligations of the

States. However, none of the States favoured the scheme because of the financial obligations

involved in it. On receiving the reactions of the State governments, the subject was referred to

an Expert Committee headed by the then Chairman, Agricultural Price Commission, in July,

1970 for full examination of the economic, administrative, financial and actuarial

implications of the subject.

145

CROP INSURANCE APPROACHES:

It is important to mention in the beginning that crop insurance is based on either Area

approach or Individual approach. Area approach is based on „defined areas‟ which could be

a district, a taluk, a block/a mandal or any other smaller contiguous area. The indemnity limit

originally was 80 per cent, which was changed to 60 per cent, 80 per cent and 90 per cent

corresponding to high, medium & low risks areas. The actual average yield / hectare for the

defined area is determined on the basis of Crop Cutting Experiments (CCEs). These CCEs are

the same conducted as part of General Crop Estimation Survey (GCES) in various states. If

the actual yield in CCEs of an insured crop for the defined area falls short of the specified

guaranteed yield or threshold yield, all the insured farmers growing that crop in the area are

entitled for claims. The claims are calculated using the formula: (Guaranteed Yield - Actual

Yield) * Sum Insured of the farmer (Guaranteed Yield)

The claims are paid to the credit institutions in the case of loanee farmers and to the

individuals who insured their crops in the other cases. The credit institution would adjust the

amount against the crop loan and pay the residual amount, if any, to the farmer. Area yield

insurance is practically an all-risk insurance. This is very important for 24 developing

countries with a large number of small farms. However, there are delays in compensation

payments. In the case of individual approach, assessment of loss is made separately for each

insured farmer. It could be for each plot or for the farm as a whole (consisting of more than

one plot at different locations). Individual farm-based insurance is suitable for high-value

crops grown under standard practices. Liability is limited to cost of cultivation. This type of

insurance provides for accurate and timely compensation. However, it involves high

administrative costs. Weather index insurance has similar advantages to those of area yield

insurance. This programme provides timely compensation made on the basis of weather

index, which is usually accurate. All communities whose incomes are dependent on the

weather can buy this insurance. A basic disadvantage could arise due to changing weather

patterns and poor density of weather stations. Weather insurance helps ill-equipped

economies deal with adverse weather conditions (65% of Indian agriculture is dependent on

natural factors, especially rainfall. Drought is another major problem that farmers face). It is a

solution to financial problems brought on by adverse weather conditions. This insurance

covers a wide section of people and a variety of crops; its operational costs are low;

transparent and objective calculation of weather index ; and quick settlement of claims.

Weather Based Crop Insurance / Rainfall Insurance:

During the year 2003-04 the private sector came out with some insurance products in

agriculture based on weather parameters. The insurance losses due to vagaries of weather, i.e.

excess or deficit rainfall, aberrations in sunshine, temperature and humidity, etc. could be

covered on the basis of weather index. If the actual index of a specific weather event is less

than the threshold, the claim becomes payable as a percentage of deviation of actual index.

One such product, namely Rainfall Insurance was developed by ICICI-Lombard General

146

Insurance Company. This move was followed by IFFCO-Tokio General Insurance Company

and by public sector Agricultural Insurance Company of India (AIC). Under the scheme,

coverage for deviation in the rainfall index is extended and compensations for economic

losses due to less or more than normal rainfall are paid. ICICI Lombard, World Bank and the

Social Initiatives Group (SIG) of ICICI Bank collaborated in the design and pilot testing of

India‟ s first Index based Weather Insurance product in 2003-04. The pilot test covered 200

groundnut and castor farmers in the rain-fed district of Mahaboobnagar, Andhra Pradesh. The

policy was linked to crop loans given to the farmers by BASIX Group, a NGO, and sold

through its Krishna Bhima Samruddhi Area Bank. The weather insurance has also been

experimented with 50 soya farmers in Madhya Pradesh through Pradan, a NGO, 600 acres of

paddy crop in Aligarh through ICICI Bank‟ s agribusiness group along with the crop loans,

and on oranges in Jhalawar district of Rajasthan. Similarly, IFFCO-Tokio General Insurance

(ITGI) also piloted rainfall insurance under the name- „Baarish Bima‟ during 2004-05 in

Andhra Pradesh, Karnataka and Gujarat. Agricultural Insurance Company of India (AIC)

introduced rainfall insurance (Varsha Bima) during 2004 South-West Monsoon period.

Varsha Bima provided for five different options suiting varied requirements of farming

community. These are (1) seasonal rainfall insurance based on aggregate rainfall from June to

September, (2) sowing failure insurance based on rainfall between 15th June and 15th

August, (3) rainfall distribution insurance with the weight assigned to different weeks

between June and September, (4) agronomic index constructed based on water requirement of

crops at different pheno-phases and (5) catastrophic option, covering extremely adverse

deviations of 50 per cent and above in rainfall during the season. Varsha Bima was piloted in

20 rain gauge areas spread over Andhra Pradesh, Karnataka, Rajasthan and Uttar Pradesh in

2004-05.

Based on the experience of the pilot project, the scheme was fine-tuned and implemented as

“Varsha Bima -2005” in about 130 districts across Andhra Pradesh, Chattisgarh, Gujarat,

Karnataka, Mahrashtra, Madhya Pradesh, Orissa, Tamil Nadu, Uttarakhand and Uttar Pradesh

during Kharif 2005. On an average, 2 or 3 blocks /mandals / tehsils were covered under each

India Meteorological Department (IMD) rain gauge stations. The scheme covered the major

crops provided at least two coverage 36

options namely, Seasonal Rainfall Insurance or Rainfall Distribution Index and Sowing

Failure Insurance. Varsha Bima-2005 covered 1.25 lakh farmers with a premium income of

Rs.3.17 crore against a sum insured of Rs.55.86 crore. Claims amounting to Rs.19.96 lakh

were paid for the season. Further, during kharif 2006, the scheme was implemented as Varsha

Bima-2006 in and around 150 districts/ rain gauge station areas covering 16 states across the

country. The Weather Based Crop Insurance Scheme (WBCIS) of AIC was implemented in

the selected areas of Karnataka on a pilot basis. WBCIS is a unique weather based insurance

product designed to provide insurance protection against losses in crop yield resulting from

adverse weather incidences. It provides payout against adverse rainfall incidence (both deficit

and excess) during kharif and adverse incidence in weather parameters like frost, heat,

147

relative humidity, un-seasonal rainfall etc., during rabi. It operates on the concept of area

approach i.e., for the purpose of compensation, a reference unit area shall be linked to a

reference weather station on the basis of which weather data and claims would be processed.

This scheme is available to both loanees (compulsory) and non-loanees (voluntary). The

NAIS is not available for the locations and crops selected for WBCIS pilot. It has the

advantage to settle the claims with the shortest possible time. The AIC has implemented the

pilot WBCIS in Karnataka during kharif 2007 season, covering eight rain-fed crops, insuring

crops nearly 50,000 ha for a sum insured of Rs.50 crore. WBCIS is being implemented in

2007-08 on a larger scale in selected states of Bihar, Chattisgarh, Haryana, Madhya Pradesh,

Punjab, Rajasthan and Uttar Pradesh for rabi 2007-08 season and will be continued even in

2008-09 also as a pilot WBCIS (Union Budget 2008-09, GOI). Together these above

mentioned companies have been able to sell weather insurance policies to about 5.39 lakh

farmers across India from their inception in 2003-04 to date.

INSURANCE :

Insurance is a financial arrangement whereby losses suffered by a few are met from the

funds accumulated through small contributions made by many who are exposed to similar

risks.

CROP INSURANCE :

Crop Insurance is and insurance arrangement aiming at mitigating the financial losses

suffered by the farmers due to damage and destruction of their crops as a result of various

production risks.

Objectives of National Agricultural Insurance Scheme (NAIS)

To provide insurance coverage to all crops and financial support to all farmers in the

event of failure of any notified crop as a result of natural calamities, pests & diseases.

To encourage farmers to adopt progressive farming practices, high value in-puts and

higher technology in Agriculture.

To help stabilize farm incomes, particularly in disaster years.

Risks covered under the scheme:

The scheme provides comprehensive risk insurance for yield losses due to

Natural fire and Lightening, Storm, Hailstorm, Cytclone, Typhoon, Tempest,

Hurricane, Tornado Flood, Inundation and Landslide.

Drought, Dry spells.

Pests / Diseases etc.

Crops covered under the scheme:

Food crops (Cereals, Millets & Pulses) : Paddy, Wheat, Jowar, Bajra, Maize, Ragi,

Greengram, Blackgram, Redgram, Horsegram.

Oilseeds : Groundnut, Sunflower, Soya bean, Safflower, Castor, Sesamum.

148

Annual Commercial / Annual Horticultural Crops : Sugarcane, Cotton, Potato, Onion,

Ginger, Turmeric, Banana, Pineapple, Jute, Tapioca, Chilli, Cumin, Coriander, etc.

The crops are selected for insurance if the past yield data for 10 years are available and

the State Government agrees to conduct requisite number of Crop Cutting Experiments

(CCEs) during the proposed season.

Eligibility :

All farmers growing insurable crops and availing Seasonal Agricultural Operations

(SAO) loans from Banks / PACS are compulsorily covered under the scheme by the

Banks/PACS, whereas the no-borrowing farmers growing insurable crops can also avail the

benefit of the scheme by submitting prescribed proposal forms at the nearest Banks/PACS.

Administration of the scheme:

The scheme is being implemented by Agriculture Insurance Company of India Limited

(AICL) on behalf of the Ministry of Agriculture through its Regional Offices located at 17

State capitals.

Unit of Insurance :

The scheme operates on the basis of Area Approach i.e. defined areas for each notified

crop for widespread calamities and individual assessment is done on experimental basis of

localised calamities, such as hailstorm, landslide, cyclone and flood in certain pre-notified

areas. The size of unit area varies from State to State and crop to crop. Presently, the defined

area is Blcok /Mandal /Taluka /Patwari halka /Nyayapanchayat/Gram Panchayat/Village, etc.

Sum insured under NAIS :

a) For loanee farmers :

Compulsory coverage : The amount of crop loan availed for the notified crop is the

minimum amount of sum insured covered on compulsory basis.

Optional Coverage : If the loanee-farmer so wishes he may insured his crop for a higher sum

insured i.e, upto the value of threshold yield (ile., guaranteed yield) which is called normal

coverage even go for additional coverage upto 150% value of average yield in the notified

area. However, for additional coverage, the farmer has to pay premium at actuarial rate as

notified by the State Government.

b) For non-loanee farmers: Coverage at normal rates of premium is available upto the

value of threshold yield. Additional coverage upto 150% of the value of actual yield can be

obtained by payment of premium at actuarial rates.

The essential requirements of a farmer to become eligible for claim under the scheme:

The farmer should have availed a crop loan for the insured crop. In case of non-loanee

farmer, he should have submitted a proposal for insurance with requisite premium.

149

The proposal/cop insurance declaration with accurate and complete particulars should

have been sent to AID by the bank along with requisite premium.

The State Government conducts requisite number of crop cutting experiments for the

insured crop in the insurance unit and submits the yield data to AIC within the

prescribed date.

The yield data so submitted by the State Government shows short fall as compared to

the guaranteed yield.

Advantage of NAIS:

Be a critical instrument of development in the field of crop production, providing

financial support to the farmers in the event of crop failure.

Encourage farmers to adopt progressive farming practices and higher technology in

Agriculture.

Help in maintaining flow of agricultural credit.

Provides significant benefits not merely to the insured farmers, but to the entire

community directly and indirectly through spillover and multiplier effects in terms of

maintaining production and employment.

Streamline loss assessment procedures and help in building up huge and accurate

statistical base for crop production.

Modified National Agricultural Scheme (MNAIS) :

MNAIS has been implemented on pilot basis during Rabi 2010-11 at Gram

Panchayat (GP) level.

Features:

Unit area of insurance reduced to villages/village panchayat level for major crops.

Threshold yield based on average yield of the preceding 7years excluding upto calamity

year declared by concerned State/UT government/authority.

Uniform seasonality disciplines both for loanee & non-loanee farmers.

It is an improvement over NAIS and based on actuarial premium rates. This scheme is

expected to generate more benefits to farmers through coverage of prevented

sowing/planting risk and post-harvest losses, increases in minimum indemnity level

from 60 to 70% more precise calculation of threshold yield. Payment of upfront

premium subsidy by State and Central Governments will facilitate quick settlement of

claims.

150

Coverage of MNAIS :

Covers pre-sowing and post-harvest losses in addition to other features of NAIS.

Covers wide spread calamities, localised risks and weather parameters.

Risk in Agricultural Production

Agriculture in India is subject to variety of risks arising from rainfall aberrations,

temperature fluctuations, hailstorms, cyclones, floods, and climate change. These risks are

exacerbated by price fluctuation, weak rural infrastructure, imperfect markets and lack of

financial services including limited span and design of risk mitigation instruments such as

credit and insurance. These factors not only endanger the farmer‟ s livelihood and incomes

but also undermine the viability of the agriculture sector and its potential to become a part of

the solution to the problem of endemic poverty of the farmers and agricultural labour.

Management of risk in agriculture is one of the major concerns of the decision makers

and policy planners, as risk in farm output is considered as the primary cause for low level of

farm level investments and agrarian distress. Both, in turn, have implications for output

growth. In order to develop mechanisms and strategies to mitigate risk in agriculture it is

imperative to understand the sources and magnitude of fluctuations involved in agricultural

output. The present section is an effort in this direction. The section examines extent of risk

by estimating year to year fluctuations in national production of major crops and also analyse

whether risk in the post reforms period declined or increased. The analysis is extended to

district level as there are vast variations in agro climatic conditions across states and districts.

*****

151

3. Trends in number and area of Operational holdings during

1970-71 to 2010-11 and comments to strengthen conduct of

Agricultural Census in Karnataka*

1. Introduction:

Agricultural Census is quinquennial census conducted once in five years. Since its

inception in 1970-71, nine Agricultural Census have been conducted, the latest being 2010-11

census. Agriculture Census forms part of a broader system of collection of Agricultural

Statistics. It is a large scale statistical operation for the collection and derivation of

quantitative information about the structure of agriculture in the State. An agricultural

operational holding is the ultimate unit for taking decision for development of Agriculture at

micro level. The operational holding is taken as the statistical unit of data collection for

describing the structure of agriculture. Through Agriculture Census, it is endeavored to

collect basic data on important aspects of agricultural economy for all the operational

holdings in the State.

Before start of the Agricultural Census, the previous year of the conducting of census,

will be declared as ‘Land Records Year’ with the objective of updating all records (RTC) by

the Revenue authorities, which is required to collect correct and authentic information on land

holdings and area to be reflected in the census.

2. Objectives:

The objective of Agricultural Census is to know the structure and characteristics of

agricultural holdings operated by cultivators. Besides, data on land use, sources of irrigation,

cropping pattern and dispersal of operated area were also collected on sampling basis. As a

follow up of Agricultural Census, the Input Survey is also conducted, with the main objective

of collecting data related to number of parcels, multiple cropping, land use pattern, use of

chemical fertilizers, organic manures and pesticides, agricultural implements, live stock

details, certified seeds used and agricultural credit availed by cultivators.

3. Importance:

Periodically conducting of Agricultural Censuses is important, as these are the main

source of information on basic characteristics of operational holdings such as land use and

cropping patterns, irrigation status, and terms of leasing. This information is tabulated by

different size classes and social groups including Scheduled castes/Scheduled Tribes that are

needed for development planning, socio-economic policy formulation and establishment of

State/national priorities. The census also provides the basis for the development of a

comprehensive integrated national system of agricultural statistics and has links with various

components of the national statistical system.

*N.Sridhara, Joint Director, M.N.Chakari, Dy.Director, C.Charles, Dy.Director (Rtd.) A.R.C Division,

Directorate of Economics and Statistics.

152

4. Phase wise conduct of the Census and Coverage:

The whole project of Agriculture Census in the country is implemented in three

distinct phases, which are statistically linked together but focus on different aspects of

agricultural statistics. In Phase-I, a list of holdings with their area and social characteristics

and gender of the holders is prepared. In Phase-II, detailed data on agricultural characteristics

of holdings are collected from selected villages. In phase-III as follow up to agriculture

census, Input survey is being conducted by collected data in three stages are followed in

conduct of Agricultural Census, they are number and area of entire operational holdings

collected through census method constitute the first stage, the details of land particulars and

other agricultural characteristics collected on sampling basis (20% of sample) constitute the

second stage and the conduct of input survey which is also taken up on sampling basis (7% of

sample) constitute the third stage.

5. Size classes adopted in the Census: The major size classes for compiling Agricultural Census reports are as follows:

Marginal Holdings: An operated holding with area less than a hectare.

Small Holdings: An operational holding with area one hectare and above, but below two

hectares.

Semi-medium Holdings: An operational holding with area two hectares and above, but less

than four hectare.

Medium Holdings: An operational holding with area 4 hectares and above, but less than 10

hectare.

Large Holdings: An peritoneal holdings with 10 hectares and above.

TRENDS IN NUMBER AND AREA OF OPERATIONAL HOLDINGS

DURING 1970-71 TO 2010-11 OF AGRICULTURAL CENSUSES

I. Number of Operational Holdings: 1. It may be observed that the number and area of operational holdings which was 35.51

lakhs during 1970-71 census has increased to 78.32 lakhs showing a significant

increase of 120.5% during 2010-11 Census. The maximum percentage increase was

observed in the number of operational holdings by 17.4% between 1985-86 censuses

and 1990-91 censuses. (Refer chart-1) .

3551 3811 4309

4919 5776

6221 7079

7581 7832

0

2000

4000

6000

8000

10000

1970-71 1976-77 1980-81 1985-86 1990-91 1995-96 2000-01 2005-06 2010-11

No

. in

00

0'

CHART-1 TRENDS IN NO. OF OPERATIONAL HOLDINGS OF AGRICULTURAL

CENSUS 1970-71 TO 2010-11

153

2. In the marginal size class the number of operational holdings has increased from 10.81

lakhs in 1970-71 to 38.48 lakhs in 2010-11 census with a maximum increase of 255%

In case of small size class of holdings had increased from 8.40 lakhs in 1970-71

census to 21.38 lakhs in 2010-11 census with an increase of 154% The semi- medium

size class of holdings registered 60% increase from 7.88 lakhs holdings in 1970-71

census to 12.66 lakhs holdings in 2010-11 census. On the hand, under medium size

class the number of holdings, which was 6.23 lakhs in 1970-71 census has come down

to 5.11 lakhs during 2010-11 census, indicating about 18.13 short fall. Further a

significant decrease in holdings was observed in case of large size class, by 70% i.e.,

from 2.19 lakhs holdings in 1970-71 census to 0.67 lakhs holdings in 2010-11 census.

(Refer chart-2).

II. Area of Operational Holdings:

1. The area of operational holdings which was 113.68 hectares in 1970-71 census has

increased to 121.61 lakh hectares with an increase of 7% Observing the previous

censuses the area of operational holdings was dropped marginally by 0.01% between

1970-71 and 1976-77 census, has gradually increased in 1980-81, 1985-86 and 1990-91,

but once again declined by around 1.7% in 1995-96 census as compared to the 1990-91

and in subsequent censuses 2000-01 and in 2005-06 it was gradually increased and in

2010-11 decreased by 6%. (Refer chart-3).

1970-71 1976-77 1980-81 1985-86 1990-91 1995-96 2000-01 2005-06 2010-11

Marginal(<1Ha) 1081 1274 1489 1792 2262 2610 3252 3655 3848

Small (1 to 2Ha) 840 888 1057 1293 1586 1707 1909 2014 2138

Semi medium(2 to 4 Ha) 788 818 918 1035 1163 1204 1259 1278 1266

Medium( 4 to 10 Ha) 623 632 662 646 636 594 569 555 511

Large (10 Ha & above) 219 199 183 153 129 106 90 79 67

0

500

1000

1500

2000

2500

3000

3500

4000

4500

No

. in

00

0'

CHART-2 TRENDS IN NO. OF OPERATIONAL HOLDINGS BY MAJOR SIZE CLASSES

OF AGRICULTURAL CENSUS 1970-71 TO 2010-11

154

2. In respect of area of operational holdings under different size classes, the increase is as

high as 237% in respect of marginal size class i.e., from 5.49 lakh hectares in 1970-71

census to 18.51 lakh hectares in 2010-11census, followed by an increase of 147% among

small size class i.e., from 12.21 lakh hectares in 1970-71 census to 30.20 lakh hectares in

2010-11 census. On contrary, in medium size class holdings, the area operated was

reduced from 37.92 lakh hectares in 1970-71 census to 29.03 lakh hectares in 2010-11

census the decrease was by about 23% and in case of large size class holdings, the drastic

reduction of around 72% i.e., 36.01 lakh hectares in 1970-71 census to 9.94 lakh hectares

in 2010-11 census can be observed. (Refer chart-4) .

10800

11000

11200

11400

11600

11800

12000

12200

12400

1970-71 1976-77 1980-81 1985-86 1990-91 1995-96 2000-01 2005-06 2010-11

11368 11357

11746 11879

12321

12109

12307 12385

12161

Are

a in

00

0' h

ect

are

s CHART - 3

TRENDS IN AREA OF OPERATIONAL HOLDINGS OF AGRICULTURAL CENSUS 1970-71 TO 2010-11

155

II. Average size of Operational Holdings:

3. It is observed that, a gradual decrease can be seen in average size of operational holdings

from census to census. The average size of operational holdings which was 3.20 hectares

in 1970-71 has reduced to 1.54 hectares in 2010-11 census the reduction of average size

was by 1.66 hectares. (Refer chart-5) .

1970-71

1976-77

1980-81

1985-86

1990-91

1995-96

2000-01

2005-06

2010-11

Marginal(<1Ha) 549 638 733 866 1072 1248 1492 1651 1850

Small (1 to 2Ha) 1221 1319 1543 1888 2308 2480 2742 2876 3020

Semi medium(2 to 4 Ha) 2205 2288 2572 2880 3200 3298 3429 3468 3393

Medium( 4 to 10 Ha) 3792 3858 4018 3881 3770 3490 3317 3206 2903

Large (10 Ha & above) 3601 3254 2880 2364 1971 1593 1327 1184 994

0

500

1000

1500

2000

2500

3000

3500

4000

4500A

rea

in 0

00

' he

ctar

ea

CHART - 4 TRENDS IN AREA OF OPERATIONAL HOLDINGS BY MAJOR SIZE

CLASSES OF AGRICULTURAL CENSUS 1970-71 TO 2010-11

156

4. With regard to average size of operational holdings under different size classes, it is seen

that under marginal, small and semi – medium size classes, the average size of

operational holdings remained almost the same during every census, while in respect of

medium and large size class of holdings there is a gradual decrease in average size of

operational holdings from census to census. (Refer chart-6) .

0

0.5

1

1.5

2

2.5

3

3.5

1970-71 1976-77 1980-81 1985-86 1990-91 1995-96 2000-01 2005-06 2010-11

3.2 2.98

2.73

2.41

2.13 1.95

1.74 1.63 1.54

In H

ect

are

s

CHART - 5 TRENDS IN AVERAGE SIZE OF OPERATIONAL HOLDINGS OF AGRICULTURAL

CENSUS 1970-71 TO 2005-06

157

IV. Findings of the Census:

(a) Decreasing trend in Large Size class holdings:

The analysis from 1970-71 to 2010-11 censuses reveals that continues decreasing

trend has been observed in the large size of operational holdings (area 10 hectares and

above). That is during 1970-71 census the number of operational holding were 2.19 lakhs,

which has steeply decreased to 0.63 lakh holdings during 2010-11 , resulted in decrease of

71%.

(b) Increasing trend in number of Marginal holdings:

On the hand there is an inverse trend with respect to the Marginal size of operational

holdings (area operated less than a hectare). That is there is an increasing trend from 10.81

lakh operational holdings during 1970-71 to 38.48 lakh operational holdings during 2010-

11, registered an increase of 255%.

(c) Decreasing trend in Area Operated among Large holdings:

The similar trend is observed in respect of area of operational holdings of large

holders. That is during 1970-71 census the operated area was 36.01 lakhs, which was

decreased to 9.93 lakh hectares during 2010-11 resulted in decrease of 72%.

1970-71 1976-77 1980-81 1985-86 1990-91 1995-96 2000-01 2005-06 2010-11

Marginal(<1Ha) 0.51 0.5 0.49 0.48 0.47 0.48 0.46 0.45 0.48

Small (1 to 2Ha) 1.46 1.49 1.46 1.46 1.46 1.45 1.44 1.43 1.41

Semi medium(2 to 4 Ha) 2.8 2.8 2.8 2.8 2.75 2.74 2.72 2.71 2.68

Medium( 4 to 10 Ha) 6.09 6.11 6.07 6.01 5.93 5.88 5.83 5.79 5.69

Large (10 Ha & above) 16.43 16.35 15.69 15.45 15.28 15.02 14.74 14.79 14.71

0

2

4

6

8

10

12

14

16

18

In h

ect

are

s

CHART - 6 TRENDS IN AVERAGE SIZE OF OPERATIONAL HOLDINGS BY MAJOR SIZE CLASSES OF

AGRICULTURAL CENSUS 1970-71 TO 2010-11

158

(d) Increasing trend in Area Operated among Marginal holdings:

On the other Hand, inverse trend can be seen with respect to the area of marginal size

of operational holdings. That is there is an increasing trend from 5.49 lakh hectares during

1970-71 census to 18.50 lakh hectares 2010-11 census resulted in increase of 236%.

(e) Steep decrease in Average size

It can been seen that, the average size of operational holding has decreased from 3.20

hectares in 1970-71 to 1.54 hectares during 2010-11 .

V. Impression: Since inception of the census in 1970-71, in case of marginal, small and semi–medium

operational holdings and area have been increasing, where as large and medium operational

holdings and area have been decreasing. These changing patterns in number and area of

operational holdings are due to sub division and fragmentation of agricultural land,

acquisition of agricultural land for non – agricultural purposes and further due to urbanization

of agricultural lands.

Comments to Strengthen Agriculture Census Scheme 1. Data collection:

(a) Updation of RTC for conduct of the census is primary requisities for collecting correct

and authentic data:

i) Before commencement of Agricultural Census Year, the computerized printed

RTC should be provided fo the concerned village accountants/patwaries to

enable them to update the RTC.

ii) Season wise & cropwise area operated & mutation registers should be updated

in the RTC to get accurate & reliable data.

iii) Horticulture and floriculture crops grown in urban area, backyard, road side

plantation, tankbund areas, Gomala and etc., are not enumerated in the earlier

census. Since these cropped areas are not recorded in the RTC. It is suggested

that, to include the above said areas to get complete data coverage on

Horticulture and floriculture crops.

iv) Season wise and crop wise cultivated area are not updated in the computerized

RTC immediately after completion of the season, it is updated after a year or

more.

v) In case of computerized RTC, the unique code is assigned to the farmers within

a village. If a farmer owns a land in different villages he will be treated as a

separate holder, which may increase number of holders considerably and we

will not able to get the correct classification of the size class in respect of

holdings and total area. Whereas, as per the guidelines of current census, Taluk

is taken up as a unit and the filled in L2 schedule for villages concerned and H

for TRS villages will be transferred to the resident of the cultivator and pooled

all the area coming under a operational holder within a taluk and size class is

being classified according to the position of the total area of a holder in that

taluk.

159

2. Approach for strengthening quality and reliability of Agriculture Census data.

Effective supervision during the time of updation of RTC and also during field work

should be done to ensure the quality of data collection by assigning responsibilities to the

concerned officers/officials of Revenue, Statistics, Agriculture, Horticulture and Irrigation

departments.

3. It is suggested that the Patwari should arrange to computerize L-1 and L-2 filled in

schedules with the help of Khasra register (RTC) to enable for re-tabulation of data and

preparation of T-1 table.

4. It has been observed that, there was exorbitant delay in data processing work. Although the

fieldwork completes as per time schedule. The centralized processing of agriculture census

data took much time for validation and processing. For ex., during 2005-06 census we have

experienced the processing of „H‟ schedule took nearly three years and by the time the

error free data table completed almost we have started the next agriculture census. The

outdated data is questioned in various committees at state Government level. This requires

modification in data processing, may be decentralized and entrusted to concern NIC at

State Head Quarters. There by clarifications and other correction may be easily done and

accessibility will be quicker at State level itself.

5. It is Suggested that certain information like residential status of operational holders, gender

wise operational holders, social status, No. of wells, tube wells etc., are not available in the

land record register (RTC) and these information may be collected through oral enquiry

from households by the patwari.

6. For intensive supervision a Performa may be designed for verification of the supervisors.

7. To rationalize to get honorarium to the field staff, the existing payment of honorarium

according to the number of villages can be replaced by the quantum of work carried out by

Patwaries (as per the number operational holders in each village).

8. It has become necessary for modification of land revenue Laws/Act to write the

RTC/Khasra register as per time schedule and actual information in the field should be

collected and incorporated in the RTC, which should made as mandatory.

9. All vacant post of field worker that is Patwari should be filled up by the revenue authority

before conduct of Agriculture Census work. Further, the Patwari normally will be pre

occupied with multi various activities at grass root level. Strict instruction may be issued to

follow the job chart stipulated to the Patwaries which should also include the conduct of

agriculture census.

10. The common data entry software and validation checks should be prior tested and

confirmed before commencement of census work. A pilot study need to be conducted and

reviewed for finalizing the common data entry software and validation checks should be

kept ready and supplied to state head quarters in advance, while conducting the census.

************

160

4. ¨É¼É PÉëÃvÀæ ªÀÄvÀÄÛ GvÁàzÀ£Á CAQ CA±ÀUÀ¼À «ªÁzÁA±À «µÀAiÀÄ ªÀiÁ»w ¸ÀAUÀæºÀuÉ ªÀÄvÀÄÛ ¸ÀA¸ÀÌgÀuÉAiÀÄ°ègÀÄªÀ vÉÆqÀPÀÄ ¥ÀjºÁgÉÆÃ¥À PÀæªÀÄUÀ¼À §UÉÎ n¥ÀàtÂ*

»£Éß¯É: PÀ£ÁðlPÀ gÁdåªÀÅ PÀÈ¶ ¥ÀæzsÁ£À gÁdå«zÀÄÝ ¨É¼É PÉëÃvÀæ ºÁUÀÆ GvÁàzÀ£É CAQ CA±ÀUÀ¼ÀÄ gÁµÀÖçzÀ PÀÈ¶ ªÀÄvÀÄÛ DºÁgÀ ¤Ãw DªÀÄzÀÄ ªÀÄvÀÄÛ gÀ¥sÀÄÛUÀ¼À£ÀÄß gÀÆ¦¸ÀÄªÀ°è «±ÉÃµÀªÁV PÀÈ¶ C©üªÀÈ¢ÝUÁV C£ÀÄzÁ£À ºÀAaPÉ ªÀiÁqÀÄªÀ°è ªÀÄvÀÄÛ gÁdå ºÁUÀÆ gÁµÁÖçzÁAiÀÄ CAzÁf¸À®Ä ¤uÁðAiÀÄPÀ ¥ÁvÀæªÀ»¸ÀÄvÀÛzÉ. F GzÉÝÃ±ÀPÉÌ UÀÄuÁvÀäPÀ ªÀÄvÀÄÛ £ÀA§®ºÀð CAQ CA±ÀUÀ¼À£ÀÄß ¸ÀPÁ°PÀªÁV MzÀV¸ÀÄªÀzÀÄ. ¸ÀPÁðgÀzÀ ¥ÀæªÀÄÄR E¯ÁSÉUÀ¼ÁzÀ PÀAzÁAiÀÄ, PÀÈ¶, vÉÆÃlUÁjPÉ ºÁUÀÆ ¤ÃgÁªÀj E¯ÁSÉUÀ¼À dªÁ¨ÁÝjAiÀiÁVgÀÄvÀÛzÉ. DyðPÀ ªÀÄvÀÄÛ ¸ÁATåPÀ ¤zÉÃð±À£Á®AiÀÄªÀÅ PÉÃAzÀæ ¸ÀPÁðgÀ¢AzÀ gÁdå PÀÈ¶ CAQ CA±ÀUÀ¼À

¥Áæ¢üPÁgÀªÁV C¢ü¸ÀÆavÀ (¤zÉÃð²vÀ) (State Agricultural Statistics Authority) E¯ÁSÉAiÀiÁVgÀÄvÀÛzÉ. CAvÉAiÉÄ DyðPÀ ªÀÄvÀÄÛ ¸ÁATåPÀ ¤zÉÃð±À£Á®AiÀÄ ªÉÄÃ¯É w½¹zÀ 1) PÀAzÁAiÀÄ 2) PÀÈ¶ 3) vÉÆÃlUÁjPÉ 4) ¤ÃgÁªÀj E¯ÁSÉUÀ¼À ¸ÀªÀÄ£ÀéAiÀÄzÉÆA¢UÉ PÀÈ¶ PÉëÃvÀæ ªÀÄvÀÄÛ GvÁàzÀ£Á CAQ CA±ÀUÀ¼À£ÀÄß ¸ÀAUÀæ»¹, «±ÉèÃ¶¹ ¸ÀPÁðgÀPÉÌ ªÀgÀ¢ ªÀiÁqÀÄªÀ ¸ÀPÁðgÀzÀ ¥Àæw¤¢ü E¯ÁSÉAiÀiÁV PÁAiÀÄð¤ªÀð»¸ÀÄwÛzÉ.

