Articles | Volume 14, issue 6
https://doi.org/10.5194/gmd-14-3879-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-3879-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
24 Jun 2021
Model description paper |
| 24 Jun 2021
| 24 Jun 2021
Partitioning soil organic carbon into its centennially stable and active fractions with machine-learning models based on Rock-Eval® thermal analysis (PARTYSOCv2.0 and PARTYSOCv2.0EU)
Normandie Univ., UNIROUEN, INRAE, ECODIV, Rouen, France
Laboratoire de Géologie, École normale supérieure, CNRS,
PSL Univ., IPSL, Paris, France
François Baudin
Institut des Sciences de la Terre de Paris, Sorbonne Université,
CNRS, 75005 Paris, France
Claire Chenu
UMR 1402 ECOSYS, INRAE, AgroParisTech, Univ. Paris Saclay,
78850 Thiverval-Grignon, France
Bent T. Christensen
Department of Agroecology, Aarhus University, AU Foulum, 8830 Tjele,
Denmark
Uwe Franko
Department of soil system science, Helmholtz Centre for Environmental
Research, UFZ, 06120 Halle, Germany
Sabine Houot
UMR 1402 ECOSYS, INRAE, AgroParisTech, Univ. Paris Saclay,
78850 Thiverval-Grignon, France
Eva Kanari
Laboratoire de Géologie, École normale supérieure, CNRS,
PSL Univ., IPSL, Paris, France
Institut des Sciences de la Terre de Paris, Sorbonne Université,
CNRS, 75005 Paris, France
Thomas Kätterer
Department of Ecology, Swedish University of Agricultural Sciences,
75007 Uppsala, Sweden
Ines Merbach
Department Community Ecology, Helmholtz Centre for Environmental
Research, UFZ, 06246 Bad Lauchstädt, Germany
Folkert van Oort
UMR 1402 ECOSYS, INRAE, AgroParisTech, Univ. Paris Saclay,
78850 Thiverval-Grignon, France
Christopher Poeplau
Thünen Institute of Climate-Smart Agriculture, 38116 Braunschweig,
Germany
Juan Carlos Quezada
Laboratory 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
Swiss Federal Institute for Forest, Snow and Landscape Research WSL,
1015 Lausanne, Switzerland
Ecosystem Management, Institute of Terrestrial Ecosystems, Department
of Environmental Systems Science, ETHZ, 8092 Zürich, Switzerland
Florence Savignac
Institut des Sciences de la Terre de Paris, Sorbonne Université,
CNRS, 75005 Paris, France
Laure N. Soucémarianadin
ACTA – les instituts techniques agricoles, 75595 Paris, France
Pierre Barré
Laboratoire de Géologie, École normale supérieure, CNRS,
PSL Univ., IPSL, Paris, France
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Short summary
Partitioning soil organic carbon (SOC) into fractions that are stable or active on a century scale is key for more accurate models of the carbon cycle. Here, we describe the second version of a machine-learning model, named PARTYsoc, which reliably predicts the proportion of the centennially stable SOC fraction at its northwestern European validation sites with Cambisols and Luvisols, the two dominant soil groups in this region, fostering modelling works of SOC dynamics.
Partitioning soil organic carbon (SOC) into fractions that are stable or active on a century...
