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Spatial distribution and main controlling factors of soil organic carbon under cultivated land based on GBDT model in black soil region of Northeast China |
ZHU Yuan-li1,2,3, FENG Xiang-yang1, YAN Qing-wu1,2,3, WU Zi-hao1,2,3 |
1. School of Public Policy & Management, China University of Mining and Technology, Xuzhou 221116, China; 2. Research Center for Land Use and Ecological Security Governance in Mining Area, China University of Mining and Technology, Xuzhou 221116, China; 3. Research Center for Transformation Development and Rural Revitalization of Resource-Based Cities in China, China University of Mining and Technology, Xuzhou 221116, China |
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Abstract The nonlinear relationships between soil organic carbon (SOC) and environmental covariates have been extensively proved, the threshold and interaction effects of environmental covariates on SOC content are often ignored. This study, founded upon the SOC data collected in 2012, employs the Gradient Boosting Decision Tree (GBDT) to delve into the thresholds and interactive effects of SOC. It aims to discern the main controlling factors of SOC and unveil the spatial differentiation mechanism of SOC. The national commodity grain production base county, Wangkui County, which is also one of the high-yield counties in China, was selected as the study area. The GBDT model can more flexibly fit the complex nonlinear relationship between SOC and environmental variables, and shows superior predicting performance under all independent variables to the simple linear regression model. The model achieves an overall R2 value of 0.958, indicating its ability to effectively explain the variations of the target variable. Alkali-hydrolyzed nitrogen, available phosphorus, temperature, soil microbial system diversity, available potassium, pH and precipitation in turn are the most significant factors influencing SOC content. Alkali-hydrolyzed nitrogen holds the highest influence weight of 33.85%. All 7environmental covariates had threshold effects on SOC with two distinct thresholds. It was found that any two covariates are not independent but exhibit interactive negative effects, indicating that the influence of environmental covariates on SOC concentration is a complex interaction rather than a simple superposition. The above results emphasize that threshold and interaction effects between environment variables should be considered in determining the effective range of environmental variables, the potential process of SOC spatial differentiation, and improving the explanatory power of SOC change in farmland. It indirectly proves the fact that excessive fertilization fails to enhance soil fertility but instead leads to resource wastage and the ecological pollution of farmlands.
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Received: 13 September 2023
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Corresponding Authors:
吴子豪,副教授,6247@cumt.edu.cn
E-mail: 6247@cumt.edu.cn
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