A) ¨É¼É PÉëÃvÀæ CAQ CA±ÀUÀ¼ÀÄ:-

FV£À ªÀ¸ÀÄÛ ¹Üw: 1966 gÀ PÀ£ÁðlPÀ ¨sÀÆPÀAzÁAiÀÄ ¤AiÀÄªÀÄUÀ¼À ªÉÄÃgÉUÉ PÀÈ¶UÉ

¸ÀA§A¢ü¹zÀ CAQ CA±ÀUÀ¼À£ÀÄß UÁæªÀÄ zÁR¯ÉAiÀiÁzÀ ¥ÀºÀtÂ ¥ÀwæPÉ (Record of Rights Tenancy and

crop) AiÀÄ°è ¸ÀAUÀæ»¸À¯ÁUÀÄwÛzÉ.

¸ÀPÁðgÀ¢AzÀ PÀÈ¶ CAQ CA±ÀUÀ¼À ¸ÀAUÀæºÀuÉ «zsÁ£ÀzÀ°è PÁ® PÁ®PÉÌ C¢ü¸ÀÆZÀ£ÉUÀ¼À

ªÀÄÆ®PÀ ªÀiÁ¥ÁðqÀÄ ªÀiÁqÀ¯ÁVzÀÄÝ ¥Àæ¸ÀÄÛvÀ C¢ü¸ÀÆZÀ£É ¸ÀA.RD/23/ELR/2004 ¢.06.05.2005 gÀ ªÀiÁUÀð ¸ÀÆaUÀ¼ÀÄ eÁjAiÀÄ°ègÀÄvÀÛªÉ.

ªÉÄÃ°£À ªÀiÁUÀð ¸ÀÆaUÀ¼À£ÀéAiÀÄ UÁæªÀÄzÀ UÁæªÀÄ ¯ÉPÁÌ¢üPÁj PÀÈ¶ ¸ÀºÁAiÀÄPÀ, vÉÆÃlUÁjPÉ ¸ÀºÁAiÀÄPÀ, ¤ÃgÁªÀj ¥ÁlPÀj AiÀÄªÀgÀ£ÉÆß¼ÀUÉÆAqÀ vÀAqÀ¢AzÀ PÉëÃvÀæ vÀ¥Á¸ÀuÉ PÉÊUÉÆAqÀÄ ¥ÀºÀtÂ ¥ÀwæPÉAiÀÄ°è ªÀÄÄAUÁgÀÄ, »AUÁgÀÄ, ¨ÉÃ¹UÉ ºÁUÀÆ ªÁ¶ðPÀ IÄvÀÄUÀ½UÉ ¸ÀA§A¢ü¹zÀAvÉ (¸ÀÜ½ÃAiÀÄ/C¢üPÀ E¼ÀÄªÀj) ªÁgÀÄ ªÀÄÆ® (ªÀÄ¼ÉD²ævÀ/¤ÃgÁªÀj) ªÁgÀÄ «ªÀgÀUÀ¼À£ÀÄß ¥ÀºÀtÂAiÀÄ°è zÁR°¹ UÁæªÀÄzÀ ¨É¼É WÉÆÃµÁégÉUÀ¼À£ÀÄß ¹zÀÝ¥Àr¸À¯ÁUÀÄwÛzÉ.

UÁæªÀÄ ¨É¼É WÉÆÃµÁégÉUÀ¼À£ÀÄß DzsÀj¹ vÁ®ÆPÁ ºÁUÀÆ f¯Áè ªÀÄlÖzÀ ¨É¼É CAQ CA±ÀUÀ¼À ¸ÀªÀÄ£ÀéAiÀÄ ¸À«ÄwAiÀÄ°è ¥Àj²Ã°¹ ºÉÆÃ§½, vÁ®ÆPÁ ªÀÄvÀÄÛ f¯Áè ªÀÄlÖzÀ ¨É¼É PÉëÃvÀæ ªÀgÀ¢UÀ¼À£ÀÄß ¹zÀÝ¥Àr¸À¯ÁUÀÄvÀÛzÉ.

ªÀÄÆgÀÄ IÄvÀÄUÀ¼À ¨É¼É PÉëÃvÀæ ªÀÄgÀÄ ºÉÆAzÁtÂPÉ ªÀgÀ¢UÀ¼À£ÁßzsÀj¹ ªÁ¶ðPÀ IÄvÀÄ ªÀÄvÀÄÛ ¨É¼ÉUÀ¼À CAQ CA±ÀUÀ¼À ªÀgÀ¢AiÀÄ£ÀÄß ¹zÀÝ¥Àr¸À¯ÁUÀÄwÛzÉ. F ªÀgÀ¢AiÀÄÄ ¸ÀPÁðgÀzÀ PÀÈ¶ CAQ CA±ÀUÀ¼À KPÀªÀiÁvÀæ PÁ£ÀÆ£ÀÄ§zÀÞ ªÀgÀ¢AiÀiÁVgÀÄvÀÛzÉ.

* ²æÃ J£ï.J¸ï. £ÁUÀ±ÉnÖ, f¯Áè ¸ÀASÁå ¸ÀAUÀæºÀuÁ¢üPÁj, zsÁgÀªÁqÀ.

161

PÀÈ¶ CAQ CA±ÀUÀ¼À UÀÄtªÀÄlÖzÀ°è ¸ÀÄzsÁgÀuÉ vÀgÀÄªÀ ¤nÖ£À°è PÉÃAzÀæ ¸ÀPÁðgÀ¢AzÀ

gÀÆ¦¸À¯ÁzÀ ¸ÀPÁ°PÀ (TRS) ªÀgÀ¢ AiÉÆÃd£É ªÀÄvÀÄÛ PÀÈ¶ CAQ CA±ÀUÀ¼À (ICS) ¸ÀÄzsÁgÀuÉ AiÉÆÃd£ÉUÀ¼À£ÀéAiÀÄ gÁ¶ÖçÃAiÀÄ ªÀiÁzÀj ¸À«ÄÃPÉë ¸ÀA¸ÉÜAiÀÄ ¸ÀºÀAiÉÆÃUÀzÀ°è PÁAiÀÄð ¤ªÀð»¸À¯ÁUÀÄwÛzÉ.

F AiÉÆÃd£ÉUÀ¼Àr gÁ¶ÖçÃAiÀÄ ªÀiÁzÀj ¸À«ÄÃPÉë ¸ÀA¸ÉÜAiÀÄ C¢üPÁjUÀ¼ÀÄ, gÁdå ¸ÀPÁðgÀzÀ PÀAzÁAiÀÄ, PÀÈ¶, vÉÆÃlUÁjPÉ ªÀÄvÀÄÛ ¸ÁATåPÀ E¯ÁSÉ C¢üPÁjUÀ½AzÀ DAiÀiÁ UÁæªÀÄUÀ¼À°è ¨É¼É vÀ¥Á¸ÀuÉ ªÀiÁr¹ ¥ÀºÀtÂ zÁR¯ÁwUÀ¼À°è£À ¯ÉÆÃ¥À zÉÆÃµÀUÀ¼À ¥ÀvÉÛ ºÀaÑ ¥ÀjºÁgÉÆÃ¥À PÀæªÀÄ PÉÊUÉÆ¼Àî¯ÁUÀÄwÛzÉ.

B) 1) E¼ÀÄªÀj/GvÁàzÀ£Á CAQ CA±ÀUÀ¼ÀÄ:

PÀÈ¶ GvÀàwÛAiÀÄ£ÀÄß CAzÁf¸À®Ä E¼ÀÄªÀj zÀgÀUÀ¼ÀÄ CªÀ±ÀåPÀªÁVgÀÄvÀÛzÉ. F GzÉÝÃ±ÀPÁÌV PÉÃAzÀæ ¥ÀÄgÀ¸ÀÌøvÀ AiÉÆÃd£ÉAiÀiÁzÀ ¨É¼É CAzÁdÄ ¸À«ÄÃPÉëAiÀÄ£ÀÄß gÁdåzÀ°è PÀ¼ÉzÀ DgÀÆªÀgÉ zÀ±ÀPÀUÀ½AzÀ £ÀqÉ¹PÉÆAqÀÄ §gÀ¯ÁVzÉ.

2) GzÉÝÃ±À:

1. ¥ÀæªÀÄÄR DºÁgÀ ªÀÄvÀÄÛ DºÁgÉÃvÀgÀ ¨É¼ÉUÀ¼À ºÉPÉÖÃgÀÄªÁgÀÄ E¼ÀÄªÀjAiÀÄ£ÀÄß ºÉÆÃ§½, vÁ®ÆPÁ, f¯Áè ªÀÄvÀÄÛ gÁdå ªÀÄlÖPÉÌ PÀAqÀÄPÉÆAqÀÄ F ºÀAvÀUÀ½UÉ GvÁàzÀ£ÉAiÀÄ£ÀÄß CAzÁf¸ÀÄªÀÅzÁVgÀÄvÀÛzÉ.

2. PÀÈ¶UÉ ¥ÀÆgÀPÀªÁzÀ ¨ÉÃ¸ÁAiÀÄ ¥ÀzÀÝw ¨É¼ÉUÀ¼À gÉÆÃUÀ ªÀÄvÀÄÛ QÃl¨ÁzÉ, ªÉÄÃªÀÅ GvÁàzÀ£É ªÀÄÄAvÁzÀ ªÀiÁ»w PÀ¯ÉºÁPÀ¯ÁUÀÄwÛzÉ.

3. ¥ÀæPÀÈw «PÉÆÃ¥À¢AzÁV ¨É¼É £ÀµÀÖªÁzÀ°è gÉÊvÀjUÉ PÀÈ¶ «ªÀiÁ ¥ÀjºÁgÀ ¤ÃqÀ®Ä ¥ÀæAiÉÆÃd£ÀPÁjAiÀiÁVgÀÄvÀÛzÉ.

3) PÁAiÀÄð «zsÁ£À:

¨É¼É CAzÁdÄ ¸À«ÄÃPÉë AiÉÆÃd£ÉUÉ UÁæªÀÄUÀ¼À£ÀÄß DAiÉÄÌ ªÀiÁqÀÄªÀ°è ¸ÀPÁ°PÀ ªÀgÀ¢

AiÉÆÃd£ÉAiÀÄ UÁæªÀÄUÀ½UÉ ¥Áæ±À¸ÀÛöå ¤ÃqÀ¯ÁUÀÄwÛzÉ. UÁæªÀÄUÀ¼À DAiÉÄÌ PÀmÁªÀÅ vÁPÀÄUÀ¼À (Experimental

Plots) DAiÉÄÌ ªÀiÁqÀÄªÁUÀ §ºÀÄ ºÀAvÀzÀ (Multistage Stratified Random Sampling) C¤AiÀÄvÀ ¥ÀzÀÝwAiÀÄ£ÀÄß DzsÀj¸À¯ÁUÀÄwÛzÉ. F ¥ÀzÀÝwAiÀÄ£ÀéAiÀÄ vÁ®ÆQ£À°è UÁæªÀÄªÀÅ ¥ÁæxÀ«ÄPÀ WÀlPÀªÁVzÀÄÝ DAiÉÄÌAiÀiÁzÀ ¨É¼É PÉëÃvÀæ ¢éÃwAiÀÄ ºÀAvÀzÁÝVzÀÄÝ. DAiÉÄÌAiÀiÁzÀ ¥ÁæAiÉÆÃVPÀ vÁPÀÄ PÉÆ£ÉAiÀÄ ºÀAvÀzÁÝVgÀÄvÀÛzÉ.

¥Àæ¸ÀÄÛvÀ eÁjAiÀÄ°ègÀÄªÀ gÁ¶ÖçÃAiÀÄ PÀÈ¶ «ªÀiÁAiÉÆÃd£É ªÀiÁUÀð ¸ÀÆaAiÀÄ£ÀéAiÀÄ ¥Àæw ºÉÆÃ§½UÉ C¢ü¸ÀÆZÀ£ÉAiÀiÁzÀ ¨É¼ÉAiÀÄ ªÉÄÃ¯É ºÀvÀÄÛ ¥ÀæAiÉÆÃUÀUÀ¼À£ÀÄß (5 UÁæªÀÄUÀ¼À£ÀÄß) DAiÉÆÃf¸À¯ÁUÀÄwÛzÉ. ¸ÀPÁðgÀzÀ C¢ü¸ÀÆZÀ£ÉAiÀÄ£ÀéAiÀÄ f¯ÉèAiÀÄ°è ªÀÄÄAUÁgÀÄ, ªÁ¶ðPÀ, »AUÁgÀÄ ºÁUÀÆ ¨ÉÃ¹UÉ IÄvÀÄUÀ½UÉ ¥ÀævÉåÃPÀªÁV ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À QæAiÀiÁ AiÉÆÃd£ÉAiÀÄ£ÀÄß ¹zÀÝ¥Àr¸À¯ÁUÀÄwÛzÉ.

¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À£ÀÄß ¤ªÀð»¸À®Ä ¸ÀPÁðgÀzÀ ªÀiÁUÀð¸ÀÆaAiÀÄ£ÀéAiÀÄ UÁæªÀÄ ªÀÄlÖzÀ PÁAiÀÄðPÀvÀðgÁzÀ PÀAzÁAiÀÄ E¯ÁSÉAiÀÄ UÁæªÀÄ ¯ÉQÌUÀgÀÄ d¯Á£ÀAiÀÄ£À ºÁUÀÆ PÀÈ¶ E¯ÁSÉUÀ¼À PÀÈ¶ ¸ÀºÁAiÀÄPÀgÀÄ/¸ÀºÁAiÀÄPÀ PÀÈ¶ C¢üPÁjUÀ¼ÀÄ, vÉÆÃlUÁjPÉ E¯ÁSÉAiÀÄ vÉÆÃlUÁjPÉ ¸ÀºÁAiÀÄPÀgÀÄ/¸ÀºÁAiÀÄPÀ vÉÆÃlUÁjPÉ C¢üPÁjUÀ¼ÀÄ ªÀÄvÀÄÛ UÁæ«ÄÃt C©üªÀÈ¢Ý ªÀÄvÀÄÛ ¥ÀAZÁAiÀÄvÀ

162

gÁd E¯ÁSÉAiÀÄ ¥ÀAZÁAiÀÄvÀ PÁAiÀÄðzÀ²ðUÀ¼ÀÄ/¥ÀAZÁAiÀÄvÀ C©üªÀÈ¢Ý C¢üPÁjUÀ¼ÀÄ ªÀÄÆ® PÁAiÀÄðPÀvÀðgÁVgÀÄvÁÛgÉ.

¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À UÀÄtªÀÄlÖ PÁ¥ÁqÀ®Ä ±ÉÃ.35% ¥ÀæAiÉÆÃUÀUÀ¼À£ÀÄß ªÉÄÃ°éZÁgÀuÉUÁV PÀAzÁAiÀÄ, PÀÈ¶, vÉÆÃlUÁjPÉ ºÁUÀÆ ¸ÁATåPÀ E¯ÁSÉAiÀÄ C¢üPÁjUÀ½UÉ ªÀ»¹PÉÆqÀ¯ÁUÀÄwÛzÉ.

f¯ÁèªÀÄlÖzÀ°è ¸ÁATåPÀ E¯ÁSÉ¬ÄAzÀ QæAiÀiÁ AiÉÆÃd£É ¹zÀÝ¥Àr¸ÀÄªÀÅzÀÄ ªÀÄÆ® PÁAiÀÄðPÀvÀðjUÉ ºÁUÀÆ ªÉÄÃ°éZÁgÀuÁ¢üPÁjUÀ½UÉ vÀgÀ¨ÉÃw ¤Ãr ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À £ÀªÀÄÆ£ÉUÀ¼À£ÀÄß ¸ÀAUÀæ»¸ÀÄªÀ ºÉÆuÉUÁjPÉAiÀÄ£ÀÄß ¤ªÀð»¸À¯ÁUÀÄvÀÛzÉ. DyðPÀ ªÀÄvÀÄÛ ¸ÁATåPÀ ¤zÉÃð±À£Á®AiÀÄ¢AzÀ PÉÃA¢æÃAiÀÄªÁV E¼ÀÄªÀjUÀ¼À£ÀÄß PÀAqÀÄPÉÆAqÀÄ ¸ÀPÁðgÀPÉÌ ªÀgÀ¢ªÀiÁqÀÄªÀ dªÁ¨ÁÝjAiÀÄ£ÀÄß ¤ªÀð»¸À¯ÁUÀÄvÀÛzÉ.

C) ¨É¼É PÉëÃvÀæ ºÁUÀÆ GvÁàzÀ£Á CAQ CA±ÀUÀ¼À°è PÀAqÀÄ§gÀÄªÀ £ÀÆå£ÀvÉUÀ¼ÀÄ:

1) ¥ÀºÀtÂAiÀÄ°è ¨É¼ÉUÀ¼À zÁR¯Áw ¤AiÀÄªÀiÁ£ÀÄ¸ÁgÀ ¤RgÀªÁV DUÀÄwÛ®è.

GzÁ :

1) ¤UÀ¢ PÁ¯ÁªÀ¢üAiÀÄ°è zÁR°¸ÀzÉÃ CwÃ «¼ÀA§ªÁV zÁR°¸ÀÄªÀÅzÀÄ.

2) ¥ÀÆªÀð ªÀÄÄAUÁgÀÄ ¨É¼ÉUÀ¼ÁzÀÀ ¨É¼ÀÄî½î, zsÀ¤AiÀiÁ, ºÉ¸ÀgÀÄ, C®¸ÀA¢, £ÀªÀtÂ ¨É¼ÉUÀ¼À£ÀÄß ¥ÀÆªÀð ªÀÄÄAUÁgÀÄ IÄvÀÄ«UÉ ¥ÀævÉåÃPÀªÁV zÁR°¸ÀÄwÛ®è.

3) «Ä±Àæ ¨É¼É ¥ÀAiÀiÁðAiÀÄ ¨É¼É ªÀÄvÀÄÛ ªÁ¶ðPÀ ¨É¼ÉUÀ¼ÁzÀ vÉÆÃlUÁjPÉ ¨É¼ÉUÀ¼À£ÀÄß PÀæªÀÄ§zÀÝªÁV zÁR°¸ÀÄwÛ®è.

4) ¤ÃgÁªÀj ªÀÄÆ®UÀ¼À£ÀÄß ºÁUÀÆ PÉëÃvÀæªÀ£ÀÄß zÁR°¸ÀÄwÛ®è.

5) C£À¢üÃPÀÈvÀ ¸ÁUÀÄªÀ½ PÉëÃvÀæ ºÁUÀÆ ¨É¼ÉUÀ¼À ªÀiÁ»w ªÀgÀ¢AiÀiÁUÀÄwÛ®è.

6) ªÁ¸ÀÛ«PÀªÁV ¨É¼ÉzÀ ¨É¼ÉUÀ¼À §zÀ¯ÁV «ªÀiÁ AiÉÆÃd£ÉAiÀÄr ¸ÉÃ¥ÀðqÉAiÀiÁUÀ¢gÀÄªÀ ¨É¼ÉUÀ¼À PÉëÃvÀæªÀ£ÀÄß zÁR°¸À¯ÁUÀÄwÛzÉ. GzÁ: PÀ§Äâ ¨É¼ÉAiÀÄ §zÀ¯ÁV ¨sÀvÀÛzÀ ¨É¼É, ºÀwÛ §zÀ¯ÁV UÉÆÃ«£À eÉÆÃ¼À EvÁå¢

7) ªÀÄÆ® PÁAiÀÄðPÀvÀðjUÉ ¤AiÉÆÃd£ÉUÉÆAqÀ ªÀÄÆ®PÁAiÀÄðPÀvÀðgÀÄ RÄzÁÝV ¨É¼ÉPÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À£ÀÄß ¤ªÀð»¸ÀzÉÃ EgÀÄªÀÅzÀÄ, CxÀªÁ ¥Àæw¤¢ü¬ÄAzÀ ¤ªÀð»¸ÀÄªÀÅzÀÄ.

8) PÀÈ¶ E¯ÁSÉAiÀÄ ¸ÁªÀiÁ£Àå E¼ÀÄªÀj zÀgÀUÀ¼À §UÉÎ w¼ÀÄªÀ½PÉ E®è¢gÀÄªÀÅzÀÄ.

9) ªÀÄÆ® PÁAiÀÄðPÀvÀðgÀÄ RÄzÁÝV ¥ÀæAiÉÆÃUÀ ¤ªÀð»¹zÁUÀÆå E¼ÀÄªÀj zÁR°¸ÀÄªÁUÀ ¸ÀÜ½ÃAiÀÄ ªÀåQÛUÀ¼À ¥Àæ¨sÁªÀPÉÆÌ¼ÀUÁV ªÁ¸ÀÛ«PÀ E¼ÀÄªÀj zÁR°¸À¢gÀÄªÀÅzÀÄ.

10) ¤AiÉÆÃd£ÉUÉÆAqÀ C¢üPÁjAiÀÄªÀgÀÄ RÄzÁÝV ªÉÄÃ°éZÁgÀuÉ £ÀqÉ¸ÀzÉ ¥Àæw¤¢üUÀ¼À ªÀÄÆ®PÀ ¤ªÀð»¸ÀÄªÀÅzÀÄ.

11) ºÀ¹ªÉÄt¹£ÀPÁ¬Ä ªÀÄvÀÄÛ PÉA¥ÀÄªÉÄt¹£ÀPÁ¬Ä (Mt ªÉÄt¹£ÀPÁ¬Ä) ¨É¼ÉPÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À£ÀÄß PÉÊUÉÆ¼ÀÄîªÀ°è ¸ÀàµÀÖªÁzÀ ªÀiÁUÀð¸ÀÆa E®è¢gÀÄªÀzÀÄ.

12) §ºÀÄ ºÀAvÀzÀ PÀmÁªÀÅ ¨É¼ÉUÀ¼ÁzÀ ºÀwÛ, ªÉÄt¹£ÀPÁ¬Ä ¨É¼ÉUÀ¼À PÀmÁ«UÉ ¸ÀA§A¢ü¹zÀAvÉ JµÀÄÖ PÀmÁªÀÅUÀ¼À£ÀÄß PÉÊUÉÆ¼Àî¨ÉÃPÀÄ JA§ÄzÀgÀ §UÉÎ ¸ÀàµÀÖªÁzÀ ªÀiÁUÀð¸ÀÆa EgÀÄªÀÅ¢®è.

163

D) DyðPÀ ªÀÄvÀÄÛ ¸ÁATåPÀ C¢üPÁjUÀ¼ÀÄ UÀªÀÄ¤¸À¨ÉÃPÁzÀ ªÀÄºÀvÀézÀ ¸ÀAUÀwUÀ¼ÀÄ: 1) vÁ®ÆPÁ ªÀÄlÖzÀ C£ÀÄµÁ×£À C¢üPÁjUÀ¼ÁzÀ vÀºÀ²Ã¯ÁÝgÀgÀÄ, ¸ÀºÁAiÀÄPÀ PÀÈ¶ ¤zÉÃð±ÀPÀgÀÄ,

¸ÀºÁAiÀÄPÀ vÉÆÃlUÁjPÉ ¤zÉÃð±ÀPÀgÀÄ ªÀÄvÀÄÛ PÁAiÀÄð ¤ªÁðºÀPÀ C¢üPÁjUÀ¼ÀÄ ¨É¼É CAzÁdÄ ¸À«ÄÃPÉë/¨É¼É «ªÀiÁ AiÉÆÃd£É ¥ÀæUÀw §UÉÎ ¥Àæw wAUÀ¼À ªÀiÁ¹PÀ ¸À¨sÉAiÀÄ°è ZÀZÁð «µÀAiÀÄªÁV ¥ÀjUÀtÂ¸ÀÄªÀÅzÀÄ.

2) EwÛÃa£À ¢£ÀUÀ¼À°è ¨É¼É «ªÀiÁ PÀAvÀÄ £ÁåAiÀiÁ®AiÀÄ PÉÃ¸ÀÄUÀ¼ÀÄ zÁR¯ÁUÀÄwÛzÀÄÝ

£ÁåAiÀiÁ®AiÀÄPÉÌ CES – II £ÀªÀÄÆ£ÉUÀ¼À£ÀÄß MzÀV¸ÀÄªÀÅzÀÄ PÀµÀÖPÀgÀªÁVzÉ. DzÀÝjAzÀ PÀ¼ÉzÀ 5

ªÀµÀðzÀ CªÀ¢üUÉ CES – II £ÀªÀÄÆ£ÉUÀ¼À£ÀÄß PÁ¬ÄÝj¹ ¸À°è¸ÀÄªÀ §UÉÎ CzÀPÀÆÌ »A¢£À CªÀ¢üUÉ

f¯Áè ¸ÀASÁå ¸ÀAUÀæºÀuÁ¢üPÁj PÀbÉÃjAiÀÄ qÉÃmÁ gÀf¸ÀÖgïzÀ Extract PÉÆqÀÄªÀ §UÉÎ E¯ÁSÁ

ªÀÄlÖzÀ°è wÃªÀiÁð¤¸ÀÄªÀÅzÀÄ (Resolution) ¸ÀÆPÀÛªÉ¤¸ÀÄvÀÛzÉ. 3) ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À ¸À®PÀgÀuÉUÀ¼À£ÀÄß UÁæªÀÄ¯ÉPÁÌ¢üPÁj ªÀÈvÀÛ/¸ÁgÀhiÁPÉÌ (PÉÃAzÀæ)

MAzÀÄ ¸ÉnÖ£ÀAvÉ E¯ÁSÉ¬ÄAzÀ RjÃ¢¹ ¸ÀgÀ§gÁdÄ ªÀiÁqÀÄªÀÅzÀÄ. 4) ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À£ÀÄß ¤ªÀð»¸ÀÄªÀ ªÀÄÆ® PÁAiÀÄðPÀvÀðjUÉ ¤ÃqÀÄªÀ ¥ÉÆæÃvÁìºÀ

zsÀ£ÀªÀ£ÀÄß ¸ÀÆPÀÛ ªÀÄlÖPÉÌ Kj¸ÀÄªÀ PÀÄjvÀÄ.

E) ²¥sÁgÀ¸ÀÄìUÀ¼ÀÄ: 1) KSSDA ¢AzÀ ¥ÀÆgÉÊ¸À¯ÁzÀ «Ä¤¯Áå¥ïmÁ¥ïUÀ¼À°è ¸ÀªÉð £ÀA§gïªÁgÀÄ ¨É¼É zÁR°¸ÀÄªÀ

vÀAvÁæA±ÀªÀ£ÀÄß C©üªÀÈ¢Ý ¥Àr¹PÉÆqÀÄªÀÅzÀÄ.

2) IÄvÀÄªÁgÀÄ ¥ÀºÀtÂ §gÉAiÀÄÄªÀ PÁ¯ÁªÀ¢AiÀÄ£ÀÄß DAzÉÆÃ®£À jÃwAiÀÄ°è dgÀÄV¸ÀÄªÀzÀÄ ¸ÀÆPÀÛ PÀæªÀÄ PÉÊUÉÆ¼Àî®Ä ¸ÀA§A¢ü¹zÀ E¯ÁSÉUÀ½UÉ ¤zÉÃð±À£À ¤ÃqÀÄªÀzÀÄ.

3) EwÛÃa£À ªÀµÀðUÀ¼À°è C£À¢üPÀÈvÀ ¸ÁUÀÄªÀ½ PÉëÃvÀæ EzÁÝUÀÆå ªÀgÀ¢AiÀiÁUÀÄwÛgÀÄªÀ¢®è. PÁgÀt PÀÈ¶ E¯ÁSÉAiÀÄªÀgÀÄ PÀqÁØAiÀÄªÁV ªÀgÀ¢ ªÀiÁqÀÄªÀAvÉ ¤zÉÃð²¸ÀÄªÀzÀÄ.

4) ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À UÀÄtªÀÄlÖ PÁ¥ÁrPÉÆ¼Àî®Ä ¥ÀæwAiÉÆAzÀÄ ¥ÀæAiÉÆÃUÀUÀ¼À ¥sÉÆÃmÉÆÃ ªÀiÁqÀÄªÀÅzÀÄ CwÃ CªÀ±ÀåPÀ. F PÀÄjvÀÄ ¥ÀævÉåÃPÀ C£ÀÄzÁ£À MzÀV¸ÀÄzÀÄ.

5) ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À ªÉÄÃ°éÃZÁgÀuÉ PÉÊUÉÆ¼ÀÄîªÀ C¢üPÁj/¹§âA¢ CªÀjUÀÆ PÀÆqÀ ¥ÉÆæÃvÁìºÀ zsÀ£ÀªÀ£ÀÄß ¤ÃrzÀÝ°è ªÉÄÃ°éÃZÁgÀuÉ PÁAiÀÄðªÀÅ ªÀÄvÀÛµÀÄÖ ¥sÀ®PÁjAiÀiÁUÀ§ºÀÄzÀÄ. F PÀÄjvÀÄ ¥ÀævÉåÃPÀ C£ÀÄzÁ£À MzÀV¸ÀÄzÀÄ.

