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.

Viewed

Total article views: 1,084 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
764 303 17 1,084 50 10 14
  • HTML: 764
  • PDF: 303
  • XML: 17
  • Total: 1,084
  • Supplement: 50
  • BibTeX: 10
  • EndNote: 14
Views and downloads (calculated since 16 Feb 2021)
Cumulative views and downloads (calculated since 16 Feb 2021)

Viewed (geographical distribution)

Total article views: 865 (including HTML, PDF, and XML) Thereof 865 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 21 Sep 2021
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.