[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125212-en":3,"doc-seo-125212-105":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":4,"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},125212,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Enhancing Soil Texture and Bulk Density Mapping Using Soil Grids and Machine Learning - A Comparative Analysis with Observed Data","Digital soil mapping supports reliable characterization of soil variability and strengthens sustainable land management decisions. This study evaluates the accuracy of SoilGrids for the Kurdistan Region of Iraq by comparing global predictions with ground truth sampling data. Clay, silt, and sand fractions and bulk density are assessed through comparative error analysis, showing significant differences between observed values and SoilGrids estimates. Machine learning, especially Extreme Gradient Boosting (XGBoost), improves estimation accuracy, reducing mean errors and improving RMSE and MAPE across soil fractions and bulk density.","Research outputs 2022 to 2026  \n10-29-2024  \nEnhancing soil texture and bulk density mapping using soil gridsand machine learning: A comparative analysis with observed data  \nAram Ali  \nIsmael O. Ismael  \nHewa T. Mustafa  \nDiman Krwanji  \nEdith Cowan University Akram O. Esmail  \nFollow this and additional works at: [https://ro.ecu.edu.au/ecuworks2022-2026](https://ro.ecu.edu.au/ecuworks2022-2026)  \n Part of the Natural Resources and Conservation Commons  \n10.9734/asrj/2024/v8i4163  \nAli, A., Ismael, I. O., Mustafa, H. T., Krwanji, D., & Esmail, A. O. (2024) . Enhancing soil texture and bulk density mapping using soil grids and machine learning: A comparative analysis with observed data. Asian Soil Research  \nJournal, 8(4), 61–78 . [https://doi.org/10.9734/asrj/2024/v8i4163](https://doi.org/10.9734/asrj/2024/v8i4163)  \n[This Journal Article is posted at Research Online.](This Journal Article is posted at Research Online.)[ ](This Journal Article is posted at Research Online.)[https://ro.ecu.edu.au/ecuworks2022-2026/5587](https://ro.ecu.edu.au/ecuworks2022-2026/5587)  \nAsian Soil Research Journal  \nVolume 8, Issue 4, Page 61-78, 2024; Article no.ASRJ.125367 ISSN: 2582-3973  \nEnhancing Soil Texture and Bulk Density Mapping Using Soil Grids and Machine Learning: A Comparative Analysis with Observed Data  \nAram Ali a,b* , Ismael O. Ismael a, Hewa T. Mustafa a, Diman Krwanji c,d and Akram O. Esmail a  \na Soil and Water Department, College of Agricultural Engineering Sciences, Salahaddin UniversityErbil, Erbil, Kurdistan Region, Iraq.  \nb University of Southern Queensland, Centre for Sustainable Agricultural Systems, West St, Toowoomba, QLD 4350, Australia.  \nc Plant Protection Department, College of Agricultural Engineering Sciences, Salahaddin UniversityErbil, Erbil, Kurdistan Region, Iraq.  \nd Edith Cowan University, 270 Joondalup Drive, Joondalup, WA 6027, Australia.  \nAuthors’ contributions  \nThis work was carried out in collaboration among all authors. All authors read and approved the final manuscript.  \nArticle Information  \nDOI: [https://doi.org/10.9734/asrj/2024/v8i4163](https://doi.org/10.9734/asrj/2024/v8i4163)  \nOpen Peer Review History:  \nThis journal follows the Advanced Open Peer Review policy. Identity of the Reviewers, Editor(s) and additional Reviewers, peer review comments, different versions of the manuscript, comments of the editors, etc are available here:  \n[https://www.sdiarticle5.com/review-history/125367](https://www.sdiarticle5.com/review-history/125367)  \nOriginal Research Article  \nReceived: 20/08/2024  \nAccepted: 22/10/2024  \nPublished: 29/10/2024  \nABSTRACT  \nDigital soil mapping plays a crucial role in understanding soil variability and informing sustainable land management practices. This study focuses on the Kurdistan Region of Iraq (KRI), evaluating the accuracy of SoilGrids, a global-scale soil mapping initiative, and exploring the efficacy of machine learning algorithms in refining soil properties estimations. The aim of this research was to assess and represent the physical parameters of soils effectively by comparing ground truth soil sampling data with data obtained from SoilGrids regarding clay, silt, and sand fractions and bulk density. Comparative analyses were conducted between ground truth soil sampling data and SoilGrids predictions, revealing significant differences across soil mineral fractions including clay, silt, sand fractions, and bulk density. The results showed that the mean clay fraction in the ground truth dataset differed notably from SoilGrids estimation, with a Mean Absolute Deviation (MAD) of 124.0 g kg-1 and Root Mean Square Error (RMSE) of 152.5. However, the integration of machine learning algorithms, particularly the Extreme Gradient Boosting (XG Boost) algorithm, showed promising results in improving accuracy. The XG Boost algorithm exhibited a relatively low MAD of 97.9 g kg-1 for clay fractions, indicating a better approximation of observed values compared to SoilGr","cbCaijsIXvamvxxe","https://ap.wps.com/l/cbCaijsIXvamvxxe","pdf",2234303,1,19,"English","en",105,"# Abstract\n# Introduction\n# Materials and Methods\n## Soil sampling and ground truth data\n## SoilGrids predictions\n## Machine learning models and evaluation\n# Results\n## Soil fractions comparison\n## Bulk density comparison\n# Discussion\n# Conclusion and Future Work","[{\"question\":\"What does the study evaluate in the Kurdistan Region of Iraq?\",\"answer\":\"The study evaluates how accurately SoilGrids estimates key soil properties—clay, silt, sand fractions, and bulk density—using comparisons with ground truth soil sampling data.\"},{\"question\":\"Which machine learning algorithm shows the most promising results?\",\"answer\":\"Extreme Gradient Boosting (XGBoost) shows promising performance by improving approximation of observed values compared with SoilGrids alone.\"},{\"question\":\"What improvements are reported when using machine learning for soil mapping?\",\"answer\":\"Significant percentage improvements are reported in RMSE and MAPE across multiple soil fractions and bulk density, ranging from roughly 15% for clay to about 35% for sand fractions and about 20% for bulk density.\"}]","Enhancing Soil Texture and Bulk Density Mapping Using Soil Grids and Machine Learning - 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