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RESEARCH PAPER
Interpretable machine learning unravels hierarchical controls on soil organic carbon in hyperarid oases of the Eastern Tarim Basin
1
Urumqi Natural Resources Comprehensive Survey Center, China Geological Survey, Urumqi, 555 Xihuan North Road, 830057, China
2
Baotou Teachers College, Baotou, 24 Ziyou Road, 014030, China
3
Hami Geological Brigade, Geological Bureau of Xinjiang Uygur Autonomous Region, Hami, 38 Jianguo North Road, 839099, China
4
Bayingolin Geological Brigade, Geological Bureau of Xinjiang Uygur Autonomous Region, Korla, 83 West Tianshan Road, 841000, China
5
Hubei Provincial Land and Resources Information Center, Geological Bureau of Hubei Province, Wuhan, 27, Gongzheng Road, 430071, China
These authors had equal contribution to this work
Final revision date: 2025-05-21
Acceptance date: 2025-06-09
Publication date: 2025-08-19
Corresponding author
Int. Agrophys. 2025, 39(4): 443-456
HIGHLIGHTS
- Human activities surpass climate in SOC control (14.2% vs. <8%)
- RF model decodes SOC drivers (R²=0.81; RMSE=1.32 g kg⁻¹)
- Eostatistics-ML integration guides clay-SOC management
KEYWORDS
TOPICS
ABSTRACT
Understanding spatial drivers of soil organic carbon in arid oasis ecosystems is essential for guiding precision soil management and enhancing land sustainability. This study integrates 644 surface samples and 9 soil profiles with multisource environmental data in the hyperarid eastern Tarim Basin, employing geostatistics and machine learning (random forest, support vector machines, ordinary least squares, back propagation) to quantify driving mechanisms. Key findings: 1) extreme soil organic carbon spatial polarization (0.50-21.70 g kg-1, mean = 4.47 g kg-1) with northern and southern alluvial zones containing 2.1 times higher soil organic carbon than central deserts (p < 0.01); 2) random Forest achieved optimal prediction (coefficient of determination = 0.81, root mean square error = 1.32 g kg-1) by resolving nonlinear soil organic carbon-environment interactions; 3) pedogenic properties (texture, cation exchange capacity, salini-
ty; 47.5%) dominated soil organic carbon variation, followed by anthropogenic drivers (land use intensity, 14.2%) and soil taxo-nomy (10.9%), while climate and topography showed minimal control (< 8%). Human-modified processes override climatic constraints in shaping soil organic carbon patterns, providing actionable insights for clay-organic stabilization and irrigation optimization. This methodology establishes a transferable framework for deciphering soil organic carbon dynamics in global drylands, directly informing climate-resilient land management.
ACKNOWLEDGEMENTS
We thank to the Key Laboratory of Coupling Process and Effect of Natural Resources Elements, Geological Survey Projects of the China Geological Survey, and Science and Technology Innovation Fund for the Integrated Natural Resources Survey Command Centre. We are equally grateful to the journal editors and reviewers for their support in the publication of the article.
FUNDING
This work was founded by the Key Laboratory of Coupling Process and Effect of Natural Resources Elements (No.2024KFKT019) Geological Survey Projects of the China Geological Survey (DD20242035) (2024-2026).
CONFLICT OF INTEREST
The authors declare that they have no conflict of interest.
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