Spatial variability and potential controls of soil organic matter in the Eastern Dongting Lake Plain in southern China

Information related to spatial distribution and dominants of soil organic matter (SOM) is critical for evaluating soil quality and assessing the carbon sequestration capacity, which play essential role in soil management and climate change mitigation. Until now, no reported research has conducted an extensive survey to predict SOM content, analysed SOM spatial variability, and determined the main controls of SOM variation in areas around Dongting Lake in southern China. Therefore, this study aims to (1) explore the spatial variability of SOM content; (2) build a model to quantitatively predict SOM content with various sources of covariates and with the RF method; and (3) identify potential controls of SOM based on the relative importance of variables. A total of 8040 soil samples were collected from Yueyang County in Eastern Dongting Lake Plain. Ordinary kriging was used to produce a map of SOM and then the random forest algorithms were used to predict SOM content with 17 covariates covered terrain attributes, land use, climate, soil management policies, soil properties, and geologic information. Finally, the main dominants of SOM variability were identified. The SOM content in the survey region varied from 4.00 to 446.60 g kg−1 and had an average content of 33.17 g kg−1, which indicated fertile soil in the study area. SOM presented strong spatial variability in the area under study. The high SOM values were majorly located in the northeast and southwest parts of the survey regions. The R2 of our developed model was 0.74 and the RMSE was 0.16 g kg−1. The main controls of SOM variability in the study area were available phosphorus, precipitation, soil group, rotation system, available potassium, altitude, and slope. Our developed model showed a good performance to estimate SOM content using auxiliary variables. Soil properties and agricultural management measures played the most important roles in predicting SOM in the study area. Results obtained from this study could provide new insights for estimating SOM and contribute to the sustainable development of agriculture and better regulation of soil quality in the study area.