Spatially Varying Climate Importance in United States Agricultural Insurance Claim Severity

Abstract

Climate relationships with agricultural insurance claims are unlikely to be spatially uniform because crops, hazards, and insurance experience vary geographically. To identify geographic variation in climate predictors of claim severity, we fit six geographically weighted random forest models to monthly county observations across the conterminous United States from 1994 through 2022, pairing corn, soybeans, or wheat with drought or excess moisture/precipitation/rain. Model-specific coverage ranged from 1722 to 1934 counties, with the outcome variable being logarithmically transformed positive loss per claim, adjusted to constant 2022 dollars. Models included monthly precipitation, maximum temperature, minimum temperature, vapor-pressure deficit, actual evapotranspiration, and soil moisture, together with seasonal controls and year; each local forest used an adaptive neighborhood of the 10 nearest eligible counties. Total positive climate importance was greatest in the Great Plains and Midwest but varied by commodity and cause. Maximum temperature ranked first in five of the six models, while actual evapotranspiration ranked first for soybean excess moisture claims. Corn and soybean drought models emphasized maximum temperature and vapor pressure deficit, while excess moisture models were more heterogeneous. These findings demonstrate that a single national profile of climate importance cannot adequately represent local relationships between climate and claim severity.

The authors' abstract, as published at the source. Agriculture, 2026 · DOI ↗