[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119420-en":3,"doc-seo-119420-105":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},119420,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Estimation of Soil Temperature for Agricultural Applications in South Africa Using Machine-Learning Methods","This study investigates machine-learning approaches as alternative, cost-effective tools for estimating soil temperature for agricultural use in South Africa. Four models—multiple linear regression, artificial neural networks, random forest, and decision tree—were trained and validated using meteorological data from seven stations covering diverse climatic conditions. Daily soil temperature was predicted at six depths (10–80 cm). Results indicate reasonably accurate estimates across depths, with Nash–Sutcliffe efficiency from 0.74 to 0.87 overall, RMSE \u003C 2.79 °C, and random forest achieving the highest accuracy. Climate-specific models outperform aggregated ones.","Authors:  \nLindumusa Myeni1 ,2 Tlotlisang Nkhase3 Ramontsheng Rapolaki4 ,5  \nZaid Bello6 ,7  \nMokhele E. Moeletsi3 ,7  \nAFFILIAtIoNs:  \n1Unit for Environmental Sciences and Management, North-West University, Potchefstroom, South Africa 2Department of Geography and Environmental Studies, School of Geo-and Spatial Sciences, North-West University, Mahikeng, South Africa 3Agricultural Research Council – Natural Resources and Engineering, Pretoria, South Africa  \n4South African Weather Service, Marine Research Unit, Cape Town, South Africa 5Department of Geography, University of the Free State, Bloemfontein, South Africa  \n6Agricultural Research Council – Grain Crops, Potchefstroom, South Africa 7Centre for Global Change, University of Limpopo, Sovenga, South Africa  \nCorrEsPoNDENCE to:  \nLindumusa Myeni  \nEMAIL:  \n[lindumusa.myeni@nwu.ac.za](lindumusa.myeni@nwu.ac.za)  \nDAtEs:  \nreceived: 19 Mar. 2024  \nrevised: 09 Apr. 2025  \nAccepted: 09 Apr. 2025  \nPublished: 29 May 2025  \nhoW to CItE:  \nMyeni L, Nkhase T, Rapolaki R, Bello Z, Moeletsi ME. Estimation of soil temperature for agricultural applications in South Africa using machine-learning methods. S Afr J Sci. 2025;121(5/6), Art.  \n\\#18235 . [https://doi.org/10.17](https://doi.org/10.17)[ ](https://doi.org/10.17)[159/sajs.2025/18235](159/sajs.2025/18235)  \nArtICLE INCLuDEs:  \n☒ Peer review  \n☒ Supplementary material  \nDAtA AVAILABILItY:  \n☐ Open data set  \n☐ All data included ☒ On request from author(s)☐ Not available  \n☐ Not applicable  \nEDItors:  \nJennifer Fitchett  Pfananani Ramulifho   \nKEYWorDs:  \nagricultural applications, artificial intelligence, climatic zones, precision agriculture, random forests  \nFuNDING:  \nSouth African National Research Foundation (CSRP2330503101419)  \n© 2025. The Author(s) . Published under a Creative Commons Attribution Licence.  \nEstimation of soil temperature for agricultural applications in South Africa using machine-learning methods  \nThis study was undertaken to investigate the potential of using machine-learning approaches as alternative and cost-effective tools for estimating soil temperature from readily available meteorological data for agricultural applications in South Africa. Four machine-learning models – multiple linear regression, artificial neural networks, random forest and decision tree – were developed and tested to estimate daily soil temperature at six soil depths (viz. 10, 20, 30, 40, 60 and 80 cm) using meteorological data acquired from seven stations, representing diverse climatic conditions in South Africa. The data were randomly split into two parts: the first 80% of the data set was used for training, while the remaining 20% was utilised to validate the models. The results showed that soil temperature at various depths can be reasonably estimated by different generic machine-learning models, with average Nash–Sutcliffe efficiency values ranging from 0.74 for decision tree to 0.87 for random forest models and root mean square error values of less than 2.79 °C for all models. Among the evaluated models, random forest models showed the highest estimation accuracy across different soil depths and climatic conditions, with average Nash–Sutcliffe efficiency values ranging from 0.87 to 0.95. This study indicated that the performance of climate-specific models was better than that of the aggregated ones. Therefore, it is recommended that machine-learning approaches, particularly RF models, be developed for specific climatic conditions where possible to achieve better soil temperature estimations. The developed models can be applied with caution in other regions with similar climatological and pedological properties.  \nsignificance:  \n• This study evaluated the performance of four machine-learning models in estimating daily ST at six depths using meteorological data in diverse climatic conditions in South Africa.  \n• The results showed that ST at various depths can be reasonably estimated using different machine learning models,","cbCaifmD29928YpA","https://ap.wps.com/l/cbCaifmD29928YpA","pdf",3577876,1,11,"English","en",105,"# Introduction\n## Soil temperature relevance to agricultural processes\n## Drivers of soil temperature variation\n# Methods\n## Machine-learning models and training/validation design\n## Input meteorological data and study locations\n# Results\n## Model performance across depths and climates\n## Comparison of climate-specific versus aggregated models\n# Conclusion\n## Recommendations for RF models under similar conditions","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To evaluate machine-learning methods for estimating daily soil temperature for agricultural applications in South Africa using readily available meteorological data.\"},{\"question\":\"Which machine-learning models were developed and tested?\",\"answer\":\"Multiple linear regression, artificial neural networks, random forest, and decision tree models were developed and tested for soil temperature estimation.\"},{\"question\":\"How many soil depths and what performance indicators were used?\",\"answer\":\"Soil temperature was estimated at six depths (10, 20, 30, 40, 60, and 80 cm). Performance was assessed using Nash–Sutcliffe efficiency and RMSE, with RMSE reported as less than 2.79 °C for all models.\"}]","Estimation of Soil Temperature for Agricultural Applications in South Africa Using Machine-Learning Methods | PDF",1785724205,28,{"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},"estimation-of-soil-temperature-for-agricultural-applications-in-south-africa-using-machine-learning-methods","",{"@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/estimation-of-soil-temperature-for-agricultural-applications-in-south-africa-using-machine-learning-methods/119420/",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","2026-08-03",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 evaluate machine-learning methods for estimating daily soil temperature for agricultural applications in South Africa using readily available meteorological data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine-learning models were developed and tested?",{"text":80,"@type":76},"Multiple linear regression, artificial neural networks, random forest, and decision tree models were developed and tested for soil temperature estimation.",{"name":82,"@type":73,"acceptedAnswer":83},"How many soil depths and what performance indicators were used?",{"text":84,"@type":76},"Soil temperature was estimated at six depths (10, 20, 30, 40, 60, and 80 cm). Performance was assessed using Nash–Sutcliffe efficiency and RMSE, with RMSE reported as less than 2.79 °C for all models.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]