In this study, to discover how the growth of digital agriculture has impacted agricultural green total factor productivity (AGTFP), we take panel data from 2011 to 2019 for 30 Chinese provinces as the research object, measure the growth of AGTFP and digital agriculture development using the SBM-ML and entropy method, and use a fixed effect model to analyze the effects of digital agriculture development on AGTFP. The results demonstrate that (1) from the time-series characteristics, digital agriculture presented a steady growth state from 2011 to 2019; (2) during the study period, the technical efficiency index was slightly lower than the technological progress index in the AGTFP index, meaning that there is room for further development; (3) the relationship between the growth of digital agriculture and AGTFP presents an inverted U-shaped curve, with human capital playing a moderating role. Finally, corresponding countermeasures are proposed in four aspects: strengthening the construction of organizational mechanisms, building a standardized base for digital agriculture output, enhancing the traceability and certification of agricultural products, and improving social services in the agricultural industry.

In the current study, an index system for digital agriculture growth was constructed. The index encompasses six key dimensions, namely production, operation, service, management, sustainability, and digital information infrastructure. Data from 30 Chinese provinces between 2011 and 2020 were collected and analyzed using the entropy method, Moran index, Dagum’s Gini coefficient, and the kernel density estimate. An in-depth analysis of the development level and spatial patterns, dynamic evolution and intra- and inter-regional differences in China (i.e., eastern, western, and central regions) was conducted. From the result, an overall growing trend of digital agriculture in China was observed, with a relatively more advanced status in the eastern region. A positive spatial dependence, showing a “high-high” and “low-low” (HH, LL) trend, was obtained. However, the regional spatial dependence has generally weakened since 2019. The intra-regional differences were large in western and eastern areas, while the greatest inter-regional differences were unveiled between western and eastern regions. The country’s overall differences mainly stemmed from inter-regional differences. The overall kernel density curves moved to the right over time, showing a trend of a gradual rise in digital agricultural growth, accompanied by a polarization pattern in the western region.