Spatial Correlation Evolution and Driving Factors of Wheat Production in China Based on Social Network Analysis

Abstract

Understanding the spatial correlation network of inter-provincial wheat production is vital for ensuring food security and achieving sustainable agricultural development in China. However, the spatial correlation characteristics of wheat production and their determinants remain unclear. In this study, an improved gravity model was used to calculate the spatial correlation of inter-provincial wheat production in China based on available panel data from 2000 to 2020. The spatial-temporal evolution characteristics and the driving factors of the spatial correlation network of inter-provincial wheat production in China were analyzed using social network analysis (SNA) and a quadratic assignment procedure (QAP). The findings indicated that (1) the spatial correlation of inter-provincial wheat production first increased and then decreased. The network density increased from its lowest value (0.2598) in 2000 to its maximum value (0.2782) in 2016 and then continued to fluctuate. (2) The spatial correlation network of inter-provincial wheat production presented a “core-periphery” distribution pattern for the major wheat-producing areas (such as Jiangsu, Anhui, and Hubei) and non- major wheat-producing areas (such as Jilin, Qinghai, Guangxi, and Beijing), and the roles of the blocks in the network varied with time and space. (3) The implementation of grain-related policies (such as the abolition of agricultural taxes, the implementation of industry nurturing agriculture, and the minimum grain purchase price policy) positively affected the development of the spatial correlation network of wheat production. Since the implementation of the minimum purchase price policy for wheat in 2006, the network density reached its maximum value (0.2782), the network efficiency reached its minimum value (0.5985), and the stability of the network structure greatly improved. (4) The interactions between the internal natural conditions and the external socioeconomic factors promoted the construction of a spatial correlation network for wheat production. The differences in geographical adjacency, land resources, temperature, and sunlight hours were all significant at the 1% level, highlighting the substantial impact of these factors on the spatial correlation intensity of wheat production in China. This study provides a reference for the development of cooperative cross-regional wheat production and the formulation of distinct policies for the production of wheat and other grains.