Spatial and Temporal Analysis of Crop Frequency and Insurance Losses to Predict Crop Losses Under Certain Climate Conditions

Keywords: crop insurance, spatial clustering, agricultural geography, big data
Abstract Type: Paper Abstract

Abstract

This study analyzes corn frequency and insurance loss data for entire United States from 2008 to 2023 to understand spatial and temporal patterns in crop production and insurance claims. This will allow to build a system predicting loss calculations by crop types and climate conditions. Using geospatial tools and data from the USDA’s Cropland Data Layer and Risk Management Agency, corn frequency at the county level and integrated it with insurance data, including loss ratios and causes of loss. Methods included structural equation modeling to identify relationships among subsidies, indemnities, loss ratios, and premiums, and spatial autocorrelation techniques to detect geographic patterns in loss ratios. Temporal autocorrelation was examined using Markov chains to model dependencies in crop types and causes of loss over time. Expected results are spatial autocorrelation in the data, with high loss ratios and common causes of loss, such as excess moisture and drought, being geographically clustered. Temporal analysis is supposed to revealed that previous years’ crop types and causes of loss influence future patterns. The findings suggest that crop insurance outcomes are not randomly distributed but are influenced by spatial and temporal factors. These insights can help in predicting land use changes and improving future weather forecasting and loss mitigation strategies. Understanding these patterns is crucial for farmers, insurers, and policymakers to make informed decisions about crop planting and risk management. This study is an ongoing project, so there will be updates until the end of December.