Climate Responsive Agricultural Insurance: Bayesian Approach for Improving Risk Assessment and Pricing

Abstract Agricultural insurance is yet to achieve widespread adoption due to ongoing challenges in risk assessment. This study proposes a climate-responsive insurance framework that integrates meteorological and remote sensing data into statistical modeling to enhance risk assessment. We estimate the predictive density of yields by analyzing historical yield data and weather information across three crop life cycle phases: sowing, growing, and harvesting. A nonparametric Bayesian framework is used to derive the conditional yield distribution through a copula-based joint dependence structure between yield and climate variables. Bayesian inference is used to quantify the relative influence of climate factors at each crop phase on yield variability. These phase-specific contributions are used as weights to combine the conditional yield distributions, resulting in a more weather-informative predictive density. This approach shows improved predictive accuracy in cross-validation and outperforms conventional univariate models in out-of-sample rating performance, supporting more reliable premium rate estimation. These features make it promising for large-scale insurance programs compared to prevalent univariate models. Our findings under various climate stress scenarios underscore the need for such a framework to inform sustainable, climate-responsive insurance policies and to enhance both farmer participation and resilience to climate risks.