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Farm-scale digital soil mapping of soil classes in South Africa
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SYSNO ASEP 0562777 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Farm-scale digital soil mapping of soil classes in South Africa Author(s) Flynn, Trevan Coughlin (BC-A) RID, ORCID
Rozanov, A. (ZA)
Ellis, F. (ZA)
de Clercq, W. (ZA)
Clarke, C. (ZA)Source Title SOUTH AFRICAN JOURNAL OF PLANT AND SOIL - ISSN 0257-1862
Roč. 39, č. 3 (2022), s. 175-186Number of pages 12 s. Language eng - English Country GB - United Kingdom Keywords digital elevation model ; feature selection ; conditional Latin hypercube ; predictive model ; relative efficiency ; sample design ; spatial prediction Subject RIV DF - Soil Science OECD category Soil science Method of publishing Limited access Institutional support BC-A - RVO:60077344 UT WOS 000847305800001 EID SCOPUS 85136879172 DOI 10.1080/02571862.2022.2059115 Annotation 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). Workplace Biology Centre (since 2006) Contact Dana Hypšová, eje@eje.cz, Tel.: 387 775 214 Year of Publishing 2023 Electronic address https://www.tandfonline.com/doi/full/10.1080/02571862.2022.2059115
Number of the records: 1