ªÀÄÄPÁÛAiÀÄ :- gÁµÀÖçªÀÅ C©üªÀÈ¢Ý ¥ÀxÀzÀ°è CwÃ ªÉÃUÀªÁV ªÀÄÄ£ÀßrAiÀÄÄwÛgÀÄªÀ ¸ÀAzÀ¨sÀðzÀ°è ªÁ¸ÀÛ«PÀªÁzÀ ºÁUÀÆ ¸ÀPÁ°PÀªÁzÀ CAQ CA±ÀUÀ¼À£ÀÄß ¤ÃqÀÄªÀÅzÀÄ. ¸ÁATåPÀ E¯ÁSÉUÀ¼À ¤gÀAvÀgÀªÁzÀ dªÁ¨ÁÝjAiÀiÁVgÀÄvÀÛzÉ. PÁgÀt E¯ÁSÉAiÀÄÄ E£ÀßµÀÄÖ ¸ÀªÀÄxÀðªÁV PÁAiÀÄð¤ªÀð»¸À®Ä C£ÀÄPÀÆ®ªÁUÀÄªÀAvÉ ¥ÀÄ£ÀB±ÉÑÃvÀzÀ PÁAiÀÄðPÀæªÀÄUÀ¼À£ÀÄß ºÀ«ÄäPÉÆ¼ÀÄîªÀ CªÀ±ÀåPÀvÉ ºÉZÁÑVzÉ. F «µÀAiÀÄzÀ°è E¯ÁSÉAiÀÄ C¢üPÁj/¹§âA¢AiÀÄªÀjUÉ vÀgÀ¨ÉÃw ¤Ãr vÀ£ÀÆä®PÀ PÁAiÀÄðPÀëªÀÄvÉAiÀÄ£ÀÄß GvÀÛªÀÄ ¥Àr¸À®Ä E¯ÁSÉUÉ vÀ£ÀßzÉÃ DzÀ vÀgÀ¨ÉÃw ¸ÀA¸ÉÜAiÀÄ£ÀÄß ºÉÆAzÀÄªÀzÀÄ CªÀ±ÀåªÁVzÉ. ºÁUÀÆ DyðPÀ ªÀÄvÀÄÛ ¸ÁATåPÀ ¸ÀªÉÄäÃ¼À£ÀªÀ£ÀÄß ¥Àæw ªÀµÀð dgÀÄV¸ÀÄªÀzÀjAzÀ E¯ÁSÉ C¢üPÁj/ ¹§âA¢AiÀÄªÀgÀÄ vÀªÀÄä C¤¹PÉUÀ¼À£ÀÄß ºÁUÀÆ C£ÀÄ¨sÀªÀUÀ¼À£ÀÄß ºÀAaPÉÆ¼Àî®Ä MAzÀÄ ¸ÀÆPÀÛ ªÉÃ¢PÉAiÀÄ£ÀÄß PÀ°à¹zÀAvÁUÀÄvÀÛzÉ.

*****

164

5. Crop area and production statistics- issues in collection,

compilation and processing of data and remedies-*

1. Background/ History

Agricultural Statistics system is evolved over a period of time to reflect the

complexities and ups and downs in the agrarian economy. In earlier days the “Shanbhoug”

village level functionary of the Revenue Department use to collect and compile the village

level crop area coverage figures. In the late 60s

or early 70s this job was assigned to the

Village Accountants of the Revenue Department and they were assigned to the write the crop

data in the RTC. This system is still continued.

However, the system has recently come under criticism on accounts of reliability,

timely availability, coverage and failure to meet the emerging demand for correct statistics.

One of the chief causes of inaccuracy in the reporting of the primary reporting agency is

perhaps, the large increase in the work of Village Accountant who has a heavy burden to bear.

Being the only Government Official at the village level, he has to undertake multifarious

duties connected with various departmental and developmental schemes. As such he has little

time to devote to the proper compilation of Agricultural Statistics. He is, besides, also

employed as the primary enumerator for all kinds of ad hoc enquiries initiated by the

government, ranging from the preparation of electoral rolls to the compilation of population

census. The Village Accountant usually has no instructions as to the priority to be given to the

different enquiries and hence goes on taking all of them in the same routine manner, resulting

in slipshod work in respect of many of them including compilation of crop coverage data.

2. Present Status

In Agriculture Department crop area coverage figures are reported weekly from the

Hobli level Raitha Samparka Kendra to the Taluk Assistant Director of Agriculture, who in

turn reports the consolidated figures of his taluk to the office of the District Joint Director of

Agriculture, who again in turn consolidated the data of all taluks and sends the district level

data to the Statistical Cell in the Directorate of Agriculture. At Directorate of Agriculture,

district-wise and crop-wise area coverage is compiled on weekly basis throughout the year for

three distinct seasons viz. Kharif (April- September), Rabi (October - December) and summer

(January - March). The State Level crop area coverage details are sent to various offices and

crop development directorates of State and Central Government.

The data reported from Taluk Assistant Director is reconciled at the taluk level in the

office of taluk Tahsildar with the revenue, horticulture, irrigation and statistics departments at

the end of each season and same exercise is carried out at district level under the

Chairmanship of District Deputy Commissioner. The reconciled data is used to compile the

“Annual Season and Crop Report” of each taluk/district.

* Sri Gokul Prasad, ASO/DD, Agriculture Department.

165

Agriculture department only provides the tentative figures of area coverage during the

three seasons but uses the data finalized by the Directorate of Economics & Statistics in the

“Fully Revised Estimates of Area, Production and Productivity of Principal Crops” for all

analysis and reporting purposes.

3. Gaps

Analysis of the recent data of total area coverage under agricultural crops (cereals,

pulses, oilseeds, cotton, sugarcane and tobacco) from 2001-02 to 2009-10 indicates minimum

variation ranging from 0.22% to 2.22%. The details are as follows:

Year Area Coverage reported

(Lakh hectares)

Area

Difference

(Lakh

hectares)

%

Difference

Agriculture Dept. DE&S

2001-02 100.26 99.99 0.27 0.3

2002-03 99.09 98.87 0.22 0.2

2003-04 96.82 98.99 2.17 2.22

2004-05 110.05 111.58 1.53 1.40

2005-06 113.64 113.83 0.19 0.2

2006-07 107.96 107.43 0.54 0.5

2007-08 113.22 111.45 1.76 1.6

2008-09 107.39 106.15 1.24 1.2

2009-10 112.66 110.29 2.37 2.1

The difference in area coverage reported by the Agriculture Department and the

figures indicated in FRE is mainly under crops like Sugarcane, Paddy, Bengal gram and

Soyabean. The difference in Paddy area is on account of un-authorized cultivation, in

sugarcane due to overlap of ratoon / planted area. The difference in minor crops is not of

much significance due to very small area under these crops (minor millets, castor, niger seed,

linseed, safflower etc.)

Agriculture department uses the production estimates prepared by the Directorate of

Economics and Statistics on basis of Crop Yield data of Crop Estimation Survey. Only

indicative estimates are prepared for National Kharif/rabi Conferences and State Economic

Survey Report.

4. Recommendations to fill the Gaps

The present system that relies entirely on a large number of poorly trained

Functionaries at the ground level, belonging to different departments and charged with

multiple functions, leaves a huge scope for non-sampling errors. Checking them is difficult

without a unified, strong and professional agency to ensure that field workers follow

166

prescribed procedures strictly. Devising effective institutional arrangements that would ensure

reliable data and their timely availability is the major challenge.

The new initiative by National Crop Forecast Centre “FASAL” is a good beginning in

estimating crop area of major selected crops through remote sensing but its use is limited to a

few crops. It is essential to understand the potentials and limitations before Remote Sensing

can be put to effective use. Rapid advances in remote sensing technology are constantly

expanding the accuracy, level of detail and spatial disaggregation at which these tasks can be

handled. The basic methodology for using remote sensing information to assess crop yields is

very promising even though it is going to be a major challenge to operationalize this method.

We need to better understand the problems involved in optimizing the use of Remote Sensing

imageries of different resolutions for meeting agricultural data needs, their capacity to

discriminate between different crops and estimating area of crops (especially of minor and

mixed crops) grown on small and fragmented plots, techniques for verifying the accuracy of

Remote Sensing estimates with ground truth, and the appropriate organizational arrangements

to use them.

Use of new technology such as use of hand held devices for field data collection,

online data transmission besides computerized processing for preparation of the state

estimates based on the sampling methodology. This will help reduce the scope for human

errors and can substantially bring down time lag in the preparation of crop estimates. It should

also take the initiative to get land records and cadastral maps of the sample villages

computerized; besides facilitate the use of computers for selection of sample plots for

inspection and CCEs and speeding up the estimation procedures.

6. Conclusion

Strengthening the convergence of different departments at village/taluk would help to

minimize the differences. Strict checking by senior level officers would also be helpful in

achieving higher accuracy.

*****

167

6. ¨É¼É «¹ÛÃtð ªÀÄvÀÄÛ GvÁàzÀ£Á CAQ CA±ÀUÀ¼ÀÄ* PÀÈ¶ ¥ÀæzsÁ£ÀªÁzÀ F zÉÃ±ÀzÀ°è DºÁgÀ ªÀÄvÀÄÛ DºÁgÉÃvÀgÀ ¨É¼É PÉëÃvÀæ, ¨É¼É GvÁàzÀ£ÉUÉ

¸ÀA§A¢ü¹zÀAvÉ ¤RgÀªÁzÀ ªÀÄvÀÄÛ ºÉZÀÄÑ ªÁ¸ÀÛªÀvÉ¬ÄAzÀ PÀÆrzÀ ªÀiÁ»w ¥ÀqÉAiÀÄÄªÀÅzÀÄ §ºÀÄªÀÄÄRåªÁVgÀÄvÀÛzÉ. ¨É¼É GvÁàzÀ£Á CAQ CA±ÀUÀ¼À£ÀÄß ¸ÀAUÀæ»¸ÀÄªÀÅzÀgÀ ªÀÄÆ®PÀ zÉÃ±ÀPÉÌ ¸ÁPÁUÀÄªÀµÀÄÖ DºÁgÉÆÃvÁàzÀ£ÉAiÀiÁUÀÄwÛzÉAiÉÄÃ, DºÁgÀ zsÁ£ÀåUÀ¼À DªÀÄzÀÄ ªÀÄvÀÄÛ gÀ¥sÀÄÛ ¤Ãw gÀÆ¦¸À®Ä ¸ÀPÁðgÀPÉÌ ¨ÉÃPÁzÀ CAQ CA±ÀUÀ¼ÀÄ, GvÁà¢vÀ DºÁgÀªÀ£ÀÄß ¸ÀAgÀQë¹qÀ®Ä ¸ÀPÁðgÀªÀÅ ¸ÁPÀµÀÄÖ UÉÆÃzÁªÀÄÄUÀ¼À£ÀÄß ºÉÆA¢zÉAiÉÄÃ, eÁ£ÀÄªÁgÀÄ ªÉÄÃªÀÅ ¸ÁPÀ¶ÖzÉAiÉÄÃ, zÉÃ±ÀzÀ DAvÀjPÀ GvÁàzÀ£ÉAiÀÄ°è PÀÈ¶ PÉëÃvÀæzÀ ¥Á®Ä, ºÉZÀÄÑwÛgÀÄªÀ d£À¸ÀASÉåUÉ C£ÀÄUÀÄtªÁV DºÁgÉÆÃvÁàzÀ£É ºÉaÑ¸À®Ä ¸ÀPÁðgÀ vÉUÉzÀÄPÉÆ¼Àî§ºÀÄzÁzÀ PÀæªÀÄzÀ §UÉÎ aAvÀ£É, EvÁå¢ CA±ÀUÀ¼ÀÄ ¸ÀPÁðgÀzÀ AiÉÆÃd£Á ¤Ãw ¤gÀÆ¥ÀPÀjUÉ G¥ÀAiÀÄÄPÀÛ ªÀiÁ»wAiÀÄ£ÀÄß MzÀV¸ÀÄvÀÛzÉ. DzÀÝjAzÀ ¸ÀPÁðgÀzÀ F §ºÀÄªÀÄÄRåªÁzÀ DyðPÀ AiÉÆÃd£É gÀÆ¦¸ÀÄªÀ°è vÀ¼ÀºÀAvÀ¢AzÀ ¥ÀqÉzÀ CAQCA±ÀUÀ¼À PÉÆæÃrüÃPÀÈvÀ ªÀiÁ»wAiÀÄÄ ¤uÁðAiÀÄPÀ ¥ÁvÀæ ªÀ»¸ÀÄvÀÛzÉ. DzÀÝjAzÀ ¨É¼É PÉëÃvÀæzÀ ªÀiÁ»w ªÀÄvÀÄÛ ¨É¼É GvÁàzÀ£É ªÀiÁ»wUÀ¼À£ÀÄß CvÀåAvÀ PÀæªÀÄ§zÀÝªÁV ªÀÄvÀÄÛ ºÉZÀÄÑ ªÁ¸ÀÛªÀvÉ¬ÄAzÀ zÁR°¸À¨ÉÃPÁUÀÄvÀÛzÉ. CwªÀÈ¶Ö, C£ÁªÀÈ¶ÖAiÀÄAvÀºÀ £ÉÊ¸ÀVðPÀ «PÉÆÃ¥ÀUÀ¼ÀÄ ¸ÀA¨sÀ«¹zÁUÀ, ¨É¼É ºÁ¤ ¥ÀæªÀiÁt, CAvÀºÀ ªÀµÀðUÀ¼À°è CAzÁf¸À¯ÁUÀÄªÀ DºÁgÀ ¨É¼É GvÁà£É ªÀiÁ»wAiÀÄÄ CvÀåAvÀ ªÀÄºÀvÀé¥ÀÆtðªÁVgÀÄvÀÛzÉ.

¸ÀPÁðgÀzÀ DzÉÃ±ÀzÀAvÉ gÁdåzÀ°è DyðPÀ ªÀÄvÀÄÛ ¸ÁATåPÀ ¤zÉÃð±À£Á®AiÀÄªÀÅ ¨É¼É «¹ÛÃtðzÀ CAzÁdÄ ªÀiÁ»w, ¨É¼É E¼ÀÄªÀj ªÀÄvÀÄÛ GvÁàzÀ£ÉAiÀÄ ªÀiÁ»wAiÀÄ£ÀÄß ¤ªÀð»¸ÀÄvÀÛzÉ. PÀAzÁAiÀÄ E¯ÁSÉAiÀÄ UÁæªÀÄ ¯ÉPÁÌ¢üPÁjUÀ¼ÀÄ, UÁæªÀÄ ªÀÄlÖzÀ PÀÈ¶ C¢üPÁj, vÉÆÃlUÁjPÉ C¢üPÁj, ¤ÃgÁªÀj E¯ÁSÉ C¢üPÁjUÀ¼ÀÄ dAnAiÀiÁV ¸ÀªÉð £ÀqÉ¹, ¨É¼ÉPÉëÃvÀæzÀ ªÀgÀ¢AiÀÄ£ÀÄß ¥ÀºÀtÂAiÀÄ°è zÁR°¸À¯ÁUÀÄvÀÛzÉ. F ªÀÄÆ®ªÀgÀ¢AiÀÄ£ÀÄß DyðPÀ ªÀÄvÀÄÛ ¸ÁATåPÀ E¯ÁSÉ ¥ÀqÉzÀÄ gÁdå ªÀÄlÖzÀ°è PÉÆæÃrüÃPÀj¹ PÉÃAzÀæ ¸ÀPÁðgÀPÉÌ ¸À°è¸À¯ÁUÀÄwÛzÉ

¨É¼É «¹ÛÃtð ªÀiÁ»w: ¸ÀPÁðgÀzÀ DzÉÃ±ÀzÀ ªÉÄÃgÉUÉ ¥Àæw ªÀµÀð ªÀÄÄAUÁgÀÄ, »AUÁgÀÄ ªÀÄvÀÄÛ ¨ÉÃ¹UÉ ºÀAUÁªÀÄÄUÀ¼À°è ¥Àæw ¸ÀªÉð£ÀA§gÀÄªÁgÀÄ ¨É¼É PÉëÃvÀæPÉÌ UÁæªÀÄ ¯ÉPÁÌ¢üPÁjUÀ¼ÀÄ, UÁæªÀÄ ªÀÄlÖzÀ PÀÈ¶ C¢üPÁj, vÉÆÃlUÁjPÉ C¢üPÁj, ¤ÃgÁªÀj E¯ÁSÉ C¢üPÁjUÀ¼ÀÄ dAnAiÀiÁV ¨sÉÃn ¤ÃqÀÄªÀÅzÀgÀ ªÀÄÆ®PÀ ¨É¼É «¹ÛÃtðzÀ ªÀiÁ»wAiÀÄ£ÀÄß zÁR°¹, UÁæªÀÄ ¯ÉPÁÌ¢üPÁjUÀ¼ÀÄ ¥ÀºÀtÂAiÀÄ°è ¨É¼É «¹ÛÃtð zÁR¯Áw ªÀiÁqÀÄvÁÛgÉ. UÁæªÀÄ ¯ÉPÁÌ¢üPÁjUÀ¼ÀÄ CªÀgÀ ªÁå¦ÛAiÀÄ J¯Áè ¸ÀªÉð£ÀA§gï, ¸À¨ï¸ÀªÉð£ÀA§gÀÄªÁgÀÄ ¨É¼É «¹ÛÃtð ªÀiÁ»wAiÀÄ£ÀÄß ¨É¼ÉªÁgÀÄ WÉÆÃµÁégÉ zÁR°¸ÀÄªÀÅzÀgÀ ªÀÄÆ®PÀ DAiÀiÁ UÁæªÀÄ ªÀÄlÖzÀ°è PÉÆæÃrüÃPÀj¸À¯ÁUÀÄvÀÛzÉ. £ÀAvÀgÀ ºÉÆÃ§½ªÁgÀÄ PÀAzÁAiÀÄ ¤jÃPÀëPÀgÀÄ ¥Àj²Ã°¹, vÁ®ÆèPÀÄ ªÀÄlÖzÀ°è PÉÆæÃrüÃPÀj¹, vÀºÀ¹Ã¯ÁÝgÀjAzÀ ¥Àj²Ã°¸À¯ÁUÀÄvÀÛzÉ. »ÃUÉ PÉÆæÃrüÃPÀj¹zÀ ªÀiÁºÀ¬ÄwAiÀÄ£ÀÄß vÁ®ÆèPÀÄ ªÀÄlÖzÀ°è vÀºÀ¹Ã¯ÁÝgÀgÀ CzsÀåPÀëvÉAiÀÄ°è £ÀqÉAiÀÄÄªÀ PÀÈ¶, vÉÆÃlUÁjPÉ, ¸ÀtÚ ¤ÃgÁªÀj E¯ÁSÁ¢üPÁjUÀ¼À£ÉÆß¼ÀUÉÆAqÀ vÁ®ÆèPÀÄ ªÀÄlÖzÀ ¸À«ÄwUÉ ªÀÄAr¹, C£ÀÄªÉÆÃ¢¹ ¨É¼É PÉëÃvÀæ DªÀj¹zÀ «¹ÛÃtðzÀ WÉÆÃµÁégÉAiÀÄ£ÀÄß f¯Áè ¸ÀASÁå¢üPÁjUÀ½UÉ ¸À°è¸À¯ÁUÀÄvÀÛzÉ. f¯Áè ªÀÄlÖzÀ°è f¯Áè¢üPÁjUÀ¼À CzsÀåPÀëvÉAiÀÄ°è f¯Áè ªÀÄlÖzÀ PÀÈ¶, vÉÆÃlUÁjPÉ, ¸ÀtÚ ¤ÃgÁªÀj E¯ÁSÁ¢üPÁjUÀ¼ÀÄ ºÁUÀÆ vÁ®ÆèPÀÄUÀ¼À vÀºÀ¹Ã¯ÁÝgÀgÀ£ÉÆß¼ÀUÉÆAqÀ ¸À«ÄwAiÀÄ°è ªÀÄAr¹,ªÀÄgÀÄºÉÆAzÁtÂPÉUÉÆAqÀ C£ÀÄªÉÆÃ¢vÀ ¨É¼ÉªÁgÀÄ «¹ÛÃtðzÀ ªÀiÁ»wAiÀÄ£ÀÄß DyðPÀ ªÀÄvÀÄÛ ¸ÁATåPÀ ¤zÉÃð±À£Á®AiÀÄPÉÌ ¸À°è¸À¯ÁUÀÄwÛzÉ. DyðPÀ ªÀÄvÀÄÛ ¸ÁATåPÀ ¤zÉÃð±À£Á®AiÀÄzÀ°è gÁdåzÀ ºÀAUÁªÀÄªÁgÀÄ ««zsÀ ¨É¼É DªÀj¹zÀ «¹ÛÃtðzÀ ªÀiÁ»wAiÀÄ£ÀÄß ¥ÀæPÀn¸À¯ÁUÀÄwÛzÉ.

* ²æÃªÀÄw ©æfmï ªÀVÃð¸ï, JA.PÁA.,J¯ï.J¯ï.©., ¸ÀºÁAiÀÄPÀ ¤zÉÃð±ÀPÀgÀÄ, f¯Áè ¸ÀASÁå ¸ÀAUÀæºÀuÁ¢üPÁjUÀ¼À PÀbÉÃj, ²ªÀªÉÆUÀÎ.

168

¨É¼É GvÁàzÀ£É ªÀiÁ»w:

¨É¼É GvÁàzÀ£É ªÀiÁ»wAiÀÄ£ÀÄß ªÉÊeÁë¤PÀªÁV C¼ÀªÀr¸À¯ÁVgÀÄªÀ ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À ¸À«ÄÃPÉëAiÀÄ ªÀÄÆ®PÀ ¥ÀqÉAiÀÄ¯ÁUÀÄwÛzÉ. ¨É¼É «ªÀiÁ AiÉÆÃd£ÉAiÀÄr «ªÀiÁ ¥ÀjºÁgÀ ¤ÃrPÉAiÀÄ ¯ÉPÁÌZÁgÀPÉÌ ¸ÀºÀ ¨É¼É CAzÁdÄ ¸À«ÄÃPÉë¬ÄAzÀ ¥ÀqÉAiÀÄ¯ÁUÀÄªÀ E¼ÀÄªÀjAiÀÄ£ÀÄß CªÀ®A©¹gÀÄvÀÛzÉ. DAiÀÄÝ ¥ÀÆªÀð¤UÀ¢üvÀ ¨É¼É PÉëÃvÀæzÀ°è gÉÊvÀgÀÄ ¨É¼ÉzÀ ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ½AzÀ §AzÀ E¼ÀÄªÀjAiÀÄ DzsÁgÀzÀ ªÉÄÃ¯É ¥Àæw ºÉPÉÖÃj£À ¨É¼É E¼ÀÄªÀjAiÀÄ£ÀÄß CAzÁf¸À¯ÁUÀÄwÛzÉ. ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À£ÀÄß D ¥ÀæzÉÃ±ÀzÀ°è ¨É¼ÉAiÀÄÄªÀ J¯Áè ¥ÀæªÀÄÄR ¨É¼É, ¤ÃgÁªÀj- ªÀÄ¼ÉD²ævÀ, C¢üPÀ E¼ÀÄªÀj-¸ÀÜ½ÃAiÀÄ ¨É¼ÉUÀ½UÉ ¸ÀA§A¢ü¹zÀAvÉ ¥ÀævÉåÃPÀªÁV £ÀqÉ¸À¯ÁUÀÄvÀÛzÉ. C®èzÉ gÉÊvÀgÀÄ ¥Àæw JPÀgÉ ¨É¼É GvÁàzÀ£ÉUÁV G¥ÀAiÉÆÃV¹zÀ ¸ÁªÀAiÀÄªÀ UÉÆ§âgÀ, gÁ¸ÁAiÀÄ¤PÀ UÉÆ§âgÀ, QÃl gÉÆÃUÀ¨ÁzsÉUÀ¼ÀÄ, EªÀÅUÀ¼À ¤ªÁgÀuÉUÁV gÉÊvÀgÀÄ G¥ÀAiÉÆÃV¹zÀ ¸ÀAgÀPÀëuÁ PÀæªÀÄUÀ¼À ªÀiÁ»wAiÀÄ£ÀÄß ¸ÀºÀ PÉÆæÃrüÃPÀj¸À¯ÁUÀÄvÀÛzÉ. F jÃw ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ½AzÀ ¥ÀqÉAiÀÄ¯ÁUÀÄªÀ E¼ÀÄªÀj ªÀiÁ»wAiÀÄ£ÀÄß DAiÀiÁ ¨É¼ÉPÉëÃvÀæPÉÌ CAzÁf¹, gÁdå ªÀÄlÖzÀ°è PÉÆæÃrüÃPÀj¸À¯ÁUÀÄvÀÛzÉ. ««zsÀ gÁdåUÀ¼À ªÀiÁ»wAiÀÄ£ÀÄß PÉÃAzÀæ ªÀÄlÖzÀ°è PÉÆæÃrüÃPÀj¹, DAiÀiÁ ªÀµÀðzÀ°è zÉÃ±ÀzÀ ¨É¼É GvÁàzÀ£Á ªÀiÁ»wAiÀÄ£ÀÄß ¥ÀæPÀn¸À¯ÁUÀÄvÀÛzÉ. F ªÀiÁ»wAiÀÄÄ PÀÈ¶ PÉëÃvÀæzÀ°è ¸ÀPÁðgÀªÀÅ PÉÊUÉÆ¼Àî§ºÀÄzÁzÀ ¸ÀÄzsÁgÀuÁ PÀæªÀÄUÀ¼À AiÉÆÃd£É gÀÆ¦¸ÀÄªÀ°è ¸ÀºÀPÁjAiÀiÁVzÉ.

¨É¼É PÉëÃvÀæ ¸À«ÄÃPÉë ºÁUÀÆ ¨É¼É PÀmÁªÀÅ ¸À«ÄÃPÉëAiÀÄr PÉ®ªÀÅ £ÀÆå£ÀvÉUÀ¼À£ÀÄß UÀªÀÄ¤¸À§ºÀÄzÁVzÉ:

1. ¨É¼É PÉëÃvÀæzÀ ªÀÄÆ® ªÀiÁ»wAiÀÄÄ UÁæªÀÄ ¯ÉQÌUÀgÀÄ ¨É¼ÉPÉëÃvÀæ ¨sÉÃn ªÀiÁqÀÄªÀÅzÀgÀ ªÀÄÆ®PÀ ¥ÀºÀtÂAiÀÄ°è zÁR°¸ÀÄªÀ ªÀiÁ»wAiÀÄ£ÀÄß CªÀ®A©¹gÀÄvÀÛzÉ. DzÀgÉ §ºÀ¼ÀµÀÄÖ ¥ÀæPÀgÀtUÀ¼À°è UÁæªÀÄ ¯ÉQÌUÀgÀÄ ¨É¼ÉPÉëÃvÀæPÉÌ ¨sÉÃn ¤ÃqÀzÉAiÉÄÃ PÀbÉÃjAiÀÄ°è PÀÄ½vÀÄ ¥ÀºÀtÂ §jAiÀÄÄvÁÛgÉ. CxÀªÁ »A¢£À ªÀµÀðzÀ ¨É¼É PÉëÃvÀæªÀ£ÀÄß ¥ÀÄ£Àgï zÁR¯É ªÀiÁqÀÄvÁÛgÉ. UÁæªÀÄ¯ÉQÌUÀgÀÄ ¥ÀºÀtÂAiÀÄ°è §gÉAiÀÄÄªÀ ¨É¼É «¹ÛÃtð zÁR¯ÉUÀ¼ÀÄ ¤RgÀªÁVgÀÄªÀAvÉ ªÉÄÃ°éZÁgÀuÉ PÀæªÀÄªÀÅ ±ÀQÛAiÀÄÄvÀªÁVgÀÄªÀÅ¢®è. JµÉÆÖÃ ¥ÀæPÀgÀtUÀ¼À°è ¨sÀvÀÛ ¨É¼ÉAiÀÄÄªÀ PÉëÃvÀæzÀ°è CrPÉ ¨É¼ÉzÀÄ ¥À¸À®Ä §AzÀgÀÆ, ¥ÀºÀtÂAiÀÄ°è ¨sÀvÀÛzÀ ¨É¼É PÉëÃvÀæ JAzÉÃ zÁR¯ÁVgÀÄªÀ GzÁºÀgÀuÉUÀ¼ÀÄ ¸ÁPÀ¶ÖªÉ. EzÀÄ ¨É¼É PÉëÃvÀæzÀ CAQCA±ÀUÀ¼À UÀÄtªÀÄlÖzÀ ªÉÄÃ¯É ¥ÀæwPÀÆ® ¥ÀjuÁªÀÄ ©gÀÄwÛzÉ. C®èzÉ PÀÈ¶, vÉÆÃlUÁjPÉ ªÀÄvÀÄÛ ¤ÃgÁªÀj E¯ÁSÉ C¢üPÁjUÀ¼ÀÄ dAnAiÀiÁV ¸ÀÜ¼À¨sÉÃn ¤ÃqÀ¨ÉÃPÉA§ ¸ÀPÁðgÀzÀ ¤zÉÃð±À£À EzÀÝgÀÆ ªÁ¸ÀÛªÀªÁV F C¢üPÁjUÀ¼À dAn ¨sÉÃnAiÀÄ°è ¸ÀªÀÄ£ÀéAiÀÄvÉ ¸Á¢ü¸À¯ÁUÀÄwÛ®è.

2. EwÛÃZÉUÉ PÀA¥ÀÆåljÃPÀgÀtzÀ ªÀÄÆ®PÀ ªÀÄvÀÄÛ ¸Á¥sïÖªÉÃgï vÀAvÁæA±ÀzÀ ªÀÄÆ®PÀ ¨É¼ÉPÉëÃvÀæ CAQ CA±ÀUÀ¼À£ÀÄß ¤UÀ¢üvÀ CªÀ¢üAiÉÆ¼ÀUÁV zÁR°¸À®Ä DyðPÀ ªÀÄvÀÄÛ ¸ÁATåPÀ ¤zÉÃð±À£Á®AiÀÄªÀÅ PÀæªÀÄ ªÀ»¹gÀÄªÀÅzÀÄ F PÉëÃvÀæzÀ°è MAzÀÄ GvÀÛªÀÄ ¨É¼ÀªÀtÂUÉAiÀiÁVzÉ. DzÀgÉ ºÉaÑ£À UÁæªÀÄ ¯ÉQÌUÀjUÉ PÀA¥ÀÆålgï eÁë£À EgÀÄªÀÅ¢®è CxÀªÁ UÁæªÀÄ ªÀÄlÖzÀ°è, vÁ®ÆèPÀÄ ªÀÄlÖzÀ°è D£ï¯ÉÊ£ï zÁR¯ÁwUÉ «zÀÄåvï PÉÆgÀvÉ CxÀªÁ £ÉmïªÀPïð PÉÆgÀvÉ¬ÄAzÀ ¸ÀPÁ®zÀ°è zÁR¯ÁUÀÄwÛ®è.

3. UÁæªÀÄ ¯ÉPÁÌ¢üPÁjUÀ½UÉ PÀAzÁAiÀÄ E¯ÁSÁ PÁAiÀÄðPÀæªÀÄUÀ¼À®èzÉ ZÀÄ£ÁªÀuÉ,d£ÀUÀtw, PÀÈ¶ UÀtw, eÁ£ÀÄªÁgÀÄUÀtw, eÁwUÀtw, ¥ÀrvÀgÀ ªÉÊªÀ¸ÉÜ, §gÀ¥ÀjºÁgÀ, £ÉgÉ¥ÀjºÁgÀ ªÀÄÄAvÁzÀ C£ÉÃPÀ PÉ®¸ÀPÁAiÀÄðUÀ¼À°è vÉÆqÀV¹PÉÆ¼Àî¯ÁUÀÄwÛgÀÄªÀÅzÀjAzÀ ¨É¼ÉPÉëÃvÀæ ¨sÉÃn ªÀiÁr ¸ÀPÁ®zÀ°è ¥ÀºÀtÂAiÀÄ°è ¨É¼É zÁR¯Áw ªÀiÁqÀ®Ä ¸ÁzÀåªÁVgÀÄªÀÅ¢®è. vÁ®ÆèQ£À

169

vÀºÀ¹Ã¯ÁÝgÀgÁUÀ° ¥ÀºÀtÂAiÀÄ°è ¨É¼É zÁR¯Áw ¥ÀÆtð¥ÀæªÀiÁtzÀ°è PÉÊUÉÆ¼Àî®Ä ºÉaÑ£À D¸ÀQÛ ªÀ»¸ÀÄwÛ®è.

4. ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ½UÉ DAiÉÄÌAiÀiÁzÀ vÁQ£À E¼ÀÄªÀjAiÀÄÄ EvÀgÉ vÁPÀÄUÀ½VAvÀ ºÉZÁÑVgÀÄªÀ ¸ÀA¨sÀªÀUÀ¼ÀÄ eÁ¹Û¬ÄgÀÄvÀÛzÉ. »ÃUÁV ¨É¼ÉPÀmÁªÀÅ ¥ÀæAiÉÆÃUÀU¼À DzsÁgÀzÀ ªÉÄÃ¯É ¤zsÀðj¸À¯ÁUÀÄªÀ E¼ÀÄªÀj ªÀiÁ»wAiÀÄÄ ªÁ¸ÀÛªÁA±À¢AzÀ PÀÆrgÀÄªÀÅ¢®è.

