Internet of Things (IoT) technologies are widely recognized as one of the most important infrastructures for economic development and technological innovation. By analyzing the influencing factors of vegetable farmers’ intention to adopt agricultural IoT, it helps to formulate effective IoT promotion policies and accelerate the realization of agricultural modernization. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technology-Organization-Environment (TOE) theory, this study constructed for the first time a mechanism model of the influence of vegetable farmers’ intention to adopt IoT, expanding the scope of current research on agricultural IoT and revealing the intrinsic influence mechanism of farmers’ adoption of IoT technologies. In this study, 357 quantitative data were obtained by a questionnaire survey, and structural equation modeling was used to test the direct and indirect effects of each factor on vegetable farmers’ intention to adopt IoT. The results show that almost all variables in TOE have significant direct impacts on the intention, while no variables in UTAUT have significant direct impacts. Among variables in TOE, government support and complexity are the two most important elements influencing the intention. Although the interactions among variables in TOE and UTAUT are also found, the indirect effects of variables are non-significant. Therefore, it is proposed to reduce the complexity of operation and use of IoT technologies; improve rural information infrastructure and compatibility of IoT platforms and devices; and governments should increase subsidies, and incentives to promote the use of IoT in agriculture and agricultural practices.

To promote the resilient and coordinated development of regional economies, in this study, to construct an index system, we used the entropy weight method to measure the agricultural economic resilience of 11 prefecture-level cities in Jiangxi province, China during 2011–2020, and we analyzed the characteristics of their spatial-temporal pattern evolution. We used the Theil index method to analyze the main sources of their spatial differences. The results showed that: First, the mean value of agricultural economic resilience of prefecture-level cities in Jiangxi trended upward during 2011–2020. Among the prefecture-level cities, Ganzhou had the highest mean agricultural economic resilience value, while Yingtan had the lowest. From the perspective of regional division, the agricultural economy in southern Jiangxi was the most resilient, whereas that in northeast Jiangxi was the least. Second, since 2011, the overall difference in agricultural economic resilience in the four regions of Jiangxi has trended downward, with the inter-regional difference being the main source of the overall difference. Based on this, we provide the following policy implications: first, to continuously enhance the intrinsic dynamics of the agricultural economic resilience and strengthen policy support in Jiangxi; second, to coordinate the development of regional agricultural economic resilience and achieve overall improvement.

To accurately identify the land ecological security in the middle and lower reaches of the Yangtze River, we measured the land ecological security based on the PSR model, selected a total of 19 indicators, such as population density, and applied the methods of system evaluation value conversion, comprehensive evaluation value and coordination degree measurement to study the land ecological security in the region. Research findings: The comprehensive evaluation level showed that Jiangxi, Shanghai, and Hunan had comprehensive evaluation values according to the regional forefront. The subsystem evaluation level showed that Shanghai and Hunan had higher pressure subsystem evaluation values relative to other provinces; Shanghai, Zhejiang, and Anhui had higher state subsystem evaluation values relative to other provinces; and Jiangxi was much more responsive to land ecological security issues than were other provinces and cities in the region. The system coordination degree showed that the comprehensive evaluation value of land ecological safety and the coordination degree evaluation value of other provinces except Shanghai and Hunan showed a negative correlation, indicating certain land ecological safety problems in the region. Finally, suggested reflections based on the conclusions are presented.

The study of the evolutionary patterns and trends of agricultural carbon emission intensity and agricultural economic development levels plays an important role in promoting the green and low-carbon sustainable development of agriculture. This paper adopts the carbon emission factor method to measure the agricultural carbon emissions in Jiangxi Province from 2001 to 2020, uses the LMDI decomposition method to explore the drivers of carbon emissions, and further analyzes the coupling relationship between agricultural carbon emissions and the agricultural economy using the Tapio decoupling model, based on which a GM (1,1) model is used to forecast the agricultural carbon emissions in Jiangxi Province from 2001 to 2015. According to the research results, agricultural carbon emissions in Jiangxi Province show a trend of “rising and then falling”, with the intensity decreasing; the level of economic development is the main factor that increases carbon emissions, while the efficiency of agricultural production, the size of the labor force, and the structure of agricultural production have positive effects in terms of reducing carbon emissions. How to reduce carbon emissions while promoting agricultural economic development is an issue that remains to be addressed in the future. Further analysis found that the decoupling states of Jiangxi Province from 2001 to 2009 switched between strong decoupling and weak decoupling, with weak decoupling dominating the years 2010–2015 and strong decoupling dominating from 2016 onwards. With the continuous promotion of carbon emission reduction, agricultural carbon emissions in Jiangxi Province will continue to show a decreasing trend over the next five years. Three policy recommendations are put forth in order to advance the effort to reduce agricultural carbon emissions in Jiangxi Province: cultivating high-quality and low-carbon rice varieties, switching to green agricultural production, and coordinating the connection between economic growth and agricultural carbon emissions.

Increasing green total factor productivity is the key to achieving green development in agriculture. This study measured the green total factor productivity of Jiangxi’s agriculture, and its regional and temporal evolution characteristics were examined. The fixed-effects model was then used to investigate the model’s fundamental components empirically. The study’s findings reveal the following: During the period under review, technical change was the primary element driving the rise in the green total factor productivity of agriculture. A rising “U”-shaped trend with notable regional variances characterizes the spatial and temporal evolution. The primary factors that affect changes in green total factor productivity in agriculture include the ease of transportation, the per capita disposable income of rural residents, the level of agricultural mechanization, the degree of urbanization, the level of financial support for agriculture, and the percentage of workers in secondary industries, with the impact effect values of 0.581, 0.647, −0.126, −0.729, −0.326, and −0.559, respectively. As a result, it is suggested that in order to substantially increase agricultural green total factor productivity, agricultural green technological support should be strengthened, more fully developed, and promoted in a multi-pronged approach.

Understanding how industrial agglomeration affects agricultural green production efficiency is essential for green agricultural development. This study uses the super-efficient Epsilon-Based Measure (EBM) model and Global Malmquist–Luenberger (GML) index to measure and analyze the spatial and temporal evolution characteristics and core sources of dynamics of agricultural green production efficiency in China by using panel data from 30 Chinese provinces from 2006 to 2020. It also empirically investigates the relationships between industrial agglomeration, land transfer, and agricultural production efficiency. By using fixed, intermediary, and threshold effect models, the internal links between industrial agglomeration, land transfer, and agricultural green production efficiency are examined. The findings indicate the following. (1) The green production efficiency of Chinese agriculture exhibits the regional characteristics of being “high in the west and low in the east, high in the south and low in the north” in terms of space; in terms of time, the overall trend is that green production technology efficiency is growing, with an average annual growth rate of 11.45%, and the growth primarily depends on the “single-track drive” of green technological progress. (2) Industrial agglomeration significantly affects agricultural green production efficiency, green technology efficiency, and green technology change; the corresponding coefficient values are 0.115, 0.093, and 0.022. (3) According to the mechanism-of-action results, land transfer mediates the effects of industrial agglomeration on agricultural green production efficiency, green technology efficiency, and green technology change. These effects have effect values of 28.48%, 27.91%, and 47.75%, respectively. (4) The threshold effect’s findings demonstrate a double threshold effect of industrial agglomeration on the green production efficiency of agriculture in terms of land transfer, with threshold values of 1.468 and 3.891, respectively. As a result, this study suggests adhering to the idea of synergistic development, promoting agricultural green development, strengthening the development of industrial agglomerations, promoting the quality and efficiency of industry, improving land-transfer mechanisms, and placing a focus on resource efficiency improvements, as well as other policy recommendations.