Farm-scale digital soil mapping of soil classes in South Africa

Abstract

This study involved the evaluation of farm-scale digital soil classification in the Sandspruit catchment of the Western Cape Province, South Africa. The study aimed to evaluate a digital soil mapping (DSM) method, from feature selection, spatial predictions and sample design. The results showed that feature selection with the least absolute shrinkage and selection operator (LASSO) technique is a robust method as it had a high relative efficiency and achieved the highest accuracy for three out of the four soil classes predicted. This implies that covariate selection is the most notable aspect in DSM at the farm-scale. The top-performing predictive models achieved satisfactory results for soil associations (kappa = 0.64, accuracy = 74%), presence of a bleached topsoil (kappa = 0.64, accuracy = 74%) and soil depth (kappa = 0.48, accuracy = 74%), whereas only moderate results were achieved for soil texture (kappa = 0.43, accuracy = 66%). Lastly, the expert sampling locations had a higher average probability of occurrence (geographic and feature space distribution coverage) yet achieved similar performance to conditioned Latin hypercube sampling (cLHS).