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

Project: Research project

Project Details


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
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 date1/1/2112/31/23


  • USDA: National Institute of Food and Agriculture (NIFA): $498,935.00


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