Artificial Intelligence Enhancing Agricultural Total Factor Productivity in China: Mechanisms and Pathways

Quanzhi Lu; Jiachen Lv

doi:10.62177/amit.v1i6.779

Authors

Quanzhi Lu Guangzhou College of Commerce
Jiachen Lv Guangxi Aquatic Animal Husbandry School

DOI:

https://doi.org/10.62177/amit.v1i6.779

Keywords:

Artificial Intelligence, Agricultural Total Factor Productivity, Empowerment Mechanisms, Smart Agriculture, Optimization Pathways

Abstract

Against the dual backdrop of intensifying global food security challenges and increasingly tight resource and environmental constraints, enhancing agricultural Total Factor Productivity (TFP) has become a core driver for promoting high-quality agricultural development. Artificial Intelligence (AI), as a strategic technology leading the new round of scientific and technological revolution and industrial transformation, is profoundly reshaping agricultural production methods and industrial ecosystems. This paper systematically elucidates the driving effect of AI on agricultural TFP growth through three key mechanisms: enhancing technical efficiency, optimizing factor allocation, and fostering new business models. Simultaneously, it identifies the multiple challenges in the AI-enabled empowerment process, including underlying data deficiencies, technological application bottlenecks, institutional and talent lag, and regional disparities. To address these issues, this paper proposes systematic optimization pathways, including building a high-quality agricultural data resource system, developing adaptable AI technologies and equipment, cultivating interdisciplinary "AI + Agriculture" talent, and improving policy regulations and ethical governance frameworks. This research aims to provide a theoretical framework for understanding the intrinsic logic of AI-driven agricultural TFP growth and to offer decision-making references for formulating relevant industrial policies and promoting the practical implementation of smart agriculture.

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