Spatial variability and influencing factors of topsoil organic carbon and total nitrogen in agricultural areas of North China.

Soil organic carbon (SOC) and soil total nitrogen (STN) play crucial roles in the sustainability of soil production and ecological service function. Spatially explicit estimations of SOC and STN and the identification of factors influencing the spatial variability of them are prerequisites for formulating and implementing site-specific management measures to improve soil quality. Based on 613 topsoil observations and environmental covariates, this study revealed the spatial variability of SOC and STN in agricultural areas of North China employing the random forest (RF) model, and identified the controlling factors of them. The results indicated that SOC and STN in agricultural areas of North China displayed a similar spatial distribution pattern (high in the north and low in the south), with the mean value of 14.03 g kg-1 and 1.05 g kg-1, respectively. The spatial variability of SOC and STN in agricultural areas across of North China was found to be closely associated with the natural environmental factors such as mean annual temperature (MAT), mean annual elevation (MAE), elevation, et al. The spatial location, expressed in longitude, latitude and altitude coordinate values, was measured to explain the spatial variability of SOC and STN exceeding 40% and 30%, respectively, reflecting the value of spatial location as a covariate in digital soil mapping (DSM). The RF model using multi-temporal NDVI data had a higher accuracy and more stable output than using single-temporal data. Taking into account both model accuracy and simplicity, the multi-year monthly annual data (AvgMonths) of NDVI data was recommended to import RF in DSM. The results provide basic data for SOC and STN management tailored to local conditions in agricultural areas North of China, and also provide reference for optimizing the environmental covariate sets and their usage in DSM.