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

The federal crop insurance program (FCIP) is the most extensive direct support program for US agricultural producers, providing approximately $100 billion in crop insurance coverage. The program is structured as a private-public partnership, in which the USDA's Risk Management Agency (RMA) prices, regulates and administers the program and private insurers deliver the insurance. Crop insurance helps producers manage price and yield variability caused by market shocks, droughts, floods, disease, pest infestation, and other perils. The FCIP began in 1938 and has grown in size and popularity since the 1990s (Glauber, 2013). In general, it has surpassed traditional farm support programs in federal outlays. Crop insurance may become more important in the future as uncertainty regarding future weather events and crop price variability may increase. Crop insurance faces the challenge of adverse selection and moral hazard, and improving policy design may reduce these risks. A large body of research has examined and critiqued the FCIP program, and many of these suggestions have been adopted in design improvements (Woodard and Verteramo-Chiu, 2017; Rejesus et al., 2015; Coble et al., 2013; Finger, 2013; Woodard et al., 2011; Skees and Reed, 1986). Future efforts to improve FCIP efficiency are essential as mispriced and poorly designed crop insurance may lead to negative externalities (Adhikari et al., 2012).Over the past several years, the general premium rate-making structure of the APH and revenue policies have stayed relatively the same. Reference premium rates are developed for the county, and farm premium rates are adjusted according to a rating schedule based on the farm's actual production history. The pricing structure adjusts premium rates relative to expected farm yields for the crop type and risk classification. Due to crop rotation, the producer's expected yield and yield risk may change year to year based on the soil quality of the fields where the crops are grown or if the producer puts more land into production. For example, a producer may grow soybeans on high-quality land one year and grow soybeans on low-quality land the next year. The FCIP does not account for the change in risk of increasing the soybeans on lower quality land the second year. The difference in soil quality may also affect the expected yield, which is used for setting the yield guarantee (liability). Similarly, if a producer puts marginal land into production, the crop insurance premium may not reflect the increased yield risk. Due to these soil quality effects, there may exist an inefficiency in liability determination and rate-setting, leading to premium mispricing.Mispriced crop insurance can affect the producer's decision making and management practices and may lead to adverse environmental outcomes such as acreage expansion on marginal lands. If producers grew only one crop, it would be relatively simple to adjust premium rates relative to a soil quality score to provide a disincentive for expansion on marginal lands, and soil quality's effect on expected yield would be present in historical yields. However, because crops are grown in rotation, it is more challenging to make this adjustment as the soil quality score must be determined each year based on the field units that the crop is grown.Following the 2008 Farm Bill, the RMA started collecting field-level crop yield data geographically matched to field locations. With big data technologies from data-science and geographic information systems, there is an opportunity to quantify the effect of soil on crop yield risk. Agriculture is well suited to take advantage of large geolocated data sets and applied computing technologies, and the FCIP may benefit from improved efficiency (Coble et al., 2018). Woodard and Verteramo-Chiu (2017) and Li et al. (2016), investigated the crop insurance premium pricing efficiency impacts of using soil information. They found that including soil information significantly improved crop insurance premium pricing through improved liability setting. Following these results, they suggested that the RMA's premium pricing approach be redesigned to price premiums at the field level using soil information. They expect that soil information may improve crop insurance more in areas with higher soil heterogeneity, such as the western states. Completely redesigning the premium rate making approach to incorporate soil may be a significant undertaking that may lose key actuarial information from past loss experience. However, much of the pricing accuracy improvements may be gained by weighting a simulated premium price at the farm level with the price derived from the current premium rate-making methodology and may not require significant program changes.The goal of the research is to develop an improved premium pricing method for the federal crop insurance program (FCIP) that considers the effect of soil quality. To this end, the study will examine a big data simulation and credibility-based crop insurance pricing method that uses producers' actual production history and field-level soil quality for the upcoming growing season. The extent of possible mispricing will be examined, such as the percentage of farms affected by mispricing, and potential factors associated with mispricings such as geographic location or other factors like weather.The existing crop insurance rate-setting method uses actual farm-level yields but does not use field-level soil information. Field level soil information may be used to improve crop insurance by increasing the accuracy of liability setting (i.e., setting the insurable yield) by using expected yield estimates for the upcoming growing season that are adjusted for field-level soil effects. Field level soil information may also be used to provide premium discounts or surcharges to incentivize sustainable agricultural practices and disincentivize crop expansion on marginal lands.Since 2009, specific field-level boundary information has been collected by the RMA and may be useful for improving crop insurance. The proposed big data approach uses simulated premium rates that incorporate field-level soil quality and are simulated using the estimated weather (e.g., temperature and precipitation) at the farm location since 1895. This simulated premium price is then weighted with the premium price derived from the current rate-making method using a modified credibility approach. The proposed premium rate making method may improve on existing approaches by providing additional information by using field-level soil information for premium pricing.The rationale behind the proposed project is to improve the premium rate, making the accuracy of the FCIP. Mispriced crop insurance can affect a producer's decision making and management practices. It may also lead to producers insuring their high-risk crops and not insuring their low-risk crops (adverse selection). These effects reduce the efficiency of the FCIP, which affects taxpayers and producers.