the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Feb 2024
Investigating the complementarity of thermal and physical soil organic carbon fractions
Abstract. Partitioning soil organic carbon (SOC) in fractions with different biogeochemical stability is useful to better understand and predict SOC dynamics, and provide information related to soil health. Multiple SOC partition schemes exist but few of them can be implemented on large sample sets and therefore be considered as relevant options for soil monitoring. The well-established particulate- (POC) vs. mineral-associated organic carbon (MAOC) physical fractionation scheme is one of them. Introduced more recently, Rock-Eval® thermal analysis coupled with the PARTYSOC machine-learning model can also fractionate SOC into active (Ca) and stable SOC (Cs). A debate is emerging as to which of these methods should be recommended for soil monitoring. To investigate the complementarity or redundancy of these two fractionation schemes, we compared the quantity and environmental drivers of SOC fractions obtained on an unprecedented dataset from mainland France. About 2,000 topsoil samples were recovered all over the country, presenting contrasting land covers and pedoclimatic characteristics, and analysed. We found that the environmental drivers of the fractions were clearly different, the more stable MAOC and Cs fractions being mainly driven by soil characteristics, whereas land cover and climate had a greater influence on more labile POC and Ca fractions. The stable and labile SOC fractions provided by the two methods strongly differed in quantity (MAOC/Cs = 1.88 ± 0.46 and POC/Ca = 0.36 ± 0.17; n = 843) and drivers, suggesting that they correspond to fractions with different biogeochemical stability. We argue that, at this stage, both methods can be seen as complementary and potentially relevant for soil monitoring. As future developments, we recommend comparing how they relate to indicators of soil health such as nutrient availability or soil structural stability, and how their measurements can improve the accuracy of SOC dynamics models.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Status: closed
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RC1: 'Comment on egusphere-2024-197', Anonymous Referee #1, 12 Mar 2024
A very well-written and relevant study. I have no objections to publication.
Minor:
159: The term "fPOM" can lead to confusion because fPOM has been used for several decades and in many studies as a synonym for free POM.
190: It might be worthwhile to explain why one-hot encoding was performed. It is not immediately necessary for random forests.
342+: I have several questions regarding this matter, which may not be immediately resolvable here. It's possible that the authors possess more detailed insights and could provide further clarification. However, the importance of the publication remains unchanged, regardless of whether these questions are fully answered.
As described, it appears that the targeted SOC storage mechanisms of MAOC fractions and Cs fractions differ. The physical fractionations aim to separate OC protected by mineral binding. As demonstrated in various studies and outlined in the script, these fractions still contain fine POC and soluble compounds.
However, it remains unclear to what extent the thermostability determined in Rock-Eval is linked to SOC storage mechanisms. Is it plausible that thermostability correlates solely with the recalcitrance of the treated OC? MAOC consists to a larger extent of polysaccharides and lipids. When MAOC with a potentially high MRT is treated with Rock-Eval, will it be attributed to the Ca pool due to the low thermostability of polysaccharides and lipids?
Consequently, does this imply that the MAOC fractions will have a wider MRT range, spanning from very young (DOC/fine POM) to very old, while the Cs fraction is limited to a narrow MRT range, failing to capture either young or very old MAOC?
Citation: https://doi.org/10.5194/egusphere-2024-197-RC1 -
AC1: 'Reply on RC1', Amicie Delahaie, 12 Apr 2024
The authors warmly thank the referee for his/her supportive review!
A very well-written and relevant study. I have no objections to publication.
Minor:
159: The term "fPOM" can lead to confusion because fPOM has been used for several decades and in many studies as a synonym for free POM.
As it only appears twice in the manuscript, there is actually no need for an abbreviation; we will write fine POM in full instead of fPOM to avoid confusion, and do the same with coarse POM for consistency.
190: It might be worthwhile to explain why one-hot encoding was performed. It is not immediately necessary for random forests.