5. ªÀÄ¼É D²ævÀ JAzÀÄ zÁR¯ÁUÀÄªÀ C£ÉÃPÀ ¨É¼É PÉëÃvÀæUÀ¼À°è ºÉÆ¸À ¨ÉÆÃgïªÉ¯ï ªÀÄÆ®PÀ CxÀªÁ ¨Á«AiÀÄ ªÀÄÆ®PÀ ¤ÃgÀÄ ºÁAiÀÄÄªÀj ªÀiÁqÀÄwÛzÀÄÝ, gÉÊvÀgÀÄ F ªÀiÁ»wAiÀÄ£ÀÄß §»gÀAUÀ¥Àr¸ÀÄwÛ®è. DzÀÝjAzÀ ªÀÄ¼ÉD²ævÀ ¥ÀæzÉÃ±À JAzÀÄ zÁR¯ÁVgÀÄªÀ C£ÉÃPÀ ¥ÀæPÀgÀtUÀ¼À°è E¼ÀÄªÀj ªÀiÁ»wAiÀÄÄ ªÁ¸ÀÛªÀvÉ¬ÄAzÀ PÀÆrgÀÄªÀÅ¢®è.

6. ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À£ÀÄß ºÉÆÃ§½ ªÀÄlÖzÀ°è ªÀiÁqÀ¯ÁUÀÄwÛgÀÄªÀÅzÀÄ ¸ÀjAiÀiÁzÀ PÀæªÀÄªÁVgÀÄªÀÅ¢®è. ¥Àæ¸ÀÄÛvÀ UÁæªÀÄ ªÀÄlÖzÀ°è ªÀÄÆgÀÄ ºÀAvÀzÀ DqÀ½vÀ ªÀåªÀ¸ÉÜ¬ÄzÀÄÝ, vÀ¼ÀºÀAvÀzÀ°è UÁæªÀÄ ¥ÀAZÁAiÀÄw EgÀÄvÀÛzÉ. ¨É¼É PÉëÃvÀæ zÁR¯ÁwUÀ¼ÀÄ, ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼ÀÄ ¥ÀAZÁAiÀÄvï ºÀAvÀzÀ°è £ÀqÉ¹, ¥Àæw ¥ÀAZÁAiÀÄvïªÁgÀÄ ªÀiÁ»w PÉÆæÃrüÃPÀj¸ÀÄªÀÅzÀÄ ºÉZÀÄÑ ¸ÀªÀÄAd¸ÀªÁVgÀÄvÀÛzÉ.

7. ¨É¼É CAzÁdÄ ¸À«ÄÃPÉë, ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À ªÀÄÆ® PÁAiÀÄðPÀvÀðgÀ£ÁßV PÀÈ¶ E¯ÁSÉ, d¯Á£ÀAiÀÄ£À E¯ÁSÉ, vÉÆÃlUÁjPÉ E¯ÁSÉ, ºÁUÀÆ C©üªÀÈ¢Ý (¥ÀAZÁAiÀÄvï) E¯ÁSÁ ¹§âA¢üAiÀÄ£ÀÄß ¤AiÉÆÃf¸À®Ä ¸ÀÆZÀ£É EzÀÝgÀÆ, C©üªÀÈ¢Ý E¯ÁSÁ ¹§âA¢üUÀ¼ÁUÀ°Ã, vÁ®ÆèPÀÄ ¥ÀAZÁAiÀÄvï PÁAiÀÄð¤ªÀðºÀuÁ¢üPÁjUÀ¼ÁUÀ°Ã F PÁAiÀÄðªÀ£ÀÄß ¤®ðQë¸ÀÄwÛzÁÝgÉ. ªÀÄvÀÄÛ ¥ÀAZÁAiÀÄvï PÁAiÀÄðzÀ²ðUÀ½UÉ F PÁAiÀÄðzÀ°è D¸ÀQÛAiÀÄÄ EgÀÄªÀÅ¢®è. EzÀjAzÁV EªÀgÀÄ ¤ªÀð»¸ÀÄªÀ ¨É¼ÉPÀmÁªÀÅ ¸À«ÄÃPÉëUÀ¼ÀÄ E¯ÁSÉ ¤ÃrzÀ ¤zÉÃð±À£ÀzÀ ¥ÀæPÁgÀ £ÀqÉAiÀÄÄªÀ ¸ÀA¨sÀªÀUÀ¼ÀÄ PÀrªÉÄ EgÀÄvÀÛzÉ.

¸ÀÄzsÁgÀuÁ PÀæªÀÄUÀ¼ÀÄ:

1. UÁæªÀÄ ¯ÉQÌUÀgÀÄ ¨É¼ÉPÉëÃvÀæ ¨sÉÃnAiÀÄ ªÀÄÆ®PÀ ¥ÀºÀtÂAiÀÄ°è ¨É¼ÉPÉëÃvÀæ zÁR¯Áw PÉÊUÉÆ¼ÀÄîªÀÅzÀ£ÀÄß PÀqÁØAiÀÄUÉÆ½¸À¨ÉÃPÀÄ. EzÀPÁÌV PÉÃAzÀæ¸ÀPÁðgÀªÀÅ ¸ÀÆPÀÛ C¢ü¤AiÀÄªÀÄªÀ£ÀÄß (DPïÖ) eÁjUÉÆ½¹, ¥ÀºÀtÂAiÀÄ°è vÀ¥ÀÄà ¨É¼ÉPÉëÃvÀæ zÁR°¸ÀÄªÀ ªÀÄÆ®PÁAiÀÄðPÀvÀðgÀ£ÀÄß ªÀÄvÀÄÛ CzÀPÉÌ ¸ÀA§A¢ü¹zÀ ªÉÄÃ°éZÁgÀuÁ¢üPÁjAiÀÄ£ÀÄß zÀAqÀ£ÉUÉ/ ²¹Û£À PÀæªÀÄPÉÌ M¼À¥Àr¸À¨ÉÃPÀÄ

2. ¥ÀºÀtÂAiÀÄ°è ¨É¼É PÉëÃvÀæ zÁR¯Áw ¸ÀªÀÄAiÀÄzÀ°è UÁæªÀÄ ¯ÉPÁÌ¢üPÁjUÀ½UÉ EvÀgÉ ¸ÀªÉðPÁAiÀÄðªÀ£ÀÄß ªÀ»¸ÀÄªÀÅzÀ£ÀÄß ¤¶zÀÝUÉÆ½¸À¨ÉÃPÀÄ. vÁ®ÆèPÀÄ vÀºÀ¹Ã¯ÁÝgÀjUÉ F §UÉÎ ¸ÀPÁðgÀ¢AzÀ PÀlÄÖ¤nÖ£À ¸ÀÆZÀ£É ¤ÃqÀ¨ÉÃPÀÄ.

3. ¤UÀ¢üvÀ CªÀ¢üAiÉÆ¼ÀUÁV J¯Áè ¨É¼É PÉëÃvÀæªÀ£ÀÄß PÀA¥ÀÆålgï CAvÀeÁð®zÀ ªÀÄÆ®PÀ zÁR°ÃPÀj¸ÀÄªÀÅzÀÄ PÀqÁØAiÀÄUÉÆ½¸ÀÄªÀÅzÀÄ. F PÁAiÀÄðPÉÌ ªÀÄÆ®PÁAiÀÄðPÀvÀð£À£ÀÄß eÉÆvÉUÉ vÁ®ÆèPÀÄ ªÀÄlÖzÀ ªÉÄÃ°éZÁgÀuÁ¢üPÁjAiÀÄ£ÀÄß (vÀºÀ¹Ã¯ÁÝgï) dªÁ¨ÁÝgÀ£À£ÁßV ªÀiÁqÀÄªÀÅzÀÄ. vÁ®ÆèPÀÄ PÀbÉÃjAiÀÄ°è F PÁAiÀÄðzÀ ªÉÄÃ°éZÁgÀuÉUÁV ¤AiÀÄÄPÀÛªÁVgÀÄªÀ UÀtwzÁgÀgÀÄ ªÀÄvÀÄÛ ¸ÁATåPÀ ¤jÃPÀëPÀgÀ ºÀÄzÉÝAiÀÄÄ AiÀiÁªÁUÀ®Æ SÁ° EgÀzÀAvÉ DyðPÀ ªÀÄvÀÄÛ ¸ÁATåPÀ ¤zÉÃð±À£Á®AiÀÄ PÀæªÀÄ ªÀ»¸ÀÄªÀÅzÀÄ. CUÀvÀå PÀA¥ÀÆålgï ¹§âA¢üAiÀÄ£ÀÄß PÀAzÁAiÀÄ CxÀªÁ DyðPÀ ªÀÄvÀÄÛ ¸ÁATåPÀ E¯ÁSÉ ªÀÄÆ®PÀ ªÀåªÀ¸ÉÜUÉÆ½¸ÀÄªÀÅzÀÄ. C®èzÉ vÁ®ÆèPÀÄ

170

¥ÀAZÁAiÀÄwUÀ¼À°è ¥ÀæUÀw ¸ÀºÁAiÀÄPÀgÀ ºÀÄzÉÝUÀ¼ÀÄ §ºÀÄvÉÃPÀ SÁ° EzÀÄÝ, EzÀ£ÀÄß ¨sÀwð ªÀiÁqÀÄªÀÅzÀjAzÀ C©üªÀÈ¢Ý E¯ÁSÉ¬ÄAzÀ ªÀiÁ»w PÉÆæÃrüÃPÀgÀt ¸ÀÄ®¨sÀ¸ÁzsÀåªÁUÀÄvÀÛzÉ. ( £ÀªÀÄÆ£É 1 , 2,3 )

4. PÀÈ¶ CAQCA±ÀUÀ½UÉ ¸ÀA§A¢ü¹zÀAvÉ, PÀÈ¶, PÀAzÁAiÀÄ, vÉÆÃlUÁjPÉ, ¤ÃgÁªÀj, d¯Á£ÀAiÀÄ£À E¯ÁSÉAiÀÄ UÁæªÀÄ ªÀÄlÖzÀ C¢üPÁjUÀ¼À PÉÃAzÀæ¸ÁÜ£À ªÀÄvÀÄÛ DqÀ½vÀ PÁAiÀÄðªÁå¦ÛAiÀÄ£ÀÄß UÁæªÀÄ ¥ÀAZÁAiÀÄvï ªÀÄlÖPÉÌ §zÀ¯Á¬Ä¸À®Ä DqÀ½vÁvÀäPÀ §zÀ¯ÁªÀuÉAiÀÄ£ÀÄß vÀgÀ®Ä ¸ÀPÁðgÀPÉÌ ²¥sÁgÀ¸ÀÄì ªÀiÁqÀÄªÀÅzÀÄ.(PÉÃgÀ¼À ªÀiÁzÀj) UÁæªÀÄ ¥ÀAZÁAiÀÄvï ¸À¨sÉAiÀÄ°è ¥ÀºÀtÂAiÀÄ°è ¨É¼É PÉëÃvÀæ zÁR¯ÁVgÀÄªÀ ªÀiÁ»wAiÀÄ£ÀÄß ªÀÄAr¹, ¨É¼ÉªÁgÀÄ WÉÆÃµÁégÉAiÀÄ£ÀÄß ¤ÃgÁªÀj ªÀÄÆ®zÀ «ªÀgÀUÀ¼ÉÆA¢UÉ zÁR°¹, vÁ®ÆèPÀÄ PÀbÉÃjUÉ ¸À°è¸ÀÄªÀ ªÀåªÀ¸ÉÝUÉÆ½¸ÀÄªÀÅzÀÄ.

5. ¥Àæw f¯ÉèAiÀÄ f¯Áè¢üPÁjUÀ¼À C¢üÃ£ÀzÀ°è ¸ÁATåPÀ «¨sÁUÀªÀ£ÀÄß ¸ÀÈf¹, dAn ¤zÉÃð±ÀPÀgÀÄ ªÀÄvÀÄÛ EvÀgÉ ¹§âA¢üAiÀÄ£ÀÄß ¤AiÉÆÃf¸ÀÄªÀÅzÀÄ. EªÀgÀÄ f¯Áè¢üPÁjUÀ¼À C¢üÃ£ÀzÀ°è ºÁUÀÆ f¯Áè¢üPÁjUÀ¼À C£ÀÄªÀÄw ªÉÄÃgÉUÉ PÁ®PÁ®PÉÌ PÀAzÁAiÀÄ E¯ÁSÉ C¢üPÁjUÀ¼À ¸À¨sÉ £ÀqÉ¹, ¤RgÀªÁzÀ ªÀÄvÀÄÛ ºÉZÀÄÑ ªÁ¸ÀÛ«PÀvÉ¬ÄAzÀ PÀÆrzÀ PÀÈ¶ PÉëÃvÀæ ºÁUÀÆ ¨É¼É E¼ÀÄªÀj CAQ CA±ÀUÀ¼À£ÀÄß ¥ÀqÉAiÀÄÄªÀAvÉ DyðPÀ ªÀÄvÀÄÛ ¸ÁATåPÀ ¤zÉÃð±À£Á®AiÀÄ PÀæªÀÄ ªÀ»¸ÀÄªÀÅzÀÄ.

6. DyðPÀ ªÀÄvÀÄÛ ¸ÁATåPÀ E¯ÁSÉAiÀÄ ¹§âA¢üUÀ½UÉ ¥ÁævÀåPÀëvÉAiÀÄ£ÉÆß¼ÀUÉÆAqÀ vÀgÀ¨ÉÃwAiÀÄ£ÀÄß ¤Ãr ¨É¼ÉPÉëÃvÀæ ¸À«ÄÃPÉë ºÁUÀÆ ¨É¼É PÀmÁªÀÅ ¸À«ÄÃPÉëUÀ¼ÀÄ ºÉZÀÄÑ ¤RgÀªÁVgÀÄªÀAvÉ ªÉÄÃ°éZÁgÀuÉ £ÀqÉ¸À®Ä ¥ÉæÃgÉÃ¦¸ÀÄªÀÅzÀÄ.

ZÁ°ÛAiÀÄ°ègÀÄªÀ ¨É¼É PÉëÃvÀæ zÁR¯Áw ºÁUÀÆ ¨É¼É E¼ÀÄªÀj ¸À«ÄÃPÉëUÀ¼À£ÀÄß ºÉZÀÄÑ ¤RgÀªÁV ªÀÄvÀÄÛ ªÁ¸ÀÛªÀvÉ¬ÄAzÀ PÀÆrgÀ®Ä ¸ÀÄzsÁgÀuÁ PÀæªÀÄUÀ¼À CUÀvÀå«zÀÄÝ, PÉÃAzÀæ ¸ÀPÁðgÀzÀ ªÀÄlÖzÀ°è CUÀvÀå ¨É¼ÉPÉëÃvÀæ ºÁUÀÆ ¨É¼É E¼ÀÄªÀj CAQCA±ÀUÀ¼À C¢ü¤AiÀÄªÀÄªÀ£ÀÄß eÁjUÉÆ½¸ÀÄªÀÅzÀgÀ ªÀÄÆ®PÀ ªÀiÁvÀæ ¸ÀÄzsÁgÀuÉAiÀÄ£ÀÄß ¤jÃQë¸À§ºÀÄzÁVzÉ.

******

171

7. ¨É¼É CAQ CA±À zÁR¯Áw ªÀÄvÀÄÛ CzÀgÀ°ègÀÄªÀ ¸ÀªÀÄ¸ÉåUÀ¼ÀÄ - GqÀÄ¦ f¯Éè *

¦ÃpPÉ:

¥ÀºÀtÂAiÀÄ°è ¨É¼É ºÁUÀÆ ¨sÀÆ G¥ÀAiÉÆÃUÀzÀ zÁR¯ÁwAiÀÄ dªÁ¨ÁÝjAiÀÄ£ÀÄß §ºÀ¼À »A¢£À PÁ®¢AzÀ®Æ PÀAzÁAiÀÄ E¯ÁSÉ ¤ªÀð»¹PÉÆAqÀÄ §A¢vÀÄÛ. £ÀªÀÄÆ£É 16 gÀ°è ¥ÀºÀtÂ ¥ÀwæPÉ eÁjUÉ §gÀÄªÀ ¥ÀÆªÀðzÀ°è SÉvÀÄªÁgÀÄ jf¹Öç CxÀªÁ ¨É¼É jf¹ÖçAiÀÄ°è ¸ÀªÉð £ÀA§æªÁgÀÄ ¨É¼ÉzÀ ªÀÄÄRå ¨É¼ÉUÀ¼À «¹ÛÃtðzÀ «ªÀgÀªÀ£ÀÄß §gÉzÀÄ ¥sÀ¸À®Ä WÉÆÃµÁégÉAiÉÆAzÀ£ÀÄß ¤ªÀð»¸À¯ÁUÀÄwÛvÀÄÛ. ¸À¢æ ¨É¼É WÉÆÃµÁégÉ ªÀiÁ»wAiÀÄ£ÀÄß ¯É«¸ÀAUÀæºÀuÉ, ¸ÀgÀPÁgÀzÀ ««zsÀ AiÉÆÃd£ÉUÀ¼À C£ÀÄµÁ×£ÀPÁÌV G¥ÀAiÉÆÃV¸À¯ÁUÀÄwÛvÀÄÛ.

¨sÀÆ PÀAzÁAiÀÄ ¤AiÀÄªÀÄzÀAvÉ £ÀªÀÄÆ£É 16 gÀ°è ¥ÀºÀtÂ ¥ÀwæPÉ eÁjUÉ §AzÀ ªÉÄÃ¯É 1964-65 jAzÀ ¨É¼É ºÁUÀÆ ¨sÀÆ G¥ÀAiÉÆÃUÀzÀ «ªÀgÀªÀ£ÀÄß ¥Àæw ªÀµÀð IÄvÀÄªÁgÀÄ PÉëÃvÀæ ¸À«ÄÃPÉë £ÀqÉ¹ ¥ÀºÀtÂAiÀÄ°è zÁR°¹ ¨É¼ÉªÁgÀÄ WÉÆÃµÁégÉAiÀÄ£ÀÄß vÀAiÀiÁj¹ ªÉÄÃ¯Á¢üPÁjUÀ½UÉ ¤ÃqÀ¯ÁUÀÄwÛvÀÄÛ. F ¥ÀzÀÝw 2000£ÉÃ ¸Á°£À°è ¥ÀºÀtÂ ¥ÀwæPÉ UÀtQÃPÀgÀtUÉÆ¼ÀÄîªÀgÉUÀÆ eÁjAiÀÄ°èzÀÄÝ ¨sÀÆ PÀAzÁAiÀÄ ¤AiÀÄªÀÄ, UÁæªÀÄ¯ÉQÌUÀgÀ PÉÊ¦r ºÁUÀÆ ¥ÀºÀtÂ zÁR¯ÁwAiÀÄ PÉÊ¦rAiÀÄ£ÀéAiÀÄ IÄvÀÄªÁgÀÄ ¤UÀ¢üvÀ CªÀ¢üAiÉÆ¼ÀUÉ ¨É¼É «ªÀgÀªÀ£ÀÄß ¥ÀºÀtÂAiÀÄ°è zÁR°¸ÀÄªÀÅzÀÄ UÁæªÀÄ¯ÉQÌUÀgÀ ¥ÀæªÀÄÄR PÀvÀðªÀåªÁVzÉ. UÁæªÀÄ¯ÉQÌUÀjUÉ C°èAiÀÄªÀgÉUÀÆ ¸ÁªÀðd¤PÀjUÉ ««zsÀ G¥ÀAiÉÆÃUÀzÀ §UÉÎ, ¸Ë®¨sÀåUÀ¼À£ÀÄß ¥ÀqÉAiÀÄªÀ §UÉÎ ¥ÀºÀtÂAiÀÄ «vÀgÀuÉAiÀÄ C¢üPÁgÀªÀ£ÀÄß ¤ÃqÀ¯ÁVgÀÄªÀÅzÀjAzÀ ¸ÀA§A¢ü¹zÀ ªÀµÀð ¨É¼É zÁR¯Áw ªÀiÁr EA¢ÃPÀÈvÀ ¥ÀºÀtÂAiÀÄ£ÀÄß ¤ªÀð»¸ÀÄªÀÅzÀÄ C¤ªÁAiÀÄðªÀÇ DVvÀÄÛ.

2000 £ÉÃ ¸Á°£À°è ¥ÀºÀtÂ ¥ÀwæPÉAiÀÄ UÀtQÃPÀgÀt PÁAiÀÄð ¥ÁægÀA¨sÀUÉÆAqÀÄ J¯Áè ¥ÀºÀtÂUÀ¼À£ÀÄß UÀtQÃPÀgÀtªÁzÀ ªÉÄÃ¯É UÁæªÀÄ¯ÉQÌUÀjAzÀ CªÀgÀ UÁæªÀÄUÀ¼À 100% gÀµÀÄÖ ¥ÀºÀtÂAiÀÄ£ÀÄß CªÀÅUÀ¼À°è£À «ªÀgÀzÀ UÀtQÃPÀgÀt ¸ÀjAiÀiÁV £ÀqÉ¢gÀÄªÀÅzÀgÀÀ §UÉÎ ¥Àj²Ã®£ÉUÉ M¼À¥Àr¹ ««zsÀ ¸ÀÜgÀUÀ¼À°è PÀAzÁAiÀÄ E¯ÁSÉAiÀÄ C¢üPÁjUÀ½AzÀ ªÉÄÃ®Û¤PÉ¬ÄAzÀ 100% «ªÀgÀUÀ¼ÀÄ zÁR¯ÁVgÀÄªÀÅzÀ£ÀÄß RavÀ¥Àr¹ zÀÈrüÃPÀj¸À¯ÁVvÀÄÛ.

¥ÀºÀtÂ ¥ÀwæPÉAiÀÄ UÀtQÃPÀgÀtUÉÆAqÀ £ÀAvÀgÀ PÀAzÁAiÀÄ E¯ÁSÉAiÀÄ ¸ÀÄvÉÆÛÃ¯É £ÀA§æ Dgï.r 60 JA.Dgï.Dgï 2001 ¢£ÁAPÀ 12.4.2002 gÀAvÉ UÁæªÀÄ¯ÉQÌUÀjUÉ CªÀgÀ ªÁå¦ÛAiÀÄ UÁæªÀÄzÀ J¯Áè ¥ÀºÀtÂUÀ¼À MAzÀÄ ¸Émï Dgïn¹AiÀÄ£ÀÄß ¤Ãr CzÀ£ÀÄß ªÀÄÄA¢£À 2-3 ªÀµÀðUÀ¼ÀªÀgÉUÉ ¨É¼É zÁR¯ÁwUÁV G¥ÀAiÉÆÃV¸À vÀPÀÌzÉÝAzÀÆ ¥ÀºÀtÂ UÀtQÃPÀgÀt ¥ÀÆtðUÉÆ¼ÀîzÀ ªÀÄvÀÄÛ MAzÀÄ ¥Àæw ¥ÀºÀtÂ ¤ÃqÀ¯ÁUÀzÀ f¯ÉèUÀ¼À°è ¥ÀºÀtÂAiÀÄ£ÀÄß ºÉÆÃ®ÄªÀ £ÀªÀÄÆ£ÉUÀ¼À£ÀÄß «vÀj¸ÀÄªÀ ªÀÄÆ®PÀ ¥ÀºÀtÂAiÀÄ°è ¨É¼É zÁR¯ÉAiÀÄ PÁAiÀÄð¸ÀÜVvÀUÉÆ¼ÀîzÀAvÉ £ÉÆÃrPÉÆ¼ÀîvÀPÀÌzÉÝAzÀÆ «¢ü À¯ÁAiÀÄÄÛ.

vÀzÀ £ÀAvÀgÀ PÀAzÁAiÀÄ E¯ÁSÉ¬ÄAzÀ ¤ÃrzÀ ¸ÀÄvÉÆÛÃ¯É £ÀA§æ Dgï r 49/JA.Dgï.Dgï 2003 ¢£ÁAPÀ 27.12.2005 gÀ°è ¨sÀÆ«Ä qÁmÁ ¨ÉÃ¸ï£À°è ¨É¼ÉUÀ¼À EA¢ÃPÀgÀt PÁAiÀÄðzÀ ºÀAvÀUÀ¼À£ÀÄß w½¹ UÁæªÀÄ¯ÉQÌUÀgÀÄ ¤UÀ¢vÀ CªÀ¢üAiÉÆ¼ÀUÉ IÄvÀÄªÁgÀÄ ¨É¼ÉAiÀÄ «ªÀgÀªÀ£ÀÄß zÁR°¹zÀ £ÀAvÀgÀ PÀ¼ÉzÀ ªÀµÀðPÉÌ ºÉÆÃ°¹zÁUÀ ¨É¼É / ¨sÀÆ G¥ÀAiÉÆÃUÀ §zÀ¯ÁªÀuÉAiÀiÁVgÀÄªÀ ¸ÀªÉð £ÀA§æUÀ¼À MAzÀÄ ¥ÀnÖAiÀÄ£ÀÄß §zÀ¯ÁUÀzÀ J¯Áè ¸ÀªÉð £ÀA§æzÀ E£ÉÆßAzÀÄ ¥ÀnÖAiÀÄ£ÀÄß ªÀiÁr EA¢ÃPÀgÀtPÁÌV ¨sÀÆ«Ä ±ÁSÉUÉ PÉÆlÖ £ÀAvÀgÀ ¨É¼É §zÀ¯ÁªÀuÉUÉÆ¼ÀÄîªÀ ¸ÀªÉð £ÀA§æUÀ¼À£ÀÄß UÀÄgÀÄvÀÄ ªÀiÁr ªÀÄvÀÄÛ ¨É¼É §zÀ¯ÁªÀuÉUÉÆ¼ÀîzÀªÀÅUÀ¼À£ÀÄß PÀ¼ÉzÀ ªÀµÀð¢AzÀ F ªÀµÀðPÉÌ £ÀPÀ®Ä ªÀiÁrzÀ £ÀAvÀgÀ ¸À¢æ qÁmÁªÀ£ÀÄß ¥ÀÆªÀð ¤UÀ¢vÀ KeÉ¤ìAiÀÄªÀjUÉ EA¢ÃPÀgÀtPÁÌV KPïì¥ÉÆÃmïð ªÀiÁqÀÄªÀAvÉAiÀÄÆ ¸À¢æ KeÉ¤ìAiÀÄªÀgÀÄ * ²æÃ ©. ¸ÀÄgÉÃ±ïgÁªï, f¯Áè ¸ÀASÁå ¸ÀAUÀæºÀuÁ¢üPÁjUÀ¼ÀÄ, GqÀÄ¦

172

qÁmÁ ¦æ¥ÀgÉÃ±À£ï ²Ãmï£ÀÄß UÁæªÀÄ¯ÉQÌUÀjUÉ PÉÆlÄÖ CzÀ£ÀÄß ¨sÀwð ªÀiÁr ¥ÀqÉzÀ £ÀAvÀgÀ KeÉ¤ìAiÀÄªÀgÀÄ ¨É¼É «ªÀgÀªÀ£ÀÄß zÁR®Ä ªÀiÁr ªÉÆzÀ®£ÉAiÀÄ ZÉPï°¸ÀÖ£ÀÄß UÁæªÀÄ¯ÉQÌUÀjUÉ PÉÆlÄÖ CzÀ£ÀÄß qÁmÁ ¦æ¥ÀgÉÃ±À£ï ²Ãn£ÉÆA¢UÉ vÀÄ®£É ªÀiÁrzÀ £ÀAvÀgÀ CzÀ£ÀÄß KeÉ¤ìAiÀÄªÀgÀÄ ªÀÄvÉÛ »AzÀPÉÌ ¥ÀqÉzÀÄ vÀ¥ÀàUÀ½zÀÝ°è ¸Àj¥Àr¹zÀ £ÀAvÀgÀ 2£ÉÃ ¨ÁjAiÀÄ ZÉPï °¸ÀÖ£ÀÄß ¸ÀºÀ PÉÆlÄÖ ¥ÀÄ£ÀB vÀÄ®£É ªÀiÁr CAwªÀÄªÁV ¸Àj¥Àr¹zÀ £ÀAvÀgÀ KeÉ¤ì¬ÄAzÀ EA¢ÃPÀÈvÀ ¥ÀºÀtÂAiÀÄ ¨É¼É «ªÀgÀªÀ£ÀÄß vÀºÀ²Ã¯ÁÝgÀgÀÄ DªÀÄzÀÄ ªÀiÁrPÉÆ¼Àî¨ÉÃPÁUÀÄvÀÛzÉ. ¥ÀÆªÀð ªÀÄÄAUÁgÀÄ ªÀÄvÀÄÛ ªÀÄÄAUÁgÀÄ ºÀAUÁ«Ä£À ¨É¼ÉUÀ¼À£ÀÄß MAzÉÃ ¸À® ªÀÄÄAUÁjUÀÆ »AUÁgÀÄ ªÀÄvÀÄÛ ¨ÉÃ¹UÉAiÀÄ «ªÀgÀUÀ¼À£ÀÄß D ºÀAUÁªÀÄÄ ªÀÄÄVzÀ PÀÆqÀ¯ÉÃ ¨sÀÆ«Ä EA¢ÃPÀgÀt PÁAiÀÄðPÉÊUÉÆ¼ÀîvÀPÀÌzÉÝAzÀÆ ¸ÀÆa¸À¯ÁVzÉ. ¸ÀÄvÉÆÛÃ¯ÉAiÉÆA¢UÉ §AzÀ ¨É¼É EA¢ÃPÀgÀtzÀ ºÀAvÀUÀ¼ÀÄ ºÁUÀÆ ¤UÀ¢¥Àr¸À¯ÁzÀ PÁ¯ÁªÀPÁ±ÀzÀ «ªÀgÀ ®UÀvÀÄÛ EzÉ.

