10: System of collection of agricultural Statistics …
SYSTEM OF COLLECTION OF AGRICULTURAL STATISTICS IN INDIA INCLUDING LAND USE AND AREA STATISTICS Rajiv Lochan Directorate of Economics and Statistics, New Delhi
1
INTRODUCTION
India predominantly is an agrarian economy both from the point of view of employment as well as contribution to the national income. Availability of reliable and timely crop estimates is hence of paramount importance to the planners, administrators, policy makers and research scholars. The Government depends depends on these data in taking a number of policy decisions decisions regarding pricing, processing, procurement, storage, transport, marketing, export/import, public distribution and many other issues like li ke investment planning. The system of generating annual estimates of area, yield and production of crops in India is more than a century old. A constant evaluation of the mechanism for generation of timely and reliable agricultural production statistics, therefore, assumes vital importance and significance. The Directorate of Economics and Statistics (DES) releases estimates of area, production and yield in respect of 51 principal crops of food grains, oilseeds, sugarcane, fibers and important commercial and horticulture crops. These crops together accounts for nearly 87% of agriculture output. The organization of this lecture note is as follows. Section 1 presents the system of release of Advance and Final Estimates of area, production and yield by the DES on the basis of reports received from State Agricultural Statistics Authorities (SASAs) Section 2 gives a bird eyeview of area statistics. Section 3 deals with various aspects of yield estimates namely extent of coverage, sampling design and degree of precision of estimates. Under Section 4, some of important limitations of CES (Crop Estimation Surveys) are discussed. Section 5 gives gives an account of the schemes launched to fine tune crop statistics. Finally, section 6 presents the conclusion. 1.1
Release of Estimates of Area and Production
The period of an agricultural crop year is from July to June, during which various farm operations from preparation of seed bed, nursery, sowing, transplanting various inter-culture operations, harvesting, threshing etc. are carried out. Different crops are grown during the agricultural seasons in the crop year. Final estimates of production based on complete enumeration of area and yield through crop cutting experiments become available much after the crops are actually harvested. However, the Government requires advance estimates of production for taking various policy decisions relating to pricing, marketing, export/import, distribution, etc. Considering the genuine requirement of crop estimates much before the crops are harvested for various policy purposes, a time schedule of releasing the advance estimates has been evolved. These estimates of crops are prepared prepared and released at four points of time during a year as enumerated below: 1.2
First Advance Estimates
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10: System of collection of agricultural Statistics … The first official estimate of area and production of kharif crops is prepared in September every year, when south-west monsoon season is about to be over and kharif crops are at an advanced stage of maturity. This coincides with the holding of the National Conference of Agriculture for Rabi Campaign, where the State Governments give rough assessment of their respective kharif crops. The assessment is made by the State Governments based on the reports from the field offices of the State Department of Agriculture. They are mainly guided by visual observations. These are validated on the basis of inputs from the proceedings of Crop Weather Watch Group (CWWG) meetings, and other feedback such as relevant availability of water in major reservoirs, availability/supply of important inputs including credit to farmers, rainfall, temperature, irrigation etc. 1.3
Second Advance Estimates
The second advance estimate is made in the month of January every year when the advance estimates of kharif crops prepared during the National Conference of Agriculture for Rabi Campaign may undergo a revision in the light of flow of more precise information from the State Governments. Around this time, the first advance estimates of rabi crops are also prepared. The Second Advance Estimates then cover the second assessment in respect of Kharif Crops and the first assessment in respect of Rabi Crops. 1.4
Third Advance Estimates
The third advance estimates are prepared towards the end of March/ beginning of April every year, when the National Conference on Agriculture for Kharif campaign is convened and the State Governments come up with their assessments for both kharif and rabi crops. The earlier advance estimates of both kharif and rabi seasons are firmed up/ validated with the information available with State Agricultural Statistics Authorities (SASAs), remote sensing data, available with Space Application Centre, Ahmedabad as well as the proceedings of CWWG. 1.5
Fourth Advance Estimates
The fourth advance estimates are prepared in the month of June/July every year, when the National Workshop on Improvement of Agricultural Statistics is held. Since most of the rabi crops get harvested by the end of May, SASAs are in a position to supply the estimates of both kharif and rabi seasons as well as likely assessment of summer crops during the National Workshop. Like the third advance estimates, the fourth advance estimates are duly validated with the information available from other sources. 1.6
Final Estimates
Under the existing system of crop estimation, the fourth advance estimate is followed by final estimates in December / January of the following agricultural year. The main factors contributing to the relatively large number of crop estimates are the large variations in crop 128
10: System of collection of agricultural Statistics … seasons across the country and the resulting delay in the compilation of yield estimates based on crop cutting experiments. As agriculture is a State subject, Central Government depends upon State Governments for accuracy of these estimates . For this purpose, State Governments have setup High Level Coordination Committees (HLCC) comprising, inter-alia, senior officers from their departments of Agriculture, Economics & Statistics, Land Records and NSSO (FOD), IASRI, DES from Central Government for sorting out problems in preparation of these estimates in a timely and orderly manner. 2.