We thank the reviewer for this important question which we will clarify in the article as well. While the theoretical RF model can handle categorical features by definition, its implementation in the scikit-learn library in Python does require encoding categorical features as they are not natively supported. We decided to use one-hot encoding as it is one of the rare feature encoders that do not establish any kind of ranking between the features (as would, for example, the ordinal or target encoding). Since our only categorical feature is the land cover, it was important to make sure that the encoding does not introduce an artificial ranking of land covers, and the small number of categories (four different land covers) does not significantly increase the number of features used.
342+: I have several questions regarding this matter, which may not be immediately resolvable here. It's possible that the authors possess more detailed insights and could provide further clarification. However, the importance of the publication remains unchanged, regardless of whether these questions are fully answered.
As described, it appears that the targeted SOC storage mechanisms of MAOC fractions and Cs fractions differ. The physical fractionations aim to separate OC protected by mineral binding. As demonstrated in various studies and outlined in the script, these fractions still contain fine POC and soluble compounds.
However, it remains unclear to what extent the thermostability determined in Rock-Eval is linked to SOC storage mechanisms. Is it plausible that thermostability correlates solely with the recalcitrance of the treated OC? MAOC consists to a larger extent of polysaccharides and lipids. When MAOC with a potentially high MRT is treated with Rock-Eval, will it be attributed to the Ca pool due to the low thermostability of polysaccharides and lipids?
Consequently, does this imply that the MAOC fractions will have a wider MRT range, spanning from very young (DOC/fine POM) to very old, while the Cs fraction is limited to a narrow MRT range, failing to capture either young or very old MAOC?
Rock-Eval® thermal analyses give information on the chemical composition of organic matter (HI and OIre6 being proxies of H/C and O/C ratios) and its thermal stability. This information is likely related to the biogeochemical stability of SOC (Gregorich et al., 2015). Indeed, chemical composition can be linked to organic matter recalcitrance and thermal stability can give insights on OM-minerals interactions as organic molecules bound to mineral surfaces would probably evolve at higher temperature. However, we agree that to a large extent, the precise link between information given by Rock-Eval® thermal analyses and the mechanisms explaining SOM persistence in soils (guided by the return-on-energy-investment; Barré et al., 2016; Henneron et al., 2022) remains to be clearly established .
The calculation of Cs proportion using the PARTYsoc model takes into account both information on chemical composition and thermal stability. The Cs fraction corresponds to very stable organic matter, whether due to its chemical composition (such as PyC) or its strong interaction with minerals.
References:
Barré, P., Plante, A.F., Cécillon, L. et al. The energetic and chemical signatures of persistent soil organic matter. Biogeochemistry 130, 1–12 (2016). https://doi.org/10.1007/s10533-016-0246-0
Henneron, L., Balesdent, J., Alvarez, G. et al. Bioenergetic control of soil carbon dynamics across depth. Nat Commun 13, 7676 (2022). https://doi-org.insu.bib.cnrs.fr/10.1038/s41467-022-34951-w
Gregorich, E. G., Gillespie, A. W., Beare, M. H., Curtin, D., Sanei, H., and Yanni, S. F. Evaluating biodegradability of soil organic matter by its thermal stability and chemical composition. Soil Biol. Biochem., 91, 182–191 (2015). https://doi.org/10.1016/j.soilbio.2015.08.032
Citation: https://doi.org/10.5194/egusphere-2024-197-AC1
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AC1: 'Reply on RC1', Amicie Delahaie, 12 Apr 2024
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RC2: 'Comment on egusphere-2024-197', Anonymous Referee #2, 16 Mar 2024
Delahaie et al. present results from a large dataset of physical and thermal soil fractions, and explore the correspondence between MAOC, POC, Cs, and Ca and their environmental controls. In all this is a really interesting and well-written manuscript that was a pleasure to read. I recommend publication, and include some non-critical questions/comments below for further discussion.
The PartySOC model was trained on long-term agronomic experiments, as also stated on line 138. Are there uncertainties in applying the model directly to grasslands and forests? Especially since it is a machine learning model. Are certain soils and vegetation types outside the ranges used to train it? Does land-use history matter? There is some discussion of this on lines 323-325, but perhaps this could be expanded.
