Spatiotemporal Coupling and Driving Factors of Farmland Transfer and Labor Transfer Based on Big Data: The Case of Xinjiang, China

Abstract

Agriculture big data can use the advantages of information technology to provide data support and technical support for improving the level of farmland transfer and labor transfer. And the coupling and coordinated development of the two is an essential way to ensure the effective promotion of the rural revitalization strategy and complete the building of a moderately prosperous society in an all-round path. First, aided by the coupling coordination model and exploratory spatial data analysis, a detailed analysis of the spatiotemporal patterns and dynamics of farmland transfer and labor transfer was conducted. The driving factors were quantitatively examined using the gray correlation model. The results indicate that the degree of coupling and coordination between Xinjiang’s farmland transfer and labor transfer ranges from 0.50 to 0.70, basically on the between of slight and primary coordination, and the degree of coordination among the regions varies greatly. The labor resource endowment, agricultural equipment level, farmland ownership confirmation, nonagricultural industry development level, and urbanization level are significantly impacting the degree of coupling and coordination between Xinjiang’s farmland transfer and labor transfer.