AREA STATISTICS
From the point of view of collection of area statistics, the States in the country are divided into three broad categories:(i)
States and U.Ts which has been cadastrally surveyed and where area and land use statistics are built up as part of the land records maintained by the revenue agencies (referred to as “Land Record States” or temporarily settled states). The system of land record is being followed in 13 major states and 4 UTs of Chandigarh, Delhi, Dadar & Nagar Haveli and Pondicherry. These states/UTs account for about 86% of reporting area.
(ii)
The states where area statistics are collected on the basis of sample surveys (normally known as non-land record states or “Permanently Settled States” which are three in number viz. Kerala, Orissa and West Bengal). A scheme for Establishment of Agency for Reporting of Agricultural Statistics (EARAS) has been introduced in these three states which envisages, inter-alia, either the estimation of areas by complete enumeration or through sample surveys in a sufficiently large sample of 20% villages/ investigators zones. These states accounts for about 9% of reporting area.
(iii)
In hilly districts of Assam, the rest of the states in North-Eastern Region, Sikkim, Goa, UTs of Andaman & Nicobar Islands, Daman & Diu and Lakshadweep where no reporting agency had been functioning, the work of collection of Agricultural Statistics is entrusted with the village headmen (5%).
While the area statistics are collected on complete enumeration basis in respect of states in category (i) above, on ad-hoc methods based on impressionistic approach in case of states in category (iii) above, a scheme for Establishment of Agency for Reporting of Agricultural Statistics (EARAS) has been introduced in the three states in category (ii) above. For further details of EARAS section 6.3 may be referred to. 3. YIELD ESTIMATES
The second most important component of production statistics is yield rates. The yield estimates of major crops are obtained through analysis of scientifically designed crop cutting 129
10: System of collection of agricultural Statistics … experiments (CCE) conducted under scientifically designed general Crop Estimation Surveys (CES). During 2004-05, 8.16 lakhs such experiments covering 68 crops including 16 nonfood crops were conducted. At present over 95% of t he production of food grains is estimated on the basis of yield rates obtained from the CCE spread over 19 stat es and 4 UTs. The primary objective of CES is to obtain fairly reliable estimates of average yield of principal food and non-food crops for each of state and UTs which are important from the point of view of crop production. The estimates of yield rates thus arrived at are generally adopted for the purposes of planning, policy formulation and implementation. The CCE consist of identification and marking of experimental plots of a specified size and shape in a selected field on the principle of random sampling, threshing the produce and recording of the harvested produce for determining the percentage recovery of dry grains or the marketable form of the produce. 3.1
Coverage
A total of 68 crops comprising 52 food crops and 16 non-food crops were covered under CES during 2004-05 as per details given in table 1. Table 1: Crops Covered under CES Type of Crop
A. Food grains Crops Cereals
No. of crops Covered under CES
No.of Experiments
Percentage
7
487493
71.0
Pulses & Beans Small Millets Condiments and Spices Fruits and Vegtables Sugar (Sugarcane and Cocoa) Sub-Total (A) : B. Non-Food grains Crops Oilseeds
11 7 10 16 2 52
129898 3336 9798 33876 21985 686386
18.9 0.5 1.4 4.9 3.2 84.1
11
103787
79.9
Fibres Drugs and Narcotics Sub-Total (B) : Grand Total (A) + (B)
3 2 16 68
24560 1582 129929 816315
18.9 1.2 15.9 100.0
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10: System of collection of agricultural Statistics … 3.2
Sampling Design
Stratified multi-stage random sampling design is generally adopted for carrying out CES with tehsils/taluks/revenue inspector circles/CD blocks anchals etc. as strata, revenue villages within a stratum as first stage units of sampling, survey numbers/ fileds within each selected villages as sampling units at the second stage and experimental plot of a specified shape and size as the ultimate unit of sampling. In each selected primary unit generally 2 survey numbers/fields growing the experimental crop are selected for conducting CCE. In a bid to improve efficiency of estimates, pre-stratification of design taking into account the impact of irrigation and type of seeds was adopted during 2004-05 in some states namely A.P., Bihar, Chhattisgarh, Gujarat, Jharkhand, Karnataka, M.P., Maharashtra, Rajasthan and Tamil Nadu. The details of such stratification are given in the table 2. Table 2: Stratification According to Inputs State
