Articles | Volume 14, issue 6
https://doi.org/10.5194/gmd-14-3879-2021
https://doi.org/10.5194/gmd-14-3879-2021
Model description paper
 | 
24 Jun 2021
Model description paper |  | 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)

Lauric Cécillon, François Baudin, Claire Chenu, Bent T. Christensen, Uwe Franko, Sabine Houot, Eva Kanari, Thomas Kätterer, Ines Merbach, Folkert van Oort, Christopher Poeplau, Juan Carlos Quezada, Florence Savignac, Laure N. Soucémarianadin, and Pierre Barré

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Interactive discussion

Status: closed

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

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Lauric Cécillon on behalf of the Authors (17 Apr 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (23 Apr 2021) by Tomomichi Kato
RR by Anonymous Referee #2 (07 May 2021)
ED: Publish as is (28 May 2021) by Tomomichi Kato
AR by Lauric Cécillon on behalf of the Authors (28 May 2021)  Manuscript 
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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.