Crop Cutting experiment

In a unIque effort, which is exceptional among Random, But SuRe, numBeRS Indian states, Karnataka’s Crop Cutting experiment (CCe) is an information technology driven process to calculate the seasonal and yearly yields of selected crops. This kicks in after Crop Survey determines the crops being grown by the farmers in each of the 20 million plots in the state. now, the state wishes to know about the production of these crops. Mobile is used to conduct more than 100,000 CCes across the state each year. The yield is estimated based on statistical tools and methodology that is largely defined by the central government’s national Sample Survey office (nSSo), and tweaked and modified by the state government, wherever and whenever required, without compromising the basic mathematical principles. The entire process is administered by the Karnataka’s Directorate of economics and Statistics, which falls under the purview of the Planning Department. While this remains the administrative authority, the field-level surveys and actions are conducted by multiple official departments such as agriculture, horticulture, rural development and panchayat raj, and revenue. once the yield is available, it is used for various government requirements. for example, CCe data is used by Samrakshane (see Chapter on Samrakshane) to estimate crop losses and crop insurance claims and compensation. CCe figures form the basis for the payments under the central Pradhan Mantri fasal Bima Yojana. Hence, the farmers, government agencies and insurance firms use the CCe data. The fact remains that comprehensive and realistic farm production information is crucial for efficient and transparent governance. for example, yield estimates of key crops – there are 300 crops in the country – contribute to calculations related to possible imports and exports of specific crops.

no governMent, eItHer Central or state, can estimate the yield in each of StatiStical calculation the 20 million plots. In addition, there are production variations across the fields within the same and different regions. Hence, the solution lies in random, statistical, and proven methodology. This is used to calculate the yield in a given area (cluster of villages or Panchayats), and collated at the state level. for CCe in a given area, for instance, five out of 20 villages may be chosen randomly, and two experiments each are conducted in each village, totalling 10 in a Hobli (cluster of villages). This is narrowed down further to generate a

tudy s Case

Shashidhar K, 30

Gram Panchayat Secretary, Bellagatta village, Chitradurga District

A Gram Panchayat employee, the CCE work is assigned to him by the District Administration. He says that the project is sensitive as a mistake in details can affect the farmer Associated with the project for five years, he marks a 5x10 m patch on a plot. He measures the crop in it. Based on this calculation, he arrives at the yield estimate for the entire plot. When he visits the plot, he gets the details of the land and survey number, farmer’s name, crop grown details on the mobile app. Form 1 is filled at the time of growing. Depending on the crop type, there is another visit after 90 or 150 days. Other information related to fertilisers used and seeds are included. The app is useful but one has to fill in the details carefully. A mistake can affect the farmers. If a few growers get the insurance claims, and others don’t, the latter get angry. In the last three years, farmers who cultivated Bengal gram, didn’t get the compensation. The app is easy to use and is in Kannada language. He is a Gram Panchayat employee, and the CCE work is assigned by the District Administration.

survey number randomly in selected villages. Crop yields may vary across the selected fields for the experiments, and are averaged. a juxtaposition with actual production in the past – five best years out of seven – helps in the calculation of threshold yield limit for each crop for the purpose of crop insurance. even if the CCe is manipulated and not exact, the averaging process covers the risks. two main activities constitute the CCe process. under form 1, the crop in the chosen field is confirmed. It is established if the field and crop are suitable for the yield estimation. If yes, the process continues. If not, the surveyor moves on to the next field, or even the next village. There is an additional buffer survey to ensure transparency and correct estimation. form 2 relates to harvest time, and field study is on the same plots mentioned in form 1, whose data is downloaded and available for the second step. The data at the second stage is generated in real-time on the same day and processed after the server receives it. The information is available for further use.

aS MentIoneD earlIer, tHe Data at tHe HarveSt Stage is collected and collated in real time, and uploaded on the same day on the server. Thus, at the region and state levels, the information is instantly available for further use. The photographs and videos of the CCes are included in the centralised database for later and further examination. The concrete advantages and benefits are reflected in crop insurance. The insurance companies are involved in the CCe process, and act as intrinsic stakeholders. This completely avoids objections that may be raised at the later stages – calculation of claims and payment of compensation – by the insurers. farmers too have the option to file complaints and raise objections against the CCe methodology and estimates. There is a strong and systematic It-based procedure and structure to handle and solve the farmers’ grievances by the various state Deputy Commissioners. Prior to digitisation, there were several challenges in the area of crop insurance. There was paucity of relevant and correct information when required, verification and monitoring by different agencies (including the insurance companies) was lax, and the approach was haphazard and a bit chaotic. The Samrakshane-CCe-Crop Survey combination addresses these issues, and makes the process transparent and accessible.

Real-time data availaBle