Drought Risk Mapping and Parametric Insurance in Agriculture: A Machine Learning-Based Framework

Abstract

Climate change has increased both the frequency and intensity of agricultural droughts, reinforcing the need for risk transfer instruments that rely on objective environmental indicators. This study introduces a comprehensive framework for the development of drought index insurance using openly available climatic and satellite data. The approach builds synthetic indicators that combine precipitation-based measures (SPI, SPEI) with vegetation indices (NDVI), selected through statistical learning techniques such as Lasso regression, Random Forest, and Partial Least Squares. These indicators are then embedded in parametric indemnity functions whose shape depends on their correlation with crop yields, ensuring decreasing or increasing payouts as appropriate. Calibration with historical yield data is performed to minimize basis risk, and pure premiums are derived through simulation methods including empirical resampling, bootstrap, Monte Carlo, and kernel density estimation. The framework is applied to three Moroccan regions with contrasting agro-climatic conditions and representative crops. Results indicate that the proposed design substantially lowers basis risk while preserving transparency, interpretability, and reproducibility. More broadly, the framework provides a transferable methodological contribution by linking machine learning with actuarial tools, supporting the development of weather index insurance in contexts where data availability remains limited.