Weather extremes are increasingly threatening food security across Sub-Saharan Africa (SSA), where maize serves as a critical staple for millions of smallholder farmers. While most research to date concentrated on climate impacts on crop yields, the climatic determinants of harvested area (ha) which is an equally important component of production, remain insufficiently explored. Employing a machine learning framework on historical maize production and climate datasets, this study evaluates how both mean climate conditions and weather extremes have influenced maize yield and ha across SSA from 1979 to 2021. Our findings indicate that reported yield reductions are predominantly attributable to heat and drought stress, while contractions in ha are primarily associated with episodes of excessive wetness. Notably, our models account for a greater proportion of variance in ha (R2 up to 0.75) than in yield (R2 ≈ 0.43). This suggests that fluctuations in cultivation area are more readily anticipated from climate signals, potentially reflecting farmers’ proactive adjustments to wet conditions, than the more complex physiological responses of yield to heat stress. The rising frequency of area-related shocks, in contrast to relatively stable trends in yield-shock, signals a shifting landscape of production risks. Collectively, these findings elucidate two principal climate-risk pathways: heat and drought diminishing yields, and wetness extremes constraining cultivated area. These insights underscore the urgent need for agronomic innovation, targeted economic incentives, and infrastructural investment to enhance the resilience of maize-based systems in SSA.

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