The total factor productivity (hereafter TFP) of grain production is important to achieve balanced development, while environmental factors are an important part of TEP. In order to explore the characteristics and patterns of the temporal and spatial evolution of the environmental total factor productivity (hereafter ETFP), the Malmquist-Luerberger index, and the spatial autoregressive panel (SAR panel) model were adopted to analyze the evolutionary rules and the influencing factors of ETFP. In this study, we took Poyang Lake, one of China’s main grain production areas, as a study area, and carried out empirical research based on grain production statistical data. The results show that: (1) ETFP shows a growth trend with the increase of grain production from 2001 to 2017, and a great potential for improvement exists. Moreover, from the perspective of time sequence evolution and decomposition of ETFP, which belongs to the dual-track driver of environmental technical efficiency and environmental technological progress, relevant technologies play an important role in promoting the improvement of TEFP; (2) Given that the objective conditions of gain production remain unchanged, the fact that the urbanization rate and average annual rainfall have a negative effect on ETFP, the explanatory variables such as the business scale per worker, the proportion of grain growing population, industrial agglomeration, the proportion of grain sown area and the average annual temperature all play a positive role. Among the variables, the business scale per worker and the proportion of grain growing population significantly affect ETFP at the 1% level. The average annual rainfall, industrial agglomeration and the proportion of grain sown area significantly affect the ETFP at the 5% level. The average annual temperature significantly affects the ETFP at the 10% level.

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Based on the grain production data of the counties (cities, districts) in Poyang Lake Basin, this paper uses the productivity index of Epsilon Based Measure of Malmquist Luenberger (EBM-ML Index) to analyse the green total factor productivity (GTFP) of grain in Poyang Lake Basin. Kernel density function and Markov analysis are used to discuss the dynamic evolution process of the distribution of GTFP of grain. The results show the following: (1) From the time dimension, the GTFP of grain is on the rise and fluctuates more frequently from 2001 to 2017, and its trend of change is determined by the combination of technical efficiency and technological progress. Moreover, from a spatial dimension, the number of counties (cities, districts) with GTFP of grain greater than 1.0 has shown an overall increase, indicating that the overall level of GTFP of grain is increasing. (2) According to the kernel density estimation results, the crest of the main peak of the kernel density curve corresponding to the GTFP of grain in Poyang Lake Basin shifts to the right, and the area formed by the right part of the GTFP of grain corresponding to the crest of the main peak of its kernel density curve gradually increases. The peak of the kernel density curve changes from “multi-peak mode” to “single-peak mode,” and the height of the main peak of the kernel density curve of GTFP of grain shows an overall decrease. Meanwhile, the right tail of the kernel density curve shows an overall extending trend. (3) According to the estimation results of the Markov chain, the GTFP of grain in Poyang Lake Basin is highly mobile from 2001 to 2017, and the counties (cities, districts) have a certain degree of agglomeration in the low, medium-low, medium-high and high levels. In other words, the long-term equilibrium state of growth of GTFP of grain remains dispersed in the state space of four level types, indicating that the divergence state of GTFP of grain in counties (cities, districts) of Poyang Lake Basin will continue for a long time in the future. The study reveals the evolution and dynamic change of GTFP of grain in Poyang Lake Basin, which has important theoretical significance and practical value for optimizing the spatial pattern and realizing the balanced development of GTFP among counties (cities, districts) of Poyang Lake Basin and consolidating China’s food security strategy.

The sustainable and coordinated development of agriculture directly associated with the national economy and social development, and the agriculture development is closely affected by several societal, economic and environmental effects. Based on the panel data of agricultural production from 2004 to 2015 in China, 21 indicators were selected to construct a five-dimensional index system of sustainable agricultural green development, including population, society, economy, environment and resources perspective. Using entropy method and coordination degree method, the spatial-temporal dynamics and coordination degree of agricultural green development index (AGDI) are explored. The results show that: sustainable agricultural green development is mostly affected by the sustainability of population system, followed by the sustainability of environmental system, resource system, economic system and societal system. In terms of the spatial dimension, it has large differences between different regions. In terms of the coordination degree of AGDI between the five dimensions, it shows a trend of "continuous decline and then rising fluctuation" from 2004 to 2015. From the spatial distribution of the coordination degree between these subsystems, the number of provinces with "coordination" and "comparative coordination" between the sustainability of each subsystem is increasing, at the same time, the provinces belong "coordination" and "comparative coordination" are mainly distributed in the central and eastern regions of China. This paper analyses the spatial-temporal dynamics and coordination degree of AGDI based on the evidence collected in China, it furthered explores the great significance of the five-dimensional systems in improving the level of agricultural sustainable development.

The rich ecological resources in underdeveloped resource-rich areas are difficult to transform into economic advantages under the system of low-cost resources, a priceless environment and highpriced industrial products, resulting in a backward economic development level, serious relative poverty and a possible return to poverty at any time. Correctly evaluating the effect of green poverty reduction in underdeveloped resource-rich areas holds great significance for reducing relative poverty and ending the phenomenon of returning to poverty. Taking Jiangxi Province as an example, this paper uses the entropy weight method and the coefficient of variation to calculate the green poverty reduction index and each subdimension index of 80 counties (cities/districts) in Jiangxi Province, and analyzes the characteristics of the province's spatial and temporal evolution. It is found that the green poverty reduction index of Jiangxi Province rises overall, but the regional differences are obvious, and the increase in each subdimension index leads to an increase in the green poverty reduction index. The increase in the economic poverty reduction index is the main reason. The regional differences will persist, and balanced development will not be achieved in the short term.