Struggling with the Rain : Weather Variability and Food Insecurity Forecasting in Mauritania

Weather-related shocks and climate variability contribute to hampering progress toward povertyreduction in Sub-Saharan Africa. Droughts have a direct impact on weather-dependent livelihood means and thepotential to affect key dimensions of households’ welfare, including food consumption. Yet, the ability to forecastfood insecurity for intervention planning remains limited and current approaches mainly rely on qualitative methods.This paper incorporates microeconomic estimates of the effect of the rainy season quality on food consumption intoa catastrophe risk modeling approach to develop a novel framework for early forecasting of food insecurity atsub-national levels. The model relies on three usual components of catastrophe risk models that are adapted forestimation of the impact of rainy season quality on food insecurity: natural hazards, households’ vulnerability andexposure. The paper applies this framework in the context of rural Mauritania and optimizes the model calibration with amachine learning procedure. The model can produce fairly accurate lean season food insecurity predictions very earlyon in the agricultural season (October-November), that is six to eight months ahead of the lean season. Comparisons ofmodel predictions with survey-based estimates yield a mean absolute error of 1.2 percentage points at the nationallevel and a high degree of correlation at the regional level (0.84).