1 Andhra Pradesh Bihar Chhattisgarh Gujarat Karnataka
Jharkhand Karnataka
Madhya Pradesh Maharashtra
Crop for which pre-stratification is done according to Irrigation & seed both Irrigation only Seed only 2 3 4 Paddy (kharif & rabi) Paddy (Agh.) & Wheat Paddy (Bha.) & Paddy (Gar.) & Maize Sugarcane (Bha. & Gar.) Paddy* & Wheat* Paddy (Kh.), Wheat & Cotton Rice (kharif), jowar Groundnut (kh.)@, Rice (rabi & summer), (kh. & rabi), bajra cotton (kh.), Gram, Maize (rabi & (kh.), maize (kh.), ragi Sunflower (kh. and summer), (kh. & rabi**) and rabi) Jowar (summer), wheat Ragi (summer) Paddy (agh.) & Wheat Paddy(kh.,Rabi.* * Groundnut@ Sum.**), Jowar (kh.&Sum.), Cotton (Kh.,Rabi.&Sum**), (Kh.), Gram & Bajra (kh.), Maize Sunflower (Kh. Rabi (Kh.Rabi & Sum**), and Sum.) Ragi (Kh.,Rabi & Sum.**) & Wheat Paddy * & Wheat* -
-
Rajasthan
-
Tamil Nadu
-
Paddy (kh.) , jowar (rabi), bajra, wheat and cotton Rapeseed & mustard & gram & Wheat Jowar, bajra, ragi, groundnut, seasmum & cotton 131
-
-
10: System of collection of agricultural Statistics …
* @ **
Pre-stratification according to seed is only for irrigated. Pre-stratification only in the relevant districts and not for state as a whole. Pre-stratification according to irrigation, is only for HYV.
Theoretically, it is desirable to evolve a general design for stratification according to inputs as it may provide valuable results. However, there are practical difficulties in attempting stratification according to inputs mainly due to the following two reasons:
3.3
Lack of availability of required data in the presently available system of area statistics at different level; and
Stratification according to inputs may require conduct of larger number of experiments (CCE) to draw statistically acceptable estimates of desired parameters. Given the resources, it does not appear feasible to increase number of CCE at present. Degree of Precision
The magnitude of standard error reflects the precision of the estimates and the degree of precision reduced with increase in the standard error. It is generally agreed that desirable level of standard error (SE) for crop yields is 0% to 5% . However, the experience shows that in good number of cases, S.Es are above the desirable limits in some of states like Andhra Pradesh, Bihar, Chhattisgarh, Gujarat, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Orissa, Rajasthan and West Bengal which put a question mark on the reliability of the estimates. Concerted efforts are required on the part of State Governments by increasing sample size and supervision to ensure that the SEs do not exceed 5%. 4. LIMITATIONS OF CES CES have been quite useful in providing desired estimates. However, it has the following important limitations:
Non response
Errors in CCE
Substitution of experiments
Delegation to Junior Officials
Non availability of suitable equipments
Non response
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10: System of collection of agricultural Statistics … The reliability of estimates depends to a great extent on the response level in conducting CCE. In 2004-05, the overall response recorded was 94 percent as in the previous year. Percentage response for food crops during Kharif and Rabi 2004-05 was 95% & 94% respectively and for non-food crops, it was 96% & 92% respectively. Response was below 90 percent in Himachal Pradesh (86%) and Delhi (79%). Special measures are needed to improve the response to more than 90 percent in these States to increase the acceptability and reliability of estimates based on CCE. 4.1
Errors in CCE
An analysis of the results of CCE through the sample check under ICS few years before revealed that about 90% of experiments could be conducted without error at All India level. However the position is quite different once State-wise analysis is made. Table 3 indicates the position of different types of errors observed during the conduct of CCE in kharif season. Table 3: Incidence of Errors in Crop Cutting Experiments Sl.no.
