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

0562777 - BC 2023 RIV GB eng J - Journal Article
Flynn, Trevan Coughlin - Rozanov, A. - Ellis, F. - de Clercq, W. - Clarke, C.
Farm-scale digital soil mapping of soil classes in South Africa.
SOUTH AFRICAN JOURNAL OF PLANT AND SOIL. Roč. 39, č. 3 (2022), s. 175-186. ISSN 0257-1862
EU Projects: European Commission(XE) LIFE17 IPE/CZ/000005 - LIFE-IP: N2K Revisited
Institutional support: RVO:60077344
Keywords : digital elevation model * feature selection * conditional Latin hypercube * predictive model * relative efficiency * sample design * spatial prediction
OECD category: Soil science
Impact factor: 0.9, year: 2022
Method of publishing: Limited access
https://www.tandfonline.com/doi/full/10.1080/02571862.2022.2059115

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).
Permanent Link: https://hdl.handle.net/11104/0336920