LimeSoDa: A dataset collection for benchmarking of machine learning regressors in digital soil mapping
Digital soil mapping (DSM) relies on a broad pool of statistical methods, yet determining the optimal method for a given context remains challenging and contentious. Benchmarking studies on multiple datasets are needed to reveal strengths and limitations of commonly used methods. Existing DSM studie...
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2025-07-01
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| author | Jonas Schmidinger Sebastian Vogel Viacheslav Barkov Anh-Duy Pham Robin Gebbers Hamed Tavakoli Jose Correa Tiago R. Tavares Patrick Filippi Edward J. Jones Vojtech Lukas Eric Boenecke Joerg Ruehlmann Ingmar Schroeter Eckart Kramer Stefan Paetzold Masakazu Kodaira Alexandre M.J.-C. Wadoux Luca Bragazza Konrad Metzger Jingyi Huang Domingos S.M. Valente Jose L. Safanelli Eduardo L. Bottega Ricardo S.D. Dalmolin Csilla Farkas Alexander Steiger Taciara Z. Horst Leonardo Ramirez-Lopez Thomas Scholten Felix Stumpf Pablo Rosso Marcelo M. Costa Rodrigo S. Zandonadi Johanna Wetterlind Martin Atzmueller |
| author_facet | Jonas Schmidinger Sebastian Vogel Viacheslav Barkov Anh-Duy Pham Robin Gebbers Hamed Tavakoli Jose Correa Tiago R. Tavares Patrick Filippi Edward J. Jones Vojtech Lukas Eric Boenecke Joerg Ruehlmann Ingmar Schroeter Eckart Kramer Stefan Paetzold Masakazu Kodaira Alexandre M.J.-C. Wadoux Luca Bragazza Konrad Metzger Jingyi Huang Domingos S.M. Valente Jose L. Safanelli Eduardo L. Bottega Ricardo S.D. Dalmolin Csilla Farkas Alexander Steiger Taciara Z. Horst Leonardo Ramirez-Lopez Thomas Scholten Felix Stumpf Pablo Rosso Marcelo M. Costa Rodrigo S. Zandonadi Johanna Wetterlind Martin Atzmueller |
| author_sort | Jonas Schmidinger |
| collection | DOAJ |
| description | Digital soil mapping (DSM) relies on a broad pool of statistical methods, yet determining the optimal method for a given context remains challenging and contentious. Benchmarking studies on multiple datasets are needed to reveal strengths and limitations of commonly used methods. Existing DSM studies usually rely on a single dataset with restricted access, leading to incomplete and potentially misleading conclusions. To address these issues, we introduce an open-access dataset collection called Precision Liming Soil Datasets (LimeSoDa). LimeSoDa consists of 31 field- and farm-scale datasets from various countries. Each dataset has three target soil properties: (1) soil organic matter or soil organic carbon, (2) clay content and (3) pH, alongside a set of features. Features are dataset-specific and were obtained by optical spectroscopy, proximal- and remote soil sensing. All datasets were aligned to a tabular format and are ready-to-use for modeling. We demonstrated the use of LimeSoDa for benchmarking by comparing the predictive performance of four learning algorithms across all datasets. This comparison included multiple linear regression (MLR), support vector regression (SVR), categorical boosting (CatBoost) and random forest (RF). The results showed that although no single algorithm was universally superior, certain algorithms performed better in specific contexts. MLR and SVR performed better on high-dimensional spectral datasets, likely due to better compatibility with principal components. In contrast, CatBoost and RF exhibited considerably better performances when applied to datasets with a moderate number (<20) of features. These benchmarking results illustrate that the performance of statistical methods can be highly context-dependent. LimeSoDa therefore provides an important resource for improving the development and evaluation of statistical methods in DSM. |
| format | Article |
| id | doaj-art-d9049181b7e0408fb32fcade915178a7 |
| institution | DOAJ |
| issn | 1872-6259 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
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| series | Geoderma |
