Cropland carbon stocks driven by soil characteristics, rainfall and elevation

Soil organic carbon (SOC) can influence atmospheric CO2 concentration and then the extent to which the climate emergency is mitigated globally. It follows the elucidation of the driving factors of cropland SOC stocks, which is fundamental to reducing soil carbon loss and promoting soil carbon seques...

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Veröffentlicht in:The Science of the total environment 2023-03, Vol.862, p.160602-160602, Article 160602
Hauptverfasser: Chen, Fangzheng, Feng, Puyu, Harrison, Matthew Tom, Wang, Bin, Liu, Ke, Zhang, Chenxia, Hu, Kelin
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creator Chen, Fangzheng
Feng, Puyu
Harrison, Matthew Tom
Wang, Bin
Liu, Ke
Zhang, Chenxia
Hu, Kelin
description Soil organic carbon (SOC) can influence atmospheric CO2 concentration and then the extent to which the climate emergency is mitigated globally. It follows the elucidation of the driving factors of cropland SOC stocks, which is fundamental to reducing soil carbon loss and promoting soil carbon sequestration. Here, we examined the influence of 16 environmental variables on SOC stocks and sequestration based on three machine learning soil mapping methods, i.e. multiple linear regression (MLR), random forest (RF) and extreme gradient boosting (XGBOOST), with 2875 observed soil samples from cropland topsoil across Hunan Province, China in 2010. We employed a structural equation model (SEM) to extricate the driving mechanisms of environmental variables on SOC stocks at the regional scale. Our results show that XGBOOST had the most reliable performance in predicting SOC stocks, explaining 66 % of the total SOC stock variation. Croplands with high SOC stocks were distributed in low-altitude and water-sufficient areas. The partial dependence of SOC on precipitation showed a trend of increasing and then slowly decreasing. In addition, the grid-based SEM results clearly presented the direct and indirect routes of environmental variables' impacts on cropland SOC stocks. Soil properties regulated by elevation, were the most influential natural factor on SOC stocks. Precipitation and elevation drove SOC stocks through direct and indirect effects respectively. Our SEM combined with machine learning approach can provide an effective explanation of the driving mechanism for SOC accumulation. We expect our proposed modelling approach can be applied to other regions and offer new insights, as a reference for mitigating cropland soil carbon loss under climate emergency conditions. [Display omitted] •Croplands with high SOC stocks were distributed in low-altitude and water-sufficient areas.•Partial dependence of SOC on precipitation showed a trend of increasing and then slowly decreasing.•Grid-based SEM results clearly explained the driving mechanism of the SOC stocks in croplands.•Soil properties regulated by elevation, are the most influential natural factor on SOC stocks.•Precipitation and elevation drive SOC stocks through direct and indirect effects respectively.
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It follows the elucidation of the driving factors of cropland SOC stocks, which is fundamental to reducing soil carbon loss and promoting soil carbon sequestration. Here, we examined the influence of 16 environmental variables on SOC stocks and sequestration based on three machine learning soil mapping methods, i.e. multiple linear regression (MLR), random forest (RF) and extreme gradient boosting (XGBOOST), with 2875 observed soil samples from cropland topsoil across Hunan Province, China in 2010. We employed a structural equation model (SEM) to extricate the driving mechanisms of environmental variables on SOC stocks at the regional scale. Our results show that XGBOOST had the most reliable performance in predicting SOC stocks, explaining 66 % of the total SOC stock variation. Croplands with high SOC stocks were distributed in low-altitude and water-sufficient areas. The partial dependence of SOC on precipitation showed a trend of increasing and then slowly decreasing. In addition, the grid-based SEM results clearly presented the direct and indirect routes of environmental variables' impacts on cropland SOC stocks. Soil properties regulated by elevation, were the most influential natural factor on SOC stocks. Precipitation and elevation drove SOC stocks through direct and indirect effects respectively. Our SEM combined with machine learning approach can provide an effective explanation of the driving mechanism for SOC accumulation. We expect our proposed modelling approach can be applied to other regions and offer new insights, as a reference for mitigating cropland soil carbon loss under climate emergency conditions. [Display omitted] •Croplands with high SOC stocks were distributed in low-altitude and water-sufficient areas.•Partial dependence of SOC on precipitation showed a trend of increasing and then slowly decreasing.•Grid-based SEM results clearly explained the driving mechanism of the SOC stocks in croplands.•Soil properties regulated by elevation, are the most influential natural factor on SOC stocks.•Precipitation and elevation drive SOC stocks through direct and indirect effects respectively.</description><identifier>ISSN: 0048-9697</identifier><identifier>EISSN: 1879-1026</identifier><identifier>DOI: 10.1016/j.scitotenv.2022.160602</identifier><identifier>PMID: 36493831</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><subject>Altitude ; Carbon - chemistry ; Carbon Sequestration ; Cropland ; Crops, Agricultural ; Driving mechanisms ; Machine learning ; Soil - chemistry ; Soil organic carbon ; Structural equation modelling</subject><ispartof>The Science of the total environment, 2023-03, Vol.862, p.160602-160602, Article 160602</ispartof><rights>2022 Elsevier B.V.</rights><rights>Copyright © 2022 Elsevier B.V. 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In addition, the grid-based SEM results clearly presented the direct and indirect routes of environmental variables' impacts on cropland SOC stocks. Soil properties regulated by elevation, were the most influential natural factor on SOC stocks. Precipitation and elevation drove SOC stocks through direct and indirect effects respectively. Our SEM combined with machine learning approach can provide an effective explanation of the driving mechanism for SOC accumulation. We expect our proposed modelling approach can be applied to other regions and offer new insights, as a reference for mitigating cropland soil carbon loss under climate emergency conditions. 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subjects Altitude
Carbon - chemistry
Carbon Sequestration
Cropland
Crops, Agricultural
Driving mechanisms
Machine learning
Soil - chemistry
Soil organic carbon
Structural equation modelling
title Cropland carbon stocks driven by soil characteristics, rainfall and elevation
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