the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Submitted as: model description paper 16 Feb 2021
Submitted as: model description paper | 16 Feb 2021
Partitioning soil organic carbon into its centennially stable and active fractions with statistical models based on Rock-Eval® thermal analysis (PARTYSOCv2.0 and PARTYSOCv2.0EU)
- 1Laboratoire ECODIV, Univ. Normandie, UNIROUEN, INRAE, FR Scale CNRS 3730, Rouen, 76000, France
- 2Laboratoire de Géologie, CNRS, École normale supérieure, PSL University, IPSL, Paris, France
- 3Institut des Sciences de la Terre de Paris, Sorbonne Université, CNRS, Paris, 75005, France
- 4UMR 1402 ECOSYS, INRAE, AgroParisTech, Univ. Paris Saclay, Thiverval-Grignon, 78850, France
- 5Department of Agroecology, Aarhus University, AUFoulum, 8830 Tjele, Denmark
- 6Department of soil system science, Helmholtz Centre for Environmental Research, UFZ, 06120 Halle Germany
- 7Department of Ecology, Swedish University of Agricultural Sciences, 75007 Uppsala, Sweden
- 8Department Community Ecology, Helmholtz Centre for Environmental Research, UFZ, 06246 Bad Lauchstädt, Germany
- 9Thünen Institute of Climate-Smart Agriculture, 38116 Braunschweig, Germany
- 10Laboratory of Ecological Systems ECOS and Laboratory of Plant Ecology Research PERL, School of Architecture, Civil and Environmental Engineering ENAC, École Polytechnique Fédérale de Lausanne EPFL, 1015 Lausanne, Switzerland
- 11Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 1015 Lausanne, Switzerland
- 12Ecosystem Management, Institute of Terrestrial Ecosystems, Department of Environmental Systems Science, ETHZ, 8092 Zürich, Switzerland
- 13ACTA – les instituts techniques agricoles, 75595 Paris, France
- 1Laboratoire ECODIV, Univ. Normandie, UNIROUEN, INRAE, FR Scale CNRS 3730, Rouen, 76000, France
- 2Laboratoire de Géologie, CNRS, École normale supérieure, PSL University, IPSL, Paris, France
- 3Institut des Sciences de la Terre de Paris, Sorbonne Université, CNRS, Paris, 75005, France
- 4UMR 1402 ECOSYS, INRAE, AgroParisTech, Univ. Paris Saclay, Thiverval-Grignon, 78850, France
- 5Department of Agroecology, Aarhus University, AUFoulum, 8830 Tjele, Denmark
- 6Department of soil system science, Helmholtz Centre for Environmental Research, UFZ, 06120 Halle Germany
- 7Department of Ecology, Swedish University of Agricultural Sciences, 75007 Uppsala, Sweden
- 8Department Community Ecology, Helmholtz Centre for Environmental Research, UFZ, 06246 Bad Lauchstädt, Germany
- 9Thünen Institute of Climate-Smart Agriculture, 38116 Braunschweig, Germany
- 10Laboratory of Ecological Systems ECOS and Laboratory of Plant Ecology Research PERL, School of Architecture, Civil and Environmental Engineering ENAC, École Polytechnique Fédérale de Lausanne EPFL, 1015 Lausanne, Switzerland
- 11Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 1015 Lausanne, Switzerland
- 12Ecosystem Management, Institute of Terrestrial Ecosystems, Department of Environmental Systems Science, ETHZ, 8092 Zürich, Switzerland
- 13ACTA – les instituts techniques agricoles, 75595 Paris, France
Abstract. Partitioning soil organic carbon (SOC) into two kinetically different fractions that are centennially stable or active is key information for an improved monitoring of soil health and for a more accurate modelling of the carbon cycle. However, all existing SOC fractionation methods isolate SOC fractions that are mixtures of centennially stable and active SOC. If the stable SOC fraction cannot be isolated, it has specific chemical and thermal characteristics that are quickly (ca. 1 h per sample) measureable using Rock-Eval® thermal analysis. An alternative would thus be to (1) train a machine-learning model on the Rock-Eval® thermal analysis data of soil samples from long-term experiments where the size of the centennially stable and active SOC fractions can be estimated, and (2) apply this model on the Rock-Eval® data of unknown soils, to partition SOC into its centennially stable and active fractions. Here, we significantly extend the validity range of the machine-learning model published by Cécillon et al. [Biogeosciences, 15, 2835–2849, 2018, https://doi.org/10.5194/bg-15-2835-2018], and built upon this strategy. The second version of this statistical model, which we propose to name PARTYSOC, uses six European long-term agricultural sites including a bare fallow treatment and one South American vegetation change (C4 to C3 plants) site as reference sites. The European version of the model (PARTYSOCv2.0EU) predicts the proportion of the centennially stable SOC fraction with a conservative root-mean-square error of 0.15 (relative root-mean-square error of 0.27) in a wide range of agricultural topsoils from Northwestern Europe. We plan future expansions of the PARTYSOC global model using additional reference soils developed under diverse pedoclimates and ecosystems, and we already recommend the application of PARTYSOCv2.0EU in European agricultural topsoils to provide accurate information on SOC kinetic pools partitioning that may improve the simulations of simple models of SOC dynamics.
