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Projected Reductions in Staple Crop Production and Nutritional Quality Under Climate Change: Machine Learning Evidence From China
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DOI：10.1029/2025EF006781.png)
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Abstract
En 中文
Staple crops are essential sources of sustenance and macronutrients. While previous studies have overlooked the combined impacts of climate extremes, elevated CO2-induced nutrient penalties, and resulting nutritional inequities across cities. Moreover, adaptive strategies often emphasize top-down policies, neglecting tailored measures such as behavioral changes by farmers. Here, we examine the interplay of annual-mean climate, climate extremes, and atmospheric CO2 on staple crop (rice, maize, and wheat) production and nutrition by developing machine-learning models based on a 20-year panel data set for China's prefecture-level cities. We further project staple grain, iron and protein supplies under SSP126, SSP245, SSP370, and SSP585 from 2030 to 2100 in China. Results show that climate extremes and rising CO2 have comparable or even stronger impacts than annual-mean climate. Without interventions, the combined stressors are projected to reduce national major staple crop production by 1.04%–6.21%, iron supply by 2.42%–12.92%, and protein supply by 2.24%–20.08%. Lower-income regions, which depend more heavily on staple crops, face disproportionately severe nutritional losses, raising equity concerns. Tailored adaptation measures, including adjusting crop sowing dates, responding to extreme heat, and optimizing irrigation rates, can mitigate up to 6.76% of production losses but not fully offset declines in crop production and nutrients.
Keywords:
climate change
crop production
machine learning
inequality
adaptation
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