Analysis on the spatiotemporal patterns and driving mechanisms of China's agricultural production efficiency from 2000 to 2015. (December 2020)

• Record Type: Journal Article
• Authors: Guo, Baishu; He, Dawei; Zhao, Xiaodong; Zhang, Zhengyu; Dong, Yin
• Is Part Of: Physics and chemistry of the earth. Volume 120(2020)
• Publication Date: 2020-12
• Subjects: Agricultural production efficiency -- Stochastic frontier analysis -- Spatial correlation analysis -- Driving mechanism analysis -- China; Geophysics -- Periodicals; Geochemistry -- Periodicals; Earth sciences -- Periodicals; Geodesy -- Periodicals; Astrophysics -- Periodicals; 550
• DOI: 10.1016/j.pce.2020.102909
• Languages: English
• ISSNs: 1474-7065

Abstract

Abstract: Agricultural modernization is a key measure for China to promote the quality and efficiency of agricultural production in the new period. It aims to strengthen the basic status of agriculture's national economy and advocate a sustainable path plan for rural revitalization. Systematic analyses of the spatio-temporal evolution, spatial correlation characteristics, and external driving mechanisms of China's agricultural production efficiency is the scientific basis for the layout of agricultural space, the schedules formulation of agricultural modernization, and the implementation of the rural revitalization strategic goals. Based on the data sets of agricultural production, natural conditions, and socio-economic development at the municipal level in China from 2000 to 2015, the basic characteristics of agricultural production in China were explored using stochastic frontier analysis, spatial correlation analysis, and driving mechanism analysis. The results showed that: (1) The agricultural production efficiency has been reduced from 0.715 to 0.648, which has continued to maintain a steady decline, while the agricultural production efficiency has gradually become spatially heterogeneous and presents a significant 'striped' distribution. (2) The Moran's I of agricultural production efficiency increased from 0.4834 to 0.4908, and the spatial positive correlation clustering characteristics of H–H and L-L were relatively stable. (3) Differentiation of natural and socio-economic factors affects agricultural production efficiency. Natural conditions such as the diversity index and NDVI, and socio-economic factors such as the proportion of cropland and the proportion of GDP have become the dominant factors determining the agricultural production efficiency. Highlights: Agricultural production efficiency at the city level in China was measured by stochastic frontier model. Spatiotemporal characteristics of agricultural production efficiency were portrayed by the spatial correlation model. The driving mechanism between agricultural production and natural-social-economic factors was identified by Tobit model.