There is a newer version of the record available.

Published February 28, 2025 | Version 1.0.0
Dataset Open

Precision Liming Soil Datasets (LimeSoDa) Zenodo Repository

Creators

  • 1. Osnabrück University, Joint Lab Artificial Intelligence and Data Science, Osnabrück, Germany
  • 2. Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Department of Agromechatronics, Potsdam, Germany
  • 3. University of São Paulo (USP), Center of Nuclear Energy in Agriculture (CENA), Piracicaba, Brazil.
  • 4. The University of Sydney, Sydney Institute of Agriculture, Sydney, Australia
  • 5. Mendel University in Brno, Department of Agrosystems and Bioclimatology, Brno, Czech Republic
  • 6. Leibniz Institute of Vegetable and Ornamental Crops, Next Generation Horticultural Systems, Grossbeeren, Germany
  • 7. Eberswalde University for Sustainable Development, Landscape Management and Nature Conservation, Eberswalde, Germany
  • 8. University of Bonn, Institute of Crop Science and Resource Conservation (INRES)—Soil Science and Soil Ecology, Bonn, Germany
  • 9. Tokyo University of Agriculture and Technology, Institute of Agriculture, Tokyo, Japan
  • 10. LISAH, Univ. Montpellier, AgroParisTech, INRAE, IRD, L'Institut Agro, Montpellier, France
  • 11. Agroscope, Field-Crop Systems and Plant Nutrition, Nyon, Switzerland
  • 12. University of Wisconsin-Madison, Department of Soil Science, Madison, USA.
  • 13. Federal University of Viçosa, Department of Agricultural Engineering, Viçosa, Brazil
  • 14. Woodwell Climate Research Center, Falmouth, USA
  • 15. Federal University of Santa Maria (UFSM), Academic Coordination, Santa Maria, Brazil
  • 16. Federal University of Santa Maria (UFSM), Soil Department, Santa Maria, Brazil
  • 17. Norwegian Institute of Bioeconomy Research (NIBIO), Division of Environment and Natural Resources, Aas, Norway
  • 18. University of Rostock, Chair of Geodesy and Geoinformatics, Rostock, Germany
  • 19. Federal Technological University of Paraná, Dois Vizinhos, Brazil
  • 20. BÜCHI Labortechnik AG, Data Science Department, Flawil, Switzerland
  • 21. Imperial College London, Imperial College Business School, London, UK
  • 22. University of Tübingen, Department of Geosciences, Tübingen, Germany
  • 23. University of Tübingen, DFG Cluster of Excellence 'Machine Learning for Science'
  • 24. Bern University of Applied Sciences, Competence Center for Soils, Zollikofen, Switzerland
  • 25. Leibniz Centre for Agricultural Landscape Research (ZALF), Simulation and Data Science, Müncheberg, Germany
  • 26. Federal University of Jataí, Institute of Agricultural Sciences, Jatai, Brazil
  • 27. Federal University of Mato Grosso, Instute of Agricultural and Environmental Scinces, Sinop, Brazil
  • 28. Swedish University of Agricultural Sciences (SLU), Department of Soil and Environment, Skara, Sweden
  • 29. German Research Center for Artificial Intelligence (DFKI), Research Department Plan-Based Robot Control, Osnabrück, Germany

Description

Overview

Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are "ready-to-use" for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1) soil organic matter (SOM) or soil organic carbon (SOC), (2) pH, and (3) clay content, while the features for modeling are dataset-specific. The primary goal of `LimeSoDa` is to enable more reliable benchmarking of machine learning methods in digital soil mapping and pedometrics. All the associated materials and data from LimeSoDa can be downloaded in this data repository. However, for a more in-depth analysis, we refer to the published paper "LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping" by Schmidinger et al. (2025). You may also use our R and Python package likewise called LimeSoDa.

 

Citation

Upon usage of datasets from LimeSoDa, please cite our associated paper:

Schmidinger, J., Vogel, S., Barkov, V., Pham, A.-D., Gebbers, R., Tavakoli, H., Correa, J., Tavares, T.R., Filippi, P., Jones, E. J., Lukas, V., Boenecke, E., Ruehlmann, J., Schroeter, I., Kramer, E., Paetzold, S., Kodaira, M., Wadoux, A.M.J.-C., Bragazza, L., Metzger, K., Huang, J., Valente, D.S.M., Safanelli, J.L., Bottega, E.L., Dalmolin, R.S.D., Farkas, C., Steiger, A., Horst, T. Z., Ramirez-Lopez, L., Scholten, T., Stumpf, F., Rosso, P., Costa, M.M., Zandonadi, R.S., Wetterlind, J. & Atzmueller, M. (2025). LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping. https://doi.org/10.48550/arXiv.2502.20139

 

Files

Zenodo_Repository.zip

Files (21.3 MB)

Name Size Download all
md5:cd9608539ea8cfc9bcd65cc2591352fb
21.3 MB Preview Download

Additional details

Funding

Federal Ministry of Education and Research
031B1069A
Niedersächsisches Ministerium für Wissenschaft und Kultur
ZN4072