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.

Data sets

lauric-cecillon/PARTYsoc: Second version of the PARTYsoc statistical model (Version v2.0) Lauric Cécillon https://doi.org/10.5281/zenodo.4446138

Model code and software

lauric-cecillon/PARTYsoc: Second version of the PARTYsoc statistical model (Version v2.0) Lauric Cécillon https://doi.org/10.5281/zenodo.4446138

Download
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.