Divergent Climate Sensitivity in C3 Crops—Evidence from 20 Cassava and Wheat-Producing Countries (1961–2024)

Climate volatility poses a critical threat to global food security, yet the differential impacts of seasonal weather anomalies on diverse staple crops remain poorly understood. This study analyses a nearly balanced panel dataset spanning 64 years (1961–2024) across 20 major cassava- and wheat-producing countries to evaluate the impacts of quarterly temperature and precipitation anomalies on yields. Methodologically, the research integrates machine learning (random forest) to capture non-linear thresholds with Mundlak-type Linear Mixed Models (LMMs) to effectively isolate long-term regional climate features from short-term annual weather shocks. The empirical results reveal a stark contrast in climate vulnerability between the two staples. Wheat is highly sensitive to weather. Sudden heat waves between June and August disrupt its growth, while warmer temperatures from September to November lead to lower long-term yields. In contrast, cassava handles yearly weather changes much better and shows no major damage from single-year shocks, though warmer temperatures from March to May historically reduce its yield. However, cassava’s structural productivity is heavily constrained by long-term regional climate characteristics and indicates that permanent shifts in regional climate regimes alter its vegetative dynamics. The formal Mundlak test robustly rejects standard random effects specifications, confirming that ignoring unobserved country heterogeneity introduces substantial statistical bias. In conclusion, while cassava serves as a resilient buffer crop against immediate seasonal weather volatility, permanent shifts in long-term climate baselines threaten the future yield security of both crops, necessitating target-oriented, climate-smart agronomic interventions rather than extensive land expansion.