Preprints
https://doi.org/10.5194/bg-2021-246
https://doi.org/10.5194/bg-2021-246

  23 Sep 2021

23 Sep 2021

Review status: a revised version of this preprint was accepted for the journal BG and is expected to appear here in due course.

A robust initialization method for accurate soil organic carbon simulations

Eva Kanari1,2, Lauric Cécillon1,3, François Baudin2, Hugues Clivot4, Fabien Ferchaud5, Sabine Houot6, Florent Levavasseur6, Bruno Mary5, Laure Soucémarianadin7, Claire Chenu6, and Pierre Barré1 Eva Kanari et al.
  • 1Laboratoire de Géologie, École normale supérieure, CNRS, Université PSL, IPSL, Paris, France
  • 2ISTeP, UMR 7193 Sorbonne Université, CNRS, Paris, France
  • 3Normandie Univ, UNIROUEN, INRAE, ECODIV, Rouen, France
  • 4Université de Reims Champagne Ardenne, INRAE, FARE, UMR A 614, 51097 Reims, France
  • 5BioEcoAgro Joint Research Unit, INRAE, Université de Liège, Université de Lille, Université Picardie Jules Verne, F-02000, Barenton-Bugny, France
  • 6UMR ECOSYS, INRAE, AgroParisTech, Université Paris-Saclay, Thiverval-Grignon, France
  • 7ACTA - les instituts techniques agricoles, Paris, France

Abstract. Changes in soil organic carbon (SOC) stocks are a major source of uncertainty for the evolution of atmospheric CO2 concentration during the 21st century. They are usually simulated by models dividing SOC into conceptual pools with contrasted turnover times. The lack of reliable methods to initialize these models, by correctly distributing soil carbon amongst their kinetic pools, strongly limits the accuracy of their simulations. Here, we demonstrate that PARTYsoc, a machine-learning model based on Rock-Eval® thermal analysis optimally partitions the active and stable SOC pools of AMG, a simple and well validated SOC dynamics model, accounting for effects of soil management history. Furthermore, we found that initializing the SOC pool sizes of AMG using machine-learning strongly improves its accuracy when reproducing the observed SOC dynamics in nine independent French long-term agricultural experiments. Our results indicate that multi-compartmental models of SOC dynamics combined with a robust initialization can simulate observed SOC stock changes with excellent precision. We recommend exploring their potential before a new generation of models of greater complexity becomes operational. The approach proposed here can be easily implemented on soil monitoring networks, paving the way towards precise predictions of SOC stock changes over the next decades.

Eva Kanari et al.

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

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

Eva Kanari et al.

Eva Kanari et al.

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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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