Prediction of soil total nitrogen for rubber plantation at regional scale based on environmental variables and random forest approach

Soil total nitrogen (STN) plays an important role in soil fertility and N cycle. Detailed information about the spatial distribution of STN is vital to effective management of soil fertility and better understanding of the process of N cycle. To date, however, few studies have been conducted to digitally map the spatial variation of STN for rubber plantation at the regional scale in Hainan Island, China. In this study, a relatively new method, random forest (RF) was proposed to predict and map the spatial pattern of STN for the rubber plantation. In this study, stepwise linear regression (SLR), generalized additive mixed model (GAMM), classification and regression tree (CART), and random forest (RF) were used to predict and map the spatial distribution of STN for the rubber plantation. The results suggested that RF is a promising approach in predicting spatial distribution of STN for rubber plantation at regional scale, and can be applied to predict other soil properties in regions with complex soil-environme