1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
States
2 Andhra Pradesh Assam Bihar Gujarat Haryana Himachal Pradesh Jammu &Kashmir Karnataka Kerala Madhya Pradesh Maharashtra Orissa Punjab Rajasthan Tamil Nadu U.P. West Bengal Delhi Pondicherry
% of expts. Where no error was noticed 3 85 94 92 92 90 68
% of expts. Where error was noticed E1
E2
E3
E4
E5
E6
E7
E8
4 0 0 0 0 2 3
5 0 0 0 0 1 1
6 1 0 0 0 1 0
7 0 0 0 0 5 4
8 1 0 0 0 0 2
9 7 0 4 9 17 14
10 4 0 0 0 2 3
11 5 6 4 0 3 10
75
0
0
4
0
0
15
4
3
79 99 90 44 96 92 76 75 53 100 55 100
1 0 0 5 0 0 0 0 2 0 0 0
2 0 0 4 0 0 0 0 1 0 0 0
0 0 0 2 0 1 2 3 1 0 0 0
11 0 0 36 0 0 0 0 13 0 0 0
1 0 1 4 0 0 0 0 2 0 0 0
1 1 2 37 1 4 29 28 15 0 20 0
5 0 4 15 0 0 0 0 10 0 0 0
8 1 2 15 3 5 1 2 16 0 35 0
Note: E1: Error in selection of Survey /Sub survey nos., E2: Error in selection of field within Survey /Sub-survey No., E3: Error in the measurement of the field, E4: Error in selection of random nos., location and marking of plots, E5: Error in weighment of produce, E6: Error in recording ancillary information, E7: Inadequate arrangements for
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10: System of collection of agricultural Statistics … storing of produce for driage and incorrect reporting of constituents in mixture, E8: Others Mistakes that occur in the process of selection of survey numbers, selection of fields, measurement of plots and selection of random numbers for location of plots have a direct impact on the objectivity envisaged and results in upsetting the representative character of the experiments. Instances of errors in reporting ancillary information provides adequate evidence about the casual approach adopted by the primary worker even in eliciting the relevant data. It is imperative to take strong administrative measures and impart intensive training to field staff to ensure that prescribed procedure for conducting CCE is adhered to. 4.3
Substitution of Experiments
Instructions for conduct of CES prohibit substitution of sampling units once selected. But instances of substitution of sampling units at village and field level are observed. During 1996-97, incidences relating to substitution was around 5% in Bihar, Karnataka and Madhya Pradesh. In majority of the cases, the fields were substituted either because the crop was harvested prior to selection of the field or crop harvested before the date fixed. Substitution of duly selected sampling units by any other convenient unit may result in distorted results. Better liaison between the primary workers and the cultivators would go a long way to control such high incidence of substitution. 4.4 Delegation to Junior Officials
The field work of crop estimation surveys is entrusted with the officials who are normally one rank higher than the primary worker for area enumeration. Delegation of crop cutting work particularity to the junior rank has been observed in several states. For example, the delegation of work was of the order of 10% in case of U.P. during summer 1996-97. Adequate arrangements are needed for ensuring proper training to the field workers entrusted with CES to avoid improper conduct of crop cutting experiments. 4.5
Non availability of suitable equipments
While an untrained worker cannot conduct the experiment properly, supply of essential equipments and its proper use is required for accuracy in measurements. The position is far from satisfactory particularly in the case of Bihar, Haryana, Himachal Pradesh, J&K, Karnataka, Maharastra, Punjab, Rajasthan and UP. Even the supplied equipments were reported to have not been carried to the field for the conduct of CCE in many cases in Karnataka, M.P., Maharastra, Rajasthan and U.P. This calls for strong administrative measures for effecting further improvement. Table 4 gives the percentage of experiments conducted without use/ improper use of crop cutting equipments during 5 years preceding 1997-98 as observed through ICS. Table 4: Supply and Use of Equipments for CCE During the Last Five Years Year
