Multivariate Copula Modeling for Improving Agricultural Risk Assessment under Climate Variability

Agricultural production is highly vulnerable to both short-term extreme weather events and long-term climate variability and change. These impacts propagate further and result in socioeconomic changes affecting farmers, insurers, and other stakeholders across agricultural supply chains. As a result, the most recent challenges in addressing resiliency and sustainability at the face of climate change require development of innovative multivariate methods for quantifying crop yield risk driven by factors that are strongly spatially and temporally dependent. Copulas offer a systematic solution to tackle this spatio-temporal uncertainty quantification problem. However, utility of copulas in agricultural risk assessment and insurance remains largely under-explored. We introduce multivariate copula modeling (MCM) for capturing yield-climate dependence and evaluate its utility by benchmarking its performance on a multi-scale yield-climate dataset against state-of-the-art competing model-based approaches. MCM is found to outperform traditional statistical approaches and better explain complex dependence structure over time and space between crop yield and climate. Our findings highlight the benefits of MCM for reducing basis risk and improving the robustness of insurance premium rate-making.