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 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.

Item Type:	Refereed Article
Keywords:	Soil; carbon; organic; machine learning; measurement; structural equation modeling; rainfall; elevation; climate; weather; extremes; climate emergency; climate crisis; water-use efficiency; SOC; stocks; sequestration; environment; food security
Research Division:	Agricultural, Veterinary and Food Sciences
Research Group:	Agriculture, land and farm management
Research Field:	Agricultural systems analysis and modelling
Objective Division:	Environmental Policy, Climate Change and Natural Hazards
Objective Group:	Adaptation to climate change
Objective Field:	Climate change adaptation measures (excl. ecosystem)
UTAS Author:	Harrison, MT (Associate Professor Matthew Harrison)
UTAS Author:	Liu, K (Dr Ke Liu)
ID Code:	154374
Year Published:	2022
Deposited By:	TIA - Research Institute
Deposited On:	2022-11-27
Last Modified:	2023-02-21
Downloads:	0