Climate change impacts on crop yields: A review of empirical findings, statistical crop models, and machine learning methods

Understanding crop responses to climate change is crucial for ensuring food security. Here, we reviewed ∼230 statistical crop modeling studies for major crops and summarized recent progress in estimating climate change impacts on crop yields. Evidence was strong that increasing temperatures reduce crop yields. A 1 °C warming decreased the yields by 7.5 ± 5.3% (maize), 6.0 ± 3.3% (wheat), 6.8 ± 5.9% (soybean), and 1.2 ±5.2% (rice) across the world, but spatial heterogeneity was noticeable, due partly to asymmetric nonlinear crop responses to temperature (e.g., warming-induced gains in cold regions). Yield responses to precipitation were not consistent across the studies or geographical areas. On average, climate explained 37% of yield variability. We also observed a methodological shift from linear regression to machine learning (e.g., explainable AI and interpretable machine learning), which on average reduced predictve errors by 44%. Furthermore, we discussed the opportunities and challenges facing statistical crop modeling, such as ensemble modeling, physics-informed machine learning, spatiotemporal heterogeneity in crop responses, climate extremes, extrapolation under novel climates, and the confounding from technology, management, CO₂, and O₃.