Analysis of the spatial variations of determinants of agricultural production efficiency in China

• Published in: Computers and electronics in agriculture 2021-01, Vol.180, p.105890, Article 105890
• Main Authors: Ma, Li, Long, Hualou, Tang, Lisha, Tu, Shuangshuang, Zhang, Yingnan, Qu, Yi
• Format: Article
• Language: English
• Subjects: Agglomeration; Agricultural land; Agricultural production; Agricultural production efficiency; China; Coastal zone; Efficiency; Geographically weighted regression model; Mathematical analysis; Mountainous areas; Population density; Rural areas; Rural development; Sustainability
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2020.105890

•City-level agricultural production efficiency is mapped by Super-Efficiency Data Envelopment Analysis model.•The agricultural production efficiency is featured as spatial agglomerations.•The OLS and GWR models are used to explore the spatial heterogeneity and driving mechanisms of agricultural production efficiency.•Series of policy recommendations are provided. The level of agricultural production efficiency (APE) affects farmers' livelihood decisions and the transformation and reconstruction of the rural territorial system. This paper constructs an evaluation index system of China's APE from the perspective of input–output. The spatial autocorrelation model and econometric model are used to calculate the spatio-temporal evolution characteristics and influencing factors of China's APE in 1990 and 2017. The results show that China's APE level is relatively low, and there are significant spatial differences. The extremely high efficiency regions are distributed in the eastern plains of China; the regions with extremely low efficiency are mainly distributed in the southwest and the western regions of China. The results of the spatial correlation effect show that the H-H agglomeration regions of APE are usually distributed in the eastern coastal areas and northeastern areas, while the L-L agglomeration regions are mainly distributed in the central mountainous areas and the border areas of western Yunnan. From 1990 to 2017, the influencing factors of APE have gradually changed, and showing a more complex trend, mainly including farmland management scale, multiple crop index, rural per capita net income, population density, and precipitation per unit area. Finally, the corresponding policy and suggestions are provided.