Using Field Level Soil Quality Data for Crop Insurance: A Big Data Simulation and Credibility Approach to Improve Crop Insurance Pricing and Agricultural Land Sustainability Practice Using Field Level Soil Quality Data for Crop Insurance: A Big Data Simulation and Credibility Approach to Improve Crop Insurance Pricing and Agricultural Land Sustainability Practice This project proposes an improved crop insurance premium pricing method that uses soil information and big weather data to increase premium pricing accuracy. Crop insurance may be improved by an improved liability setting (i.e. setting the insurable yield) procedure in which the expected farm yield for the upcoming growing season is adjusted for field-level soil effects. Also, crop insurance may be improved by providing premium discounts or surcharges to incentivize sustainable production practices and disincentivize crop expansion on marginal lands. The extent of crop insurance premium pricing accuracy improvement that can be gained by using soil and weather information will be investigated. The proposed premium pricing method uses premium rates simulated at the farm that account for soil effects and the RMA premium rate that is derived from the farm's yield history and county loss history. A credibility approach is used to optimally weight the two derived premium rates, to obtain a more accurate premium rate. The proposed big data approach requires field-level crop yield data, large weather data sets, and soil information that is measured on a 30m by 30m grid for the continental USA. Most of this data is publicly available. This method would not require a large program design change to implement and may be a cost-effective way to benefit from field level soil information.
|Effective start/end date||1/1/21 → 12/31/23|
- USDA: National Institute of Food and Agriculture (NIFA): $498,935.00
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