Research on Driving Forces of Spatiotemporal Patterns in Cotton Cultivation Considering Spatial Heterogeneity

Meng Du, Deyu Shen, Xun Yang, Fenfang Lin (), Chunfa Wu and Dongyan Zhang
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Meng Du: School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Deyu Shen: School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Xun Yang: School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Fenfang Lin: School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Chunfa Wu: School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Dongyan Zhang: Shanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Northwest A&F University, Xianyang 712100, China

Agriculture, 2025, vol. 15, issue 20, 1-19

Abstract: Cotton is increasingly important in global development. The exploration of drivers of spatiotemporal patterns for cotton planting, considering spatial heterogeneity, is essential for optimizing its distribution and supporting sustainable production. This study combined the locally explained stratified heterogeneity (LESH) model with geographically weighted regression (GWR) to investigate the factors shaping cotton-planting patterns in the northern slope of the Tianshan Mountains (NSTM), China, from 2000 to 2020. Cotton distribution was derived from long-term Landsat image series, and its expansion showed an average annual growth rate of 2.10 × 10 3 km 2 , with intensive cultivation primarily distributed across the central and western counties. The dominant drivers of cotton distribution were elevation (ELE), sunshine duration (SD), slope (SLO), temperature (TEM), runoff (RO), and gross domestic product (GDP). ELE explained about 40% of the spatial heterogeneity. SD showed a declining influence, SLO remained stable, TEM increased in importance, and GDP exhibited a progressive upward trend, although weaker. Moreover, nonlinear weakening interactions, especially between ELE and other factors, as well as between socio-economic and climatic variables, substantially enhanced explanatory power. These findings highlight the significance of accounting for spatial heterogeneity and factor interactions in guiding the spatial optimization and sustainable management of cotton cultivation.

Keywords: cotton cultivation; spatiotemporal dynamics; spatial heterogeneity; driving forces; LESH; GWR; Xinjiang (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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