Comparison of terrain-based drift models to improve the quality of soil predictive mapping at a field scale

The ecological, economic, and agricultural benefits of accurate interpolation of spatial distribution patterns of soil properties are well recognized. In the present study different approaches to build the drift model for the regression kriging are analyzed and compared for estimating the spatial variation of humus and physical clay at soil depth (0-20 cm) in Tatarstan, Russian Federation. The soil sampling was performed according to an agrochemical sampling design: the field was divided into 60 sections; within each section 12-15 sampling points were taken using a hand auger at the depth of 10-20 cm to produce one mixed sample. Three terrain-based drift models: principal component regression (PCR), partial least squares (PLS), and random forest were used to predict the spatial distribution of humus and physical clay. Cross-validation was applied to evaluate the accuracy of interpolation methods through mean error (ME), root mean square error (RMSE), root mean square standardized error (RMSSE), and ratio of the observed and the predicted variances (RVar). The results indicate that ordinary kriging (OK) is superior when the data have strong spatial dependence. But in other cases, the PLS approach had the best prediction performance