Mitigating Risks Around Increases in Crop Insurance
Over the past decade, the focus of U.S. federal agricultural policy has shifted away from direct payments towards risk management, and federal crop insurance has become a cornerstone of U.S. agricultural policy. More than 265 million acres were enrolled in the crop insurance program in 2011, with $114 billion in estimated total liability. The corresponding costs to the federal government in 2011 were estimated at over $11 billion. The shift of agricultural policy focus continued with the Agricultural Act of 2014, which eliminated direct government payments and expanded crop insurance. The Congressional Budget Office (2014) estimated that the Agricultural Act of 2014 would increase spending on agricultural insurance programs by $5.7 billion, to a total of $89.8 billion over the next decade (2014–2023).
This growing crop insurance industry needs increasingly precise data. SaraniaSat’s™ remote-sensing data and information products can be used for assessing risk of crop loss and damage to crops for both indemnity insurance as well as index-based insurance. The “early warning” aspect of SaraniaSat’s™ data and information products allows both farmers and insurers alike to assess the effect of both natural and man-made disasters on the ultimate crop yield. Detecting crop stress early in the crop cycle will not only allow the use of mitigation strategies to restore crop health and ultimate harvest yield, but also provide insurers with early predictions of crop performance and risk assessment.
Crop insurance can alter producers’ incentives in two broad ways. First, premium subsidies based on the “fair” premium, by definition, add to expected revenue for crop production. As such, subsidized crop insurance may create incentives for farmers to expand crop production to marginal lands. Second, crop insurance reduces the riskiness of growing covered crops relative to other crops, thus potentially affecting farmers’ crop mix and input use. SaraniaSat’s™ data-driven solutions can provide a robust, multi-year record of analytics that would enable insurers to make predictions based on historical performance and set realistic premiums accordingly. The “early warning” aspect of SaraniaSat’s™ agricultural prescriptions would also enable insurance companies to forecast their claims payouts at the end of each season.
Providing crop insurance has historically been a difficult undertaking. Saraniasat’s™ non-linear, weak-signal detection and sensor fusion algorithms monitor and analyze data specifically targeted to identifying key factors impacting crop health and ultimate yield at harvest. These include: weather, soil condition, pests, and irrigation. Crop insurance is a complex industry that can be greatly impacted by sophisticated Big Data analytics, and SaraniaSat™ is here to help. For more information, contact SaraniaSat™ here.
Woodard J.D. 2013. Theme Overview: Current Issues in Risk Management and U.S. Agricultural Policy. Choices 28 3: 1–2.
Glauber J.W. 2013. The Growth of the Federal Crop Insurance Program, 1990–2011. American Journal of Agricultural Economics 95 2: 482–8.
Roger Claassen, Christian Langpap, JunJie Wu, Impacts of Federal Crop Insurance on Land Use and Environmental Quality, American Journal of Agricultural Economics, Volume 99, Issue 3, April 2017, Pages 592–613.
Walters C.G., Shumway C.R., Chouinard H.H., Wandschenider P.R.. 2012. Crop Insurance, Land Allocation, and the Environment. Journal of Agricultural and Resource Economics 37 2: 301–20.
Goodwin B.K., Smith V.H.. 2003. An Ex Post Evaluation of the Conservation Reserve, Federal Crop Insurance, and Other Government Programs: Program Participation and Soil Erosion. Journal of Agricultural and Resource Economics 28 2: 201–16.
Young C.E., Vandeveer M.L., Schnepf R.D.. 2001. Production and Price Impacts of U.S. Crop Insurance Programs. American Journal of Agricultural Economics 83 5: 1196–203.
Goodwin B.K., Vandeveer M.L, Deal J.L.. 2004. An Empirical Analysis of Acreage Effects of Participation in the Federal Crop Insurance Program. American Journal of Agricultural Economics 86 4: 1058–77.