Articles | Volume 14, issue 6
Geosci. Model Dev., 14, 3879–3898, 2021
https://doi.org/10.5194/gmd-14-3879-2021
Geosci. Model Dev., 14, 3879–3898, 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 et al.

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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
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
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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.