ªÁ¸ÀÛªÀvÉ

¸ÀgÀPÁj ¤zÉÃð±À£À ¸ÀjAiÀiÁVzÀÝgÀÆ ¸ÀºÀ

1. UÁæªÀÄ¯ÉQÌUÀgÀÄ ªÀÄvÀÄÛ ¸ÀA§A¢ü¹zÀ PÀAzÁAiÀÄ E¯ÁSÉAiÀÄ ªÉÄÃ¯Á¢üPÁjUÀ¼ÀÄ ¸ÀºÀ UÀtQÃPÀgÀtUÉÆAqÀ £ÀAvÀgÀ UÀtQÃPÀÈvÀ ¥ÀºÀtÂAiÀÄ°è ¨É¼É «ªÀgÀ §gÉAiÀÄÄªÀ CªÀ±ÀåPÀvÉ E®è JAzÀÄ ¨sÁ«¹gÀÄªÀÅzÀÄ ªÀÄvÀÄÛ CA¢¤AzÀ E°èAiÀÄªÀgÉUÀÆ ¨É¼ÉAiÀÄ£ÀÄß UÀtQÃPÀÈvÀ ¥ÀºÀtÂAiÀÄ°è zÁR°¹ §zÀ¯ÁªÀuÉUÀ¼À£ÀÄß ¥ÀnÖ ªÀiÁqÀzÉÃ ªÀÄÆ® UÀtQÃPÀgÀtzÀ ¥ÀºÀtÂAiÀÄ°ègÀÄªÀ ¨É¼ÉAiÀÄ «ªÀgÀªÉÃ ªÀÄÄAzÀÄªÀj¢zÉ. PÉ®ªÉÇAzÀÄ UÁæªÀÄ¯ÉQÌUÀgÀÄ ¥ÀºÀtÂ §gÉAiÀÄ¢zÀÝgÀÆ §zÀ¯ÁªÀuÉUÉÆAqÀ ¨É¼ÉUÀ¼À ¥ÀnÖAiÀÄ£ÀÄß ªÀiÁr EA¢ÃPÀgÀtPÁÌV PÉÆnÖzÀÝgÀÆ ªÀÄvÀÄÛ qÁmÁ ¦æ¥ÀgÉÃ±À£ï ²Ãn£À°è ¸ÀjAiÀiÁV «ªÀgÀ §gÉ¢zÀÝgÀÆ KeÉ¤ìAiÀÄªÀgÀÄ ¸ÀjAiÀiÁV PÉ®¸À ¤ªÀð»¸ÀzÀ PÁgÀt §zÀ¯ÁzÀ ¨É¼ÉUÀ¼ÀÄ ¸ÀA§A¢ü¹zÀ ¥ÀºÀtÂAiÀÄ°è £ÀªÀÄÆzÁV®è. C®èzÉ KeÉ¤ì¬ÄAzÀ ¸À¢æ ¨É¼É «ªÀgÀ EA¢ÃPÀgÀtPÁÌV vÀgÀ¨ÉÃw E®èzÀ ªÀÄvÀÄÛ «µÀAiÀÄzÀ w¼ÀÄªÀ½PÉ E®èzÀ ºÉÆgÀUÀÄwÛUÉAiÀÄ ²§âA¢UÀ¼À£ÀÄß £ÉÃªÀÄPÀ ªÀiÁr¸ÀÄªÀÅzÀjAzÀ ¨É¼É ºÁUÀÄ ¨sÀÆ G¥ÀAiÉÆÃUÀPÉÌ ¤ÃrzÀ ¸ÀAPÉÃvÁPÁëgÀUÀ¼À£ÀÄß vÀ¥ÁàV £ÀªÀÄÆ¢¹ QvÀÛ¼É, zÁæQë E®èzÀ f¯ÉèAiÀÄ ¥ÀºÀtÂUÀ¼À°è QvÀÛ¼É, zÁæQë ¨É¼ÉUÀ¼ÀÄ £ÀªÀÄÆ¢¸À®àlÖ «¥ÀAiÀiÁð¸À ¸ÀÈ¶×AiÀiÁVzÉ.

2. ¥Àæw ªÀµÀð UÁæªÀÄ¯ÉQÌUÀjUÉ ¨É¼É «ªÀgÀ zÁR¯Áw PÀæªÀÄzÀ §UÉÎ f¯Áè ¸ÀASÁå ¸ÀAUÀæºÀuÁ¢üPÁjUÀ½AzÀ vÀgÀ¨ÉÃwUÀ¼ÀÄ £ÀqÉAiÀÄÄwÛzÉ. ¸À¢æ vÀgÀ¨ÉÃwAiÀÄ°è PÀvÀðªÀå ¤µÀ× UÁæªÀÄ¯ÉQÌUÀgÀÄ qÁmÁ ¦æ¥ÀgÉÃ±À£ï ²Ãn£À°è C°èAzÀ E°èAiÀÄªÀgÉUÉ IÄvÀÄªÁgÀÄ §zÀ¯ÁzÀ ¨É¼ÉUÀ¼À «ªÀgÀUÀ¼À£ÀÄß ¸ÀjAiÀiÁV PÉÆnÖzÀÝgÀÆ ¥ÀºÀtÂAiÀÄ°è EA¢ÃPÀgÀtUÉÆ¼Àî¢gÀÄªÀÅzÀjAzÀ vÀgÀ¨ÉÃw ²©gÀzÀ°è UÀtQÃPÀÈvÀ ¥ÀºÀtÂAiÀÄ°è ¨É¼É «ªÀgÀªÀ£ÀÄß §gÉAiÀÄÄªÀ OavÀéªÀ£ÀÄß ¥Àæ²ß¸ÀÄvÁÛgÉ.

3. ¨sÀÆ«ÄAiÀÄ°è ¨É¼É «ªÀgÀ EA¢ÃPÀgÀt ¥ÀæQæAiÉÄ ªÀÄÄAUÁjUÉ CPÉÆÖÃ§gï , »AUÁjUÉ ¥sÉ§æªÀj ºÁUÀÆ ¨ÉÃ¹UÉUÉ ªÉÄÃ wAUÀ¼À°è ªÀÄÄV¸À¨ÉÃPÁVzÀÝgÀÆ CzÀÄ C£ÀUÀvÀå «¼ÀA§ªÁUÀÄvÀÛzÉ. C®èzÉ gÉÊvÀjUÉ ZÁ°Û ¸Á°£À ¨É¼É «ªÀgÀUÀ¼ÀÄ ¸ÀPÁ®zÀ°è ¹UÀzÉ ¸ÀPÁðgÀ¢AzÀ ¹UÀÄªÀ ¸ÀªÀ®vÀÄÛ ªÀAavÀ£ÁUÀÄvÁÛ£É.

4. ¨É¼ÉUÀ¼ÀÄ ¸ÀPÁ®zÀ°è EA¢ÃPÀgÀtªÁUÀ¢zÀÝgÉ ©vÀÛ£É zÀÈqÀ ¥ÀvÀæ CxÀªÁ ¨É¼É zÀÈrÃPÀgÀtPÁÌV ¸ÁªÀðd¤PÀgÀÄ UÁæªÀÄ¯ÉQÌUÀgÀ£ÀÄß £ÉaÑPÉÆ¼ÀÄîªÀ ¥Àæ¸ÀAUÀ GzÀâ«¹zÉ. CzÀÄzÀjAzÀ UÁæªÀÄ¯ÉQÌUÀgÀÄ ¸ÀºÀ ¨É¼É zÁR°¸ÀÄªÀ PÁAiÀÄðzÀ°è C£Á¸ÀQÛ vÉÆÃj¸ÀÄvÁÛgÉ.

173

5. ¨sÀÆ«Ä vÀAvÁæA±ÀzÀ°è SÁvÁ §zÀ¯ÁªÀuÉAiÀÄAvÉ ¸ÀA§A¢ü¹zÀ gÉÊvÀgÀÄ CªÀgÀ d«ÄÃ¤£À°ègÀÄªÀ ¨É¼ÉUÀ¼À «ªÀgÀªÀ£ÀÄß £ÉÃgÀªÁV EA¢ÃPÀgÀtPÁÌV ¸À°è¹zÀ°è CzÀ£ÀÄß EA¢ÃPÀj¹ ¤ÃqÀ®Ä CªÀPÁ±À«zÀÝgÀÆ UÁæªÀÄ¯ÉPÀÌUÀjUÁUÀ¯ÉÃ , ¸ÁªÀðd¤PÀjUÁUÀ¯ÉÃ F «µÀAiÀÄ w½¢®è.

6. ºÉaÑ£À UÁæªÀÄ¯ÉQÌUÀgÀÄ UÀtQÃPÀÈvÀ ¥ÀºÀtÂAiÀÄ°è ¨É¼É zÁR¯Áw ªÀiÁqÀzÉ ¨É¼É WÉÆÃµÁégÉAiÀÄ£ÀÄß CAzÁf£À ªÉÄÃ¯É ¤ÃqÀÄªÀ ¥Àj¥ÁoÀ ºÉaÑzÉ. C®èzÉ PÉëÃvÀæ ¸À«ÄÃPÉë ªÀiÁr ¨É¼ÉUÀ¼À£ÀÄß zÁR°¹zÀÝgÀÆ CªÀÅUÀ¼À£ÀÄß PÉÆæÃrÃPÀj¹zÀ°è PÀ¼ÉzÀ ªÀµÀð ¤ÃrzÀ CAQ CA±ÀPÀÆÌ F ¸Á°£À CAQCA±ÀPÀÆÌ CdUÀeÁAvÀgÀ ªÀåvÁå¸ÀªÁUÀÄwÛzÀÄÝ EA¢ÃPÀj¹ PÉÆæÃrÃPÀj¸À¢gÀÄªÀÅzÀgÀ OavÀå w½zÀÄ §gÀÄªÀÅ¢®è.

7. UÁæªÀÄ¯ÉQÌUÀgÀÄ ¥Àæw ªÀµÀð ªÀÄÄAUÁgÀÄ ºÀAUÁ«ÄUÉ zÁR°¹zÀgÀÆ ºÉa£À UÁæªÀÄ¯ÉQÌUÀjUÉ »AUÁgÀÄ ªÀÄvÀÄÛ ¨ÉÃ¹UÉUÉ ¨É¼É zÁR®Ä ªÀiÁqÀÄªÀ ¥Àj¥ÁoÀ«gÀÄªÀÅ¢®è.

PÀ£ÁðlPÀ gÁdå PÁAiÀÄðvÁAwæPÀ ¸ÁATåPÀ AiÉÆÃd£ÉAiÀÄr vÁ®ÆPÀÄUÀ½UÉ «Ä¤ ¯Áå¥ïmÁ¥ïUÀ¼À£ÀÄß ¸ÀgÀ§gÁdÄ ªÀiÁqÀ¯ÁVzÀÄÝ EªÀÅUÀ¼À°è ¨É¼É WÉÆÃµÁégÉ , ¨É¼É PÀmÁ«£À ¥ÀæAiÉÆÃUÀUÀ¼À £ÀªÀÄÆ£É ªÀgÀ¢UÀ¼À zÁR¯ÁwUÁV vÀAvÁæA±ÀªÀ£ÀÄß C©üªÀÈ¢Þ¥Àr¹ UÁæªÀÄ¯ÉQÌUÀgÀÄ vÀºÀ²Ã¯ÁÝgÀgÀ PÀZÉÃj¬ÄAzÀ ¸À¢æ UÀtPÀAiÀÄAvÀæªÀ£ÀÄß ¥ÀqÉzÀÄ ¨É¼É WÉÆÃµÁégÉAiÀÄ£ÀÄß D¼ÀªÀr¸À¨ÉÃPÉAzÀÄ CxÀªÁ ¨É¼É PÀmÁ«£À ¥ÀæAiÉÆÃUÀzÀ ªÀiÁ»wAiÀÄ£ÀÄ D£ï¯ÉÊ£ï zÁR°¸À¨ÉÃPÉAzÀÄ «¢ü¸À¯ÁVzÉ. F ¸ÀA§AzsÀ J¯Áè UÁæªÀÄ¯ÉQÌUÀjUÉ vÀgÀ¨ÉÃwAiÀÄ£ÀÄß ¤ÃqÀ¯ÁVzÉ.

PÉÃªÀ® ¨É¼É WÉÆÃµÁégÉ, ¨É¼É PÀmÁ«£À ¥ÀæAiÉÆÃUÀUÀ¼À ªÀiÁ»wAiÀÄ zÁR¯ÁwUÁV ¸À¢æ ¯Áå¥ïmÁ¥ïUÀ¼À£ÀÄß §¼À¸À¯ÁUÀÄwÛzÀÄÝ CªÀÅUÀ¼À ¤gÀAvÀgÀ G¥ÀAiÉÆÃUÀ E®èzÁVzÉ. CµÀÄ× ªÉZÀÑ ªÀiÁr CªÀÅUÀ¼À£ÀÄß ªÀÄvÀÄÛ ¥ÀÆtð ¥ÀæªÀiÁtzÀ°è §¼À¸À®Ä CªÀPÁ±À«gÀÄªÀÅ¢®è.

EwÛÃZÉV£À ¢£ÀUÀ¼À°è ¥ÀºÀtÂAiÀÄ°è ¨É¼É CAQ CA±ÀUÀ¼À zÁR¯ÁwUÉ UÁæªÀÄ¯ÉQÌUÀgÀÄ UÀªÀÄ£À PÉÆqÀÄwÛ®è. CªÀjVgÀÄªÀ PÉ®¸À PÁAiÀÄðUÀ¼À MvÀÛqÀ, PÉëÃvÀæ ¥Àj²Ã®£ÉUÉ ¸ÀªÀÄAiÀiÁªÀPÁ±ÀzÀ C¨sÁªÀ ¥ÀæwAiÉÆAzÀÄ UÁæªÀÄPÉÌ ¸ÀgÁ¸Àj 3500 ¥ÀºÀtÂUÀ½zÀÄÝ CªÀÅUÀ¼À£ÀÄß ¤UÀ¢vÀ CªÀ¢üAiÉÆ¼ÀUÉ ¥Àj«ÃQë¹ ¨É¼É zÁR¯Áw ªÀiÁqÀÄªÀÅzÀÄ PÀµÀ× ¸ÁzsÀåªÁVgÀÄªÀÅzÀjAzÀ F §UÉÎ D¸ÉÛ ªÀ»¸À¢gÀÄªÀÅzÀÄ ªÀÄvÀÄÛ PÀ¼ÉzÀ ¸Á°£À «ªÀgÀUÀ¼À£ÀÄß AiÀÄxÁªÀvÁÛV zÁR°¸ÀÄªÀ ¥Àj¥ÁoÀ ªÀÄÄAzÀÄªÀj¢zÉ.

¸ÀgÀPÁgÀzÀ PÁAiÀÄðzÀ²ðUÀ¼ÀÄ, PÀAzÁAiÀÄ E¯ÁSÉ EªÀgÀ CgÉ¸ÀPÁðj ¥ÀvÀæ ¸ÀA.Dgïr/07/ JADgïDgï/2004/¢£ÁAPÀ 1.8.2009 gÀAvÉ UÁæªÀÄ¯ÉQÌUÀgÀÄ PÀÈ¶, vÉÆÃlUÁjPÉ ºÁUÀÆ ¤ÃgÁªÀj E¯ÁSÉAiÀÄ PÉëÃvÀæªÀÄlÖzÀ C¢üPÁjUÀ¼ÉÆA¢UÉ dAnAiÀiÁV ¨É¼É ¥Àj²Ã®£É £ÀqÉ¹zÀ £ÀAvÀgÀ ¸ÀªÉð ¸À¨ï £ÀA§æªÁgÀÄ ¨É¼ÉAiÀÄ «ªÀgÀªÀ£ÀÄß PÉÆæÃrÃPÀj¹ ¸À¢æ E¯ÁSÉUÀ¼ÉÆA¢UÉ ¨É¼É PÉëÃvÀæ ºÉÆAzÁtÂPÉ £ÀqÉ¹zÀ £ÀAvÀgÀ dAn ¸À»AiÉÆA¢UÉ ¨É¼É WÉÆÃµÁégÉAiÀÄ£ÀÄß ¸À°è¸À¨ÉÃPÁVgÀÄvÀÛzÉ. ¥Àæ¸ÀÄÛvÀ J¯Áè UÁæªÀÄ¯ÉQÌUÀgÀ ªÀÈvÀÛzÀ UÁæªÀÄ¯ÉQÌUÀgÀ ºÀÄzÉÝAiÀÄ£ÀÄß ¨sÀwðUÉÆ½¹zÀÝgÀÆ ¸ÀºÀ F ¥ÀæQæAiÉÄAiÀÄ°è M¼ÀUÉÆ¼ÀÄîªÀ G½zÀ PÀÈ¶ ªÀÄvÀÄÛ vÉÆÃlUÁjPÁ E¯ÁSÉAiÀÄ°è PÉëÃvÀæ ²§âA¢UÀ¼ÀÄ E®è¢gÀÄªÀÅzÀjAzÀ E¯ÁSÉAiÀÄªÀgÀÄ PÉëÃvÀæ ¸À«ÄÃPÉëUÉ ºÁdgÁUÀzÉ CxÀªÁ PÉÊUÉÆ¼ÀîzÉ UÁæªÀÄ¯ÉQÌUÀgÀ CAQ CA±ÀPÉÌ M¦àUÉ ¸ÀÆa¸À¨ÉÃPÁzÀ CxÀªÁ CAzÁdÄ «¹ÛÃtðªÀ£ÀÄß PÉÆqÀÄªÀ ¥ÀæªÉÄÃAiÀÄ GzÀâ«¹zÉ. F »£Àß¯ÉAiÀÄ°è ¨É¼É «¹ÛÃtð ºÉÆAzÁtÂPÉAiÀÄ ¥ÀæQæÃAiÉÄ PÉÃªÀ® PÁmÁZÁgÀPÉÌ / C¤ªÁAiÀÄðvÉUÉ zÁR¯ÁwUÁV £ÀqÉ¸ÀÄªÀ ¥ÀæQæAiÉÄAiÀiÁVzÉ. gÉÊvÀ ¸ÀA¥ÀPÀð PÉÃAzÀæPÉÌ ¹Ã«ÄvÀªÁVgÀÄªÀ ªÀÄvÀÄÛ PÉëÃvÀæ ²§âA¢UÀ½®èzÀ PÀÈ¶ E¯ÁSÁ C¢üPÁjUÀ¼ÀÄ PÉëÃvÀæ ¸À«ÄÃPÉëAiÀÄ°è ¥Á¯ÉÆÎ¼ÀîzÉ ªÀÄvÀÄÛ »A¢£À CAQ CA±ÀUÀ¼À DzsÁgÀzÀ°è ºÉÆAzÁtÂPÉ £ÀqÉ¸ÀÄªÀÅzÉÃ DVzÉ.

174

UÁæªÀÄ¯ÉQÌUÀgÀÆ ¸ÀºÀ CªÀjVgÀÄªÀ EvÀgÀ PÉ®¸ÀUÀ¼À MvÀÛqÀ¢AzÁV ªÀÄvÀÄÛ F ¥ÀæQæAiÉÄ ¤UÀ¢üvÀ ¹Ã«ÄvÀ CªÀ¢üAiÉÆ¼ÀUÉ DUÀ¨ÉÃPÁVgÀÄªÀÅzÀjAzÀ ¥ÀºÀtÂ zÁR°¸ÀzÉ ªÀÄvÀÄÛ ¨É¼ÉUÀ¼À£ÀÄß ¸ÀªÉð £ÀA§æªÁgÀÄ PÀÆærüÃPÀj¸ÀzÉ PÀÈ¶ CxÀªÁ vÉÆÃlUÁjPÉ E¯ÁSÉAiÀÄ CAzÁdÄ CAQ CA±ÀUÀ½UÉ ºÉÆA¢PÉÆ¼ÀÄîªÀ ¥Àj¹Üw ¤ªÀiÁðtªÁVzÉ.

F J¯Áè PÁgÀtUÀ¼À »£Àß¯ÉAiÀÄ°è ¨É¼É zÁR¯Áw, EA¢ÃPÀgÀt, PÉÆæÃrüÃPÀgÀt ºÁUÀÆ «¹ÛÃtð ºÉÆAzÁtÂPÉAiÀÄ PÉ®¸ÀUÀ¼ÀÄ PÉÃªÀ® PÁmÁZÁgÀPÉÌ ªÀiÁqÀÄªÀ ¥ÀæQAiÉÄUÀ¼ÁUÀÄvÀÛzÉ JAzÀgÀÆ vÀ¥ÁàUÀÄªÀÅ¢®è. DzÀÄzÀjAzÀ ¥Àæ¸ÀÄÛvÀzÀ ¥Àj¹ÜwAiÀÄ°è PÀÈ¶ CAQ CA±ÀUÀ¼À UÀÄtªÀÄlÖ GvÀÛªÀÄªÁUÀ¨ÉÃPÁzÀ°è F PÉ¼ÀV£À «µÀAiÀÄUÀ¼ÀvÀÛ UÀªÀÄ£À ºÀj¸ÀÄªÀÅzÀÄ CªÀ±ÀåªÁVzÉ.

CªÀ±ÀåPÀvÉ

(1) IÄvÀÄªÁgÀÄ ¨É¼ÉAiÀÄ¯ÁUÀÄªÀ ¨É¼ÉUÀ¼ÀÄ DAiÀiÁ IÄvÀÄ«£À¯ÉèÃ UÀtw ªÀiÁqÀvÀPÀÌzÁVzÀÄÝ ¥sÀ¸À®Ä PÀmÁªÀÅ ªÀÄÄVzÀ £ÀAvÀgÀ ¸À¢æ ¨É¼É JtÂPÉUÉ ¹UÀ¢gÀÄªÀÅzÀjAzÀ ºÀAUÁªÀÄÄªÁgÀÄ ¤UÀ¢ü¥Àr¹zÀ ªÉÃ¼ÁªÀ¢üAiÀÄ¯Éè UÁæªÀÄ¯ÉQÌUÀgÀÄ ¨É¼É vÀ¤SÉ £ÀqÉ¸À¨ÉÃPÀÄ ªÀÄvÀÄÛ UÀtQÃPÀÈvÀ ¥ÀºÀtÂAiÀÄ°è ¨É¼É zÁR¯ÁUÀÄªÀAvÉ PÀæªÀÄªÀ»¸ÀÄªÀÅzÀÄ.

(2) ¸ÀgÀPÁj ¸ÀÄvÉÆÛÃ¯ÉAiÀÄAvÉ ¨É¼É vÀ¤SÉ, ¨É¼ÉUÀ¼À «¹ÛÃtð ºÉÆAzÁtÂPÉ UÁæªÀÄªÀÄlÖzÀ°è DUÀ¨ÉÃPÀÄ. «¹ÛÃtð JtÂPÉAiÀÄ¯ÁèUÀÄªÀ ¯ÉÆÃ¥ÀzÉÆÃµÀUÀ¼À£ÀÄß ¸Àj¥Àr¸À¨ÉÃPÀÄ.

(3) UÀtQÃPÀÈvÀ ¥ÀºÀtÂAiÀÄ°è zÁR¯ÁzÀ ¨É¼ÉAiÀÄ «ªÀgÀªÀ£ÀÄß PÀ¼ÉzÀ ªÀµÀðPÉÌ ºÉÆÃ°¹zÁUÀ §zÀ¯ÁªÀuÉUÀ¼À£ÀÄß PÀÆqÀ¯ÉÃ ¥ÀnÖ ªÀiÁr ¨sÀÆ«ÄAiÀÄ°è EA¢ÃPÀgÀtPÁÌV ¤ÃqÀ¨ÉÃPÀÄ.

(1) ªÀµÀðzÀ°è ¥Àæw IÄvÀÄ ªÀÄÄVzÀ PÉÆqÀ¯ÉÃ ¸ÁªÀðd¤PÀjUÉ ¨É¼ÉAiÀÄ «ªÀgÀ £ÀªÀÄÆ¢¹zÀ ¥ÀºÀtÂ zÉÆgÉAiÀÄÄªÀAvÁUÀ¨ÉÃPÀÄ.

(2) PÀÈ¶ ªÀµÀðzÀ°è ¨É¼É «ªÀgÀUÀ¼À£ÀÄß ¥ÀºÀtÂAiÀÄ°è zÁR°¸ÀÄªÀªÀgÉUÉ F »A¢£À ªÀµÀðzÀ ¨É¼É «ªÀgÀ UÀtQÃPÀÈvÀ ¥ÀæwAiÀÄ°è ®¨sÀå«gÀ¨ÉÃPÀÄ.

ªÉÄÃ°£ÀAvÉ ¸ÀPÁ®zÀ°è ¨É¼É vÀ¤SÉ, zÁR¯Áw «¹ÛÃtðUÀ¼À PÉÆæÃrüÃPÀgÀt, «¹ÛÃtð ºÉÆAzÁtÂPÉ, ¨sÀÆ«Ä EA¢ÃPÀgÀt ºÁUÀÆ ¸ÁªÀðd¤PÀjUÉ ¨É¼É zÁR°¹zÀ ¥ÀºÀtÂ ¥ÀvÀæUÀ¼ÀÄ «vÀgÀuÉAiÀiÁUÀÄªÀAvÉ ªÀiÁqÀ®Ä F PÉ¼ÀV£ÀAvÉ PÀæªÀÄªÀ»¸À§ºÀÄzÁVzÉ.

¨É¼É vÀ¤SÉUÁV ºÀAUÁªÀÄÄªÁgÀÄ ¸ÀgÀPÁgÀ ¤UÀ¢ü¥Àr¹zÀ MAzÀÄ wAUÀ¼À CªÀ¢üAiÀÄ°è UÁæªÀÄ¯ÉQÌUÀjUÉ E¯ÁSÉAiÀÄ EvÀgÀ PÉ®¸ÀUÀ¼À MvÀÛqÀªÀ£ÀÄß PÀrªÉÄ ªÀiÁr ¨É¼É ¥Àj²Ã®£É, zÁR¯Áw ºÁUÀÆ PÉÆæÃrüÃPÀgÀtPÉÌ C£ÀÄªÀÅ ªÀiÁrPÉÆqÀÄªÀÅzÀÄ.

UÁæªÀÄ¯ÉQÌUÀgÀÄ ¨É¼É §zÀ¯ÁªÀuÉAiÀÄ ¥ÀnÖAiÀÄ£ÀÄß ªÀiÁr qÁmÁ ¦æ¥ÀgÉÃ±À£ï ²Ãmï£À°è ¨sÀwð ªÀiÁqÀÄªÀ ZÉPï°¸ïÖUÀ¼À£ÀÄß ¸ÀÈf¹ ¥Àj²Ã®£Á PÁAiÀÄðzÀ §zÀ®Ä FUÁUÀ¯ÉÃ ¨É¼É CAQ CA±ÀUÀ¼À£ÀÄß zÁR°¸À®Ä C©üªÀÈ¢Þ ¥Àr¸À¯ÁzÀ ¯Áå¥ïmÁ¥ï£À°è UÁæªÀÄzÀ J¯Áè ¸ÀªÉð £ÀA§æzÀ ¥ÀºÀtÂAiÀÄ PÁ®A 12 ªÀÄvÀÄÛ 13 £ÀÄß C¥ï¯ÉÆÃqï ªÀiÁrPÉÆlÖ°è ZÀÄ£Á¬ÄvÀ ¸ÀzÀ¸ÀågÀÄ, gÉÊvÀgÀÄ ºÁUÀÆ GUÁætÂUÀ¼ÀÄ ¤ÃqÀÄªÀ ¨É¼ÉUÀ¼À «ªÀgÀUÀ¼À£ÀÄß PÉëÃvÀæ ¸À«ÄÃPÉë £ÀqÉ¹ CAwªÀÄUÉÆ½¹ C°èAiÉÄÃ ¨É¼É §zÀ¯ÁªÀuÉUÀ¼À£ÀÄß ¸Àj¥Àr¸À®Ä ¤ÃrzÀ°è ¸ÀªÀÄAiÀÄzÀ G½vÁAiÀÄ ºÁUÀÆ ¤RgÀvÉAiÀÄ ¸À«ÄÃ¥À«gÀÄvÀÛzÉ. EzÀjAzÀ F zÉ¸ÉAiÀÄ°è UÁæªÀÄ¯ÉQÌUÀgÀ dªÁ¨ÁÝÝjAiÀÄÆ ºÉZÀÄÑvÀÛzÉ. PÉÃªÀ® ¨É¼É CAQ CA±ÀUÀ¼À/ ¨É¼É WÉÆÃµÁégÉUÁV UÁæªÀÄ¯ÉQÌUÀjUÉ ¤ÃqÀ¯ÁUÀÄªÀ ¯Áå¥ïmÁ¥ïUÀ¼À ¸ÀA¥ÀÆtð G¥ÀAiÉÆÃUÀªÀÇ CzÀAvÁUÀÄvÀÛzÉ.

175

UÁæªÀÄ¯ÉQÌUÀgÀªÁgÀÄ ¸ÀgÀ§gÁdÄ ªÀiÁr CªÀÅUÀ¼À ¸ÀAgÀPÀëvÉAiÀÄ dªÁ¨ÁÝjAiÀÄ£ÀÄß UÁæªÀÄ¯ÉQÌUÀjUÉ ¤ÃqÀ§ºÀÄzÀÄ.

ªÉÄÃ°£ÀAvÉ ¨sÀÆ«Ä vÀAvÁæA±ÀzÀ°è ¨É¼ÉAiÀÄ «ªÀgÀªÀ£ÀÄß PÉëÃvÀæªÀÄlÖzÀ°è zÁR°¸À®Ä C£ÀÄªÀÅ ªÀiÁrPÉÆqÀÄªÀÅzÀgÀ dvÉUÉ ¨É¼ÉªÁgÀÄ, ¨sÀÆ G¥ÀAiÉÆÃUÀªÁgÀÄ CAQ CA±ÀUÀ¼À PÉÆæÃrüÃPÀgÀtzÀ vÀAvÁæA±ÀªÀ£ÀÄß C©üªÀÈ¢Þ¥Àr¹ C¼ÀªÀr¸ÀÄªÀÅzÀjAzÀ UÁæªÀÄ¯ÉQÌUÀgÀ ªÀiÁå£ÀÄªÀ¯ï PÉÆæÃrüÃPÀgÀtPÁÌV ªÀåAiÀÄªÁUÀÄªÀ ¸ÀªÀÄAiÀÄzÀ G½vÁAiÀÄªÁUÀÄªÀÅzÀ®èzÉ ¥ÀºÀtÂAiÀÄ°è zÁR¯ÁVgÀÄªÀ ¨É¼É, ¨sÀÆ G¥ÀAiÉÆÃUÀzÀ CAQ CA±ÀUÀ½UÀÆ ªÁ¸ÀÛªÀvÉUÀÆ vÁ¼É EgÀÄvÀÛzÉ ªÀÄvÀÄÛ CAzÁdÄ ªÀiÁrzÀ CAQ CA±ÀUÀ¼À£ÀÄß ¤ÃqÀÄªÀÅzÀPÉÌ CªÀPÁ±À«gÀÄªÀÅ¢®è ¨É¼É EA¢ÃPÀgÀtzÀ ºÉÆgÀUÀÄwÛUÉUÁV ¸ÀgÀPÁgÀPÉÌ ªÀåAiÀÄªÁUÀÄªÀ RZÀÄð vÀ¥ÀÄàvÀÛzÉ.

E¯ÁSÉ¬ÄAzÀ¯ÉÃ ºÉÆgÀUÀÄwÛUÉAiÀÄ°è UÁæªÀÄ¥ÀAZÁAiÀÄvÀÄªÁgÀÄ ¥ÀºÀtÂAiÀÄ°è EA¢ÃPÀgÀtPÁÌV UÀtwzÁgÀgÀ£ÀÄß £ÉÃªÀÄPÀ ªÀiÁr ¨sÀÆ«Ä vÀAvÁæA±ÀzÀ ªÀÄÆ®PÀ ¨É¼É EA¢ÃPÀgÀtzÀ PÉ®¸ÀªÀ£ÀÄß E¯ÁSÉ¬ÄAzÀ¯ÉÃ PÉÊUÉÆ¼ÀÄîªÀ ¥Àæ¸ÁÛªÀ£ÉAiÀÄ£ÀÄß ¥ÀjUÀtÂ¸À§ºÀÄzÀÄ.

MmÁÖgÉAiÀiÁV ¨sÀÆ zÁR¯ÉUÀ¼ÀÄ UÀtQÃPÀgÀtUÉÆAqÀ £ÀAvÀgÀ ¨É¼É CAQ CA±ÀUÀ¼À ¤RgÀvÉ QëÃtÂ¸ÀÄwÛzÀÄÝ CªÀÅUÀ¼À£ÀÄß ¸ÀÄzsÁgÀuÉAiÀÄ zÉ¸ÉAiÀÄ°è vÀÄvÀÄð PÀæªÀÄUÀ¼À£ÀÄß PÉÊUÉÆ¼ÀÄîªÀÅzÀÄ CwÃ CªÀ±Àå«gÀÄvÀÛzÉ.

