Malnutrition and Climate in Niger: Findings from Climate Indices and Crop Yield Simulations

TL;DR

This study explores how climate affects malnutrition in Niger, finding that crop yields from the previous year strongly correlate with malnutrition rates.

Contribution

The study introduces a novel approach combining satellite rainfall data and crop yield simulations to assess climate's indirect impact on malnutrition.

Findings

Malnutrition in Niger is strongly correlated with crop production from the previous year (R = −0.72).

Climate indices like temperature and intra-seasonal rainfall show no significant links to malnutrition.

A one-year lag in the climate-malnutrition relationship allows for early prediction of food crises.

Abstract

Malnutrition, particularly its impact on child morbidity and mortality, is one of the top five health effects of climate change. However, quantifying the portion of malnutrition attributed to climate remains challenging due to various confounding factors. This study examines the relationship between climate and acute malnutrition in Niger, a country highly vulnerable to climate change and disasters. Since climate’s effect on malnutrition is indirect, mediated by crop production, we combine rainfall data from TAMSAT satellite estimates with the SARRA-O crop model, which simulates the impact of rainfall variability on crop yields. Our analysis reveals a significant correlation between malnutrition and both rainfall and crop production from the previous year, but not within the same year. The strongest correlation (R = −0.72) was found with the previous year’s crop production. No significant links were found with temperature or intra-seasonal rainfall indices, like the start or duration of the rainy season. Although national correlations between global malnutrition, rainfall, and crop yields were stronger, they were weaker or absent at the regional level and, for Severe Acute Malnutrition crises, are less likely driven by climate variability. However, the one-year lag in the correlation allows for the prediction of future food crises, providing an opportunity to implement early intervention measures.

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