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| Prediction of soil cation exchange capacity in China based on machine learning and future climate scenarios of CMIP6, 2030 |
| Received:May 20, 2025 |
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| KeyWord:environmental variables;XGBoost;SHAP;uncertainty analysis;cation exchange capacity |
| Author Name | Affiliation | | XU Xiangming | School of Geography and Environmental Engineering, Gannan Normal University, Ganzhou 341000, China | | ZHANG Xinyi | School of Geography and Environmental Engineering, Gannan Normal University, Ganzhou 341000, China | | QIN Linghua | School of Geography and Environmental Engineering, Gannan Normal University, Ganzhou 341000, China | | JI Lingling | School of Geography and Environmental Engineering, Gannan Normal University, Ganzhou 341000, China | | LI Rui | School of Geography and Environmental Engineering, Gannan Normal University, Ganzhou 341000, China |
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| Abstract: |
| In order to make an accurate prediction of soil cation exchange capacity（CEC）under future climate scenarios, this study systematically compared the performance of five machine learning models in predicting soil CEC across China. These models were based on a multi- source environmental covariate dataset. Additionally, the study quantified the spatial differentiation characteristics of soil CEC content in China in 2030 under the SSP2-4.5 and SSP3-7.0 future climate scenarios, along with the spatial uncertainty of the prediction results. The findings suggest that the XGBoost model demonstrates optimal performance across all metrics. The primary drivers of CEC are thus determined as soil organic carbon（contribution rate of 18.41%）and clay content（18.03%）, followed by temperature, elevation, and pH（all with contribution rates exceeding 10%）. Among these factors, soil organic carbon, clay content, total nitrogen, precipitation, slope, and human activity index have been shown to positively impact on CEC, while the remaining factors exhibit inhibitory effects. Projections indicate that under the SSP2-4.5 scenario, soil CEC content will remain stable by 2030; under the SSP3-7.0 scenario, CEC content will increase to 17.48 cmol·kg-1. From a spatial distribution perspective, The Northeast Region has the highest CEC content, followed by The South China Region, The Southwest China Region, The Central China Region, and The North China Region, with The Northwest Region having the lowest. The overall uncertainty in predictions under the two scenarios is similar, but regional differences exist. Under the SSP2-4.5 scenario, the highest uncertainty is observed in The East China Region and The South China Region, with the lowest in The North China Region. Under the SSP3-7.0 scenario, The Central China Region has the widest confidence interval. In summary, the XGBoost model is the optimal model for predicting soil CEC under future climate scenarios. The spatial distribution and uncertainty analysis of soil CEC content in China in 2030 demonstrate significant regional variations. |
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