| spelling | doaj-art-d9049181b7e0408fb32fcade915178a72025-08-20T03:22:11ZengElsevierGeoderma1872-62592025-07-0145911733710.1016/j.geoderma.2025.117337LimeSoDa: A dataset collection for benchmarking of machine learning regressors in digital soil mappingJonas Schmidinger0Sebastian Vogel1Viacheslav Barkov2Anh-Duy Pham3Robin Gebbers4Hamed Tavakoli5Jose Correa6Tiago R. Tavares7Patrick Filippi8Edward J. Jones9Vojtech Lukas10Eric Boenecke11Joerg Ruehlmann12Ingmar Schroeter13Eckart Kramer14Stefan Paetzold15Masakazu Kodaira16Alexandre M.J.-C. Wadoux17Luca Bragazza18Konrad Metzger19Jingyi Huang20Domingos S.M. Valente21Jose L. Safanelli22Eduardo L. Bottega23Ricardo S.D. Dalmolin24Csilla Farkas25Alexander Steiger26Taciara Z. Horst27Leonardo Ramirez-Lopez28Thomas Scholten29Felix Stumpf30Pablo Rosso31Marcelo M. Costa32Rodrigo S. Zandonadi33Johanna Wetterlind34Martin Atzmueller35Osnabrück University, Joint Lab Artificial Intelligence and Data Science, Osnabrück, Germany; Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Department of Agromechatronics, Potsdam, Germany; Corresponding author.Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Department of Agromechatronics, Potsdam, GermanyOsnabrück University, Joint Lab Artificial Intelligence and Data Science, Osnabrück, Germany; Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Department of Agromechatronics, Potsdam, GermanyOsnabrück University, Joint Lab Artificial Intelligence and Data Science, Osnabrück, Germany; Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Department of Agromechatronics, Potsdam, GermanyLeibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Department of Agromechatronics, Potsdam, GermanyLeibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Department of Agromechatronics, Potsdam, GermanyLeibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Department of Agromechatronics, Potsdam, GermanyUniversity of São Paulo (USP), Center of Nuclear Energy in Agriculture (CENA), Piracicaba, BrazilThe University of Sydney, Sydney Institute of Agriculture, Sydney, AustraliaThe University of Sydney, Sydney Institute of Agriculture, Sydney, AustraliaMendel University in Brno, Department of Agrosystems and Bioclimatology, Brno, Czech RepublicLeibniz Institute of Vegetable and Ornamental Crops, Next Generation Horticultural Systems, Grossbeeren, GermanyLeibniz Institute of Vegetable and Ornamental Crops, Next Generation Horticultural Systems, Grossbeeren, GermanyEberswalde University for Sustainable Development, Landscape Management and Nature Conservation, Eberswalde, GermanyEberswalde University for Sustainable Development, Landscape Management and Nature Conservation, Eberswalde, GermanyUniversity of Bonn, Institute of Crop Science and Resource Conservation (INRES)—Soil Science and Soil Ecology, Bonn, GermanyTokyo University of Agriculture and Technology, Institute of Agriculture, Tokyo, JapanLISAH, Univ. Montpellier, AgroParisTech, INRAE, IRD, L’Institut Agro, Montpellier, FranceAgroscope, Field-Crop Systems and Plant Nutrition, Nyon, SwitzerlandAgroscope, Field-Crop Systems and Plant Nutrition, Nyon, SwitzerlandUniversity of Wisconsin-Madison, Department of Soil Science, Madison, USAFederal University of Viçosa, Department of Agricultural Engineering, Viçosa, BrazilWoodwell Climate Research Center, Falmouth, USAFederal University of Santa Maria (UFSM), Academic Coordination, Santa Maria, BrazilFederal University of Santa Maria (UFSM), Soil Department, Santa Maria, BrazilNorwegian Institute of Bioeconomy Research (NIBIO), Division of Environment and Natural Resources, Aas, NorwayUniversity of Rostock, Chair of Geodesy and Geoinformatics, Rostock, GermanyFederal Technological University of Paraná, Dois Vizinhos, BrazilBÜCHI Labortechnik AG, Data Science Department, Flawil, Switzerland; Imperial College London, Imperial College Business School, London, UKUniversity of Tübingen, Department of Geosciences, Tübingen, Germany; University of Tübingen, DFG Cluster of Excellence ‘Machine Learning for Science’, GermanyBern University of Applied Sciences, Competence Center for Soils, Zollikofen, SwitzerlandLeibniz Centre for Agricultural Landscape Research (ZALF), Simulation and Data Science, Müncheberg, GermanyFederal University of Jataí, Institute of Agricultural Sciences, Jatai, BrazilFederal University of Mato Grosso, Instute of Agricultural and Environmental