{"id":13711,"date":"2025-06-23T05:21:00","date_gmt":"2025-06-23T10:21:00","guid":{"rendered":"https:\/\/www.agron.iastate.edu\/glsi\/?p=13711"},"modified":"2025-09-25T10:22:47","modified_gmt":"2025-09-25T15:22:47","slug":"mapping-soil-drainage-classes-comparing-expert-knowledge-and-machine-learning-strategies","status":"publish","type":"post","link":"https:\/\/www.agron.iastate.edu\/glsi\/manuscripts\/mapping-soil-drainage-classes-comparing-expert-knowledge-and-machine-learning-strategies\/","title":{"rendered":"Mapping Soil Drainage Classes: Comparing Expert Knowledge and Machine Learning Strategies"},"content":{"rendered":"
\n

Soil drainage is an essential factor that influences plant growth and various biophysical processes, such as nutrient cycling and greenhouse gas fluxes. Therefore, soil drainage maps are fundamental tools for managing crops, forests, and the environment. This study compared two approaches to mapping soil drainage classes in the state of S\u00e3o Paulo, Brazil, using geographic information systems (GIS). The first approach employed expert knowledge (EK) to develop a simple model based on soil color and texture, while the second used machine learning (ML) with an extensive set of covariates and a decision tree algorithm. To evaluate the full operational implementation of soil mapping, this study assessed the two approaches in terms of accuracy, labor efficiency, transferability, interpretability, and agreement\/disagreement statistical methods. In terms of accuracy, the ML-based strategy showed greater agreement with the reference map (53%) compared to the EK approach (50%). However, the EK strategy was more time- and resource-efficient, as well as being more transferable and interpretable due to the simplicity of its rules based on soil properties. Given its higher interpretability and ease of application, the EK approach was recommended as the most suitable for operational soil drainage mapping in tropical environments.<\/p>\n<\/div><\/div>\n\n

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C\u00e9sar de Mello, D., N.E.Q. Silvero, B.A. Miller, N.A. Rosin, J.T.F. Rosas, B.A. Bartsch, G.V. Veleso, J.J.M. Novais, R. Falcioni, M.R. Nanni, M.R. Alves, E.I. Fernandes-Filho, U.J. Santos, J.A.M. Dematt\u00ea. 2025. Mapping soil drainage classes: Comparing expert knowledge and machine learning strategies. Soil Advances 3:100028. doi: 10.1016\/j.soilad.2024.100028<\/a>.<\/p>\n<\/div><\/div>\n\n

\n

<\/p>\n<\/div><\/div>","protected":false},"excerpt":{"rendered":"

Soil drainage is an essential factor that influences plant growth and various biophysical processes, such as nutrient cycling and greenhouse gas fluxes. Therefore, soil drainage maps are fundamental tools for managing crops, forests, and the environment. This study compared two approaches to mapping soil drainage classes in the state of S\u00e3o Paulo, Brazil, using geographic […]<\/p>\n","protected":false},"author":3216,"featured_media":13713,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"ngg_post_thumbnail":0,"footnotes":""},"categories":[5,7],"tags":[329,34,121],"class_list":["post-13711","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-manuscripts","category-miller","tag-brazil","tag-digital-soil-mapping","tag-soil-maps"],"acf":[],"featured_image_urls_v2":{"full":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2025\/06\/Mapping-soil-drainage-classes-Brazil.jpg",303,259,false],"thumbnail":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2025\/06\/Mapping-soil-drainage-classes-Brazil-150x150.jpg",150,150,true],"medium":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2025\/06\/Mapping-soil-drainage-classes-Brazil-300x256.jpg",300,256,true],"medium_large":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2025\/06\/Mapping-soil-drainage-classes-Brazil.jpg",303,259,false],"large":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2025\/06\/Mapping-soil-drainage-classes-Brazil.jpg",303,259,false],"1536x1536":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2025\/06\/Mapping-soil-drainage-classes-Brazil.jpg",303,259,false],"2048x2048":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2025\/06\/Mapping-soil-drainage-classes-Brazil.jpg",303,259,false]},"post_excerpt_stackable_v2":"

Soil drainage is an essential factor that influences plant growth and various biophysical processes, such as nutrient cycling and greenhouse gas fluxes. Therefore, soil drainage maps are fundamental tools for managing crops, forests, and the environment. This study compared two approaches to mapping soil drainage classes in the state of S\u00e3o Paulo, Brazil, using geographic information systems (GIS). The first approach employed expert knowledge (EK) to develop a simple model based on soil color and texture, while the second used machine learning (ML) with an extensive set of covariates and a decision tree algorithm. To evaluate the full operational implementation…<\/p>\n","category_list_v2":"Manuscripts<\/a>, Miller<\/a>","author_info_v2":{"name":"Bradley Miller","url":"https:\/\/www.agron.iastate.edu\/glsi\/author\/millerba\/"},"comments_num_v2":"0 comments","yoast_head":"\nMapping Soil Drainage Classes: Comparing Expert Knowledge and Machine Learning Strategies - Geospatial Laboratory for Soil Informatics<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.agron.iastate.edu\/glsi\/manuscripts\/mapping-soil-drainage-classes-comparing-expert-knowledge-and-machine-learning-strategies\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Mapping Soil Drainage Classes: Comparing Expert Knowledge and Machine Learning Strategies - Geospatial Laboratory for Soil Informatics\" \/>\n<meta property=\"og:description\" content=\"Soil drainage is an essential factor that influences plant growth and various biophysical processes, such as nutrient cycling and greenhouse gas fluxes. 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