Percentage of experiments for which 134
10: System of collection of agricultural Statistics …
1 1997-98 1996-97 1995-96 1994-95 1993-94
Concerned primary workers not supplied with Tape Pegs Balance Weight 2 3 4 5 15 16 36 39 18 61 37 40 17 60 37 39 16 60 34 39 16 60 37 38
Concerned primary workers did not use the supplied items Tape Pegs Balance Weight 6 7 8 9 20 18 26 24 18 17 25 24 18 16 24 23 18 17 23 22 18 18 24 23
5. SCHEMES FOR FINE TUNNING OF CROP STATISTICS
Availability of reliable and timely estimates of area and production assumes prime importance as these estimates are used by the government for taking a number of policy decisions regarding production, pricing, processing, procurement, storage, transport, exportimport, public distribution etc. In its quest for improving quality, reliability and timeliness of agricultural statistics, DES has initiated the following important schemes:
TRS
ICS
EARAS
FASAL
5.1 Timely Reporting Scheme (TRS)
The agricultural statistics has come under severe criticism within and outside the Government for considerable time-lag in the availability of desired statistics. To positively respond to this, DES (Directorate of Economics & Statistics) initiated TRS in 1969-70 in the land record States to reduce time lag between the period of sowing and the availability of estimates of area sown on one hand and between completion of harvesting and availability of estimates of production in respect of important crops on the other hand. The primary objective of this scheme is to obtain reliable estimates of area and production of principal crops in each season with break up of area under irrigated/ unirrigated and traditional/high yielding varieties of crops on the basis of priorities enumeration conducted in 20% of villages. These estimates are required to be furnished to the Govt. of India by 30 th November for kharif crops and by 30th April for rabi crops. Under the scheme, Girdwari is conducted by revenue agency of the state on priority basis in a sample of 20% villages every year. The enumeration of
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10: System of collection of agricultural Statistics … area done by Patwaris is supervised by trained staff of NSSO in random sample of 20% villages in each season in a state. Thus, five randomly selected non overlapping samples surveyed each year cover all the villages in five years. The TRS results have been found extremely useful both by states and DES. In addition to timely and reliable area estimates, TRS also serves as a sampling frame for CES and related aspects. Thus, the TRS scheme has an important role in serving the following dimensions of Agricultural Statistics System:
Sample based reliability of crop area statistics taking into consideration the heavy work load of State primary workers.
Timeliness in the aggregation and flow of the area estimates envisaging the area enumeration on priority for the sampled villages.
Frame for the General Crop Enumeration Surveys facilitating the methodological commitments of scientifically designed crop yield estimation exercise.
Frame for the Agricultural Census Surveys.
Frame for Input Surveys.
5.2 The Scheme for the Improvement of Crop Statistics (ICS)
The scheme for Improvement of Crop Statistics was initiated in 1973-74 with the main objective of locating through joint efforts of Central and State agencies from year to year, the deficiencies in the State system of crop statistics and suggesting remedial measures to effect lasting improvement in the system. The programme of ICS is in operation in 16 land record States/UTs. and the 3 non-land record States of Kerala, Orissa and West Bengal. The scheme provides for exercising checks at every stage of work relating to estimation of crop production on an equal matching basis by the Central as well as State supervisory staff on three basic aspects namely area enumeration, area aggregation and conduct of CCE. The programme envisages the following:
Studying the State system of crop estimation in its normal operative conditions.
