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<p>This study investigates the transformative role of artificial ıntelligence (AI) technologies in the agricultural sector, focusing on the United Kingdom from 1973 to 2023. As AI increasingly integrates into agriculture, it promises to revolutionize productivity, resource efficiency, and environmental sustainability. Using advanced econometric models, such as panel data analysis and the Difference-in-Differences (DiD) approach, this research quantifies AI’s direct and indirect effects on farm productivity, operational efficiency, and market competitiveness. The analysis reveals that AI adoption can lead to an increase in crop yields by 20–30% and a 15% reduction in operational costs, particularly through precision agriculture applications and predictive analytics. However, the study also highlights barriers to AI adoption, including high costs, technical challenges, and infrastructure deficits, especially in developing regions. The findings underscore the need for policy support and investment in infrastructure to fully harness AI’s potential in agriculture. This research contributes to the growing body of literature by offering new insights into the economic opportunities and challenges presented by AI in the global agricultural sector.</p>

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The economic and operational impacts of artificial intelligence on agriculture

  • 期刊论文

  • 发表日期：

    2025年9月19日

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  • Elsun Nabatov,
  • Meral Uzunöz Altan

摘要

This study investigates the transformative role of artificial ıntelligence (AI) technologies in the agricultural sector, focusing on the United Kingdom from 1973 to 2023. As AI increasingly integrates into agriculture, it promises to revolutionize productivity, resource efficiency, and environmental sustainability. Using advanced econometric models, such as panel data analysis and the Difference-in-Differences (DiD) approach, this research quantifies AI’s direct and indirect effects on farm productivity, operational efficiency, and market competitiveness. The analysis reveals that AI adoption can lead to an increase in crop yields by 20–30% and a 15% reduction in operational costs, particularly through precision agriculture applications and predictive analytics. However, the study also highlights barriers to AI adoption, including high costs, technical challenges, and infrastructure deficits, especially in developing regions. The findings underscore the need for policy support and investment in infrastructure to fully harness AI’s potential in agriculture. This research contributes to the growing body of literature by offering new insights into the economic opportunities and challenges presented by AI in the global agricultural sector.