******

176

8. ¨É¼ÉUÀ¼À PÉëÃvÀæ CAQ-CA±ÀUÀ¼ÀÄ* »£ÉßÃ¯É

1) §ºÀ¼À »A¢£À PÁ®¢AzÀ®Æ zÉÃ±ÀzÀ°è PÀÈ¶UÉ ¸ÀA§A¢¹zÀ ªÀiÁ»wAiÀÄ£ÀÄß ¸ÀAUÀæ»¸À¯ÁUÀÄwÛzÉ. PÀ£ÁðlPÀzÀ°è §ºÀÄ »A¢£À PÁ®¢AzÀ®Æ ªÀA±À ¥ÁgÀA¥ÀgÀåªÁzÀ ±Á£ÀÆ¨sÉÆÃVPÉ ªÀÄvÀÄÛ ¥ÀmÉÃ°PÉUÀ¼ÀÄ EzÀÄÝ ±Á£ÀÄ¨ÉÆÃUÀgÀÄ vÀªÀÄUÉ ¸ÉÃjzÀ ºÀ½îUÀ¼À°è PÀÈ¶UÉ ¸ÀA§A¢¹zÀ CAQ-CA±ÀUÀ¼À£ÀÄß ªÀÄvÀÄÛ EvÀgÉ ªÀiÁ»wUÀ¼À£ÀÄß ¸ÀAUÀæ»¸ÀÄwÛzÀÝgÀÄ.DzÀgÉ EwÛÃaUÉ gÁdåzÀ°è ±Á£ÀÄ¨ÉÆÃUÀgÀ §zÀ°UÉ ¸ÀPÁðj £ËPÀgÀgÁzÀ UÁæªÀÄ ¯ÉQÌUÀgÀ£ÀÄß £ÉÃ«Ä¸À¯ÁVzÉ.FUÀ UÁæªÀÄ ¯ÉQÌUÀgÀÄ PÀÈ¶AiÀÄ §UÉÎ CAQ-CA±ÀUÀ¼À£ÀÄß ¸ÀAUÀæ»¸ÀÄwÛzÁÝgÉ.

2) 1966 gÀ PÀ£ÁðlPÀ ¨sÀÆPÀAzÁAiÀÄ ¤AiÀÄªÀÄUÀ¼À ¥ÀæPÁgÀ PÀÈ¶ ¸ÀA§AzÀ CAQ ¸ÀASÉåUÀ¼À£ÀÄß 16

gÀ £ÀªÀÄÆ£ÉAiÀÄ°è ¸ÀAUÀæ»¸À¯ÁUÀÄwÛzÉ.EzÀ£ÀÄß ºÀPÀÄÌ zÁR¯Áw UÉÃtÂzÁgÀ(RTC) ªÀÄvÀÄÛ ¨É¼É vÀ¤SÉAiÀÄ gÀf¸ÀÖgï JAzÀÄ PÀgÉAiÀÄ¯ÁVzÉ. EAvÀºÀ gÀf¸ÀÖgÀUÀ¼À£ÀÄß ¥ÀæwAiÉÆAzÀÄ UÁæªÀÄPÀÆÌ ¥ÀævÉåÃPÀªÁV EqÀ¯ÁVzÉ.zÁgÀt ªÀiÁrgÀÄªÀ PÀgÀ ¤zÀðgÀuÉ,¤ÃgÀÄzÀgÀ,ªÀÄtÂÚ£À ªÀVÃðPÀgÀt,¨sÀÆ¸Áéw£ÀvÉAiÀÄ ¸ÀégÀÆ¥À ¨É¼ÉAiÀÄ¯ÁzÀ ¨É¼ÉUÀ¼À«ªÀgÀ,¤ÃgÁªÀj ªÀÄÆ®,¨sÀÆ«ÄAiÀÄ G¥ÀAiÀÄÄPÀÛvÉ,EvÁå¢ «ªÀgÀ M¼ÀUÉÆAqÀ CwÃ ¥ÁæªÀÄÄRå PÀAzÁAiÀÄ zÁR¯ÉAiÀiÁVzÉ.

3) F zÁR¯ÁwAiÀÄ ªÉÄÃgÉUÉ ¥ÀæwAiÉÆAzÀÄ IÄvÀÄ«£À®Æè ¨É¼ÉzÀ ¨É¼ÉUÀ¼À PÉæÃvÀæ ªÀÄvÀÄÛ ªÀiÁzÀj ¸À«ÄÃPÉëUÀ¼À£ÀÄß £ÀqÉ¹ ¸ÀPÁðgÀPÉÌ PÀÈ¶ ¸ÀA§AzÀ ªÀiÁ»wAiÀÄ£ÀÄß MzÀV¸ÀÄªÀ KPÉÊPÀ ªÀÄÆ®ªÁVzÉ.

FV£À ªÀ¸ÀÄÛ ¸ÀÜw

F zÁR¯ÁwAiÀÄ£ÀÄß ¨sÀÆ«Ä vÀvÁæA±ÀzÀ ªÉÄÃgÉUÉ UÀtQÃPÀÈvÀ zÁR¯ÁwAiÀÄ£ÀÄß ªÀiÁqÀ¯ÁUÀÄwÛzÀÄÝ,PÀÈ¶/vÉÆÃlUÁjPÉ/¤ÃgÁªÀj E¯ÁSÉUÀ¼À AiÉÆÃd£ÉUÀ¼À C£ÀÄµÁ×£À ªÀÄvÀÄÛ ¸ÁUÀÄªÀ½zÁgÀgÀÄ vÀªÀÄUÉ ¸ÀA§A¢¹zÀ «ªÀgÀUÀ¼À£ÀÄß ¨sÀÆ«Ä vÀvÁæA±ÀzÀ ªÀÄÆ®PÀ AiÀiÁªÁUÀ ¨ÃPÁzÀgÀÆ «ÃPÀëuÉAiÀÄ£ÀÄß ªÀiÁqÀ§ºÀÄzÁVzÉ.

1) ¥ÀÆªÀð ¥ÀæªÀiÁtzÀ PÉëÃvÀæzÀ §UÉÎ zÁR¯ÁwAiÀiÁUÀÄwÛ®è

2) PÀÈ¶AiÉÄÃvÀgÀ G¥ÀAiÉÆÃV¸ÀÄwÛgÀÄªÀ ¨sÀÆ«ÄAiÀÄ §UÉÎ ¤RgÀvÉ PÀAqÀÄ §gÀÄwÛ®è.

3) ¹zÀÝ¥Àr¹gÀÄªÀ ªÀiÁ»wAiÀÄ£ÀÄß ¸ÀjAiÀiÁV ªÉÄÃ°éZÁgÀuÉ ªÀiÁqÀ®Ä ¹§âA¢UÀ¼À PÉÆgÀvÉ¬ÄAzÀ ¤RgÀvÉ §gÀÄwÛ®è.

4) PÀAzÁAiÀÄ,PÀÈ¶,vÉÆÃlUÁjPÉ,¤ÃgÁªÀj E¯ÁSÉAiÀÄªÀgÀÄ F PÉ®¸À ¸ÁATåPÀ E¯ÁSÉAiÀÄªÀjUÉ ¸ÀA§A¢¹gÀÄªÀzÉAzÀÄ w½zÀÄPÉÆArzÁÝgÉ.

5) ªÀÄgÀÄ ºÉÆAzÁtÂPÉ ¸À¨sÉUÀ¼ÀÄ ¸ÀªÀÄ¥ÀðPÀªÁV PÁAiÀÄð¤ªÀð»¸ÀÄwÛ®è.EzÀjAzÁV ¤UÀ¢vÀ ¸ÀªÀÄAiÀÄzÀ°è PÉÆæÃrPÀÈvÀ ªÀiÁ»w ¸À°è¸ÀÄªÀ°è «¼ÀA§ªÁUÀÄwÛzÉ.

6) PÀ¼ÉzÀ ªÀµÀð ¥ÀºÀtÂ zÁR¯Áw §UÉÎ CjªÀÅ ªÀÄÆr¸ÀÄªÀ PÁAiÀÄðPÀæªÀÄªÁzÀ £ÀAvÀgÀ gÉÊvÀgÀÄ,gÉÊvÀ ªÀÄÄRAqÀgÀÄ ¥ÀºÀtÂ zÁR¯ÁwAiÀÄ §UÉÎ ªÀÄÄvÀªÀfð ªÀ»¸ÀÄwÛgÀÄªÀzÀÄ PÀAqÀÄ §gÀÄªÀzÀÄ.

7) UÁæªÀÄ ¯ÉPÁÌ¢PÁjUÀ¼ÀÄ ªÀiÁrgÀÄªÀ ¥ÀºÀtÂAiÀÄ°è ¨É¼É PÉëÃvÀæ zÁR¯ÁwAiÀÄ£ÀÄß PÀZÁÑ ¥ÀºÀtÂ £ÀªÀÄÆ£É £ÀA.13 gÀ°è j.¸À£ÀA ¥ÀæPÁgÀ KjPÉ PÀæªÀÄzÀ°è ¤ªÀð»¸ÀÄwÛzÁÝgÉ.EzÀ£ÀÄß ¨sÀÆ«Ä vÀvÁæA±ÀPÉÌ ¸ÀªÀÄ¥ÀðPÀªÁV C¼ÀªÀr¸À¯ÁUÀÄwÛ®è.

8) ¸ÀªÉð/¸À¨ï ¸ÀªÉð £ÀA UÀ¼À UÁæªÀÄ £ÀPÁ±ÉUÀ¼À PÉÆgÀvÉ EgÀÄªÀzÀÄ.

* ²æÃ zÉÆqÀØªÀÄ¤, f¯Áè ¸ÀASÁå ¸ÀAUÀæºÀuÁ¢üPÁjUÀ¼ÀÄ, ¨É¼ÀUÁ«

177

£ÀÆå£ÀvÉ/PÀÄAzÀÄ PÉÆgÀvÉUÀ¼ÀÄ UÁæªÀÄ ¯ÉPÁÌ¢üPÁjUÀ¼ÀÄ E£ÀÄß ºÉaÑ£À ¤RgÀªÁzÀ ªÀÄvÀÄÛ ¸ÀPÁ®zÀ°è PÀÈ¶ CAQ-CA±ÀUÀ¼À£ÀÄß zÁR¯ÁwAiÀÄ ªÉÄÃgÉUÉ ¤ÃqÀ®Ä ¸ÁzÀåªÁVgÀÄªÀ¢®è PÁgÀt ¹§âA¢ PÉÆgÀvÉ ¸ÀPÁðgÀzÀ EvÀgÉ AiÉÆÃd£ÉUÀ¼À C£ÀÄµÁ×£À ªÀÄvÀÄÛ ¹§âA¢UÉ ºÉÆ¸ÀzÁzÀ vÀvÁæA±ÀPÉÌ C£ÀÄUÀÄtªÁV ºÉÆA¢PÉÆ¼Àî®Ä ¸ÁPÀµÀÄÖ PÁ¯ÁªÀPÁ±À ¨ÉÃPÁVgÀÄªÀzÀjAzÀ ºÁUÀÆ UÁæªÀÄ ªÁ¸ÀÛªÀå ªÀiÁqÀ®Ä ¸ÀjAiÀiÁzÀ ªÀåªÀ¸ÉÜAiÀÄ£ÀÄß ªÀiÁqÀzÉÃ EgÀÄªÀzÀjAzÀ , ¸ÀPÁðgÀzÀ PÉ®¸ÀUÀ¼À C£ÀÄµÁÖ£ÀzÀ°è ¸ÁªÀðd¤PÀjAzÀ ºÀ¸ÀÛ PÉëÃ¥À¢AzÀ «¼ÀA§ªÀ£ÀÄß vÀqÉzÀ°è ªÀiÁvÀæ £ÀÆå£ÀvÉUÀ¼À£ÀÄß ¸Àj ¥Àr¸À§ºÀÄzÁVzÉ.

ªÀÄºÀvÀézÀ ¸ÀAUÀwUÀ¼ÀÄ

1) ªÀÄÆ® PÁAiÀÄðPÀvÀðjUÉ ¸ÀªÀÄ¥ÀðPÀ w¼ÀÄªÀ½PÉ ¤ÃqÀÄªÀzÀgÉÆA¢UÉ ¤UÀ¢vÀ CªÀ¢üAiÉÆ¼ÀUÉ ¥ÀºÀtÂ ¥ÀwæPÉ §gÉAiÀÄÄªÀAvÉ PÀlÖ½AiÀÄ£ÀÄß ºÉÆgÀr¸ÀÄªÀzÀÄ.

2) ¥ÀæZÁgÀ PÁAiÀÄðªÀ£ÀÄß ªÀiÁqÀÄªÀzÀÄ.

3) vÁ®ÆèPÁ ªÀÄlÖzÀ°è ¸ÁATåPÀ «AUï MAzÀ£ÀÄß ¥ÀævÉåÃPÀªÁV EgÀÄªÀzÀjAzÀ CªÀjUÉ ¸ÀªÀÄ¥ÀðPÀ C¢üPÁgÀ ¤Ãr vÀºÀ²Ã¯ÁÝgÀgÀ D¢üÃ£ÀzÀ°è ¤ªÀð»¸ÀÄªÀAvÉ DzÉÃ²¸ÀÄªÀzÀÄ.

4) PÀÈ¶ CAQ-CA±ÀUÀ¼À PÀÄjvÀÄ vÀºÀ²Ã¯ÁÝgÀgÀÄ/G¥À vÀºÀ²Ã¯ÁÝgÀgÀÄ/PÀAzÁAiÀÄ ¤ÃjPÀëPÀgÀÄ /¸ÁATåPÀ ¹§âA¢AiÀÄªÀgÀÄ ¸ÀªÀÄ¥ÀðPÀ ªÉÄÃ°éZÁgÀuÉAiÀÄ£ÀÄß ¤UÀ¢vÀ ¸ÀªÀÄAiÀÄzÉÆ¼ÀUÉ PÉÊPÉÆ¼ÀÄîªÀzÀÄ

£ÀÆå£ÀvÉ UÀ¼À£ÀÄß vÀÄA§®Ä ²¥ÁàgÀ¸ÀÄìUÀ¼ÀÄ

1) UÁæªÀÄ ¯ÉPÁÌ¢PÁjUÀ¼ÀÄ,PÀÈ¶,vÉÆÃlUÁjPÉ,¤ÃgÁªÀj IÄvÀÄªÁgÀÄ ¨É¼ÉUÀ¼À PÉëÃvÀæ vÀ¤SÉAiÀÄ PÁ¯ÁªÀPÁ±ÀzÀ°è EvÀgÉ PÉ®¸À ªÀ»¸ÀzÉ PÀlÄÖ ¤nÖ£À PÀæªÀÄ ªÀ»¸ÀÄªÀzÀÄ.

2) £ÀÆjvÀ vÀgÀ¨ÉÃwzÁgÀjAzÀ vÀgÀ¨ÉÃw PÉÆqÀÄªÀzÀÄ CªÀ±ÀåPÀvÉ EgÀÄªÀzÀÄ.

3) ªÀÄÆ® PÁAiÀÄðPÀvÀðgÀÄ ¥ÀºÀtÂAiÀÄ°è ¨É¼É zÁR¯ÁwAiÀiÁVgÀÄªÀ §UÉÎ ªÉÄÃ¯Á¢üPÁjUÀ¼ÀÄ RavÀ¥Àr¹PÉÆAqÀÄ CªÀgÀ ªÁ¶ðPÀ PÁAiÀÄð ¤ªÀðºÀuÁ ªÀgÀ¢AiÀÄ°è zÁR¯Áw ªÀiÁqÀvÀPÀÌzÀÄÝ.

4) ¸ÀªÉð ¸Él®ªÉÄAmï DUÀvÀPÀÌzÀÄÝ EzÀjAzÁV 9 ¨sÀÆ«Ä ªÀVðPÀgÀt ªÀiÁqÀ®Ä ¸ÁzÀåªÁUÀÄvÀÛzÉ.

5) ¥ÀÆtð ¥ÀæªÀiÁtzÀ zÁR¯ÁwAiÀÄ §UÉÎ ºÉÆÃ§½ ªÀÄlÖzÀ°è ¸ÁA.¤Ã/UÀtwzÁgÀgÀ ºÀÄzÉÝAiÀÄ£ÀÄß ¸ÀÈf¸ÀÄªÀzÀÄ.

¸ÀªÀiÁgÉÆÃ¥À

¤AiÀÄªÀiÁ½vÀ ¥ÀæPÁgÀ ¹§âA¢AiÀÄªÀgÀ£ÀÄß UÁæªÀÄ ªÀÄlÖzÀ°è £ÉÃªÀÄPÁw ªÀiÁr vÀgÀ¨ÉÃwUÉÆ½¸ÀÄªÀzÀÄ ªÀÄvÀÄÛ ¤UÀ¢vÀ PÉ®¸À PÁAiÀÄðªÀ£ÀÄß ºÉÆgÀvÀÄ ¥Àr¹ C£ÀåvÁ PÉ®¸ÀªÀ£ÀÄß ªÀ»¸À¢gÀÄªÀzÀÄ.

178

¨É¼É GvÁàzÀ£É CAQ CA±ÀUÀ¼ÀÄ

Crop Production : ¨É¼É GvÁàzÀ£ÉAiÀÄ£ÀÄß PÀAqÀÄ »rAiÀÄ®Ä ¨É¼É CAzÁdÄ ¸À«ÄPÉë JA§

ªÉÊeÁÕ¤PÀ ¥ÀzsÀÞwAiÀÄ£ÀÄß G¥ÀAiÉÆÃV¸À¯ÁUÀÄwÛzÉ.

»£Àß¯É :

¨sÁgÀvÀ zÉÃ±ÀzÀ ¸ÁévÀAvÀæöå ¥ÀqÉAiÀÄªÀÅzÀQÌAvÀ ªÀÄÄAa¤AzÀ®Æ CAzÀgÉ ©ænµÀgÀ PÁ®¢AzÀ®Æ ¸ÀzÀj

¸À«ÄÃPÉëAiÀÄ£ÀÄß PÉÊPÉÆ¼Àî¯ÁUÀÄwÛzÉ. §ºÀÄºÀAvÀzÀ ªÀVÃðPÀÈvÀ C¤AiÀÄvÀ ªÀiÁzÀj «zsÁ£ÀªÀ£ÀÄß ¸ÀzÀj

¸À«ÄÃPÉëUÉ C¼ÀªÀr¹PÉÆ¼Àî¯ÁVzÉ.

FV£À ªÀ¸ÀÄÛ ¹Üw :

vÁ®ÆPÀ ¸À«ÄÃPÉëAiÀÄ ¥ÀzsÁ£À ªÀUÀðªÁVAiÀÄÆ F ªÀUÀðzÀ ªÁå¦ÛAiÀÄ°è ºÀ½îUÀ¼ÀÄ ¸À«ÄÃPÉëAiÀÄ ¥ÁæxÀ«ÄPÀ

WÀlPÀUÀ¼ÁVAiÀÄÄ DAiÉÄÌAiÀiÁzÀ ºÀ½îUÀ¼À°è£À ºÉÆ®UÀ¼ÀÄ ¸À«ÄÃPÉëAiÀÄ ¢éwAiÀÄ ºÀAvÀzÀ WÀlPÀUÀ¼ÁVAiÀÄÆ

DAiÉÄÌAiÀiÁzÀ ºÉÆ®zÀ°è£À ¤¢ðµÀÖ C¼ÀvÉAiÀÄ ¥ÁæAiÉÆÃVPÀ ¥ÁèlÄUÀ¼ÀÄ ¸À«ÄÃPÉëAiÀÄ CAwªÀÄ

WÀlPÀªÁVAiÀÄÆ EgÀÄvÀÛzÉ.

««zsÀ ¨É¼ÉUÀ¼À vÁ®ÆPÀÄªÁgÀÄ ºÉÆÃ§½ªÁgÀÄ «¹ÛtðªÀ£ÀÄß DzsÀj¹ ¨É¼É PÀmÁªÀÅ

¥ÀæAiÉÆÃUÀ¼À£ÀÄß ºÀAaPÉ ªÀiÁqÀ¯ÁUÀÄwÛzÀÄÝ ¥Àæ¸ÀÄÛvÀ ¨É¼É «ªÉÄ AiÉÆÃd£É eÁjAiÀÄ°ègÀÄªÀÅzÀjAzÀ MAzÀÄ

ºÉÆÃ§½UÉ ºÀvÀÄÛ ¥ÀæAiÉÆÃUÀUÀ¼À£ÀÄß PÉÊPÉÆ¼Àî¯ÁUÀÄwÛzÉ. ¸ÀzÀj ¸À«ÄÃPÉëUÉ ªÀÄÆ®PÁAiÀÄðPÀvÀðgÁV PÀÈ¶,

PÀAzÁAiÀÄ, vÉÆÃlUÁjPÉ, UÁæ«ÄÃt, C©üªÀÈ¢üÝ & ¥ÀAZÁAiÀÄvïgÁeï PÁqÁ ºÁUÀÆ d¯Á£ÀAiÀÄ£À

E¯ÁSÉUÀ¼À ¹§âA¢UÉ ªÀ»¸À¯ÁUÀÄvÀÛzÉ. ¸ÀzÀj ¸À«ÄÃPÉëUÉ ¸ÀA§A¢ü¹zÀ vÁAwæPÀ CA±ÀUÀ¼ÀÄ gÁ¶æöÖÃAiÀÄ

ªÀiÁzÀj ¸À«ÄÃPÉë ¸ÀA¸ÉÜAiÀÄ dªÁ¨ÁÝjAiÀiÁVgÀÄvÀÛzÉ. ¸ÀzÀj ¸À«ÄÃPÉëAiÀÄ ªÉÄÃ°éZÁgÀPÀgÁV ¸ÁATåPÀ,

PÀAzÁAiÀÄ, PÀÈ¶ vÉÆÃlUÁjPÉ, UÁæ«ÄÃt C©üªÀÈ¢üÝ C¢üPÁjUÀ¼ÀÄ & ¥ÀAZÁAiÀÄvÀ gÁeï E¯ÁSÉUÀ¼À

C¢üPÁjUÀ¼ÀÄ PÉÊUÉÆ¼ÀÄîªÀgÀÄ ¸ÁATåPÀ E¯ÁSÉAiÀÄÄ AiÉÆÃfvÀ ¥ÀæAiÉÆÃUÀUÀ¼À ±ÉÃ. 15% ªÉÄÃ°éZÁgÀuÉ

PÉÊPÉÆ¼ÀÄîªÀgÀÄ & DAiÀiÁ E¯ÁSÉUÉ ºÀAaPÉ ±ÉÃ. 20% gÀµÀÄÖ ªÉÄÃ°éZÁgÀuÉAiÀÄ£ÀÄß PÀAzÁAiÀÄ, PÀÈ¶,

vÉÆÃlUÁjPÉ & UÁæ«ÄÃt C¢üPÁj & ¥ÀAZÁAiÀÄvï gÁeï E¯ÁSÉAiÀÄªÀgÀÄ PÉÆ¼ÀÄîªÀgÀÄ CªÀ±Àå«zÀÝ°è

PÀmÁ«£À £ÀAvÀgÀzÀ ªÉÄÃ°éZÁgÀuÉAiÀÄ£ÀÄß PÉÊPÉÆ¼Àî¯ÁUÀÄwÛzÉ.

£ÀÆå£ÀvÉ/ PÀÄAzÀÄPÉÆgÀvÉUÀ¼ÀÄ :

ªÀÄÆ® PÁAiÀÄðPÀvÀðgÀÄ ¤RgÀªÁzÀ & ¸ÀPÁ®zÀ°è CA±ÀUÀ¼À£ÀÄß ¤ÃqÀ®Ä ¸ÁzsÀåªÁUÀÄwÛ®è PÁgÀt

¹§âA¢ PÉÆgÀvÉ & CwÃ ºÉaÑ£À ¥ÀæAiÉÆÃUÀUÀ¼ÀÄ ºÀAaPÉAiÀiÁUÀÄwÛgÀÄªÀÅzÀÄ & ¸ÀPÁðgÀzÀ EvÀgÉ PÉ®¸À

PÁAiÀÄðUÀ¼À°è ¨sÁVAiÀiÁUÀÄwÛgÀÄªÀÅzÀÄ. ªÀÄÆ® PÁAiÀÄðPÀvÀðjUÉ ¤RgÀªÁzÀ E¼ÀÄªÀj zÁR°¸À®Ä vÀÆPÀ

ªÀiÁ¥À£ÀUÀ¼À£ÀÄß MzÀV¸ÀzÉÃ ¸ÀÜ½AiÀÄªÁV ¥ÀqÉAiÀÄÄwÛgÀÄªÀÅzÀÄ PÁgÀtªÁVzÉ.

179

ªÀÄºÀvÀézÀ ¸ÀAUÀwUÀ¼ÀÄ :

1) ªÀÄÆ® PÁAiÀÄðPÀvÀðjUÉ ¸ÀªÀÄ¥ÀðPÀ vÀgÀ¨ÉÃw/w¼ÀÄªÀ½PÉ ¤ÃqÀÄªÀÅzÀgÉÆA¢UÉ ¤UÀ¢ ¥Àr¹zÀ

CªÀ¢üAiÉÆ¼ÀUÁV PÁAiÀÄð ¤ªÀð»¸À®Ä vÁ®ÆQ£À C¢üPÁjUÀ¼ÀÄ PÀlÄÖ¤nÖ£À PÀæªÀÄ PÉÊPÉÆ¼ÀÄîªÀÅzÀÄ.

2) ¥ÀæZÁgÀ PÁAiÀÄðPÉÊPÉÆ¼ÀÄîªÀÅzÀÄ.

3) vÁ®ÆPÀÄ ªÀÄlÖzÀ°è ¸ÁATåPÀ «¨sÁUÀ ¸ÁÜ¦¸ÀÄªÀÅzÀÄ & PÀ¤µÀ× ¥ÀzÀ« (¸ÁATåPÀ) & UÀtPÀAiÀÄAvÀæ

C£ÀÄ¨sÀªÀ«gÀÄªÀ ¹§âA¢AiÀÄ£ÀÄß DAiÉÄÌ ªÀiÁqÀÄªÀÅzÀÄ.

4) EvÀgÉ E¯ÁSÉAiÀÄ ¹§âA¢AiÀÄªÀgÀÄ ¸ÀªÀÄ¥ÀðPÀ ªÉÄÃ°éZÁgÀuÉ PÉÊPÉÆ¼ÀÄîªÀÅzÀÄ.

£ÀÆå£ÀvÉUÀ¼À£ÀÄß vÀÄA§®Ä ²¥sÁgÀ¸ïUÀ¼ÀÄ :

1) ªÀÄÆ® PÁAiÀÄðPÀvÀðgÀÄ/ªÉÄÃ°éZÁgÀuÁ¢üPÁjUÀ¼ÀÄ vÀªÀÄUÉ ªÀ»¹zÀ PÁAiÀÄðªÀ£ÀÄß ¤UÀ¢vÀ

CªÀ¢üAiÀÄ°è PÀgÁgÀÄªÀPÁÌV ¤ªÀð»¸ÀÄªÀÅzÀÄ.

2) £ÀÄjvÀ vÀgÀ¨ÉÃwzÁgÀjAzÀ gÁdå ªÀÄlÖzÀ°è/f¯Áè ªÀÄlÖzÀ°è/vÁ®ÆPÀÄ ªÀÄlÖzÀ°è vÀgÀ¨ÉÃw

¤ÃqÀÄªÀÅzÀÄ & J¯Áè ¸ÀA§A¢ü¹zÀ ¹§âA¢/C¢üPÁjAiÀÄªÀgÀÄ PÀqÁØAiÀÄªÁV ºÁdjgÀÄªÀÅzÀÄ

3) vÀ¦àvÀ¸ÀÛgÀ ªÉÄÃ¯É ²¸ÀÄÛ PÀæªÀÄ dgÀÄV¸ÀÄªÀÅzÀÄ.

4) ªÀÄÆ®PÁAiÀÄðPÀvÀðjUÉ PÉ®¸ÀzÀ MvÀÛqÀPÀrªÉÄ ªÀiÁqÀÄªÀÅzÀÄ.

¸ÀªÀiÁgÉÆÃ¥À :

PÉ®¸ÀzÀ MvÀÛqÀ PÀrªÉÄªÀiÁr ªÀ»¹zÀ PÉ®¸ÀªÀ£ÀÄß CZÀÄÑPÀmÁÖV ¤ªÀð»¸À®Ä ¹§âA¢/

C¢üPÁjAiÀÄªÀjUÉ C£ÀÄªÀÅ ªÀiÁrPÉÆqÀÄªÀÅzÀÄ.

*****

180

9. ¨É¼É CAzÁdÄ ¸À«ÄÃPÉë : ¨É¼É «ªÀiÁ AiÉÆÃd£ÉAiÀÄ£ÀÄß ¸ÀÄzsÁj¸ÀÄªÀ §UÉÎ*

¨É¼É CAzÁdÄ ¸À«ÄÃPÉëAiÀÄ£ÀÄß PÀ¼ÉzÀ DgÀÄ zÀ±ÀPÀÀUÀ½AzÀ ¤zÉðÃ±À£Á®AiÀÄªÀÅ PÉÊUÉÆ¼ÀîvÁÛ

§A¢gÀÄvÀÛzÉ. DºÁgÀ ªÀÄvÀÄÛ DºÁgÉÃvÀgÀ ¨É¼ÉUÀ¼À ¸ÀgÁ¸Àj E¼ÀÄªÀjAiÀÄ£ÀÄß PÀAqÀÄ »rAiÀÄÄªÀÅzÀÄ F

¸À«ÄÃPÉëAiÀÄ GzÉÝÃ±ÀªÁVgÀÄvÀÛzÉ. ¨É¼É CAzÁdÄ ¸À«ÄÃPÉëAiÀÄÄ ¨É¼É E¼ÀÄªÀj ªÀiÁ»w, PÀÈ¶

GvÀà£ÀßUÀ¼À£ÀÄß CAzÁdÄ ªÀiÁqÀ®Ä ¸ÀºÀPÁjAiÀiÁVgÀÄvÀÛzÉ. F AiÉÆÃd£ÉAiÀÄÄ gÁdå ºÁUÀÆ gÁµÀÖç

ªÀÄlÖzÀ°è PÀÈ¶ ¤ÃwAiÀÄ£ÀÄß gÀÆ¦¸À®Ä G¥ÀAiÀÄÄPÀÛªÁVzÉ. F AiÉÆÃd£É¬ÄAzÀ zÉÃ±ÀzÀ DyðPÀ

¥Àj¹ÜwAiÀÄ£ÀÄß w½AiÀÄ®Ä ¸ÀºÀPÁjAiÀiÁUÀÄvÀÛzÉ.

