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)

Cécillon, Lauric; Baudin, François; Chenu, Claire; Christensen, Bent T.; Franko, Uwe; Houot, Sabine; Kanari, Eva; Kätterer, Thomas; Merbach, Ines; van Oort, Folkert; Poeplau, Christopher; Quezada, Juan Carlos; Savignac, Florence; Soucémarianadin, Laure N.; Barré, Pierre

doi:10.5194/gmd-14-3879-2021

Articles | Volume 14, issue 6

https://doi.org/10.5194/gmd-14-3879-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-14-3879-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 6

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 (PARTY_SOCv2.0 and PARTY_SOCv2.0_EU)

Lauric Cécillon, François Baudin, Claire Chenu, Bent T. Christensen, Uwe Franko, Sabine Houot, Eva Kanari, Thomas Kätterer, Ines Merbach, Folkert van Oort, Christopher Poeplau, Juan Carlos Quezada, Florence Savignac, Laure N. Soucémarianadin, and Pierre Barré

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Apr 2025	66	15	0	81
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Jul 2025	48	23	3	74
Aug 2025	62	32	2	96
Sep 2025	80	20	2	102
Oct 2025	61	47	2	110
Nov 2025	99	33	5	137
Dec 2025	82	45	1	128
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Cumulative views and downloads (calculated since 16 Feb 2021)

Month	HTML	PDF	XML	Total
Feb 2021	194	39	3	236
Mar 2021	90	33	3	126
Apr 2021	92	38	7	137
May 2021	42	28	0	70
Jun 2021	150	54	2	206
Jul 2021	114	52	2	168
Aug 2021	52	33	0	85
Sep 2021	54	53	1	108
Oct 2021	39	106	2	147
Nov 2021	43	65	0	108
Dec 2021	42	23	0	65
Jan 2022	34	26	2	62
Feb 2022	44	19	4	67
Mar 2022	29	19	1	49
Apr 2022	51	16	1	68
May 2022	66	19	3	88
Jun 2022	37	15	4	56
Jul 2022	43	8	2	53
Aug 2022	167	10	1	178
Sep 2022	55	18	0	73
Oct 2022	31	18	2	51
Nov 2022	52	18	0	70
Dec 2022	32	13	1	46
Jan 2023	31	22	2	55
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Mar 2023	38	32	3	73
Apr 2023	14	18	0	32
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Feb 2024	37	22	3	62
Mar 2024	54	31	1	86
Apr 2024	55	20	2	77
May 2024	67	22	6	95
Jun 2024	67	18	7	92
Jul 2024	41	12	3	56
Aug 2024	42	12	4	58
Sep 2024	59	20	2	81
Oct 2024	41	15	1	57
Nov 2024	43	8	2	53
Dec 2024	30	6	0	36
Jan 2025	52	14	0	66
Feb 2025	58	16	2	76
Mar 2025	53	13	0	66
Apr 2025	66	15	0	81
May 2025	70	7	1	78
Jun 2025	77	27	0	104
Jul 2025	48	23	3	74
Aug 2025	62	32	2	96
Sep 2025	80	20	2	102
Oct 2025	61	47	2	110
Nov 2025	99	33	5	137
Dec 2025	82	45	1	128
Jan 2026	84	22	6	112
Feb 2026	96	33	5	134
Mar 2026	74	34	4	112
Apr 2026	23	3	1	27

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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 PARTY_soc, 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.


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