Combining Digital Covariates and Machine Learning Models to Predict the Spatial Variation of Soil Cation Exchange Capacity

Cation exchange capacity (CEC) is a soil property that significantly determines nutrient availability and effectiveness of fertilizer applied in lands under different managements. CEC’s accurate high-resolution spatial information needed for the sustainability agricultural management on farms Nagaland state (northeast India) which are fragmented intertwined with forest ecosystem. The current study digital mapping (DSM) methodology, based CEC values determined samples obtained from 305 points region, mountainous difficult to access. Firstly, auxiliary data were three open-access sources, including indices generated time series Landsat 8 OLI satellite, topographic variables derived elevation model (DEM), WorldClim dataset. Furthermore, used Lasso regression (LR), stochastic gradient boosting (GBM), support vector (SVR), random (RF), K-nearest neighbors (KNN) machine learning (ML) algorithms systematically compared R-Core Environment Program. Model performance evaluated square root mean error (RMSE), determination coefficient (R2), absolute (MAE) 10-fold cross-validation (CV). lowest RMSE was by RF algorithm 4.12 cmolc kg−1, while others following order: SVR (4.27 kg−1) <KNN (4.45 <LR (4.67 <GBM (5.07 kg−1). In particular, WorldClim-based climate covariates such as annual temperature (BIO-1), precipitation (BIO-12), elevation, solar radiation most important all algorithms. High uncertainty (SD) have been found areas low sampling density this finding be considered future surveys.