Comments on “Drought Risk Mapping and Parametric Insurance in Agriculture: A Machine Learning-Based Framework” by Belhsen et al., (2026), Journal of Risk Analysis and Crisis Response, https://doi.org/10.54560/jracr.v16i2.726

Abstract

Abstract: Accurate assessment of drought-related crop losses is essential for designing effective agricultural index insurance. An examination of the datasets and methods reported for parametric drought insurance in three Moroccan agricultural regions highlighted three issues: temporal data matching, drought index standardization, and independent evaluation of basis risk reduction. The reported correlation significance levels appear inconsistent with the stated annual sample size, while recurrent extreme drought index values require further diagnostic checks. Basis risk metrics restricted to loss years and premium point estimates also provide an incomplete assessment of contract performance. The framework integrates environmental indicators and actuarial methods, but its operational reliability depends on clarifying these issues and independently comparing it with simpler insurance contracts.