Scinces, Sinop, BrazilSwedish University of Agricultural Sciences (SLU), Department of Soil and Environment, Skara, SwedenOsnabrück University, Joint Lab Artificial Intelligence and Data Science, Osnabrück, Germany; German Research Center for Artificial Intelligence (DFKI), Research Department Plan-Based Robot Control, Osnabrück, GermanyDigital soil mapping (DSM) relies on a broad pool of statistical methods, yet determining the optimal method for a given context remains challenging and contentious. Benchmarking studies on multiple datasets are needed to reveal strengths and limitations of commonly used methods. Existing DSM studies usually rely on a single dataset with restricted access, leading to incomplete and potentially misleading conclusions. To address these issues, we introduce an open-access dataset collection called Precision Liming Soil Datasets (LimeSoDa). LimeSoDa consists of 31 field- and farm-scale datasets from various countries. Each dataset has three target soil properties: (1) soil organic matter or soil organic carbon, (2) clay content and (3) pH, alongside a set of features. Features are dataset-specific and were obtained by optical spectroscopy, proximal- and remote soil sensing. All datasets were aligned to a tabular format and are ready-to-use for modeling. We demonstrated the use of LimeSoDa for benchmarking by comparing the predictive performance of four learning algorithms across all datasets. This comparison included multiple linear regression (MLR), support vector regression (SVR), categorical boosting (CatBoost) and random forest (RF). The results showed that although no single algorithm was universally superior, certain algorithms performed better in specific contexts. MLR and SVR performed better on high-dimensional spectral datasets, likely due to better compatibility with principal components. In contrast, CatBoost and RF exhibited considerably better performances when applied to datasets with a moderate number (<20) of features. These benchmarking results illustrate that the performance of statistical methods can be highly context-dependent. LimeSoDa therefore provides an important resource for improving the development and evaluation of statistical methods in DSM.http://www.sciencedirect.com/science/article/pii/S0016706125001752Machine learningBenchmarkingOpen dataDataset collectionDigital soil mappingPedometrics |
| spellingShingle | Jonas Schmidinger Sebastian Vogel Viacheslav Barkov Anh-Duy Pham Robin Gebbers Hamed Tavakoli Jose Correa Tiago R. Tavares Patrick Filippi Edward J. Jones Vojtech Lukas Eric Boenecke Joerg Ruehlmann Ingmar Schroeter Eckart Kramer Stefan Paetzold Masakazu Kodaira Alexandre M.J.-C. Wadoux Luca Bragazza Konrad Metzger Jingyi Huang Domingos S.M. Valente Jose L. Safanelli Eduardo L. Bottega Ricardo S.D. Dalmolin Csilla Farkas Alexander Steiger Taciara Z. Horst Leonardo Ramirez-Lopez Thomas Scholten Felix Stumpf Pablo Rosso Marcelo M. Costa Rodrigo S. Zandonadi Johanna Wetterlind Martin Atzmueller LimeSoDa: A dataset collection for benchmarking of machine learning regressors in digital soil mapping Geoderma Machine learning Benchmarking Open data Dataset collection Digital soil mapping Pedometrics |
| title | LimeSoDa: A dataset collection for benchmarking of machine learning regressors in digital soil mapping |
| title_full | LimeSoDa: A dataset collection for benchmarking of machine learning regressors in digital soil mapping |
| title_fullStr | LimeSoDa: A dataset collection for benchmarking of machine learning regressors in digital soil mapping |
| title_full_unstemmed | LimeSoDa: A dataset collection for benchmarking of machine learning regressors in digital soil mapping |
| title_short | LimeSoDa: A dataset collection for benchmarking of machine learning regressors in digital soil mapping |
| title_sort | limesoda a dataset collection for benchmarking of machine learning regressors in digital soil mapping |
| topic | Machine learning Benchmarking Open data Dataset collection Digital soil mapping Pedometrics |
| url | http://www.sciencedirect.com/science/article/pii/S0016706125001752 |
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