The discussion of POC/MAOC ratios on lines 334-336 is quite interesting, also in the context of this ratio being used as a proxy to assess vulnerability (e.g., as in Viscarra Rossel et al. 2019; Sanderman et al. 2021, though their fractionation schemes are slightly different than the ones here). The authors could consider extending this discussion to include implications for the broader use of this ratio.
The finding that the ratios of MAOC/Cs and POC/Ca differ from 1 is interesting, but these ratios themselves do not necessarily tell us how much of the Ca or Cs might come from POC or MAOC. Indeed, even if POC/Ca = 1 and MAOC/Cs = 1, there could still be many possible ways to achieve this; for instance, with most of the Ca coming from POC or MAOC (which also relates to Reviewer #1’s question on interpreting thermostability). Do the authors have any ideas on how to further constrain how much Ca comes from POC vs MAOC, and similarly for Cs? This doesn’t need to be resolved here, of course, but maybe the authors could discuss further insights or challenges for future work.
The ratio (MAOC/Cs)/(POC/Ca), as provided in Figure A2 and referenced on line 266, seems somewhat difficult to interpret and is not discussed again in the text. How do the authors think about this quantity?
Could the authors include bar graphs of absolute fraction quantities colored by land use in the supplement; as a Figure A3, for instance?
Line 249: Could state the mean value of the Ca proportion for vineyards and orchards here for consistency.
Line 252: Possibly change to “As with the median values, …” instead of “As for...”, otherwise reword for clarity.
References noted above:
Sanderman et al. Soil organic carbon fractions in the Great Plains of the United States: an application of mid-infrared spectroscopy. Biogeochemistry (2021).
Viscarra Rossel et al. Continental-scale soil carbon composition and vulnerability modulated by regional environmental controls. Nature Geoscience (2019).
Citation: https://doi.org/10.5194/egusphere-2024-197-RC2 -
AC2: 'Reply on RC2', Amicie Delahaie, 12 Apr 2024
The authors warmly thank the referee for his/her stimulating review!
Delahaie et al. present results from a large dataset of physical and thermal soil fractions, and explore the correspondence between MAOC, POC, Cs, and Ca and their environmental controls. In all this is a really interesting and well-written manuscript that was a pleasure to read. I recommend publication, and include some non-critical questions/comments below for further discussion.
The PartySOC model was trained on long-term agronomic experiments, as also stated on line 138. Are there uncertainties in applying the model directly to grasslands and forests? Especially since it is a machine learning model. Are certain soils and vegetation types outside the ranges used to train it? Does land-use history matter? There is some discussion of this on lines 323-325, but perhaps this could be expanded.
This is a good point. The major issue raised by models such as Random Forest is extrapolation: by their tree-based nature, these can't extrapolate outside values that are present in the calibration dataset. PARTYsoc was trained on long-term agronomic experiments, however, a few grassland samples were also included in the training set. The TOC content of samples included in PARTYsoc’s training set ranges from ca. 5 to 42 gC/kg. 85% of the RMQS samples analyzed in our study fall within this range, preventing most of the problems due to said extrapolation. Even though the model has not been trained with forest soils, the results obtained on forest samples appear realistic and consistent. Nonetheless, we agree that some discussion on this aspect has to be added in the revised draft.
We do agree that land-use history matters, and we think that PARTYsoc can reflect land-use history: our hypothesis is that the croplands with “low” Cs proportions are former grasslands or forests recently converted to agriculture, while grasslands or forests with high Cs proportions were likely croplands not so long ago, before being afforested or converted to grasslands. However, this hypothesis remains to be validated.
The discussion of POC/MAOC ratios on lines 334-336 is quite interesting, also in the context of this ratio being used as a proxy to assess vulnerability (e.g., as in Viscarra Rossel et al. 2019; Sanderman et al. 2021, though their fractionation schemes are slightly different than the ones here). The authors could consider extending this discussion to include implications for the broader use of this ratio.
