Abstract
Purpose
Integrating process-based models with machine learning (ML) is an important way to derive spatially-explicit information on the dynamics of cropland soil organic carbon (SOC) stock. However, the performance of integrating process-based models with different structures remains unclear. The purpose of this study is to develop integrated models by combining multiple process-based models with ML for improving the spatiotemporal dynamics modelling of cropland SOC.
Methods
A total of 1219 cropland SOC stock data (0–20 cm, sampled in 1980, 2000, and 2015) were collected from southern Jiangsu Province of China. Three models were built by integrating either Rothamsted carbon model (RothC), microbial-mineral carbon stabilization model (MIMICS), or the both with space-for-time random forest (RF-SFTS).
Results
The overall spatiotemporal patterns of SOC dynamics that modelled by RothC, MIMICS, the integration of RothC with RF-SFTS (RF-RothC), the integration of MIMICS with RF-SFTS (RF-MIMICS), and the integration of RothC and MIMICS with RF-SFTS (RF-RothC-MIMICS) were similar, but their prediction accuracies differed significantly. The RF-RothC-MIMICS model had the best performance among the six models, being a lowest root mean squared error (RMSE) of 0.79 kg C m−2 and a highest coefficient of determination (R2) of 0.54, compared to the prediction accuracy of other models.
Conclusions
Integrating process-based models differing in structures with ML significantly improved the spatiotemporal modelling of SOC dynamics. Outputs of this study may provide some guidance on more realistic projection of SOC dynamics under future changes in climate, land use, and management practices.
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Data availability
The datasets analysed during the current study are available from the corresponding author on reasonable request.
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This work was supported by the National Natural Science Foundation of China (41971067).
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Zhang, X., Xie, E., Chen, J. et al. Modelling the spatiotemporal dynamics of cropland soil organic carbon by integrating process-based models differing in structures with machine learning. J Soils Sediments 23, 2816–2831 (2023). https://doi.org/10.1007/s11368-023-03516-9
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DOI: https://doi.org/10.1007/s11368-023-03516-9