ArticleFrontiers in nutrition2026
Can artificial intelligence applications improve food supply capacity? Evidence from 296 Chinese cities.
Article in Frontiers in nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Against the backdrop of the rapid development of the digital economy, artificial intelligence (AI) has become an increasingly important driver of agricultural modernization and food supply capacity improvement. This study examines the impact of AI development on food supply capacity and further explores the mechanisms and regional differences underlying this relationship. Methods: Using panel data from 296 Chinese cities from 2010 to 2024, this study constructs a two-way fixed-effects model to evaluate the effect of AI development on food supply capacity. A series of robustness tests, endogeneity tests, lagged-effect tests, mechanism analyses, and heterogeneity analyses are conducted to verify the reliability of the findings and identify the channels through which AI exerts its influence. Results: The empirical results show that AI development significantly enhances food supply capacity, and this conclusion remains robust after multiple robustness and endogeneity checks. The lagged-effect analysis indicates that the positive impact of AI persists over time rather than being limited to the short term. Mechanism tests further reveal that AI improves food supply capacity mainly by increasing agricultural production efficiency and promoting the more efficient allocation of agricultural resources. Heterogeneity analysis shows that the promoting effect of AI is more pronounced in central China, major grain-producing regions, areas with relatively abundant precipitation, and regions with more advanced digital infrastructure. Discussion: These findings suggest that AI has gradually become a strategic technological support for agricultural modernization and the governance of national food supply capacity. Accordingly, policymakers should accelerate the construction of rural digital infrastructure, promote the integration of AI technologies into agricultural production systems, and formulate differentiated regional policies based on local agricultural foundations, resource endowments, and climatic conditions.
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