Published May 17, 2023 | Version 1.0
Dataset Open

Reproducibility of the wet part of the soil water retention curve: a European interlaboratory comparison (dataset)

Authors/Creators

  • 1. Uliège - Gembloux Agro-Bio Tech, TERRA Teaching and Research Centre, Passage des Déportés 2, 5030 Gembloux, Belgium
  • 2. nstrumentation, Moyens Analytiques, observatoire en Géophysique et Océanographie (UAR IMAGO), Institut de Recherche pour le Développement (IRD)
  • 3. Wageningen University and Research, Netherlands
  • 4. Institute of Agrophysics, Polish Academy of Sciences, Poland
  • 5. Ghent University, Belgium
  • 6. Technische Universität Braunschweig, Germany
  • 7. Instrumentation, Moyens Analytiques, observatoire en Géophysique et Océanographie (UAR IMAGO), Institut de Recherche pour le Développement (IRD)
  • 8. Department of Agroecology, Aarhus University, Denmark
  • 9. UCLouvain, Earth and Life Institute, Belgium
  • 10. Institute of Soil Science and Land Evaluation, University of Hohenheim, Germany
  • 11. Federal Institute for Geosciences and Natural Resources, Germany
  • 12. Department of Soil Physics and Water Management, Institute for Soil Sciences, Centre for Agricultural Research, Herman Ottó street 15, 1022 Budapest, Hungary
  • 13. Swedish University of Agricultural Sciences, Sweden
  • 14. Norwegian Institute of Bioeconomy Research, Norway
  • 15. Agrosphere Institute IBG-3, Forschungszentrum Jülich GmbH, Germany

Description

The soil water retention curve (SWRC) is a key soil property required for predicting basic hydrological processes. The SWRC is often obtained in the laboratory with non-harmonized methods. Moreover, procedures associated with each method are not standardized. This can induce a lack of reproducibility between laboratories using different methods and procedures or using the same methods with different procedures. The goal here was to estimate the inter- and intralaboratory variability of the measurement of the wet part (from 10 to 300 hPa) of the SWRC. An interlaboratory comparison was carried out between 14 laboratories, using artificially constructed, porous reference samples that were transferred between laboratories according to a statistical design. The retention measurements were modelled by a series of linear mixed models using a Bayesian approach. This allowed the detection of sample-to-sample variability, interlaboratory variability, intralaboratory variability and the effects of sample changes between measurements. Here we present the dataset containing measured data from the interlaboratory comparison, R code to implement models, and final model results.

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References
Preprint: 10.5194/egusphere-2022-1496 (DOI)