Thank you for the suggestion. Indeed, discussing the results of these articles will enrich our draft. The fractionation schemes are indeed different but it would be interesting to compare the thermal fractionation (Ca/Cs) and the 3 components physical fractionation schemes used in the two articles. These articles also provide correlations between POC content and pedo-climatical variables that are worth discussing with our results. Nonetheless, our results suggest that the “potential vulnerability” proposed in Viscarra Rossel et al. (2019) can be misleading in some cases, such as low TOC soils with ligneous POM.
The finding that the ratios of MAOC/Cs and POC/Ca differ from 1 is interesting, but these ratios themselves do not necessarily tell us how much of the Ca or Cs might come from POC or MAOC. Indeed, even if POC/Ca = 1 and MAOC/Cs = 1, there could still be many possible ways to achieve this; for instance, with most of the Ca coming from POC or MAOC (which also relates to Reviewer #1’s question on interpreting thermostability). Do the authors have any ideas on how to further constrain how much Ca comes from POC vs MAOC, and similarly for Cs? This doesn’t need to be resolved here, of course, but maybe the authors could discuss further insights or challenges for future work.
It is difficult to constrain the composition of the Ca and Cs compartments. An idea could be to analyze directly POC and MAOC fractions with Rock-Eval thermal analysis, and then apply the PartySOC model to the results. However, in this case, the first limitation this review pointed out will be even more true: the PartySOC model has not been designed to be applied to fractions. Another option may be to analyze using Rock-Eval® soil samples before and after removing specifically POM (by flotation). This is indeed an interesting research avenue.
The ratio (MAOC/Cs)/(POC/Ca), as provided in Figure A2 and referenced on line 266, seems somewhat difficult to interpret and is not discussed again in the text. How do the authors think about this quantity?
This ratio is indeed confusing. We thought it would help understand the size of the different compartments depending on the land use, but it seems not to be suitable for clear interpretation. We propose to remove this panel and discussion in the revised version, as it does not provide precise information we were looking for.
Could the authors include bar graphs of absolute fraction quantities colored by land use in the supplement; as a Figure A3, for instance?
This figure will be added to the revised version of the manuscript.
Line 249: Could state the mean value of the Ca proportion for vineyards and orchards here for consistency.
This information will be added to the revised version of the manuscript.
Line 252: Possibly change to “As with the median values, …” instead of “As for...”, otherwise reword for clarity.
This change will be done in the revised version of the manuscript.
References noted above:
Sanderman et al. Soil organic carbon fractions in the Great Plains of the United States: an application of mid-infrared spectroscopy. Biogeochemistry (2021).
Viscarra Rossel et al. Continental-scale soil carbon composition and vulnerability modulated by regional environmental controls. Nature Geoscience (2019).
Citation: https://doi.org/10.5194/egusphere-2024-197-AC2
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AC2: 'Reply on RC2', Amicie Delahaie, 12 Apr 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-197', Anonymous Referee #1, 12 Mar 2024
A very well-written and relevant study. I have no objections to publication.
Minor:
159: The term "fPOM" can lead to confusion because fPOM has been used for several decades and in many studies as a synonym for free POM.
190: It might be worthwhile to explain why one-hot encoding was performed. It is not immediately necessary for random forests.
342+: I have several questions regarding this matter, which may not be immediately resolvable here. It's possible that the authors possess more detailed insights and could provide further clarification. However, the importance of the publication remains unchanged, regardless of whether these questions are fully answered.
As described, it appears that the targeted SOC storage mechanisms of MAOC fractions and Cs fractions differ. The physical fractionations aim to separate OC protected by mineral binding. As demonstrated in various studies and outlined in the script, these fractions still contain fine POC and soluble compounds.
However, it remains unclear to what extent the thermostability determined in Rock-Eval is linked to SOC storage mechanisms. Is it plausible that thermostability correlates solely with the recalcitrance of the treated OC? MAOC consists to a larger extent of polysaccharides and lipids. When MAOC with a potentially high MRT is treated with Rock-Eval, will it be attributed to the Ca pool due to the low thermostability of polysaccharides and lipids?
Consequently, does this imply that the MAOC fractions will have a wider MRT range, spanning from very young (DOC/fine POM) to very old, while the Cs fraction is limited to a narrow MRT range, failing to capture either young or very old MAOC?
