PM’s crop insurance scheme needs tweaking to make it work better on the ground
Financial Express, Published: November 21, 2019
Since the launch of the Pradhan Mantri Fasal Bima Yojana (PMFBY) in kharif 2016, there have been mixed reactions to the Narendra Modi government’s flagship crop insurance scheme from different stakeholders. While each of them — insurance and reinsurance companies, farmers, state governments, agricultural scientists, economists and media — have their own perception about the scheme, what is not in doubt, however, is the fact that it is very ambitious and probably an ‘ideal one’ any country should have. Also, the government has been very forthcoming in using technology, right from the process of registration of farmers to the settlement of claims and transferring of money to beneficiaries.
The whole infrastructure for making this process a success may not have been in place from the start, but the individual pillars are being strengthened with each passing crop season and issues resolved through consensus approach. And this isn’t easy, given the vast differences within the country in terms of last-mile penetration of technology, crop types, seed varieties, agronomic practices, landholding sizes, microclimate and soil-moisture regimes, etc. In addition to these major constraints is the small time window available for farmer enrolment, monitoring of crop through the season, conducting crop cutting experiments (CCE) for quantitative assessment of losses, if any, and settling claims — not to speak of the ‘human factor’ operating at every level of undertaking these exercises.
Taking into account the above realities, here are some suggestions that may be worth considering for improving the implementation of a fundamentally well-conceived scheme:
Historic Yield data: For calculation of threshold yields, against which crop loss assessment is made, PMFBY relies on data aggregated at district or, at best, sub-district level. This is problematic when field or plot-level insurance is what should be a desirable aim. Any benchmark based on historic long-term data also needs relooking, as the field that gave ‘x’ quintals of produce in the past may currently yield more, thanks to availability of better seeds, irrigation or better crop management practices. To arrive at threshold yields, it would be best to take the past three-years data and exclude drought/flood years to decide threshold yields, while giving more weight to the latest year. In the past few years, yield data is being collected through digitisation of CCEs involving geo-tagging, date-timing and photographs of the plots where these are being conducted. Such data, which correlates better with local conditions than area averages, should be used for determination of threshold yields as well.
Selection of Insurance Units (IUs) for CCEs: Sowing acreages aren’t uniform across any district or even block. While some IUs in a district or block may have significant area sown under a particular notified crop, this wouldn’t be the case with others. It makes sense, therefore, to merge IUs having less sown area under that crop with the nearest significant ones. Alternatively, a number of IUs, whose individual sown area under the crop is below a pre-decided level, can be merged to form a bigger IU. All this will reduce the total number of IUs in which CCEs are required to be conducted. A further reduction can be achieved through smart site selection of IUs using satellite-mapping tools such as normalised difference vegetation, water or leaf area indices (which would help identify sites where more or less CCEs may be required, depending on the crop health status revealed by these values).
Selection of fields: CCEs are now done on random plot selection basis, independent of whether a farmer’s crop is insured or not. Insurance is sought primarily by farmers whose yields are generally low and prone to fluctuations. That being so, it would be unfair to decide the fate of insured farmers based on data collected from uninsured farmers’ plots. To be fair to insured farmers, yields from only their plots ought to be considered, as opposed to taking all farmers for computing the average yield of an IU. One way to do this to simply take the list of all insured plots in the IU and picking randomly (even a digital lottery system isn’t bad).
Adopting Satellite Remote-Sensing Technology (RST): Ideally, RST should be employed at all stages in the crop season, whether for tracking progress of sowing area or estimation of yields and determining sites for CCEs. One reason for RST solutions not being adopted for crop insurance is the lack of standardisation in the approaches towards their use. Secondly, optical remote-sensing images in the red, green, blue, and near infrared electromagnetic spectrum bands are not available during the major part of the kharif (monsoon) season, when they are needed the most. Now that we have satellite technology using microwaves, which can penetrate through cloud cover, light rain and haze, its utility for determination of crop-wise sown area and yields is beyond doubt. RST can, in fact, today be widely and more effectively deployed to simplify many aspects of PMFBY implementation.
Weather-based insurance: In an ideal world, crop yield loss at the individual farm should be the basis for compensation. But from a simplicity and scalability point of view, weather-based crop insurance is the best and fastest solution. Automated weather stations that measure simple parameters such as rainfall, temperature, relative humidity and wind speed — which correlate well with crop growth performance — can help supplement or even be an alternative to CCEs. Below-normal rain during July-August is bad for most kharif crops (when they are in sowing or vegetative growth stage), just as we know from this year’s experience how too much rainfall in September-October (when they are in grain formation or near-harvesting stage) isn’t also good. It should further be possible to offer insurance products based on crop yield proxies from satellite datasets such as normalised difference vegetation index. These should preferably be microwave satellite datasets, which are all-season.
No doubt, a lot of tweaking still needs to be done to PMFBY. Addressing the issues highlighted above should result in reduction of CCEs and more standardisation in procedures. These would ultimately help distressed farmers, who are looking for immediate relief in case of crop loss from natural factors beyond their control. They will benefit from a crop insurance scheme that expeditiously settles claims.
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