<p>The research aims to systematically explore the core issue of how agricultural digital transformation affects the level of building a strong agricultural country. To this end, the study first constructs a comprehensive evaluation system covering five dimensions: digital infrastructure, technology applications, service platforms, talent cultivation, and policy environment. The entropy method is used to measure the level of agricultural digitization in various provinces of China from 2014 to 2023. Meanwhile, the Constant Returns to Scale (CRS) is used to evaluate the fluctuations in overall agricultural productivity in various regions over the past decade, thereby measuring progress in building a strong agricultural country. Finally, the spatiotemporal evolution characteristics are analyzed using kernel density estimation, Markov chain, and σ convergence models. The findings demonstrate that from 2014 to 2023, the degree of agricultural digitization in China’s eastern, central, and western regions exhibits a sustained growth trend. The level of agricultural digitization development is highest in the east, followed by the central region, and while the starting point is lower in the west, it also experiences annual increases. The total factor productivity of agriculture increases from 1.031 in 2014 to 1.089 in 2023, indicating a significant enhancement in China’s agricultural production efficiency, which is generally higher in eastern provinces, albeit with variations. The regression coefficient of the core variable calculated through multiple linear regression analysis shows that the coefficient of agricultural digitization is 2.789 and significant at the 1% level. This coefficient indicates that for every 1 unit increase in the level of agricultural digital development index, the total factor productivity index of agriculture will significantly increase by 2.789 units. This indicates that after controlling for individual fixed effects, agricultural digitization has shown a strong driving effect on the level of building an agricultural powerhouse. Moreover, the impact exceeds core mediating variables such as agricultural production efficiency and agricultural technological innovation capability. Consequently, to further promote the development of a strong agricultural sector, the digital advancement of agriculture should be encouraged from multiple dimensions. The main contribution of the research is the construction of a systematic framework for measuring agricultural digitization, and the revelation of its internal mechanism of driving the construction of an agricultural powerhouse through multiple paths, providing a theoretical basis for precise policy formulation. Future research directions include constructing comprehensive evaluation indicators for agricultural power that cover multiple dimensions such as economy, society, and ecology, deepening mechanism research through micro surveys and big data technology, and exploring more effective instrumental variables to address endogeneity challenges.</p>

Driving agricultural strength through digital transformation

期刊论文
出版模式：

开放获取
发表日期：

2025年12月1日

查看全文

Jijie Dong,
Jianguo Xu

摘要

The research aims to systematically explore the core issue of how agricultural digital transformation affects the level of building a strong agricultural country. To this end, the study first constructs a comprehensive evaluation system covering five dimensions: digital infrastructure, technology applications, service platforms, talent cultivation, and policy environment. The entropy method is used to measure the level of agricultural digitization in various provinces of China from 2014 to 2023. Meanwhile, the Constant Returns to Scale (CRS) is used to evaluate the fluctuations in overall agricultural productivity in various regions over the past decade, thereby measuring progress in building a strong agricultural country. Finally, the spatiotemporal evolution characteristics are analyzed using kernel density estimation, Markov chain, and σ convergence models. The findings demonstrate that from 2014 to 2023, the degree of agricultural digitization in China’s eastern, central, and western regions exhibits a sustained growth trend. The level of agricultural digitization development is highest in the east, followed by the central region, and while the starting point is lower in the west, it also experiences annual increases. The total factor productivity of agriculture increases from 1.031 in 2014 to 1.089 in 2023, indicating a significant enhancement in China’s agricultural production efficiency, which is generally higher in eastern provinces, albeit with variations. The regression coefficient of the core variable calculated through multiple linear regression analysis shows that the coefficient of agricultural digitization is 2.789 and significant at the 1% level. This coefficient indicates that for every 1 unit increase in the level of agricultural digital development index, the total factor productivity index of agriculture will significantly increase by 2.789 units. This indicates that after controlling for individual fixed effects, agricultural digitization has shown a strong driving effect on the level of building an agricultural powerhouse. Moreover, the impact exceeds core mediating variables such as agricultural production efficiency and agricultural technological innovation capability. Consequently, to further promote the development of a strong agricultural sector, the digital advancement of agriculture should be encouraged from multiple dimensions. The main contribution of the research is the construction of a systematic framework for measuring agricultural digitization, and the revelation of its internal mechanism of driving the construction of an agricultural powerhouse through multiple paths, providing a theoretical basis for precise policy formulation. Future research directions include constructing comprehensive evaluation indicators for agricultural power that cover multiple dimensions such as economy, society, and ecology, deepening mechanism research through micro surveys and big data technology, and exploring more effective instrumental variables to address endogeneity challenges.