Citation: https://doi.org/10.5194/egusphere-2024-197-RC1 -
AC1: 'Reply on RC1', Amicie Delahaie, 12 Apr 2024
The authors warmly thank the referee for his/her supportive review!
A very well-written and relevant study. I have no objections to publication.
Minor:
159: The term "fPOM" can lead to confusion because fPOM has been used for several decades and in many studies as a synonym for free POM.
As it only appears twice in the manuscript, there is actually no need for an abbreviation; we will write fine POM in full instead of fPOM to avoid confusion, and do the same with coarse POM for consistency.
190: It might be worthwhile to explain why one-hot encoding was performed. It is not immediately necessary for random forests.
We thank the reviewer for this important question which we will clarify in the article as well. While the theoretical RF model can handle categorical features by definition, its implementation in the scikit-learn library in Python does require encoding categorical features as they are not natively supported. We decided to use one-hot encoding as it is one of the rare feature encoders that do not establish any kind of ranking between the features (as would, for example, the ordinal or target encoding). Since our only categorical feature is the land cover, it was important to make sure that the encoding does not introduce an artificial ranking of land covers, and the small number of categories (four different land covers) does not significantly increase the number of features used.
342+: I have several questions regarding this matter, which may not be immediately resolvable here. It's possible that the authors possess more detailed insights and could provide further clarification. However, the importance of the publication remains unchanged, regardless of whether these questions are fully answered.
As described, it appears that the targeted SOC storage mechanisms of MAOC fractions and Cs fractions differ. The physical fractionations aim to separate OC protected by mineral binding. As demonstrated in various studies and outlined in the script, these fractions still contain fine POC and soluble compounds.
However, it remains unclear to what extent the thermostability determined in Rock-Eval is linked to SOC storage mechanisms. Is it plausible that thermostability correlates solely with the recalcitrance of the treated OC? MAOC consists to a larger extent of polysaccharides and lipids. When MAOC with a potentially high MRT is treated with Rock-Eval, will it be attributed to the Ca pool due to the low thermostability of polysaccharides and lipids?
Consequently, does this imply that the MAOC fractions will have a wider MRT range, spanning from very young (DOC/fine POM) to very old, while the Cs fraction is limited to a narrow MRT range, failing to capture either young or very old MAOC?
Rock-Eval® thermal analyses give information on the chemical composition of organic matter (HI and OIre6 being proxies of H/C and O/C ratios) and its thermal stability. This information is likely related to the biogeochemical stability of SOC (Gregorich et al., 2015). Indeed, chemical composition can be linked to organic matter recalcitrance and thermal stability can give insights on OM-minerals interactions as organic molecules bound to mineral surfaces would probably evolve at higher temperature. However, we agree that to a large extent, the precise link between information given by Rock-Eval® thermal analyses and the mechanisms explaining SOM persistence in soils (guided by the return-on-energy-investment; Barré et al., 2016; Henneron et al., 2022) remains to be clearly established .
The calculation of Cs proportion using the PARTYsoc model takes into account both information on chemical composition and thermal stability. The Cs fraction corresponds to very stable organic matter, whether due to its chemical composition (such as PyC) or its strong interaction with minerals.
References:
Barré, P., Plante, A.F., Cécillon, L. et al. The energetic and chemical signatures of persistent soil organic matter. Biogeochemistry 130, 1–12 (2016). https://doi.org/10.1007/s10533-016-0246-0
Henneron, L., Balesdent, J., Alvarez, G. et al. Bioenergetic control of soil carbon dynamics across depth. Nat Commun 13, 7676 (2022). https://doi-org.insu.bib.cnrs.fr/10.1038/s41467-022-34951-w
Gregorich, E. G., Gillespie, A. W., Beare, M. H., Curtin, D., Sanei, H., and Yanni, S. F. Evaluating biodegradability of soil organic matter by its thermal stability and chemical composition. Soil Biol. Biochem., 91, 182–191 (2015). https://doi.org/10.1016/j.soilbio.2015.08.032
Citation: https://doi.org/10.5194/egusphere-2024-197-AC1
-
AC1: 'Reply on RC1', Amicie Delahaie, 12 Apr 2024
-
RC2: 'Comment on egusphere-2024-197', Anonymous Referee #2, 16 Mar 2024
Delahaie et al. present results from a large dataset of physical and thermal soil fractions, and explore the correspondence between MAOC, POC, Cs, and Ca and their environmental controls. In all this is a really interesting and well-written manuscript that was a pleasure to read. I recommend publication, and include some non-critical questions/comments below for further discussion.
