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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Taylor & Francis Group
2025-12-01
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| Series: | GIScience & Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/15481603.2025.2533487 |
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| author | Kai Ren Yongze Song Linchao Li Francesco Mancini Zhuoyao Xiao Xueyuan Zhang Rui Qu Qiang Yu |
| author_facet | Kai Ren Yongze Song Linchao Li Francesco Mancini Zhuoyao Xiao Xueyuan Zhang Rui Qu Qiang Yu |
| author_sort | Kai Ren |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bcd921c063f34562baa18e90a28fae13 |
| institution | Kabale University |
| issn | 1548-1603 1943-7226 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj-art-bcd921c063f34562baa18e90a28fae132025-08-20T03:50:31ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262025-12-0162110.1080/15481603.2025.2533487Identifying climate and environmental determinants of spatial disparities in wheat production using a geospatial machine learning modelKai Ren0Yongze Song1Linchao Li2Francesco Mancini3Zhuoyao Xiao4Xueyuan Zhang5Rui Qu6Qiang Yu7College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi, ChinaSchool of Design and the Built Environment, Curtin University, Perth, WA, AustraliaCollege of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi, ChinaSchool of Design and the Built Environment, Curtin University, Perth, WA, AustraliaSchool of Design and the Built Environment, Curtin University, Perth, WA, AustraliaSchool of Design and the Built Environment, Curtin University, Perth, WA, AustraliaSpatial Sciences Discipline, School of Earth and Planetary Sciences, Curtin University, Perth, AustraliaCollege of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi, ChinaWheat 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.https://www.tandfonline.com/doi/10.1080/15481603.2025.2533487Agricultural remote sensingspatial heterogeneityspatial statisticsgeographical detectorgeospatial intelligence |
| spellingShingle | Kai Ren Yongze Song Linchao Li Francesco Mancini Zhuoyao Xiao Xueyuan Zhang Rui Qu Qiang Yu Identifying climate and environmental determinants of spatial disparities in wheat production using a geospatial machine learning model GIScience & Remote Sensing Agricultural remote sensing spatial heterogeneity spatial statistics geographical detector geospatial intelligence |
| title | Identifying climate and environmental determinants of spatial disparities in wheat production using a geospatial machine learning model |
| title_full | Identifying climate and environmental determinants of spatial disparities in wheat production using a geospatial machine learning model |
| title_fullStr | Identifying climate and environmental determinants of spatial disparities in wheat production using a geospatial machine learning model |
| title_full_unstemmed | Identifying climate and environmental determinants of spatial disparities in wheat production using a geospatial machine learning model |
| title_short | Identifying climate and environmental determinants of spatial disparities in wheat production using a geospatial machine learning model |
| title_sort | identifying climate and environmental determinants of spatial disparities in wheat production using a geospatial machine learning model |
| topic | Agricultural remote sensing spatial heterogeneity spatial statistics geographical detector geospatial intelligence |
| url | https://www.tandfonline.com/doi/10.1080/15481603.2025.2533487 |
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