Bayesian Approach to Climate-Responsive Agricultural Insurance for Improved Risk Assessment and Pricing

Abstract

The adoption of agricultural insurance remains constrained, primarily due to persistent challenges in accurately assessing risk. This study develops a climate-responsive insurance framework that integrates meteorological observations and remote sensing indicators into advanced statistical modeling to improve risk assessment. Historical yield observations and weather data are analyzed across three key crop life cycle stages: sowing, growing, and harvesting, to estimate the predictive yield density. A nonparametric Bayesian methodology is employed to derive the conditional yield distribution by incorporating a copula-based dependence structure that captures the joint relationships between yield and climate variables. The Bayesian likelihoods were used to quantify the relative contribution of climatic factors at each crop life cycle stage to yield variability. These stage-specific contributions are used as weights to combine the conditional yield distributions, resulting in a weighted conditional predictive density that effectively reflects weather variability. The proposed approach demonstrates superior predictive accuracy in cross-validation and achieves better out-of-sample premium rate estimation compared to conventional univariate models, indicating its suitability for large-scale insurance programs. Under diverse climate scenarios, the results underscore the value of such a framework in supporting climate-responsive insurance policies, enhancing farmer participation, and strengthening resilience to climate-related risks.