Artificial intelligence and agricultural production efficiency

Vollständiger Abstract

Worum geht es in dieser Arbeit?

As a core driving force of the new round of technological revolution, artificial intelligence (AI) is profoundly reshaping agricultural production modes and the pattern of factor allocation. However, the effect of AI on agricultural production efficiency is not a linear process. The mechanisms through which this effect operates, and how these mechanisms evolve as technology penetration deepens, remain theoretical and practical questions that require urgent answers. Using panel data for 31 Chinese provincial-level regions from 2005 to 2023, this paper applies a multi-period difference-in-differences (DID) method to systematically examine the impact of AI on agricultural production efficiency and the dynamics of mechanism switching. Three findings emerge. First, AI development has a significant positive effect on agricultural production efficiency. Second, this effect is transmitted through two mechanisms: land use efficiency and smart technology adoption. Third, the two mechanisms exhibit a sequential switch in dominance, following a pattern in which smart technology adoption dominates first and land use efficiency improvement dominates later. This study extends static mechanism testing to a dynamic analysis of mechanism switching. It provides a theoretical basis and a quantitative reference for designing differentiated agricultural digitalization policies.