Abstract: Basedon "resource-energy-economy-environment" to construct a theoretical frameworkfor agricultural green total factor productivity, design and measure indicators, improve EBM model and measure growth sources from static and dynamic perspectives with ML index, using nuclear density function and space Marcov chain to analyzes the dynamic evolution of China's agricultural green total factor productivity from the perspective of time and space. Results show that:Firstly,Agricultural green total factor productivity is more realistic and reasonable reflects agricultural efficiency. Second, China's agricultural green total factor productivity shows a slight upward trend increasing from 0.607 to 0.684 in 2006-2016,and the source of growth is the advancement of agricultural green technology. The regional differences in the total factor productivity of agricultural greens are obvious, and the regional differences will not shrink in the long run. The root of regional differences lies in the efficiency of agricultural green technology. The four-agricultural green total factor productivity has spatial agglomeration characteristics, and the spatial lag type has a significant impact on the degree of regional transfer stability; the neighboring is good and the neighboring is the same as the neighboring; and the agricultural green total factor productivity level is higher, the lowering will be lower. The probability of horizontal transfer, but cross-border growth is difficult to achieve.The policy implication is to strengthen the positive spatial spillover effect, weaken the negative spillover effect, and formulate regionally differentiated strategies to achieve coordinated green growth in agriculture in China.

Key words: agricultural green total factor productivity, time-spaceevolution, space markov chain