[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122606-en":3,"doc-seo-122606-105":30,"detail-sidebar-cat-0-en-105":91},{"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122606,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Assessment of Soil Suitability Using Machine Learning in Arid and Semi-Arid Regions - Research Report","Increasing agricultural production is a major concern focused on raising income, reducing hunger, and improving well-being. Soil-suitability prediction has become a key topic for researchers, policymakers, and socio-economic analysts. This study uses physico-chemical attributes and remotely sensed phenological parameters to generate soil-suitability maps with machine-learning models in semi-arid and arid regions. An inventory of 238 suitability points and multiple parameters supports five ML algorithms. Results show phenological variables as most influential, with ROC validation achieving AUC above 0.82, and XgbTree reaching AUC 0.97, supporting sustainable development and food security.","agronomy  \nArticle  \nAssessment of Soil Suitability Using Machine Learning in Arid and Semi-Arid Regions  \nMaryem Ismaili 1,2,*, Samira Krimissa 1, Mustapha Namous 1, Abdelaziz Htitiou 1, Kamal Abdelrahman 3, Mohammed S. Fnais 3, Rachid Lhissou 4, Hasna Eloudi 5, Elhousna Faouzi 1 and Tarik Benabdelouahab 2  \nCitation: Ismaili, M.; Krimissa, S.; Namous, M.; Htitiou, A.;  \nAbdelrahman, K.; Fnais, M.S.; Lhissou, R.; Eloudi, H.; Faouzi, E.; Benabdelouahab, T. Assessment of Soil Suitability Using Machine Learning in Arid and Semi-Arid Regions. Agronomy 2023, 13, 165 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)agronomy13010165  \nAcademic Editors: Xiuliang Jin, Hao Yang, Zhenhai Li, Changping Huang and Dameng Yin  \nReceived: 30 November 2022  \nRevised: 20 December 2022  \nAccepted: 29 December 2022  \nPublished: 4 January 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Data4Earth Laboratory, Department of Geology, Sultan Moulay Slimane University, Beni Mellal 23000, Morocco  \n2 National Agronomic Research Institute, Rabat 10000, Morocco  \n3 Department of Geology & Geophysics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia  \n4 Centre ETE, INRS, 490 rue de la Couronne, Qu²bec, QC GIK 9A9, Canada  \n5 Department of Geology, Ibn Zohr University, Agadir 80000, Morocco  \n* [Correspondence: maryem.ismaili17@gmail.com](Correspondence: maryem.ismaili17@gmail.com)  \nAbstract: Increasing agricultural production is a major concern that aims to increase income, reduce hunger, and improve other measures of well-being. Recently, the prediction of soil-suitability has become a primary topic of rising concern among academics, policymakers, and socio-economic analysts to assess dynamics of the agricultural production. This work aims to use physico-chemical and remotely sensed phenological parameters to produce soil-suitability maps (SSM) based on Machine Learning (ML) Algorithms in a semi-arid and arid region. Towards this goal an inventory of 238 suitability points has been carried out in addition to14 physico-chemical and 4 phenological parameters that have been used as inputs of machine-learning approaches which are five MLA prediction, namely RF, XgbTree, ANN, KNN and SVM. The results showed that phenological parameters were found to be the most influential in soil-suitability prediction. The validation of the Receiver Operating Characteristics (ROC) curve approach indicates an area under the curve and an AUC of more than 0.82 for all models. The best results were obtained using the XgbTree with an AUC = 0.97 in comparison to other MLA. Our findings demonstrate an excellent ability for ML models to predict the soil-suitability using physico-chemical and phenological parameters. The approach developed to map the soil-suitability is a valuable tool for sustainable agricultural development, and it can play an effective role in ensuring food security and conducting a land agriculture assessment.  \nKeywords: precision agriculture; sentinel-2; random forest; XgbTree; digital soil mapping; remote sensing; agricultural management  \n1. Introduction  \nAs the world's population is rapidly growing day by day, so too does the pressure to expand and intensify the use of agricultural land; additionally, putting a strain on natural resources to meet the rising demand for food and agricultural products [1–3] . This pressure can cause a degradation in the potential of agricultural lands, causing a list of issues, such as soil degradation, waterlogging, salinization/alkalization, and pollution [4], which have a direct impact on food production and food security [5–8] . In addition,","cbCaioKmoDW1Eacn","https://ap.wps.com/l/cbCaioKmoDW1Eacn","pdf",3433667,1,16,"English","en",105,"# Abstract\n# Keywords\n# 1. Introduction","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To generate soil-suitability maps in arid and semi-arid regions using machine-learning models fed with physico-chemical and remotely sensed phenological parameters.\"},{\"question\":\"Which input data and parameters are used for model training?\",\"answer\":\"The approach uses 238 suitability points plus 14 physico-chemical and 4 phenological parameters as inputs to five machine-learning algorithms.\"},{\"question\":\"Which machine-learning model performed best, and how was it validated?\",\"answer\":\"XgbTree achieved the best performance with AUC = 0.97. Validation used ROC curve analysis, with all models reaching AUC greater than 0.82.\"}]","Assessment of Soil Suitability Using Machine Learning in Arid and Semi-Arid Regions - Research Report | PDF",1785811697,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"assessment-of-soil-suitability-using-machine-learning-in-arid-and-semi-arid-regions-research-report","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/assessment-of-soil-suitability-using-machine-learning-in-arid-and-semi-arid-regions-research-report/122606/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the study?","Question",{"text":75,"@type":76},"To generate soil-suitability maps in arid and semi-arid regions using machine-learning models fed with physico-chemical and remotely sensed phenological parameters.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which input data and parameters are used for model training?",{"text":80,"@type":76},"The approach uses 238 suitability points plus 14 physico-chemical and 4 phenological parameters as inputs to five machine-learning algorithms.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine-learning model performed best, and how was it validated?",{"text":84,"@type":76},"XgbTree achieved the best performance with AUC = 0.97. 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