Articles | Volume 19, issue 2
Biogeosciences, 19, 375–387, 2022
https://doi.org/10.5194/bg-19-375-2022
Biogeosciences, 19, 375–387, 2022
https://doi.org/10.5194/bg-19-375-2022

Research article 24 Jan 2022

Research article | 24 Jan 2022

A robust initialization method for accurate soil organic carbon simulations

Eva Kanari 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 bg-2021-246', Adrián Andriulo, 28 Oct 2021
    • AC1: 'Reply on RC1', Eva Kanari, 17 Nov 2021
  • RC2: 'Comment on bg-2021-246', Anonymous Referee #2, 05 Nov 2021
    • AC2: 'Reply on RC2', Eva Kanari, 17 Nov 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (19 Nov 2021) by Alexey V. Eliseev
AR by Eva Kanari on behalf of the Authors (25 Nov 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (02 Dec 2021) by Alexey V. Eliseev
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Short summary
Soil organic carbon (SOC) is crucial for climate regulation, soil quality, and food security. Predicting its evolution over the next decades is key for appropriate land management policies. However, SOC projections lack accuracy. Here we show for the first time that PARTYSOC, an approach combining thermal analysis and machine learning optimizes the accuracy of SOC model simulations at independent sites. This method can be applied at large scales, improving SOC projections on a continental scale.
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