The Impact of Farmers’ Digital Capability on Large-Scale Farmland Management: Evidence from the Perspective of Farmland Inflow Behavior

This study empirically investigates the impact and underlying mechanisms of farmers’ digital capability (DC) on large-scale farmland management, utilizing micro-survey data from 1144 rural households across five provinces in China: Anhui, Henan, Shaanxi, Hebei, and Shandong. The analysis employs a double machine learning model (DML). The results demonstrate that DC is positively related to farmers’ farmland inflow, thereby facilitating the realization of large-scale land management. Mechanism analysis reveals that farmers’ DC affects large-scale farmland management by expanding the transaction radius and improving agricultural production efficiency. Heterogeneity analysis indicates that the positive effect of DC on farmland inflow is more pronounced when farmers possess advantages in human capital, income levels, business entity characteristics, and natural endowments. This finding suggests that the impact of farmers’ DC on large-scale farmland management is not yet inclusive. Accordingly, the government should actively construct a cultivation system for farmers’ DC, build an inclusive digital service platform for farmland transfer, help farmers bridge the digital divide, and further unleash digital dividends. In future research, we will conduct follow-up surveys on fixed farmer households to expand the survey scope, optimize the measurement of key variables, and carry out comparative analyses across different institutional contexts, thereby providing a more systematic scientific basis for the development of agricultural modernization driven by digital empowerment.