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The impact of artificial intelligence technology application on total factor productivity in agricultural enterprises: Evidence from China

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  • Ding, Mengqi
  • Gao, Qijie

Abstract

The agricultural sector exhibits significant differences in production methods, efficiency, and models compared to other industries. The question remains whether the application of artificial intelligence (AI) in agriculture can positively impact total factor productivity (TFP). This study investigates the effect and mechanism of AI application on the TFP of agricultural enterprises, using A-share listed agricultural companies from 2011 to 2022 as the research sample. The findings reveal that AI acts as an “agricultural accelerator” for production efficiency, significantly enhancing the TFP of agricultural enterprises. This conclusion holds even after a series of robustness tests and the use of instrumental variables to address endogeneity. In terms of the impact mechanism, AI promotes TFP improvement in agricultural enterprises by enhancing innovation capacity, optimizing the human capital structure, and reducing costs while increasing efficiency. Additionally, the impact of AI is more pronounced in enterprises whose main business is edible agricultural products, larger-scale operations, private enterprises, and those located in the eastern regions. This study provides theoretical guidance for developing precise AI application plans for agricultural enterprises in China and other developing countries, and offers important policy implications for sustainable agricultural development.

Suggested Citation

  • Ding, Mengqi & Gao, Qijie, 2025. "The impact of artificial intelligence technology application on total factor productivity in agricultural enterprises: Evidence from China," Economic Analysis and Policy, Elsevier, vol. 86(C), pages 399-415.
    • Handle: RePEc:eee:ecanpo:v:86:y:2025:i:c:p:399-415
    DOI: 10.1016/j.eap.2025.03.032
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    1. Oleksandr Melnychenko, 2025. "Artificial Intelligence in Regulating Production Volumes for Sustainable Development: Qualitative and Quantitative Aspects," Virtual Economics, The London Academy of Science and Business, vol. 8(1), pages 40-57, March.
    2. Lehenchuk, Serhii & Zakharov, Dmytro & Fedorova, Olha & Horodyskyi, Mykola & Vavilov, Dmytro, 2025. "Digital transformation, research and development, and financial performance of agricultural companies," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 11(3), September.

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