Preprints
https://doi.org/10.5194/gmd-2021-16
https://doi.org/10.5194/gmd-2021-16

Submitted as: model description paper 16 Feb 2021

Submitted as: model description paper | 16 Feb 2021

Review status: a revised version of this preprint is currently under review for the journal GMD.

Partitioning soil organic carbon into its centennially stable and active fractions with statistical models based on Rock-Eval® thermal analysis (PARTYSOCv2.0 and PARTYSOCv2.0EU)

Lauric Cécillon1,2, François Baudin3, Claire Chenu4, Bent T. Christensen5, Uwe Franko6, Sabine Houot4, Eva Kanari2,3, Thomas Kätterer7, Ines Merbach8, Folkert van Oort4, Christopher Poeplau9, Juan Carlos Quezada10,11,12, Florence Savignac3, Laure N. Soucémarianadin13, and Pierre Barré2 Lauric Cécillon et al.
  • 1Laboratoire ECODIV, Univ. Normandie, UNIROUEN, INRAE, FR Scale CNRS 3730, Rouen, 76000, France
  • 2Laboratoire de Géologie, CNRS, École normale supérieure, PSL University, IPSL, Paris, France
  • 3Institut des Sciences de la Terre de Paris, Sorbonne Université, CNRS, Paris, 75005, France
  • 4UMR 1402 ECOSYS, INRAE, AgroParisTech, Univ. Paris Saclay, Thiverval-Grignon, 78850, France
  • 5Department of Agroecology, Aarhus University, AUFoulum, 8830 Tjele, Denmark
  • 6Department of soil system science, Helmholtz Centre for Environmental Research, UFZ, 06120 Halle Germany
  • 7Department of Ecology, Swedish University of Agricultural Sciences, 75007 Uppsala, Sweden
  • 8Department Community Ecology, Helmholtz Centre for Environmental Research, UFZ, 06246 Bad Lauchstädt, Germany
  • 9Thünen Institute of Climate-Smart Agriculture, 38116 Braunschweig, Germany
  • 10Laboratory of Ecological Systems ECOS and Laboratory of Plant Ecology Research PERL, School of Architecture, Civil and Environmental Engineering ENAC, École Polytechnique Fédérale de Lausanne EPFL, 1015 Lausanne, Switzerland
  • 11Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 1015 Lausanne, Switzerland
  • 12Ecosystem Management, Institute of Terrestrial Ecosystems, Department of Environmental Systems Science, ETHZ, 8092 Zürich, Switzerland
  • 13ACTA – les instituts techniques agricoles, 75595 Paris, France

Abstract. Partitioning soil organic carbon (SOC) into two kinetically different fractions that are centennially stable or active is key information for an improved monitoring of soil health and for a more accurate modelling of the carbon cycle. However, all existing SOC fractionation methods isolate SOC fractions that are mixtures of centennially stable and active SOC. If the stable SOC fraction cannot be isolated, it has specific chemical and thermal characteristics that are quickly (ca. 1 h per sample) measureable using Rock-Eval® thermal analysis. An alternative would thus be to (1) train a machine-learning model on the Rock-Eval® thermal analysis data of soil samples from long-term experiments where the size of the centennially stable and active SOC fractions can be estimated, and (2) apply this model on the Rock-Eval® data of unknown soils, to partition SOC into its centennially stable and active fractions. Here, we significantly extend the validity range of the machine-learning model published by Cécillon et al. [Biogeosciences, 15, 2835–2849, 2018, https://doi.org/10.5194/bg-15-2835-2018], and built upon this strategy. The second version of this statistical model, which we propose to name PARTYSOC, uses six European long-term agricultural sites including a bare fallow treatment and one South American vegetation change (C4 to C3 plants) site as reference sites. The European version of the model (PARTYSOCv2.0EU) predicts the proportion of the centennially stable SOC fraction with a conservative root-mean-square error of 0.15 (relative root-mean-square error of 0.27) in a wide range of agricultural topsoils from Northwestern Europe. We plan future expansions of the PARTYSOC global model using additional reference soils developed under diverse pedoclimates and ecosystems, and we already recommend the application of PARTYSOCv2.0EU in European agricultural topsoils to provide accurate information on SOC kinetic pools partitioning that may improve the simulations of simple models of SOC dynamics.

Lauric Cécillon et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-16', Emanuele Lugato, 03 Mar 2021
    • AC1: 'Reply on RC1', Lauric Cécillon, 17 Apr 2021
  • RC2: 'Comment on gmd-2021-16', Anonymous Referee #2, 06 Apr 2021
    • AC2: 'Reply on RC2', Lauric Cécillon, 17 Apr 2021

Lauric Cécillon et al.

Lauric Cécillon et al.

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