The PartySOC model was trained on long-term agronomic experiments, as also stated on line 138. Are there uncertainties in applying the model directly to grasslands and forests? Especially since it is a machine learning model. Are certain soils and vegetation types outside the ranges used to train it? Does land-use history matter? There is some discussion of this on lines 323-325, but perhaps this could be expanded.
The discussion of POC/MAOC ratios on lines 334-336 is quite interesting, also in the context of this ratio being used as a proxy to assess vulnerability (e.g., as in Viscarra Rossel et al. 2019; Sanderman et al. 2021, though their fractionation schemes are slightly different than the ones here). The authors could consider extending this discussion to include implications for the broader use of this ratio.
The finding that the ratios of MAOC/Cs and POC/Ca differ from 1 is interesting, but these ratios themselves do not necessarily tell us how much of the Ca or Cs might come from POC or MAOC. Indeed, even if POC/Ca = 1 and MAOC/Cs = 1, there could still be many possible ways to achieve this; for instance, with most of the Ca coming from POC or MAOC (which also relates to Reviewer #1’s question on interpreting thermostability). Do the authors have any ideas on how to further constrain how much Ca comes from POC vs MAOC, and similarly for Cs? This doesn’t need to be resolved here, of course, but maybe the authors could discuss further insights or challenges for future work.
The ratio (MAOC/Cs)/(POC/Ca), as provided in Figure A2 and referenced on line 266, seems somewhat difficult to interpret and is not discussed again in the text. How do the authors think about this quantity?
Could the authors include bar graphs of absolute fraction quantities colored by land use in the supplement; as a Figure A3, for instance?
Line 249: Could state the mean value of the Ca proportion for vineyards and orchards here for consistency.
Line 252: Possibly change to “As with the median values, …” instead of “As for...”, otherwise reword for clarity.
References noted above:
Sanderman et al. Soil organic carbon fractions in the Great Plains of the United States: an application of mid-infrared spectroscopy. Biogeochemistry (2021).
Viscarra Rossel et al. Continental-scale soil carbon composition and vulnerability modulated by regional environmental controls. Nature Geoscience (2019).
Citation: https://doi.org/10.5194/egusphere-2024-197-RC2 -
AC2: 'Reply on RC2', Amicie Delahaie, 12 Apr 2024
The authors warmly thank the referee for his/her stimulating review!
Delahaie et al. present results from a large dataset of physical and thermal soil fractions, and explore the correspondence between MAOC, POC, Cs, and Ca and their environmental controls. In all this is a really interesting and well-written manuscript that was a pleasure to read. I recommend publication, and include some non-critical questions/comments below for further discussion.
The PartySOC model was trained on long-term agronomic experiments, as also stated on line 138. Are there uncertainties in applying the model directly to grasslands and forests? Especially since it is a machine learning model. Are certain soils and vegetation types outside the ranges used to train it? Does land-use history matter? There is some discussion of this on lines 323-325, but perhaps this could be expanded.
This is a good point. The major issue raised by models such as Random Forest is extrapolation: by their tree-based nature, these can't extrapolate outside values that are present in the calibration dataset. PARTYsoc was trained on long-term agronomic experiments, however, a few grassland samples were also included in the training set. The TOC content of samples included in PARTYsoc’s training set ranges from ca. 5 to 42 gC/kg. 85% of the RMQS samples analyzed in our study fall within this range, preventing most of the problems due to said extrapolation. Even though the model has not been trained with forest soils, the results obtained on forest samples appear realistic and consistent. Nonetheless, we agree that some discussion on this aspect has to be added in the revised draft.
