[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126067-en":3,"doc-seo-126067-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126067,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Mapping soil drainage classes - Comparing expert knowledge and machine learning strategies","Soil drainage is an essential factor shaping plant growth and key biophysical processes, including nutrient cycling and greenhouse gas fluxes, making drainage maps important for managing crops, forests, and the environment. This study compared two GIS-based approaches to map soil drainage classes in São Paulo, Brazil: an expert-knowledge model using soil color and texture, and a machine-learning decision-tree model using many covariates. Results showed higher accuracy agreement for ML (53%) versus EK (50%), while EK was more time-, resource-efficient, transferable, and interpretable, so it is recommended for operational tropical mapping.","Soil Advances 3 (2025) 100028  \nContents lists available at ScienceDirect  \nSoil Advances  \njournal [homepage:](homepage: www.sciencedirect.com/journal/soil-advances)[ www.sciencedirect.com/journal/soil-advances](homepage: www.sciencedirect.com/journal/soil-advances)  \n| Mapping soil drainage classes: Comparing expert knowledge and machine   learning strategies\u003Cbr>Danilo C´esar de Melloa, N´elida E.Q. Silveroa, Bradley A. Miller b, Nicolas Augusto Rosina, Jorge Tadeu Fim Rosasa, Bruno dos Anjos Bartscha, Gustavo Vieira Veloso a,\u003Cbr>Jean Jesus Macedo Novais a, Renan Falcionic, Marcos Rafael Nannic, Marcelo Rodrigo Alves d, Elpídio In´acio Fernandes-Filho e, Uemeson Jos´e dos Santos f, Jos´e Alexandre Melo Demattˆe a,*\u003Cbr>a Department of Soil Science, Luiz de Queiroz College of Agriculture, University of S˜ao Paulo, Av. Pa´dua Dias, 11, Piracicaba, S˜ao Paulo 13418-260, Brazil b Department of Agronomy, Iowa State University, Ames, IA, USA\u003Cbr>c Department of Agronomy, State University of Maring´a, Av. Colombo, 5790, Maring´a, Paran´a 87020-900, Brazil\u003Cbr>d Department of Soil Science, University of Western Paulista, Rod. Raposo Tavares, km 572-Limoeiro, Presidente Prudente, S˜ao Paulo, Brazil e Department of Soil and Plant Nutrition, Federal University of Viçosa, campus UFV, Viçosa 36570-900, Brazil\u003Cbr>f Federal Institute of Education, Science, and Technology of Para´, Campus Santar´em, Av. Mal. Castelo Branco, 621, Interventoria, Santar´em, PA 68020-820, Brazil |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Environmental covariates Ferralsol\u003Cbr>Predictive modeling Remote sensing\u003Cbr>Soil color |  | 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˜ao 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 MLbased 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. |\n\n1. Introduction  \nDemand for detailed soil information has increased over the years asa means of supporting not only land management for agriculture but also the roles of soil in ecosystems (Brevik et al., 2016). The gap between this demand and the state of legacy soil maps is becoming even greater in developing countries such as Brazil. Here, most available soil maps are incomplete or insufficient to support public policies and other applications involving soil resources (Santos et al., 2015).  \nAn important characteristic lacking in most Brazilian maps is the soil drainage class, defined as the frequency and duration of wet periods or  \ndegree and frequency the soil matrix is free of water saturation (Soil Science Division Staff, 2017). A soil drainage class is used as an indicator of the general conditions of water movement in the soil, which is primarily influenced by climate and regulated by soil texture, structure, and topography (Troeh, 1964; Gerardin and Duerue, 1990; Fa","cbCaib1qS1jlAjxc","https://ap.wps.com/l/cbCaib1qS1jlAjxc","pdf",7904113,7,1,11,"English","en",105,"# Introduction\n## Soil drainage classes and their importance\n# Methods\n## GIS mapping approaches (expert knowledge vs machine learning)\n## Model inputs and algorithms\n# Results and Discussion\n## Accuracy, labor efficiency, transferability, and interpretability","[{\"question\":\"为什么土壤排水（soil drainage）信息对环境与农业管理很重要？\",\"answer\":\"土壤排水影响植物生长，并控制多种土壤过程，如养分循环和温室气体通量，因此排水制图对作物与森林管理及环境用途至关重要。\"},{\"question\":\"文中如何构建两种土壤排水等级制图方法？\",\"answer\":\"第一种方法使用专家知识，基于土壤颜色和质地建立简化模型；第二种方法使用机器学习，采用大量协变量并用决策树算法完成制图。\"},{\"question\":\"与机器学习策略相比，专家知识策略的主要优势是什么？\",\"answer\":\"尽管机器学习在准确性一致性上更高（53% vs 50%），专家知识方法在时间与资源效率方面更优，并且规则更简单，因此具有更好的可转移性与可解释性，适合在热带环境中业务化应用。\"}]","Mapping soil drainage classes - Comparing expert knowledge and machine learning strategies | PDF",1785902888,28,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"mapping-soil-drainage-classes-comparing-expert-knowledge-and-machine-learning-strategies","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/mapping-soil-drainage-classes-comparing-expert-knowledge-and-machine-learning-strategies/126067/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"为什么土壤排水（soil drainage）信息对环境与农业管理很重要？","Question",{"text":77,"@type":78},"土壤排水影响植物生长，并控制多种土壤过程，如养分循环和温室气体通量，因此排水制图对作物与森林管理及环境用途至关重要。","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"文中如何构建两种土壤排水等级制图方法？",{"text":82,"@type":78},"第一种方法使用专家知识，基于土壤颜色和质地建立简化模型；第二种方法使用机器学习，采用大量协变量并用决策树算法完成制图。",{"name":84,"@type":75,"acceptedAnswer":85},"与机器学习策略相比，专家知识策略的主要优势是什么？",{"text":86,"@type":78},"尽管机器学习在准确性一致性上更高（53% vs 50%），专家知识方法在时间与资源效率方面更优，并且规则更简单，因此具有更好的可转移性与可解释性，适合在热带环境中业务化应用。","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,117,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":115,"slug":116},6,"Technology",50,"technology",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":108,"slug":139},19,"General","general"]