1985jAzÀ ¸ÀªÀÄUÀæ ¨É¼É «ªÀiÁ AiÉÆÃd£ÉAiÀÄÄ eÁjUÉ §A¢gÀÄvÀÛzÉ. ¸ÀªÀÄUÀæ ¨É¼É «ªÀiÁ

AiÉÆÃd£ÉAiÀÄrAiÀÄ°è ¨É¼É £ÀµÀÖ ºÉÆA¢zÀ gÉÊvÀjUÉ ¥ÀjºÁgÀ PÉÆr¸ÀÄªÀÅzÀÄ F AiÉÆÃd£ÉAiÀÄ ªÀÄÄRå

GzÉÝÃ±ÀªÁVzÉ. ¨É¼É £ÀµÀÖ ºÉÆA¢zÀ gÉÊvÀgÀÄ F AiÉÆÃd£ÉAiÀÄ DzsÁgÀ¢AzÀ ¥ÀjºÁgÀªÀ£ÀÄß «ªÀiÁ

PÀA¥À¤¬ÄAzÀ ¥ÀqÉAiÀÄ§ºÀÄzÀÄ. ªÀµÀðªÁgÀÄ ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À ¸ÀASÉå ºÉZÁÑUÀÄwÛzÀÄÝ, PÉëÃvÀæ

ªÀÄlÖzÀ ¹§âA¢ ¸ÁPÀµÀÄÖ ¥ÀæªÀiÁtzÀ°è E®è¢gÀÄªÀÅzÀjAzÀ PÁ®«ÄwAiÉÆ¼ÀUÉ ¨É¼É PÀmÁªÀÅ

¥ÀæAiÉÆÃUÀUÀ¼À£ÀÄß ¤ªÀð»¸À®Ä PÀµÀÖ¸ÁzsÀåªÁVzÉ. PÉëÃvÀæ PÁAiÀÄðPÀvÀðgÀÄUÀ½UÉ vÀªÀÄä vÀªÀÄä E¯ÁSÉUÀ¼À

PÉ®¸ÀzÀ MvÀÛqÀ¢AzÀ F AiÉÆÃd£ÉAiÀÄ£ÀÄß ¤RgÀªÁV ¤ªÀð»¸À®Ä ¸ÁzsÀåªÁUÀÄwÛ®è. ¥ÀæwAiÉÆAzÀÄ

E¯ÁSÉUÀÆ «ÄvÀªÁV ¥ÀæAiÉÆÃUÀUÀ¼À£ÀÄß ¤AiÉÆÃf¹zÀgÉ ¤RgÀªÁzÀ E¼ÀÄªÀjAiÀÄ£ÀÄß ¥ÀqÉAiÀÄ®Ä

¸ÁzsÀåªÁUÀÄvÀÛzÉ. PÀAzÁAiÀÄ, PÀÈ¶, vÉÆÃlUÁjPÉ ªÀÄvÀÄÛ C©üªÀÈ¢Þ E¯ÁSÉAiÀÄ°è ªÀÄÆ® PÁAiÀÄðPÀvÀðgÀ

ºÀÄzÉÝUÀ¼ÀÄ SÁ° EgÀÄªÀÅzÀjAzÀ, ºÁ° PÁAiÀÄð¤ªÀð»¸ÀÄwÛgÀÄªÀ ªÀÄÆ® PÁAiÀÄðPÀvÀðgÀÄUÀ½UÉ ºÉaÑ£À

¥ÀæªÀiÁtzÀ°è PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À£ÀÄß ¤AiÉÆÃf¹zÀÄÝ, ¨ÉÃgÉ PÀZÉÃj PÉ®¸ÀUÀ¼À eÉÆvÉUÉ ¸ÀzÀj

PÉ®¸ÀUÀ¼À£ÀÄß ¤ªÀð»¸À®Ä PÀµÀÖªÁUÀÄwÛzÉ. ¥ÀæAiÀÄÄPÀÛ, PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À£ÀÄß £ÀqÉ¸ÀÄªÀ E¯ÁSÉUÀ¼À°è

¹§âA¢ PÉÆgÀvÉAiÀÄ£ÀÄß ¤ÃV¹zÀ°è PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À£ÀÄß £ÉgÀªÉÃj¹ ¤RgÀªÁzÀ CAQ-CA±ÀUÀ¼À£ÀÄß

¥ÀqÉAiÀÄ®Ä ¸ÁzsÀåªÁUÀÄvÀÛzÉ. EzÀjAzÀ ¸ÀPÁðgÀzÀ AiÉÆÃd£ÉUÀ¼ÀÄ ¸ÀªÀÄ¥ÀðPÀªÁV

C£ÀÄµÁ×£ÀUÉÆ½¸À§ºÀÄzÁVzÉ.

¨É¼É PÉëÃvÀæ ªÀÄvÀÄÛ GvÁàzÀ£É:

¨sÁgÀvÀ zÉÃ±ÀªÀÅ PÀÈ¶ ¥ÀæzsÁ£À zÉÃ±ÀªÁVzÀÄÝ, §ºÀÄvÉÃPÀ d£ÀgÀÄ PÀÈ¶AiÀÄ£ÀÄß CªÀ®A©¹, fÃªÀ£À £ÀqÉ¸ÀÄwÛzÁÝgÉ. zÉÃ±ÀzÀ C©üªÀÈ¢ÞUÉ PÀÈ¶AiÀÄÄ CwÃ ªÀÄÄRåªÁzÀ ¥ÁvÀæ ªÀ»¹zÉ. F ¤nÖ£À°è gÁdå ªÀÄvÀÄÛ gÁ¶ÖçÃAiÀÄ ªÀÄlÖzÀ°è PÀÈ¶ PÉëÃvÀæ ªÀÄvÀÄÛ ¨É¼ÉUÀ¼À GvÁàzÀ£ÉAiÀÄÄ ¸ÀºÀ CµÉÖÃ ªÀÄÄRåªÁzÀ ¥ÁvÀæ ªÀ»¹zÉ.

ªÀÄÄRåªÁV gÁµÀÖçzÀ C©üªÀÈ¢Þ zÀÈ¶×¬ÄAzÀ ªÀÄvÀÄÛ d£À fÃªÀ£À ªÀÄlÖ ¸ÀÄzsÁgÀuÉUÉ ¨É¼ÉUÀ¼À

GvÁàzÀ£É UÀt¤ÃAiÀÄ ªÀÄlÖzÀ°è ºÉaÑ¸À¨ÉÃPÁVzÀÄÝ, F ¤nÖ£À°è CAvÀgïgÁ¶ÖçÃAiÀÄ ªÀÄlÖzÀ°è SÁåw

ºÉaÑ¸À§ºÀÄzÁVzÉ.

* ²æÃ ZÀAzÀæ¥Àà, ªÀÄÄRå AiÉÆÃd£Á¢üPÁj, f¯Áè ¥ÀAZÁAiÀÄvï, avÀæzÀÄUÀð

181

ªÀÄÆ®¨sÀÆvÀªÁV ¨É¼ÉUÀ¼À GvÁàzÀ£ÉAiÀÄ°è PÉ¼ÀªÀÄlÖzÀ°è PÀAzÁAiÀÄ, PÀÈ¶, vÉÆÃlUÁjPÉ EªÉÃ

ªÉÆzÀ¯ÁzÀ E¯ÁSÉUÀ¼À ¹§âA¢UÀ¼À ¥Àj±ÀæªÀÄ CwÃ ªÀÄÄRåªÁzÀzÀÄÝ, EªÀgÀÄ MzÀV¸ÀÄªÀ CAQ-CA±ÀUÀ¼À

DzsÁgÀzÀ ªÉÄÃ¯É ¨É¼É PÉëÃvÀæ ºÁUÀÆ GvÁàzÀ£ÉAiÀÄ£ÀÄß CAzÁf¸À¯ÁUÀÄvÀÛzÉ. UÁæªÀÄ¯ÉQÌUÀgÀÄ, PÀÈ¶

¸ÀºÁAiÀÄPÀgÀÄ ªÀÄvÀÄÛ UÁæªÀÄ ¥ÀAZÁ¬ÄÛ C©üªÀÈ¢Þ C¢üPÁjUÀ¼ÀÄ/PÁAiÀÄðzÀ²ðUÀ½UÉ ¨É¼ÉPÉëÃvÀæ ªÀÄvÀÄÛ

E¼ÀÄªÀj ¤ÃqÀÄªÀ §UÉÎ ºÉaÑ£À jÃwAiÀÄ°è vÀgÀ¨ÉÃw ¤Ãr, ¤RgÀªÁzÀ CAQ-CA±ÀUÀ¼À£ÀÄß UÁæªÀÄ

ªÀÄlÖzÀ°è ¸ÀAUÀæ»¹, MzÀV¸À®Ä ¸ÀºÀPÁjAiÀiÁUÀÄvÀÛzÉ. ¥ÀæAiÀÄÄPÀÛ, UÁæªÀÄªÀÄlÖzÀ ¹§âA¢UÀ½UÉ ªÉÊeÁÕ¤PÀ

¸À®PÀgÀuÉUÀ¼À£ÀÄß MzÀV¸À®Ä CªÀ±ÀåªÁzÀ PÁAiÀÄðPÀæªÀÄUÀ¼À£ÀÄß gÀÆ¦¸À¨ÉÃPÁVzÉ. F ¤nÖ£À°è J¯Áè

C¢üPÁj ªÀUÀðzÀªÀgÀÄ, ¹§âA¢UÀ¼ÀÄ vÀªÀÄä vÀªÀÄä dªÁ¨ÁÝjUÀ¼À£ÀÄß CjvÀÄ gÁdå ªÀÄvÀÄÛ zÉÃ±ÀzÀ

C©üªÀÈ¢ÞAiÀÄ°è PÀÈ¶AiÀÄ ¥ÁæªÀÄÄRåvÉAiÀÄ£ÀÄß CjvÀÄPÉÆAqÀÄ PÁAiÀÄð ¤ªÀð»¹zÀ°è PÀÈ¶ GvÁàzÀ£ÉAiÀÄ£ÀÄß

ºÉaÑ¸À®Ä ¸ÁzsÀåªÁUÀÄvÀÛzÉ.

****

182

10. ¨É¼ÉUÀ¼À PÉëÃvÀæ CAQ-CA±ÀUÀ¼ÀÄ

¨sÁgÀvÀ zÉÃ±ÀªÀÅ PÀÈ¶ ¥ÀæzsÁ£À zÉÃ±ÀªÁVzÀÄÝ §ºÀÄvÉÃPÀ d£ÀgÀÄ PÀÈ¶AiÀÄ£ÀÄß CªÀ®A©¹, fÃªÀ£À

£ÉqÉ¸ÀÄwÛzÁÝgÉ, zÉÃ±ÀzÀ C©üªÀÈ¢ÞUÉ PÀÈ¶AiÀÄÄ CwÃ ªÀÄÄRåªÁzÀ ¥ÁævÀæ ªÀ»¹zÉ, F ¤nÖ£À°è gÁdå

ªÀÄvÀÄÛ gÁ¶ÖçÃAiÀÄ ªÀÄlÖzÀ°è PÀÈ¶ PÉëÃvÀæ ªÀÄvÀÄÛ ¨É¼ÉUÀ¼À GvÁàzÀ£ÉAiÀÄÄ ¸ÀºÀ CµÉÖ ªÀÄÄRåªÁzÀ ¥ÁvÀææ

ªÀ»¹zÉ.

ªÀÄÄRåªÁV gÁµÀÖçzÀ C©üªÀÈ¢Þ zÀÈ¶×¬ÄAzÀ ªÀÄvÀÄÛ d£À fÃªÀ£À ªÀÄlÖ ¸ÀÄzÁgÀtUÉ ¨É¼ÉUÀ¼À

GvÁàzÀ£É UÀt¤ÃAiÀÄ ªÀÄlÖzÀ°è ºÉaÑ¸À¨ÉÃPÁVzÀÄÝ F ¤nÖ£À°è CAvÀgÀ gÁ¶ÖçÃAiÀÄ ªÀÄlÖzÀ°è SÁåw

ºÉZÀÑ¸À§ºÀÄzÁVzÉ PÀÈ¶ GvÁàzÀ£É PÀrªÉÄAiÀiÁzÀ°è gÁµÀÖç ªÀÄlÖzÀ°è £ÀªÀÄä ¸ÀézÉðUÉ »£ÉßqÉAiÀiÁUÀÄªÀÅzÀÄ

F ¤nÖ£À°è ¥Àæw ªÀµÀð PÀÈ¶ PÉëÃvÀæ ªÀÄvÀÄÛ ¨É¼ÉUÀ¼À GvÁàzÀ£É ¥ÀæªÀiÁt ºÉZÀÑ ÀÄvÀÛ ªÉÊeÁÕfPÀ jÃwAiÀÄ°è

ªÀåªÀ¸ÁAiÀÄ ªÀiÁr gÁdå ªÀÄvÀÄÛ gÁµÀÖçªÀ£ÀÄß ¥Àæ§®ªÁV PÀlÖ®Ä CªÀgÀÄ ¸ÀºÀ PÀj¸À¨ÉÃPÁVzÉ.

ªÀÄÆ®¨sÀÆvÀªÁV ¨É¼ÉUÀ¼À GvÁàzÀ£ÉAiÀÄ°è PÉ¼ÀªÀÄlÖzÀ°è PÀAzÁAiÀÄ, PÀÈ¶. vÉÆÃlUÁjPÉ EªÉÃ

ªÉÆzÀ¯ÁzÀ E¯ÁSÉUÀ¼À ¹§âA¢UÀ¼À ¥Àj±ÀæªÀÄ CwÃ ªÀÄÄRåªÁVzÀÄÝ, EªÀgÀÄ MzÀV¸ÀÄªÀ CAQ CA±ÀUÀ¼À

CzÁgÀzÀ ªÉÄÃ¯É ¨É¼ÉPÉëÃvÀæ ºÁUÀÆ GvÁàzÀ£É CªÀ®A©¹gÀÄvÀÛzÉ F ¹§âA¢UÀ¼ÀÄ vÁªÀÅ ¤ªÀð»¸ÀÄªÀ

PÉ®¸ÀUÀ¼À°è ¨ÉdªÁ¨ÁÝj ºÁUÀÆ ¤®ðPÀëvÀ£À vÉÆÃjzÀÝ°è J¯Áè AiÉÆÃd£ÉUÀ¼ÀÄ vÀ¯ÉPÉ¼ÀUÁV AiÀiÁªÀÅzÉÃ

¥ÀæAiÉÆÃd£ÉªÁUÀÄªÀÅ¢®è F ¤nÖ£À°è UÁæªÀÄ¯ÉQÌUÀgÀÄ, PÀÈ¶ ¸ÀºÁAiÀÄPÀgÀÄ ªÀÄvÀÄÛ UÁæªÀÄ ¥ÀAZÁ¬Äw

PÁAiÀÄðzÀ²ðUÀ½UÉ ºÉaÑ£À jÃwAiÀÄ°è vÀgÀ¨ÉÃw ¤Ãr ¨É¼ÉPÉëÃvÀæ ªÀÄvÀÄÛ E¼ÀÄªÀj ¤ÃqÀÄªÀ «ZÁgÀzÀ°è

§ºÀ¼À ZÁPÀÄZÀPÀåvÉ¬ÄAzÀ ¤zÀðj¹ ¤RgÀªÁzÀ CAQ CA±ÀUÀ¼À£ÀÄß UÁæªÀÄ ªÀÄlÖzÀ°è

ªÉÄÃ¯Á¢üPÁjUÀ½UÉ ¤ÃqÀÄªÀAvÉ JZÀÑjPÉ ªÀ»¸ÀÄªÀÅzÀÄ CwÃ CªÀ±ÀåAiÀÄªÁVzÉ, ¥ÀæAiÀÄÄPÀÛ UÁæªÀÄ ªÀÄlÖzÀ

¹§âA¢UÀ½UÉ E£ÀÄß ºÉZÀÑ£À dªÁ¨ÁÝj ¤Ãr, ºÉaÑ£À ¸À®PÀgÀtÂUÀ¼À£ÀÄß MzÀV¹ vÀgÀ¨ÉÃw

w¼ÀÄªÀ½PÉUÀ¼À£ÀÄß ¤ÃqÀ®Ä CªÀ±ÀåªÁzÀ PÁAiÀÄðPÀæªÀÄUÀ¼À£ÀÄß gÀÆ¦¸À¨ÉÃPÁVzÉ F ¤nÖ£À°è J¯Áè

C¢üPÁj ªÀUÀðzÀªÀgÀÄ ¹§âA¢UÀ¼ÀÄ, vÀªÀÄä vÀªÀÄä dªÁ¨ÁÝjUÀ¼À£ÀÄß CjvÀÄ gÁdå ªÀÄvÀÄÛ zÉÃ±ÀzÀ

C©üªÀÈ¢ÝAiÀÄ°è PÀÈ¶AiÀÄ ¥ÁæªÀÄÄRåvÉAiÀÄ£ÀÄß CjvÀÄ PÉÆAqÀÄ MUÀÎmÁÖV ±Àæ«Ä¹zÀÝ°è ªÀiÁvÀæ zÉÃ±ÀzÀ°è

C©üªÀÈ¢ÞAiÀÄ°è PÀÈ¶ PÉëÃvÀæ ªÀÄvÀÄÛ GvÁàzÀ£ÉAiÀÄ£ÀÄß ºÉaÑ¹ vÀ£ÀÆä®PÀ gÁµÀÖçªÀ£ÀÄß C©üªÀÈ¢Þ ¥ÀxÀzÀ°è

PÉÆAqÀAiÀÄå®Ä ¸ÁzÀåªÁUÀÄvÀÛzÉ.

* ²æÃ ZÀAzÀæ¥Àà, f¯Áè ¸ÀASÁå ¸ÀAUÀæºÀuÁ¢üPÁj, avÀæzÀÄUÀð.

183

11. ¨É¼É PÉëÃvÀæ ªÀÄvÀÄÛ GvÁàzÀ£Á CAQ CA±ÀUÀ¼ÀÄ*

¨sÁgÀvÀ PÀÈ¶ ¥ÀæzsÁ£À zÉÃ±ÀªÁVzÀÄÝ, ±ÉÃ 75 QÌAvÀ®Ä ºÉZÀÄÑ d£À¸ÀASÉå PÀÈ¶AiÀÄ£Éß ¥ÀæªÀÄÄR PÀ¸ÀÄ§£ÁßV CªÀ®A©ü¹zÉ. zÉÃ±ÀzÀ gÁ¶ÖçÃAiÀÄ ªÀgÀªÀiÁ£ÀzÀ°è PÀÈ¶ PÉëÃvÀæzÀ ¥Á®Ä CvÀåAvÀ ¥ÀæªÀÄÄRªÁVgÀÄvÀÛzÉ.

¨sÁgÀvÀ ¸ÀPÁðgÀ ºÁUÀÆ gÁdå ¸ÀPÁðgÀUÀ¼ÀÄ PÀÈ¶ PÉëÃvÀæzÀ C©üªÀÈ¢üÞUÁV C¥ÁgÀ ªÉÆvÀÛzÀ ºÀtªÀ£ÀÄß ªÀå¬Ä¸ÀÄwÛzÉ.

F ¢±ÉAiÀÄ°è PÀÈ¶ CAQ CA±ÀUÀ¼ÀÄ PÀÈ¶UÉ ¸ÀA§A¢ü¹zÀ AiÉÆÃd£ÉUÀ¼À£ÀÄß gÀÆ¦¸ÀÄªÀ°è CvÀåAvÀ ªÀÄºÀvÀézÀ ¸ÁzsÀ£ÀUÀ¼ÁVgÀÄvÀÛªÉ. PÀ£ÁðlPÀzÀ°è §ºÀÄ »A¢£À PÁ®¢AzÀ®Ä PÀÈ¶ CAQ CA±ÀUÀ¼À£ÀÄß ¸ÀAUÀæ»¸À¯ÁUÀÄwÛzÉ. »AzÉ ªÀA±À ¥ÁgÀA¥ÀgÀåªÁzÀ ±Á£ÀÄ¨sÉÆÃVPÉ ªÀÄvÀÄÛ ¥ÀmÉÃ°PÉUÀ¼ÀÄ EzÀÝªÀÅ, ±Á£ÀÄ¨sÉÆÃUÀgÀÄ vÀªÀÄUÉ ¤AiÉÆÃf¹zÀ ºÀ½îUÀ¼À°è PÀÈ¶UÉ ¸ÀA§A¢ü¹zÀ CAQ CA±ÀUÀ¼À£ÀÄß ºÁUÀÆ PÀgÀ ªÀ¸ÀÆ° PÁAiÀÄðªÀ£ÀÄß ªÀiÁqÀÄwÛzÀÝgÀÄ.

FUÀ ±Á£ÀÄ¨sÉÆÃUÀgÀ §zÀ¯ÁV ¸ÀPÁðj £ËPÀgÀgÁzÀ UÁæªÀÄ¯ÉQÌUÀgÀÄ ¸ÀzÀj PÁAiÀÄð ¤ªÀð»¸ÀÄwÛzÁÝgÉ.

1966 gÀ PÀ£ÁðlPÀ ¨sÀÆ PÀAzÁAiÀÄ ¤AiÀÄªÀÄUÀ¼À ¥ÀæPÁgÀ PÀÈ¶UÉ ¸ÀA§A¢üvÀ CAQ CA±ÀUÀ¼À£ÀÄß £ÀªÀÄÆ£É-16 gÀ°è ¸ÀAUÀæ»¸À¯ÁUÀÄwÛzÉ. EzÀ£ÀÄß ºÀPÀÄÌ zÁR¯Áw UÉÃtÂzÁj ªÀÄvÀÄÛ ¨É¼É vÀ¤SÉAiÀÄ ( Dgï.n.¹) jf¸ÀÖgÀ JAzÀÄ PÀgÉAiÀÄÄvÁÛgÉ.

¸ÀzÀj Dgï.n.¹ AiÀÄ°è ¥ÀæwAiÉÆAzÀÄ ¸À.£ÀA/¸À¨ï ¸À.£ÀAUÀ½UÉ ¸ÀA§A¢ü¹zÀAvÉ «ªÀgÀªÁzÀ PÀÈ¶ CAQ CA±ÀUÀ¼À£ÀÄß ¸ÀAUÀæ»¸À¯ÁUÀÄwÛzÉ. ¸ÀzÀj Dgï.n.¹ AiÀÄ°è ¸ÀAUÀæ»¹zÀ ªÀiÁ»wAiÀÄÄ J¯Áè E¯ÁSÉUÀ½UÀÆ PÁ£ÀÆ£ÀÄ§zÀÞ DzsÁgÀªÁVgÀÄvÀÛzÉ. gÁdå ¸ÀPÁðgÀ ¨É¼É «ªÀgÀªÀ£ÀÄß zÁR°¸ÀÄªÀ PÀÄjvÀÄ PÀAzÁAiÀÄ E¯ÁSÉAiÀÄ ¨sÀÆ«Ä ¸ÀÄvÉÆÛÃ¯É ¸ÀASÉå:PÀA.E/49/JA.Dgï.Dgï/2003, ¢:16-04-2003 gÀ°è ¸ÀÄvÉÆÛÃ¯É ºÉÆgÀr¹ ¥ÀºÀtÂ §gÉAiÀÄÄªÀ §UÉÎ «ªÀgÀªÁzÀ ªÀiÁUÀð¸ÀÆaUÀ¼À£ÀÄß ¤ÃqÀ¯ÁVgÀÄvÀÛzÉ.

F jÃwAiÀiÁV ¸À.£ÀA/¸À¨ï ¸ÀªÉð £ÀA§gÀªÁgÀÄ ¨É¼É PÉëÃvÀæ «ªÀgÀUÀ¼À£ÀÄß zÁR°¹ £ÀAvÀgÀ UÁæªÀÄzÀ WÉÆÃµÁégÉ vÀAiÀiÁj¹ ¸ÀA§A¢ü¹zÀ E¯ÁSÁ C¢üPÁjUÀ¼ÀÄ / ¹§âA¢UÀ¼ÀÄ zÀÈrüÃPÀj¹zÀ £ÀAvÀgÀ CªÀÅUÀ¼À£ÁßzsÀj¹ PÀAzÁAiÀÄ ¤jÃPÀëPÀgÀÄ ºÉÆ§½ªÁgÀÄ ªÀgÀ¢AiÀÄ£ÀÄß vÀAiÀiÁj¹, ºÉÆ§½ ªÀÄlÖzÀ°è ªÀÄgÀÄ ºÉÆAzÁtÂPÉ ªÀiÁr vÀºÀ²Ã¯ÁÝgÀjUÉ ¸À°è¸ÀÄvÁÛgÉ. £ÀAvÀgÀ vÀºÀ²Ã¯ÁÝgÀgÀÄ vÁ®ÆPÀÄ ªÀgÀ¢ vÀAiÀiÁj¹ vÁ®ÆPÀÄ ªÀÄlÖzÀ ªÀÄgÀÄºÉÆAzÁtÂPÉ ªÀgÀ¢ vÀAiÀiÁj¹ f¯Áè ªÀÄlÖPÉÌ ¸À°è¸ÀÄvÁÛgÉ. £ÀAvÀgÀ f¯Áè ªÀÄlÖzÀ°è f¯Áè ¸ÁATåPÀ C¢üPÁjUÀ¼ÀÄ f¯Áè ªÀgÀ¢ vÀAiÀiÁj¹ ¸ÀA§A¢üvÀ E¯ÁSÁ C¢üPÁjUÀ¼À ¸À»AiÉÆA¢UÉ ªÀiÁ£Àå f¯Áè¢PÁjUÀ¼À zÀÈrüÃPÀgÀtzÉÆA¢UÉ gÁdå ªÀÄlÖPÉÌ ¸À°è¸ÀÄvÁÛgÉ.

¥ÀºÀtÂAiÀÄ°è£À PÀÈ¶ CAQ CA±ÀUÀ¼À PÉÆæÃrüPÀgÀt PÁAiÀÄð ªÀÄÄRåªÁV UÁæªÀÄ¯ÉPÁÌ¢üPÁjUÀ¼ÀzÉÝ DVgÀÄvÀÛzÉ.

DzÀgÉ EwÛaUÉ UÁæªÀÄ¯ÉPÁÌ¢üPÁjUÀ¼ÀÄ ¨ÉÃgÉ ¨ÉÃgÉ PÉ®¸ÀUÀ¼À MvÀÛqÀ¢AzÁV ºÁUÀÆ ¹§âA¢UÀ¼À PÉÆgÀvÉ¬ÄAzÁV ¥ÀºÀtÂ §gÉAiÀÄÄªÀ°è ¤jÃQëvÀ ªÀÄlÖzÀ ¤RgÀ CAQ CA±ÀUÀ¼À£ÀÄß ¸ÀAUÀæ»¸ÀÄªÀzÀÄ PÀµÀ× ¸ÁzsÀåªÁUÀÄwÛzÉ.

* f¯Áè ¸ÀASÁå ¸ÀAUÀæºÀuÁ¢üPÁj, ºÁªÉÃj.

184

F ¤nÖ£À°è EvÀgÉ ¸ÀA§A¢üvÀ E¯ÁSÉUÀ¼ÁzÀ PÀÈ¶ E¯ÁSÉ, vÉÆÃlUÁjPÉ E¯ÁSÉ, gÉÃµÉä E¯ÁSÉ, ¤ÃgÁªÀj E¯ÁSÉUÀ¼ÀÄ vÀªÀÄä PÀÈ¶ CAQ CA±ÀUÀ¼À£ÀÄß ¥ÀºÀtÂ §gÉAiÀÄÄªÀ ¸ÀªÀÄAiÀÄQÌAvÀ ªÀÄÄAZÉ CxÀªÁ ¥ÀºÀtÂ §gÉAiÀÄÄªÀ ¸ÀªÀÄAiÀÄzÀ°è UÁæªÀÄªÀÄlÖzÀ CAQ CA±ÀUÀ¼À£ÀÄß PÀAzÁAiÀÄ E¯ÁSÉUÉ ¥ÀÆgÉÊ¹ CªÀÅ ¥ÀºÀtÂAiÀÄ°è zÁR°¹zÀÝ£ÀÄß RavÀ¥Àr¹PÉÆ¼ÀÄîªÀ PÁAiÀÄð ªÀiÁrzÀgÉ E£ÀÄß ¤RgÀªÁzÀ CAQ CA±ÀUÀ¼À£ÀÄß ¸ÀAUÀæ»¸À®Ä ¸ÁzsÀåªÁUÀÄvÀÛzÉ. ºÁUÀÆ ¥ÀºÀtÂ §gÉAiÀÄÄªÀ ¸ÀªÀÄAiÀÄzÀ°è UÁæªÀÄ¯ÉPÁÌ¢üPÁjUÀ½UÉ EvÀgÉÃ ¨ÉÃgÉ AiÀiÁªÀÅzÉÃ PÉ®¸À ¤AiÉÆÃf¸À¢zÀÝgÉÃ UÁæªÀÄ ¯ÉPÁÌ¢üPÁjUÀ¼ÀÄ vÀªÀÄä ¸ÀA¥ÀÆtð UÀªÀÄ£ÀªÀ£ÀÄß ¥ÀºÀtÂ §gÉAiÀÄÄªÀzÀgÀ §UÉÎ ¤ÃrzÀgÉ ºÉaÑ£À UÀÄtªÀÄlÖªÀ£ÀÄß ¥ÀqÉAiÀÄ§ºÀÄzÁVgÀÄvÀÛzÉ. F §UÉÎ ¥ÀºÀtÂ §gÉAiÀÄÄªÀ CªÀ¢üAiÀÄ£ÀÄß “¥ÀºÀtÂ zÁR¯Áw ªÀiÁ¸ÁZÀgÀuÉ” CAvÁ ¥Àæw ªÀµÀð dgÀÄV¹ EzÀgÀ §UÉÎ ªÁå¥ÀPÀ ¥ÀæZÁgÀ ¥Àr¹ d£À¸ÁªÀiÁ£ÀåjUÀÆ EzÀgÀ ªÀiÁ»w ¥ÀÆgÉÊ¹zÀgÉ ¤RgÀªÁzÀ CAQ CA±ÀUÀ¼À£ÀÄß ¸ÀAUÀæ»¸À°PÉÌ ¸ÁzsÀåªÁUÀÄvÀÛzÉ. F jÃw ªÀiÁ¸ÁZÀgÀuÉ PÁAiÀÄðPÀæªÀÄªÀ£ÀÄß ¸ÀA¥ÀÆtðªÁV PÀAzÁAiÀÄ E¯ÁSÉUÉ ªÀ»¸ÀÄªÀzÀÄ ¸ÀÆPÀÛªÁzÀzÁVgÀÄvÀÛzÉ.

FUÁUÀ¯ÉÃ eÁjAiÀÄ°ègÀÄªÀ ¨É¼É CAQ CA±ÀUÀ¼À C©üªÀÈ¢Þ AiÉÆÃd£É, ¥ÀºÀtÂ zÁR¯Áw CAzÉÆÃ®£À ºÁUÀÆ PÉ.J¸ï.J¸ï.J¸ï.¦ ªÀw¬ÄAzÀ ºÀ«ÄäPÉÆ¼Àî¯ÁVgÀÄªÀ PÀÈ¶ CAQ CA±ÀUÀ¼À ªÀÄºÀvÀézÀ §UÉÎ ¸ÀA§A¢ü¹zÀ C¢üPÁjUÀ½UÉ / ¹§âA¢UÀ½UÉ ºÁUÀÆ d£À ¥Àæw¤¢üUÀ½UÉ vÀgÀ¨ÉÃw PÁAiÀiÁðUÁgÀUÀ¼ÀAvÀºÀ PÁAiÀÄð PÀæªÀÄUÀ¼ÀÄ PÀÈ¶ CAQ CA±ÀUÀ¼À UÀÄtªÀÄlÖªÀ£ÀÄß ºÉaÑ¸ÀÄªÀ°è CvÀåAvÀ AiÀÄ±À¹éAiÀiÁVgÀÄvÀÛªÉ.

¨É¼É PÉëÃvÀæ ªÀÄgÀÄºÉÆAzÁtÂPÉ ªÀgÀ¢UÉ ¥ÀºÀtÂAiÉÄÃ ¥ÀæªÀÄÄR DzsÁgÀªÁVgÀÄªÀzÀjAzÀ ¥ÀºÀtÂAiÀÄ°è ¨É¼É PÉëÃvÀæ zÁR®Ä ªÀiÁqÀ°PÉÌ EgÀÄªÀAvÀºÀ PÁ®AUÀ¼À£ÀÄß E£ÀÄß ¸ÀgÀ½ÃPÀgÀt ªÀiÁqÀ§ºÀÄzÁVgÀÄvÀÛzÉ.

9 «zsÀzÀ ¨sÀÆ ªÀVðPÀgÀt ªÀiÁ»wUÀ¼À£ÀÄß ¸ÀAPÉÃvÀ CPÀëgÀUÀ¼À°è §gÉAiÀÄÄªÀzÀgÀ §zÀ¯ÁV D J¯Áè ªÀVðPÀgÀtUÀ½UÉ ¥ÀºÀtÂAiÀÄ¯ÉèÃ PÁ®AUÀ¼À£ÀÄß MzÀV¸ÀÄªÀzÀÄ ºÉaÑ£À ¸ÀÆPÀÛªÁUÀ§ºÀÄzÀÄ. C®èzÉÃ ¤ÃgÁªÀj ªÀÄvÀÄÛ ªÀÄ¼É D²æÃvÀ PÉëÃvÀæªÀ£ÀÄß zÁR°¸ÀÄªÀzÀPÉÌ JgÀqÀÄ ¥ÀvÉåÃPÀ PÁ®AUÀ¼À£ÀÄß ¥ÀºÀtÂAiÀÄ°è MzÀV¹zÀgÉ ¸ÀgÀ¼À ªÀiÁ»w ¸ÀAUÀæ»¸À®Ä C£ÀÄPÀÆ®ªÁUÀÄvÀÛzÉ.