We do agree that land-use history matters, and we think that PARTYsoc can reflect land-use history: our hypothesis is that the croplands with “low” Cs proportions are former grasslands or forests recently converted to agriculture, while grasslands or forests with high Cs proportions were likely croplands not so long ago, before being afforested or converted to grasslands. However, this hypothesis remains to be validated.
The discussion of POC/MAOC ratios on lines 334-336 is quite interesting, also in the context of this ratio being used as a proxy to assess vulnerability (e.g., as in Viscarra Rossel et al. 2019; Sanderman et al. 2021, though their fractionation schemes are slightly different than the ones here). The authors could consider extending this discussion to include implications for the broader use of this ratio.
Thank you for the suggestion. Indeed, discussing the results of these articles will enrich our draft. The fractionation schemes are indeed different but it would be interesting to compare the thermal fractionation (Ca/Cs) and the 3 components physical fractionation schemes used in the two articles. These articles also provide correlations between POC content and pedo-climatical variables that are worth discussing with our results. Nonetheless, our results suggest that the “potential vulnerability” proposed in Viscarra Rossel et al. (2019) can be misleading in some cases, such as low TOC soils with ligneous POM.
The finding that the ratios of MAOC/Cs and POC/Ca differ from 1 is interesting, but these ratios themselves do not necessarily tell us how much of the Ca or Cs might come from POC or MAOC. Indeed, even if POC/Ca = 1 and MAOC/Cs = 1, there could still be many possible ways to achieve this; for instance, with most of the Ca coming from POC or MAOC (which also relates to Reviewer #1’s question on interpreting thermostability). Do the authors have any ideas on how to further constrain how much Ca comes from POC vs MAOC, and similarly for Cs? This doesn’t need to be resolved here, of course, but maybe the authors could discuss further insights or challenges for future work.
It is difficult to constrain the composition of the Ca and Cs compartments. An idea could be to analyze directly POC and MAOC fractions with Rock-Eval thermal analysis, and then apply the PartySOC model to the results. However, in this case, the first limitation this review pointed out will be even more true: the PartySOC model has not been designed to be applied to fractions. Another option may be to analyze using Rock-Eval® soil samples before and after removing specifically POM (by flotation). This is indeed an interesting research avenue.
The ratio (MAOC/Cs)/(POC/Ca), as provided in Figure A2 and referenced on line 266, seems somewhat difficult to interpret and is not discussed again in the text. How do the authors think about this quantity?
This ratio is indeed confusing. We thought it would help understand the size of the different compartments depending on the land use, but it seems not to be suitable for clear interpretation. We propose to remove this panel and discussion in the revised version, as it does not provide precise information we were looking for.
Could the authors include bar graphs of absolute fraction quantities colored by land use in the supplement; as a Figure A3, for instance?
This figure will be added to the revised version of the manuscript.
Line 249: Could state the mean value of the Ca proportion for vineyards and orchards here for consistency.
This information will be added to the revised version of the manuscript.
Line 252: Possibly change to “As with the median values, …” instead of “As for...”, otherwise reword for clarity.
This change will be done in the revised version of the manuscript.
References noted above:
Sanderman et al. Soil organic carbon fractions in the Great Plains of the United States: an application of mid-infrared spectroscopy. Biogeochemistry (2021).
Viscarra Rossel et al. Continental-scale soil carbon composition and vulnerability modulated by regional environmental controls. Nature Geoscience (2019).
Citation: https://doi.org/10.5194/egusphere-2024-197-AC2
-
AC2: 'Reply on RC2', Amicie Delahaie, 12 Apr 2024
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Amicie A. Delahaie
Lauric Cécillon
Marija Stojanova
Samuel Abiven
Pierre Arbelet
Dominique Arrouays
François Baudin
Antonio Bispo
Line Boulonne
Claire Chenu
Jussi Heinonsalo
Claudy Jolivet
Kristiina Karhu
Manuel P. Martin
Lorenza Pacini
Christopher Poeplau
Céline Ratié
Pierre Roudier
Nicolas P. A. Saby
Florence Savignac
Pierre Barré
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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