HUBEI AGRICULTURAL SCIENCES ›› 2024, Vol. 63 ›› Issue (10): 44-52.doi: 10.14088/j.cnki.issn0439-8114.2024.10.008

• Resource & Environment • Previous Articles     Next Articles

Spatio-temporal differentiation and influencing factors of “non-grain” cultivated land in Hubei Province

KUO Dong-dong1,2, QU Li-ping1,3   

  1. 1. School of Public Administration, China University of Geosciences, Wuhan 430074, China;
    2. Three Gorges Group Yunnan Energy Investment Company, Kunming 650051, China;
    3. Key Laboratory of Legal Assessment Project, Ministry of Land, Resources, Wuhan 430074, China
  • Received:2023-10-09 Online:2024-10-25 Published:2024-11-05

Abstract: The gravity shift model, spatial auto-correlation analysis and geographically weighted regression model were used to study the spatio-temporal evolution characteristics and influencing factors of “non-grain” cultivated land in Hubei Province from 2000 to 2020. The results showed that from 2000 to 2020, the non-grain rate of cultivated land in Hubei Province showed a certain fluctuation and was generally stable. In terms of space, “non-grain” had evolved from “high in the middle and east, low in the west” to a spatial pattern of “high in the east and west, low in the middle”. The center of gravity of “non-grain” was moving westward as a whole. The degree of “non-grain” had a significant global spatial auto-correlation, and the local auto-correlation showed the characteristics of a high-high concentration centered on Wuhan City and a low-low concentration transferring from the west to the middle over time. Among the influencing factors, climate potential productivity index, soil pH and the average amount of agricultural diesel used per land were negatively correlated with non-grain production, while the difference in disposable income between urban and rural residents and land productivity were positively correlated with non-grain production. The size of the impact had strong spatial heterogeneity.

Key words: “non-grain” cultivated land, spatio-temporal differentiation, center of gravity transfer model, geographically weighted regression model, influencing factors, Hubei Province

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