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Abstract
Using the provincial panel data from 1978 to 2020 as the research object, this study employs the fixed effect SFA-Malmquist model to measure the agricultural total factor productivity of each province and city, and the spatial correlation of China’s agricultural total factor productivity is determined by Moran’s I. On this basis, three weights (adjacency, economy, geography) are included as spatial factors in three spatial β-convergence models (SAR, SEM and SDM), and the spatial convergence characteristics of China’s agricultural total factor productivity are analyzed in different time periods and different regions. The study found that: First, China’s agricultural total factor productivity shows a growing trend, but as time goes on, its growth rate gradually slows down, and the growth rate in the eastern region is higher than that in the central and western regions. Second, China’s agricultural total factor productivity has significant spatial correlation and spatial convergence characteristics. The differences in agricultural total factor productivity in various regions are shrinking over time, and the spatial spillover effect significantly shortens the convergence process. Due to spatial convergence, while carrying out agricultural production, all regions should thoroughly consider the advantages of agricultural resources in neighboring regions and strengthen cooperation and exchanges between regions.
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