Identifying the deficiencies and weaknesses in the system.
Physical verification of crop enumeration done by the Patwaris in a sample of 10,000 villages ,
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Checking the accuracy of the area statistics transmitted from the village level through crop abstract in the very same 10,000 villages.
Providing technical guidance and supervision at the harvest stage in the conduct of about 30,000 CCE distributed across principal crops in various states.
The above checks specifically relate to crop area enumeration, page totaling of khasra register and supervision of harvest stage of CCE. The sample checks are undertaken by the supervisory staff of the National Sample Survey Organization and the staff of the State agencies on a matching basis over a non-overlapping sample. The samples for this purpose are drawn following the stratified multistage random sampling design. The analytical findings of the ICS scheme bring to focus the precise lines along which the improvement in the system of crop statistics system could be effected in context of the conditions prevailing in each State. The survey operations for generating crop area and yield estimates are expanded vastly over the space and involve multiple agencies at various levels. Though these surveys provide the estimates of comparable nature, the management of the survey and control of survey operations differ significantly from sate to state. The surveys of such magnitude face a potential threat to the quality of their output from numerous sources of non-sampling errors. The scheme of ICS serves the purpose of an objective assessment of non-sampling errors in these surveys and identifies their sources as well as the possible impact on the quality of data. 5.3 Establishment of an Agency for Reporting Agricultural Statistics
In three “permanently settled states” namely Kerala, Orissa and West Bengal, there is no land record system and there is no regular agency for collection of agricultural statistics. In order to bridge data gap, a scheme namely Establishment of an Agency for Reporting Agricultural Statistics (EARAS) is being implemented in States which do not have permanent land record system. In the north-eastern States (except Assam) where no reporting agency functions, the land use statistics are generated through ad-hoc methods. EARAS scheme has been extended to NE States of Arunachal Pradesh, Nagaland and Tripura besides Sikkim during 1994-95 but data generation is yet to commence. In these states, the land use statistics are still generated through ad-hoc methods. However, the scheme of EARAS has been of immense significance in generating crop estimates for the “permanently settled states“. Under the scheme, estimates of area and yield are built on the basis of complete enumeration of 20% sample of villages every year. The enumeration is supervised by trained staff of NSSO. This scheme also has some weaknesses as primary workers either do not complete work in time or do not discharge duties with required sincerity which results in under estimation of area and production of various crops. 5.4 Crop Estimation Surveys for Fruits and Vegetables
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10: System of collection of agricultural Statistics … In order to fill up a vital gap in availability of area, production and yield statistics of important fruits and vegetables crops, a central sector scheme of crop estimation surveys on fruits, vegetables and minor crops was initiated during the Sixth Five Year Plan. The objective of this scheme is to generate estimation of area and production of major fruits and vegetable crops so that all India forecast on these crops can be released under the scheme. The scheme is being implemented in 10 states of Andhra Pradesh, Gujarat, Himachal Pradesh, Jammu & Kashmir, Karnataka, Maharashtra, Orissa, Punjab, Rajasthan and Uttar Pradesh and is likely to be extended to other states. The state of Jammu & Kashmir, however, has not yet started implementing the scheme. Regular crop estimation surveys on a number of fruits, vegetables/ minor crops are being conducted in the remaining 9 states. 5.5 Forecasting Agricultural Output Using Space, Agro meteorology and Land – Based Observations (FASAL)
Timely availability of reliable information of agricultural output and other related aspects is of great significance for planning and policymaking. The existing system of agricultural statistics, in spite of established procedures and wide coverage, has inherent limitations in the matter of providing an objective assessment of crops at the pre-harvesting stages with the desired spatial details, which are essential to identify problem areas and the nature of required interventions in terms of spatial, temporal and qualitative inferences. Capabilities of the existing system of crop forecasts and crop estimation can be enhanced with the introduction of technological advancements and the adoption of emerging methodologies. In turn, an efficient and sound information mechanism can assist considerably in the management of concerns in areas such as food security, price stability, international trade, etc. Remote Sensing (RS), Information and Communication Technology (ICT) and Geographic Information System (GIS) can be used towards this end. In the year 1987, the Department of Agriculture & Cooperation (DAC) sponsored a project called "Crop Acreage and Production Estimates (CAPE)". The project is being implemented through the Space Application Centre (SAC), Ahmedabad. The objective of CAPE is to use remote sensing (RS) techniques for crop area and production forecasting. Under the CAPE project, basic procedures, models, and software packages for crop area and production forecasting, using remote sensing and weather data have been developed. The work is being carried out jointly by Space Applications Centre (SAC), State Remote Sensing agencies (SRSA), State Departments of Agriculture, and State Agricultural Universities/Institutions. As a result, considerable human resource development has taken place and initial expertise generated. The methodology and tools developed under CAPE have been discussed in different fora and found to have attained a reasonable degree of standardization for integration into the main system. CAPE project has successfully demonstrated national level forecast of wheat and kharif rice, in addition to making district level pre-harvest production forecasting of cotton, sugarcane, rapeseed/mustard and rabi sorghum in their major growing regions in the country using RS technology and other auxiliary information. It has successfully overcome the problem of persistent cloud cover during kharif season by using Synthetic Aperture Radar (SAR) data from Radarsat. Moving a step further, the FASAL project aims at multiple in-season forecasting of crop area and production. Crop forecasting at the planting stage can be made 138
10: System of collection of agricultural Statistics … by employing econometric and agro-met models using previous years crop acreage and production, market price, current season weather forecast/data, and other auxiliary information. At this stage, farmer’s response may be unpredictable. Remote Sensing (RS) technique becomes effective for crop assessment once it attains sufficient ground coverage, say around 45 days after sowing. RS data provides information regarding area covered by crop, its condition and the likely yield. This coupled with agro-meteorological observations and limited field observations can provide fairly accurate information on likely yield of the crop. Remote Sensing, weather and field observations provide complementary and supplementary information for making crop forecasts. Thus an approach, which integrates inputs from the three types of observations, is needed to make forecasts of desired coverage, accuracy, and timeliness. As such, this project has been named as “Forecasting Agricultural output using Space, Agro-Meteorology and Land based observations” or FASAL. The FASAL project has been formulated with the following considerations:
-
Experience of CAPE in implementation of RS (Remote Sensing)-based crop production forecasting experiment,
-
The requirements of the MOA/DAC in terms of timeliness, accuracy and coverage of crops,
-
Experience and recent studies outside India for RS-based crop assessment/ forecasting,
-
Improvements available in terms of RS technology as well as in related disciplines, and
-
Use of other information such as weather based and fi eld surveys for crop forecasting.
The concept of FASAL thus seeks to strengthen the current capabilitie s of early and in-season crop estimation capabilities from econometric and weather based techniques with RS applications. Mid-season assessments can be supplemented with multi-temporal coarse resolution data based analysis. In the latter half of crop growth period, direct contribution of RS in the form of acreage estimates and yield forecasts would be available. However, in this case also, the addition of more extensive field information and weather inputs would increase the forecast accuracy. 6. CONCLUSION
Given the diversities prevailing in the domain and dimension of agrarian economy of India, timely collection of agricultural statistics has been of immense use in estimating agriculture production in the country. Some of limitations of crop estimation surveys lead to lack of precision which in turn results in distortion of estimates. The Ministry of Agriculture, in any 139
10: System of collection of agricultural Statistics … case, has made efforts to fine tune the agricultural statistical system in the country by initiating schemes mentioned above. Though these schemes have made some dent on improvement of the system of crop estimation and forecast, shortcomings in terms of delays in flow of information from field, errors in area reporting continue to persist. Considering the fact that agriculture sector is vast and diverse, it poses challenge to agricultural statisticians and scientists to make available accurate and before occurrence of harvest the estimates of crop production. The need is to constantly review the system of agricultural statistics and crop estimation so that the right kind of information is made available at right place at right time in a cost – effective manner.
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