Identifying climate and environmental determinants of spatial disparities in wheat production using a geospatial machine learning model
Wheat production is crucial in global food security and sustainable development, especially in severe global climate change, frequent extreme weather events, and significant population growth worldwide. A deeper understanding of spatial variation in wheat production and its determining factors is es...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/15481603.2025.2533487 |
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| Summary: | Wheat production is crucial in global food security and sustainable development, especially in severe global climate change, frequent extreme weather events, and significant population growth worldwide. A deeper understanding of spatial variation in wheat production and its determining factors is essential for implementing different cultivation practices, water and fertilizer management, and adaptive variety selection across different regions. However, existing methods primarily focused on identifying single-variable factors while lacking geographical spatial characteristics, which may lead to an incomplete exploration of spatial disparities in wheat production, predictions, and responses to changes in determining factors. This study develops a geospatial machine learning model by integrating spatial autocorrelation, spatial stratified heterogeneity, and decision tree to identify spatial disparities and their determinants of wheat production. The model is applied to wheat production analysis in Australia, the world’s 5th (2022) wheat-producing country. First, a spatial autocorrelation method is employed to identify the hotspot area of wheat production in Australia. Next, the geographically optimal zones-based heterogeneity (GOZH) model, an integration of spatial stratified heterogeneity and decision tree learning models, is used to identify determinants and their interactions on spatial disparities of wheat production. Finally, the developed geospatial machine learning model is evaluated by comparing its effectiveness with the commonly used geographical detector model. The results demonstrate pronounced spatial heterogeneity in Australian wheat production driven by environmental, climatic, and soil factors and their interactions. Identifying these spatial determinants enables more efficient crop management – such as targeted sub – regional practices, climate – adaptive variety selection, and soil health strategies – thereby supporting food security and sustainable agricultural systems. |
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| ISSN: | 1548-1603 1943-7226 |