This research was conducted to explore the factors affecting the technical efficiency (TE) of rice producers and its determinants at the farm level. We used a multi-stage sampling procedure to collect cross-sectional data from 800 rice growers in the Uttar Pradesh state of India, and a stochastic frontier model (SFA) was applied. The results showed that the mean technical efficiency was 72%, suggesting scope for a substantial increment in rice productivity exists while using the current level of inputs and technologies. Furthermore, the MLE results revealed that labor, irrigation, and hybrid seeds had a constructive impact on technical efficiency, while experience and tenure status showed a negative impact on technical efficiency. As unraveled by the results of the study, it can be concluded that the technical efficiency of rice farmers can be improved through timely access to credit and agricultural information delivered to them via extension services. The study, therefore, recommends that the government provide subsidized agrochemicals and focus on developing a robust network of extension services throughout the local districts for proper dissemination of inputs. About 12% of India’s rice is produced in the Uttar Pradesh state. So, this study could be an essential tool for the agriculture sector, which could help to solve rice productivity problems for future generations.

In the context of the rapid development of the global Internet, evaluating the digital village development level (DVI) in different regions is important for policy formulation and scientific research in the field of rural digital development. Based on the measurement of DVI using panel data of provinces from 2005 to 2018, we also explored the causes of DVI development differences using the Dagum Gini coefficient decomposition and spatial convergence method. The study found that: (1) During the whole sample period, the DVI in China and the four major regions showed a significant upward trend as a whole, with the highest in the eastern region and the lowest in the western region, but the development of DVI among different provinces showed regional non-equilibrium characteristics. (2) DVI has a typical agglomeration effect in the spatial and geographical distribution, and regional differences are the primary source of differences in DVI development in China, but the overall regional differences show a decreasing trend. (3) Except for the northeast region, σ-convergence exists in the other three regions; in addition, all regions have significant conditional β-convergence, but the convergence speed is significantly different. (4) Rural population density, industrial structure, and economic development level significantly affect DVI. Therefore, we suggest strengthening the inter-regional connection and element flow and realizing the improvement of the overall DVI of the region through cooperation and mutual assistance.