LimeSoDa: A dataset collection for benchmarking of machine learning regressors in digital soil mapping
2025
Schmidinger, Jonas | Vogel, Sebastian | Barkov, Viacheslav | Pham, Anh-Duy | Gebbers, Robin | Tavakoli, Hamed | Correa, Jose | Tavares, Tiago, R | Filippi, Patrick | Jones, Edward, J | Lukas, Vojtech | Boenecke, Eric | Ruehlmann, Joerg | Schroeter, Ingmar | Kramer, Eckart | Paetzold, Stefan | Kodaira, Masakazu | Wadoux, Alexandre, M. J.-C. | Bragazza, Luca | Metzger, Konrad | Huang, Jingyi | Valente, Domingos S.M. | Safanelli, Jose, L. | Bottega, Eduardo, L. | Dalmolin, Ricardo S.D. | Farkas, Csilla | Steiger, Alexander | Horst, Taciara, Z. | Ramirez-Lopez, Leonardo | Scholten, Thomas | Stumpf, Felix | Rosso, Pablo | Costa, Marcelo, M. | Zandonadi, Rodrigo, S. | Wetterlind, Johanna | Atzmueller, Martin | Universität Osnabrück - Osnabrück University | Leibniz Institute for Agricultural Engineering and Bioeconomy [Potsdam] (ATB) ; Leibniz Association | Centro de Energia Nuclear na Agricultura (CENA) ; Universidade de São Paulo = University of São Paulo (USP) | Sydney Institute of Agriculture ; The University of Sydney | Mendel University in Brno (MENDELU) | Leibniz Institute of Vegetable and Ornamental Crops (IGZ) | Eberswalde University for Sustainable Development (HNE) | Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES) ; Rheinische Friedrich-Wilhelms-Universität Bonn | Tokyo University of Agriculture and Technology (TUAT) | Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH) ; Institut de Recherche pour le Développement (IRD)-AgroParisTech-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier ; Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro) | Agroscope | University of Wisconsin-Madison | Universidade Federal de Viçosa [Brasil] = Federal University of Viçosa [Brazil] = Université fédérale de Viçosa [Brésil] (UFV [Brésil]) | Woodwell Climate Research Center | Universidade Federal de Santa Maria = Federal University of Santa Maria [Santa Maria, RS, Brazil] (UFSM) | Norsk institutt for bioøkonomi=Norwegian Institute of Bioeconomy Research (NIBIO) | University of Rostock = Universität Rostock | Universidade Tecnológica Federal do Paraná = Federal Technological University of Paraná [Curitiba, Brésil] (UTFPR) | BÜCHI Labortechnik AG ; Partenaires INRAE | Imperial College London | Eberhard Karls Universität Tübingen = University of Tübingen | Bern University of Applied Sciences (BFH) | Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF) | Universidade Federal de Jataí (UFJ) | Universidade Federal de Mato Grosso (UFMT) | Swedish University of Agricultural Sciences = Sveriges lantbruksuniversitet (SLU) | Deutsches Forschungszentrum für Künstliche Intelligenz GmbH = German Research Center for Artificial Intelligence (DFKI Lab Berlin) | This research was supported by the Lower Saxony Ministry of Science and Culture (MWK), funded through the zukunft.niedersachsen program of the Volkswagen Foundation (ZN4072) as well as the Federal Ministry of Education and Research of Germany (BMBF) through the BonaRes project I4S: Intelligence for Soil (031B1069A). Compute resources were funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) project number 456666331.
International audience
Afficher plus [+] Moins [-]anglais. 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.
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