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Abstract:
Climate change threatens global food security by reducing crop yields under rising temperatures. Accurately projecting the production of wheat, a crop supplying 20% of global calories, in a changing climate is critical for food security. Most projections, however, focus solely on how climate change affects yields in existing wheat-growing areas, failing to consider potential climate-driven shifts in wheat cultivation. This omission creates a key knowledge gap in understanding future production. Here, we use machine learning and crop simulations to jointly project future wheat area and yield. We find that a 2°C warming could expand suitable planting areas by 15.0%, offsetting yield losses and potentially increasing global production of spring and winter wheat by 29.0% and 12.5%, respectively. Our results show that accounting for dynamic cropland shifts is essential for accurate production projections and food security assessments. These insights inform more realistic climate adaptation and land-use planning policies.