: PÀÈ¶ GvÁàzÀ£Á CAQ CA±ÀUÀ¼ÀÄ B

PÀÈ¶ GvÁàzÀ£Á CAQ CA±ÀUÀ¼ÀÄ zÉÃ±ÀzÀ C©üªÀÈ¢Þ ¸ÀÆZÁåAPÀzÀ°è ªÀÄºÀvÀézÀ ¥ÁvÀæ ªÀ»¸ÀÄvÀÛªÉ. PÀÈ¶ GvÁàzÀ£É, CAQ CA±ÀUÀ¼À£ÀÄß ªÀÄÄRåªÁV ¨É¼É CAzÁdÄ ¸À«ÄÃPÉë / ¨É¼É «ªÀiÁ AiÉÆÃd£ÉAiÀÄr £ÀqÉ¸À¯ÁUÀÄªÀ ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À DzsÁgÀzÀ ªÉÄÃ¯É PÀAqÀÄ»rAiÀÄ¯ÁUÀÄvÀÛzÉ.

¸ÀzÀj ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À£ÀÄß ¥Àæw ªÀµÀð IÄvÀÄªÁgÀÄ, DAiÀÄÝ ¨É¼ÉUÀ¼À ªÉÄÃ¯É PÉÊUÉÆ¼Àî¯ÁUÀÄwÛzÉ. ¸ÀzÀj ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À£ÀÄß PÀAzÁAiÀÄ, PÀÈ¶, Dgï.r.¦.Dgï, vÉÆÃlUÁjPÉ d¯Á£ÀAiÀÄ£À E¯ÁSÉUÀ¼À ¹§âA¢UÀ¼À ªÀÄÆ®PÀ PÉÊUÉÆ¼Àî¯ÁUÀÄwÛzÉ.

¥Àæ¸ÀÄÛvÀ ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À£ÀÄß ¤RgÀªÁV ºÁUÀÆ UÀÄtªÀÄlÖvÉ¬ÄAzÀ PÉÊUÉÆ¼Àî¯ÁUÀÄwÛzÉ. DzÀgÉ ¥Àæw ªÀµÀð ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À ¸ÀASÉå ºÉZÀÑ¼ÀªÁUÀÄwÛgÀÄªÀzÀjAzÀ PÉ®ªÀÅ E¯ÁSÉUÀ¼À°è ¹§âA¢UÀ¼À°è PÉÆgÀvÉ¬ÄAzÁV (CzÀgÀ°è ªÀÄÄRåªÁV PÀÈ¶ , d¯Á£ÀAiÀÄ£À E¯ÁSÉAiÀÄ°è) ¥Àæw ¹§âA¢UÀ½UÉ ºÀAaPÉAiÀiÁUÀÄwÛgÀÄªÀ ¥ÀæAiÉÆÃUÀUÀ¼À ¸ÀASÉå KjPÉAiÀiÁVzÀÝjAzÀ ¸ÀzÀj PÁAiÀÄð ¤ªÀð»¸ÀÄªÀ°è ªÀÄÆ® PÁAiÀÄðPÀvÀðjUÉ PÀµÀÖ¸ÁzsÀåªÁUÀÄwÛzÉ.

185

DzÀÝjAzÀ PÀrªÉÄ ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À ¸ÀASÉå¬ÄAzÀ ¤RgÀªÁzÀ GvÁàzÀ£É CAQ CA±ÀUÀ¼À CAzÁf¸ÀÄªÀ ªÉÊeÁÕ¤PÀ ¥ÀzÀÝwAiÀÄ£ÀÄß C¼ÀªÀr¹PÉÆAqÀ°è E£ÀÄß ºÉaÑ£À ¤RgÀvÉ¬ÄAzÀ ªÀÄÆ®PÁAiÀÄðPÀvÀðgÀÄ ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À£ÀÄß PÉÊUÉÆ¼ÀÄîªÀ°è C£ÀÄPÀÆ®ªÁUÀÄvÀÛzÉ.

PÀÈ¶ CAQ CA±ÀUÀ¼À ªÀÄºÀvÀézÀ §UÉÎ d£À ¥Àæw¤¢üUÀ½UÉ CjªÀÅ ªÀÄÆr¸ÀÄªÀ PÁAiÀÄðPÀæªÀÄUÀ½AzÁV d£À ¥Àæw¤¢üUÀ¼À°è / gÉÊvÀgÀ°è ¨É¼É «ªÀiÁ AiÉÆÃd£É §UÉÎ ºÉaÑ£À CjªÀÅ GAmÁVzÀÄÝ, ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À°è gÉÊvÀgÀÄ ¸ÀQæÃAiÀÄªÁV ¨sÁUÀªÀ»¸À®Ä C£ÀÄPÀÆ®ªÁVgÀÄvÀÛzÉ.

******

186

12. Crop area & Production Statistics- Issues in Collection Compilation

& Processing of Data & Remedies. 1) »£ÉßÃ¯É:-

¸ÀASÁå±Á¸ÀÛçzÀ §UÉÎ CAvÀeÁð®zÀ°è eÁ¯ÁrzÁUÀ PÀ¤µÀ× Qæ.¥ÀÆ 5 £ÉÃ ±ÀvÀªÀiÁ£À¢AzÀ®Æ

§¼ÀPÉ DUÀÄwÛzÉ JA§ «µÀAiÀÄ w½zÀÄ §A¢zÉ. EAzÀÄ ¸ÀASÁå±Á¸ÀÛç

¸ÀPÁðgÀzÀ AiÉÆÃd£ÉUÀ¼À£ÀÄß gÀÆ¦¸ÀÄªÀ°è,

ªÁå¥ÁgÀ ªÀ»ªÁlÄUÀ¼À°è,

¥ÀæPÀÈw «PÉÆÃ¥ÀUÀ¼À §UÉÎ,

±ÉÊPÀëtÂPÀ PÉëÃvÀæzÀ°è

¸ÁªÀiÁfPÀ «eÁÕ£ÀUÀ¼À°è

§¼À¸À¯ÁUÀÄwÛzÉ. DgÀA¨sÀzÀ°è ¸ÁªÀiÁ£ÀåªÁV d£À¸ÀASÉå ªÀÄvÀÄÛ DyðPÀ CA±ÀUÀ¼À DzsÁgÀzÀ ªÉÄÃ¯É

gÁdåzÀ CUÀvÀåUÀ¼À §UÉÎ AiÉÆÃd£ÉUÀ¼À£ÀÄß PÉÊUÉÆ¼Àî®Ä G¥ÀAiÉÆÃV¸ÀÄwÛzÀÝgÀÄ. ¸ÀASÁå±Á¸ÀÛçªÀ£ÀÄß

PÉ®ªÀgÀÄ UÀtÂvÀ «eÁÕ£À JAzÀÄ, PÉ®ªÀgÀÄ UÀtÂvÀ ±Á¸ÀÛçzÀ ¨sÁUÀªÉAzÀÄ ºÉÃ¼ÀÄvÁÛgÉ. ¸ÁªÀiÁ£Àå d£ÀgÀÄ

zÉÊ£ÀA¢£À ªÀåªÀºÁgÀzÀ°è vÀªÀÄUÉ UÉÆwÛ®èzÉ ¸ÀASÁå±Á¸ÀÛçzÀ PË±À®åUÀ¼À£ÀÄß/ CAzÁdÄUÀ¼À£ÀÄß

§¼À¹PÉÆ¼ÀÄîvÁÛgÉ. EzÀ£ÀÄß £ÁªÀÅ ¸ÀASÁå±Á¸ÀÛçzÀ ¸ÁPÀëgÀvÉ JAzÀÄ PÀgÉAiÀÄ§ºÀÄzÀÄ.

E¯ÁSÉAiÀÄ PÁAiÀÄðPÉëÃvÀæzÀ°è ¥ÀæªÀÄÄRªÁVgÀÄªÀ PÉëÃvÀæ ªÀÄgÀÄ ºÉÆAzÁtÂPÉ ªÀÄvÀÄÛ ¨É¼É

CAzÁdÄ ¸À«ÄÃPÉëUÀ¼À ªÀgÀ¢UÀ¼À£ÀÄß ¸ÀÄªÀiÁgÀÄ ªÀµÀðUÀ½AzÀ vÀAiÀiÁj¸ÀÄvÀÛ°èzÀÄÝ, ¸ÀzÀj ªÀgÀ¢UÀ¼À°è

PÉëÃvÀæ ªÀÄgÀÄ ºÉÆAzÁtÂPÉ ªÀgÀ¢AiÀÄÄ §ºÀÄ G¥ÀAiÉÆÃV PÁAiÀÄðUÀ½UÉ ºÁUÀÆ E¯ÁSÉAiÀÄ ¨É¼É

CAzÁdÄ ¸À«ÄÃPÉëUÀÆ ¸ÀºÀ G¥ÀAiÉÆÃV¸À®àqÀÄwÛgÀÄªÀ ªÀiÁ»wAiÀiÁVgÀÄvÀÛzÉ.

2) FV£À ªÀ¸ÀÄÛ ¹Üw:-

ªÉÄÃ°£À ¸ÀzÀj ªÀgÀ¢UÀ¼À°è PÉëÃvÀæ ªÀÄgÀÄ ºÉÆAzÁtÂPÉ ªÀgÀ¢ EwÛÃa£À ¢£ÀUÀ¼À°è

UÀt¤ÃAiÀÄªÁV ªÀiÁ¥ÁðqÁVzÀÄÝ, CAzÀgÉ UÁæªÀÄªÁgÀÄ, UÁæªÀÄ¥ÀAZÁAiÀÄvÀªÁgÀÄ,

ºÉÆÃ§½ªÁgÀÄ,¤ÃgÁªÀj, C¤ÃgÁªÀjUÉ ¸ÀA§A¢ü¹zÀAvÉ ¨É¼É PÉëÃvÀæ ¹zÀÞ¥Àr¸À¨ÉÃPÁVgÀÄvÀÛzÉ. EzÀgÀ

DzsÁgÀzÀ ªÉÄÃ¯É ¨É¼É CAzÁdÄ ¸À«ÄÃPÉëAiÀÄ ¥ÀæAiÉÆÃUÀUÀ¼ÀÄ PÉÊUÉÆ¼Àî¨ÉÃPÁVgÀÄvÀÛzÉ. ¨É¼É CAzÁdÄ

¸À«ÄÃPÉëAiÀÄ ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À£ÀÄß IÄvÀÄªÁgÀÄ, ºÉÆÃ§½ªÁgÀÄ, ¥ÀAZÁAiÀÄvÀªÁgÀÄ

,¤ÃgÁªÀj/C¤ÃgÁªÀj ¥ÀæAiÉÆÃUÀUÀ¼À£ÀÄß ºÉaÑ£À ¸ÀASÉåUÀ¼À°è (ªÀiÁ¥Áðr¹zÀ PÀÈ¶ «ªÀiÁ AiÉÆÃd£É

Modified National Agricultural Insurance Scheme) Cr PÉÊUÉÆ¼Àî¨ÉÃPÁVgÀÄvÀÛzÉ.

F PÉ¼ÀPÀAqÀ PÉÆÃµÀÖPÀzÀAvÉ ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼ÀÄ ºÉZÀÑ¼ÀªÁVgÀÄvÀÛªÉ.

*²æÃ ¹gÀ¹VPÀgï £ÁUÀtÚ, f¯Áè ¸ÀASÁå ¸ÀAUÀæºÀuÁ¢üPÁjUÀ¼ÀÄ, UÀÄ®âUÁð.

187

UÀÄ®§UÁð f¯ÉèUÉ ¨É¼É CAzÁdÄ ¸À«ÄÃPÉë Cr ªÀÄÄAUÁgÀÄ/»AUÁgÀÄ/ªÁ¶ðPÀ/¨ÉÃ¹UÉ IÄvÀÄ«UÁV ªÀ»¹zÀ ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼ÀÄ vÀ:SÉÛ.

ªÀµÀð 2008-09* 2009-10* 2010-11 2011-12 2012-13

¨É¼ÉPÀmÁªÀÅ

¥ÀæAiÉÆÃUÀUÀ¼ÀÄ 8944 9216 8874 6088 6266

*-AiÀiÁzÀVj f¯ÉèAiÀÄÆ ¸ÉÃj

20 £ÉÃ ±ÀvÀªÀiÁ£ÀzÀ GvÀÛgÁzsÀð¢AzÀ ¥ÁægÀA¨sÀªÁzÀ UÀtQÃgÀtªÀÅ ¸ÀASÁå±Á¸ÀÛçzÀ «eÁÕ£ÀzÀ

§¼ÀPÉAiÀÄ£ÀÄß ¥Àæ§®UÉÆ½¹vÀÄ. DzsÀÄ¤PÀ PÀA¥ÀÆålgÀUÀ¼À §¼ÀPÉAiÀÄÄ zÉÆqÀØ ¥ÀæªÀiÁtzÀ CAQ-CA±ÀUÀ¼À

¯ÉPÀÌ ¥ÀzÀÞwAiÀÄ£ÀÄß ²ÃWÀæUÉÆ½¹vÀÄ.

3) £ÀÆå£ÀvÉUÀ¼ÀÄ PÀÄAzÀÄPÉÆgÀvÉUÀ¼ÀÄ:-

¸ÀASÁå±Á¸ÀæöÛzÀ zÀÄ§ð¼ÀPÉ¬ÄAzÀ UÀA©üÃgÀ ¸ÀégÀÆ¥ÀzÀ vÀ¥ÀÄàUÀ¼ÀÄ ¸ÀA¨sÀ«¸ÀÄvÀÛªÉ. F

¸ÀAzÀ¨sÀðzÀ°è UÀtÂvÀzÀ «ªÀgÀuÉ ªÀÄvÀÄÛ CxÉÊð¸ÀÄ«PÉ PÀ¼À¥É ªÀÄlÖzÁÝVgÀ§ºÀÄzÀÄ. ¸ÀASÁå±Á¸ÀæöÛªÀ£ÀÄß

¸ÀjAiÀiÁV C¼ÀªÀr¹zÀgÀÆ C£ÀÄ¨sÀªÀzÀ PÉÆgÀvÉ¬ÄAzÁV CzÀ£ÀÄß CxÉÊð¸ÀÄªÀÅzÀÄ PÀµÀÖªÁUÀÄvÀÛzÉ.

¸ÀASÁå±Á¸ÀæöÛzÀ°è ¤UÀ¢vÀ ¸ÀªÀÄAiÀÄzÀ°è ªÀiÁzÀjUÀ¼À ¸ÀAUÀæºÀuÉ CvÀåAvÀ ªÀÄºÀvÀé ¥ÀqÉ¢zÉ. «eÁÕ¤

¯ÁgÉ£Àì¯ÉÆÃªÉÃ¯ï ¸ÀASÁå±Á¸ÀÛçzÀ §UÉÎ »ÃUÉ ºÉÃ½zÁÝgÉ- “ªÀiÁA¸ÀzÀ vÀÄAr£ÀAvÉ, CªÀÅUÀ¼À£ÀÄß

vÀAiÀiÁj¹zÀ ªÀåQÛAiÀÄ£ÀÄß ªÀÄvÀÄÛ CzÀgÀ°ègÀÄªÀ ¥ÉÆÃµÀPÁA±ÀUÀ¼À §UÉÎ w½¢zÀÝgÉ GvÀÛªÀÄ”.

¥Àæ¸ÀÄÛvÀ E¯ÁSÁ ªÀgÀ¢UÀ¼À°è MAzÁzÀ ¨É¼É CAzÁdÄ ¸À«ÄÃPÉëAiÀÄ°è ¨É¼É PÀmÁªÀÅ

¥ÀæAiÉÆÃUÀUÀ¼À ¸ÀASÉåAiÀÄ°è UÀt¤ÃAiÀÄªÁV ºÉZÀÑ¼ÀªÁVzÀÝgÀ, ¥ÀæAiÀÄÄPÀÛ

PÀAzÁAiÀÄ/PÀÈ¶/vÉÆÃlUÁjPÉ/d¯Á£ÀAiÀÄ£À/C©üªÀÈ¢Þ E¯ÁSÉAiÀÄ°è£À ¹§âA¢UÀ¼À PÉÆgÀvÉ¬ÄAzÀ ºÁUÀÆ

CªÀgÀªÀgÀ E¯ÁSÉAiÀÄ CwAiÀiÁzÀ PÉ®¸ÀzÀ MvÀÛqÀ¢AzÀ ¤UÀ¢vÀ ¸ÀªÀÄAiÀÄzÀ°è ¨É¼É PÀmÁªÀÅ

¥ÀæAiÉÆÃUÀUÀ¼À£ÀÄß PÉÊUÉÆ¼ÀîzÉ¬ÄgÀÄªÀÅzÀjAzÀ ªÀÄvÀÄÛ UÁæªÀÄªÁgÀÄ/ºÉÆÃ§½ªÁgÀÄ PÀÈ¶ CAQ CA±ÀUÀ¼À

(Reconciliation) ªÀiÁ»wUÀ¼À£ÀÄß PÀ¯ÉºÁPÀÄªÀ°è C£ÀUÀvÀåªÁV «¼ÀA§ªÁUÀÄwÛzÉ.

4) £ÀÆ£ÀåvÉUÀ¼À£ÀÄß vÀÄA§®Ä ²¥sÁgÀ¸ÀÄìUÀ¼ÀÄ:

E¯ÁSÉAiÀÄ ¤RgÀªÁzÀ ªÀgÀ¢UÀ¼ÀÄ ªÀåªÀ¹ÜvÀªÁV ¤UÀ¢¥Àr¹zÀ ¸ÀªÀÄAiÀÄzÉÆ¼ÀUÉ ªÀiÁ»w

¥ÀqÉAiÀÄ¨ÉÃPÁzÀgÉ ¥Àæw vÁ®ÆèPÀÄ PÀZÉÃjAiÀÄ°è£À ¸ÁATåPÀ ¤jÃPÀëPÀgÀÄ/¥ÀæUÀw ¸ÀºÁAiÀÄPÀgÀÄ/

UÀtwzÁgÀgÀ ºÀÄzÉÝUÀ¼À ¨sÀwðAiÉÆA¢UÉ ¥Àæw vÁ®ÆèQUÉ ºÉZÀÄÑªÀjAiÀiÁV ºÉÆÃ§½ªÁgÀÄ UÀtwzÁgÀgÀ£ÀÄß

£ÉÃ«Ä¹zÉÝÃ DzÀ°è ¥Àæw ºÉÆÃ§½UÉ ¤UÀ¢¥Àr¸À¯ÁzÀ

* ¨É¼É PÀmÁªÀÅ ¥ÀæAiÉÆÃUÀUÀ¼À ªÉÄÃ°éZÁgÀuÉ

* ¨É¼É PÉëÃvÀæ ªÀgÀ¢

* UÁæ«ÄÃt a®ègÉ ¨É¯É ªÀgÀ¢

* PÀÈ¶ PÀÆ° ªÀgÀ¢

188

* ¸ÀPÁ°PÀ ªÀgÀ¢ AiÉÆÃd£É (TRS)

* ¸ÀPÁ°PÀ ªÀgÀ¢ AiÉÆÃd£É Cr (Non-TRS) WÉÆÃµÁégÉUÀ¼À ªÀgÀ¢

* ¥ÀºÀtÂ ¥ÀwæPÉ

* d£À£À ªÀÄgÀtUÀ¼À ªÀgÀ¢UÀ¼À ªÉÄÃ°éZÁgÀuÉ / ¥Àj²Ã®£É, ¸À®ºÉ, ¸ÀAUÀæºÀuÉ, PÁAiÀÄð

* ¸ÀPÁ°PÀ ªÀgÀ¢ AiÉÆÃd£ÉUÀ¼À PÉëÃvÀæ PÁAiÀÄð/ªÀiÁzÀj PÉëÃvÀæ PÁAiÀÄð (AS1.0/AS1.1) ªÀ£ÀÄß ¸ÀºÀ

DAiÀiÁ ºÉÆÃ§½UÉ ¤AiÉÆÃf¹zÀ UÀtwzÁgÀjUÉ dªÁ¨ÁÝj ªÀ»¹, ¸ÀzÀjAiÀÄªÀjAzÀ DAiÀiÁ

ºÉÆÃ§½UÀ¼À UÁæªÀÄªÁgÀÄ/ ¥ÀAZÁAiÀÄvÀªÁgÀÄ ªÀiÁ»w/ ªÀgÀ¢UÀ¼À£ÀÄß vÁ®ÆèPÀÄ PÀZÉÃjAiÀÄ°è

PÁAiÀÄð¤ªÀð»¸ÀÄwÛgÀÄªÀ ¸ÁATåPÀ ¤jÃPÀëPÀgÀ ¸ÀºÀAiÉÆÃUÀzÉÆA¢UÉ vÀAiÀiÁj¸À®àlÖ PÉÆæÃrüPÀÈvÀ

ªÀgÀ¢AiÀÄ°è ¤RgÀvÉ ºÉZÀÄÑªÀÅzÀ®èzÉÃ E¯ÁSÉAiÀÄ ¸ÀªÀÄUÀæ ªÀiÁ»w/ªÀgÀ¢UÀ¼À£ÀÄß ¤UÀ¢vÀ ¸ÀªÀÄAiÀÄzÀ°è

¸À°èPÉAiÀiÁUÀÄªÀÅzÀjAzÀ f¯Áè PÀZÉÃjAiÀÄ°è J¯Áè ªÀgÀ¢/ ªÀiÁ»wUÀ¼À ¥Àj²Ã®£É ªÀÄvÀÄÛ vÀÄ®£ÁvÀäPÀ

PÁAiÀÄðzÀ°è UÀt¤ÃAiÀÄªÁzÀ ¥ÀæUÀw ¸Á¢ü¸À§ºÀÄzÁVzÉ.

d£À£À ªÀgÀ¢AiÀÄ°è ºÉtÄÚ ²±ÀÄUÀ¼À £ÉÆÃAzÀtÂ UÀAqÀÄ ²±ÀÄ«£À £ÉÆÃAzÀtÂUÉ ºÉÆÃ°¹zÁUÀ

ªÀÄ£ÉAiÀÄ°è ºÉZÁÑVgÀÄªÀÅzÀÄ PÀAqÀÄ §A¢zÉ. PÀ¼ÉzÀ ªÀÄÆgÀÄ ªÀµÀðUÀ¼À d£À£À ªÀgÀ¢ F PÉ¼ÀPÀAqÀ

PÉÆÃµÀÖPÀzÀ°è £ÀªÀÄÆ¢¸À¯ÁVzÉ.

UÀÄ®âUÁð f¯ÉèAiÀÄ ªÁ¶ðPÀ d£À£À ªÀgÀ¢ PÉÆÃµÀÖPÀ

°AUÀ/ªÀµÀð 2011 2010 2009

ªÀÄ£ÉAiÀÄ°è D¸ÀàvÉæAiÀÄ°è ªÀÄ£ÉAiÀÄ°è D¸ÀàvÉæAiÀÄ°è ªÀÄ£ÉAiÀÄ°è D¸ÀàvÉæAiÀÄ°è

UÀAqÀÄ 12970 15908 11579 11540 22482 9464

ºÉtÄÚ 16704 15006 18646 13338 34857 12229

d£À£À £ÉÆÃAzÀtÂUÉ ¸ÀA§A¢ü¹zÀAvÉ, d£À£À ªÀÄgÀt £ÉÆÃAzÀuÁ¢üPÁjUÀ½AzÀ ¤UÀ¢ ¥Àr¹zÀ

£ÀªÀÄÆ£ÉAiÀÄ°è ¤¢ÃðµÀÖ ªÀiÁ»w ¨sÀwðªÀiÁqÀzÉ EgÀÄªÀÅzÀjAzÀ DgÉÆÃUÀå E¯ÁSÉAiÀÄ d£À£À ªÀgÀ¢UÉ

vÁ¼ÉAiÀiÁUÀÄwÛ®è. PÁgÀt DgÉÆÃUÀå E¯ÁSÉ¬ÄAzÀ ±ÉÃPÀqÁ 90% ªÉÄÃ®àlÄÖ D¸ÀàvÉæAiÀÄ°èAiÉÄÃ

d£À£ÀUÀ¼ÀÄ DUÀÄwÛzÀÝgÀÆ ¸ÀºÀ £ÉÆÃAzÀuÁ¢üPÁjUÀ½UÉ Cj«£À PÉÆgÀvÉ¬ÄAzÁV J®è d£À£ÀUÀ¼ÀÄ

“ªÀÄ£ÉAiÀÄ°è” JAzÀÄ £ÀªÀÄÆ¢¸ÀÄwÛgÀÄªÀÅzÀjAzÀ ªÀgÀ¢AiÀÄ°è KgÀÄ¥ÉÃgÀÄ DUÀÄwÛzÉ.

ªÀÄÄAzÀÄªÀgÉAiÀÄÄvÀÛ ªÀÄgÀt zÁR¯ÉUÀ½UÉ ¸ÀA§A¢ü¹zÀAvÉ D¸ÀàvÉæUÀ¼À°è WÀn¹zÀ

WÀl£ÉUÀ½UÉ ªÉÊzÁå¢üPÁjUÀ½AzÀ §gÀ¨ÉÃPÁzÀ ªÀÄgÀt PÁgÀt ªÉÊzÀåQÃAiÀÄ ¥ÀæªÀiÁt ¥ÀvÀæUÀ¼ÀÄ ¸ÀºÀ

¸À°è¸ÀzÉÃ EgÀÄªÀÅzÀgÀ ¥ÀæAiÀÄÄPÀÛ ºÁUÀÆ D¸ÀàvÉæ ºÉÆgÀUÀqÉ DVgÀvÀPÀÌ ªÀÄgÀt £ÀªÀÄÆ£ÉAiÀÄ°è ªÀÄgÀtPÉÌ

PÁgÀtªÁVgÀÄªÀ (ZÀlUÀ¼ÀÄ/gÉÆÃUÀUÀ¼ÀÄ) AiÀiÁªÀÅzÉÃ CA±ÀUÀ¼À£ÀÄß ¨sÀwðªÀiÁqÀzÉÃ PÉÃªÀ®

“ªÀAiÀÄ¸ÁìVzÀÝgÀ ¥ÀæAiÀÄÄPÀÛ” JAzÀÄ £ÀªÀÄÆ¢¸ÀÄwÛgÀÄªÀÅzÀjAzÀ vÀÄ®£ÁvÀäPÀªÁV ªÀgÀ¢AiÀÄ°è ºÉÆAzÁtÂPÉ

PÀµÀÖ ¸ÁzsÀåªÁUÀÄwÛzÉ. PÀ¼ÉzÀ ªÀÄÆgÀÄ ªÀµÀðUÀ¼À ªÀÄgÀt ªÀgÀ¢ F PÉ¼ÀPÀAqÀ PÉÆÃµÀÖPÀzÀ°è ¤ÃqÀ¯ÁVzÉ.

189

UÀÄ®âUÁð f¯ÉèAiÀÄ ªÁ¶ðPÀ ªÀÄgÀt ªÀgÀ¢ PÉÆÃµÀÖPÀ

°AUÀ/ªÀµÀð 2011 2010 2009

ªÀÄ£ÉAiÀÄ°è D¸ÀàvÉæAiÀÄ°è ªÀÄ£ÉAiÀÄ°è D¸ÀàvÉæAiÀÄ°è ªÀÄ£ÉAiÀÄ°è D¸ÀàvÉæAiÀÄ°è

UÀAqÀÄ 6946 1229 8246 957 20782

ºÉtÄÚ 4444 790 5252 762

¥ÁææxÀ«ÄPÀ DgÉÆÃUÀå PÉÃAzÀæUÀ¼À°è vÁ®ÆèPÀÄ DgÉÆÃUÀå C¢üPÁjUÀ¼ÀÄ ¤ÃrgÀÄªÀ d£À£À ¥ÀæªÀiÁt

¥ÀvÀæ d£ÀgÀÄ CAVÃPÀj¸ÀÄwÛ®è. £ÁªÀÅUÀ¼ÀÄ ¥Àæw ªÀµÀð ¸ÀzÀj «µÀAiÀÄPÉÌ ¸ÀA§A¢ü¹zÀAvÉ UÁæªÀÄ ªÀÄlÖzÀ

£ÉÆÃAzÀuÁ¢üPÁjUÀ½UÉ vÀgÀ¨ÉÃw ¤ÃrzÀgÀÆ ¸ÀºÀ d£À£À ªÀÄgÀt £ÀªÀÄÆ£ÉUÀ¼À°è ªÀiÁ»w ¥ÀÆtð

¥ÀæªÀiÁtzÀ°è ¨sÀwðAiÀiÁUÀÄwÛ®èzÀ ¥ÀæAiÀÄÄPÀÛ ºÉZÀÄÑªÀjAiÀiÁV ºÉÆÃ§½ ªÀÄlÖzÀ°è £ÉÃ«Ä¸À®àlÖ

UÀtwzÁgÀjUÉ ¥Àj²Ã®£Á PÁAiÀÄðzÀ dªÁ¨ÁÝj ªÀ»¹PÉÆlÖ°è ¥ÀÆtð ¥ÀæªÀiÁtzÀ ªÀiÁ»w ¥Àj²Ã®£Á

PÁAiÀið PÉÊUÉÆAqÀÄ £ÀÆå£ÀåvÉUÀ¼À£ÀÄß ¸Àj¥Àr¸À§ºÀÄzÁVzÉ.

ªÀÄÄAzÀÄªÀgÉAiÀÄÄvÁÛ E¯ÁSÉAiÀÄ C¢üPÁj/ ¹§âA¢AiÀÄªÀjUÉ ±ÁSÉªÁgÀÄ vÀgÀ¨ÉÃwAiÀÄ£ÀÄß

¤zÉÃð±À£Á®AiÀÄzÀ ªÀÄÄSÁAvÀgÀ K¥Àðr¸ÀÄªÀÅzÀjAzÀ ±ÁSÉUÀ¼À ¸ÀªÀÄUÀæ ªÀiÁ»w ºÁUÀÆ ªÀgÀ¢

vÀAiÀiÁjPÉAiÉÆA¢UÉ ªÉÊAiÀÄQÛPÀªÁV ¥ÀæwAiÉÆ§â C¢üPÁj/¹§âA¢AiÀÄ PÁAiÀÄð PÀëªÀÄvÉ ºÉaÑ ÀÄªÀÅzÀPÉÌ

¸ÀºÀPÁjAiÀiÁUÀÄªÀÅzÀgÀ eÉÆvÉUÉ E¯ÁSÉAiÀÄ ¥ÀæwAiÉÆAzÀÄ ªÀiÁ»wUÀ¼À eÉÆvÉUÉ PÁ® PÁ®PÉÌ

PÉÊUÉÆ¼ÀÄîªÀAvÀºÀ ¸À«ÄÃPÁë ªÀgÀ¢UÀ¼À UÀÄtªÀÄlÖªÀ£ÀÄß ªÀÈ¢Þ¸À§ºÀÄzÁVzÉ.£ÀªÀÄä ¤zÉÃð±À£Á®AiÀÄªÀ£ÀÄß

CAQ-CA±ÀUÀ½AzÀ zÀµÀÖ¥ÀÄµÀÖ ºÁUÀÄ ¸ÀA¥ÀzÀãjvÀªÁV ªÀiÁqÀ§ºÀÄzÀÄ.

******

1. Crop Area and Production Estimates Issues in collection ... Files/2... · 97 1. Crop Area and...

Documents

Transcript of 1. Crop Area and Production Estimates Issues in collection ... Files/2... · 97 1. Crop Area and...