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Lauric Cécillon et al.
Status: open (until 13 Apr 2021)
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RC1: 'Comment on gmd-2021-16', Emanuele Lugato, 03 Mar 2021
reply
Dear Editor
The authors present an improved version of a method previously published (Biogeosciences, 15, 2835–2849, 2018), combining thermal analysis and machine learning in order to predict a centennial stable and active soil organic pool. It is certainly an interesting approach, that can provide useful indications for monitoring soil organic carbon and quality changes.
The methodology is amply described as well as the validation process, therefore, I have only minor recommendations.
Personally, I would be more careful in recommending the application of PARTYSOCv2.0EU in European agricultural topsoils (line 43), since the performances were lower both in the ‘leave-one-out-site’ and in the two independent sites validation. This is an indication that PARTYSOCv2.0EU would benefit of additional training sites; due to great variability of pedo-climatic and agroecosystem conditions in Europe, application of machine learning methods outside the range of their training can be critical.
Also the discussion presents, sometime, some repetitive concepts. I would also have expected more about comparison with other approaches especially in term of cost-benefit. While this method can be applied on existing monitoring schemes (as it requires a soil sample), there is no information of the cost of the thermal analysis, the complexity etc., which are interesting aspects if the aim is to propose a routine method.
Line1: I would suggest adding in brackets after ‘active’ (with turnover time of months to a few years).
Line 77-80: it seems that the approach proposed is quite insensitive to the number of samples training the model. Indeed only 7 sites are used and the ‘leave-one-site-out’ validation is worse that the ‘internal’. This is a likely sign that also this model would benefit from additional training. So I don’t understand the concept that other methods ‘need to be inferred from statistical models or infrared spectroscopy’. Isn’t it also what the authors are doing, training a ML on measurements?
Line 82. I generally agree, although I would like to point out that some fractionation methods don’t necessary aim at isolating kinetically defined pools, but rather pools underlying pathways of SOC formation and stabilization (e.g. the work on MEMS by Cotrufo et al.) .
Lines 85-86: yes, unless models are built to predict model fractions (eg. MEMS, COMISSION and others)
Fig.1 left panel. Is it a conceptual figure or size fractions were effectively analyzed by RockEval?
Line 195: It seems that authors suggest that the lowest SOC treatment received less ‘unwanted’ C input, while its lower value may be due to any source of uncontrolled variability. Are the results very sensitive to this approach and is there any risk to, opposite, underestimate the centennial carbon pool?
Line 275: maybe ‘inferred’ is better than ‘calculated’ as a fitting procedure was generally used.
Table 1: while the Centennial stable SOC is a unique value per site, what does the SOC content refer to (average over all treatments and years within a site)?
Line 358: if I well understood, out of those 105 samples, the centennial stable pool was inferred only from the LTBF and, then, assumed to be the same for all other treatments within the same site. I was wandering if some agronomic pracrices (for instance organic application) can bias this assumption. In fact, as far as understood (line 315), treatments with repeated application of some types of exogenous organic matter were not considered. My question is whether this poses a limit in the wide applicability of the method, since lot of soils receive manure and compost in Europe.
Table 2. Adding one site (La Cabana), the rank of the variable importance changed as well as the predicted centennial SOC proportion on a different extent depending on sites (Fig. 2b, Fig. 3a,b). Have authors considered to introduce additional variables in the Random Forest model (eg. texture) to make it more robust?
Lauric Cécillon et al.
Lauric Cécillon et al.
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