the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Nov 2022
Elemental stoichiometry and Rock-Eval® thermal stability of organic matter in French topsoils
Abstract. The quality and quantity of soil organic matter (SOM) are key elements of soil health and climate regulation by soils. The Rock-Eval® thermal analysis technique is increasingly used as it represents a powerful method for SOM characterization by providing insights on bulk SOM chemistry and thermal stability. In this study, we applied this technique on a large soil sample set from the first campaign (2000–2009) of the French monitoring network of soil quality: RMQS. Based on our analyses on ca. 2000 composite surface (0–30 cm) samples taken all over mainland France, we observed a significant impact of land cover on both SOM thermal stability and elemental stoichiometry. Cropland soils had a lower mean value of hydrogen index (a proxy for SOM H / C ratio) and a higher thermal stability than grasslands and forests. Regarding the oxygen index (a proxy for SOM O / C ratio), we observed significant differences in values for croplands, grasslands and forests. Positive correlations between the temperature parameters on the one hand and the clay content and pH on the other hand highlight the protective effect of clay on organic matter and the impact of pH on microorganisms mineralization activity. Surprisingly, we found weak effects of climatic parameters on the thermal stability and stoichiometry of SOM. Our data suggest that topsoil SOM is on average more oxidized and biogeochemically stable in croplands. More generally, the high number and even repartition of data on the whole French territory allow to build a national interpretative referential for these indicators in surface soils.
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Notice on discussion status
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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Preprint
(1818 KB)
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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.
- Preprint
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2022-1130', Anonymous Referee #1, 10 Jan 2023
Dear Author and Editor,
The manuscript "egusphere-2022-1130" brings a massive sampling and posterior massive thermal analysis, which support great interpretations about soil organic matter properties and its stability. However, I would like to observe a few comments about:
- Are there some thermal analysis limitations? for instance, how is the impact of the mineralogical composition on SOM thermal behavior?
- Extrapolations, such as: "Are French results interpretations able to describe soil management around the World? (e.g., land use change, Climate change)
Additionally, It is interesting a last review of the text in order to avoid non-formal expressions, such as, "mess up" (L.387)).
Citation: https://doi.org/10.5194/egusphere-2022-1130-RC1 -
AC2: 'Reply on RC1', Amicie Delahaie, 07 Feb 2023
We thank the reviewer for evaluating our manuscript and for their constructive comments. Please find below our answers (plain text) to each specific comment (italics).
Dear Author and Editor,
The manuscript "egusphere-2022-1130" brings a massive sampling and posterior massive thermal analysis, which support great interpretations about soil organic matter properties and its stability. However, I would like to observe a few comments about:
- Are there some thermal analysis limitations? for instance, how is the impact of the mineralogical composition on SOM thermal behavior?
Answer: The Rock-Eval thermal analysis shows very few limitations. One of them is the presence of carbonate mineral cracking at low temperature, such as siderite (FeCO3). Some solutions exist to correct this problem (Sebag et al., 2018). However, in the geographical context of mainland France, the presence of siderite in soils in highly unlikely. Another potential limitation lies in the fact that we have little insight in the study of andosols with this technique. Once again, the fact that our study focuses on mainland France strongly limits this potential issue.
On top of these specific cases, more generally, clays can retain pyrolysis effluents (Espitalié et al., 1980). At first order, the effect of clays seems, in our study, pretty limited as we observe a small positive correlation between clay content and S2 (n=1834, r²=0.03, p-value < 10-10). However, the retention of pyrolysis effluents may have played on the shape of the S2 signal. One can speculate that the retention of pyrolysis effluents may delay its output which would slightly increase T parameters measured on the S2 signal. This may contribute to the observed positive correlation between T90_HC_PYR and clay contents. However, the retention does not impact CO and CO2 signals (Kanari et al., 2021). We therefore consider, that if any, the matrix effect does not influence significantly our results and conclusions.
- Extrapolations, such as: "Are French results interpretations able to describe soil management around the World? (e.g., land use change, Climate change)
Answer: Our interpretations can be extended to similar pedoclimatic conditions only. As mentioned above, certain soil types such as andosols have not been studied enough yet using Rock-Eval thermal analysis to ensure accuracy of the analysis; however, the range of environmental conditions explored using Rock-Eval thermal analysis is constantly growing. As a result, we can expect a global coverage of RE analyses in the coming years.
Additionally, It is interesting a last review of the text in order to avoid non-formal expressions, such as, "mess up" (L.387)).
Answer: We will replace “mess up” and operate a final careful check to avoid such expressions in the revised version.
Cited References
- Espitalie, J., Madec, M., Tissot, B., 1980. Role of Mineral Matrix in Kerogen Pyrolysis : Influence on Petroleum Generation and Migration. The American Association of Petroleum Geologists 64, 59–66.
- Kanari, E., Barré, P., Baudin, F., Berthelot, A., Bouton, N., Gosselin, F., Soucémarianadin, L., Savignac, L., & Cecillon, L. (2021). Predicting Rock Eval® thermal analysis parameters of a soil layer based on samples from its sublayers; an experimental study on forest soils. Organic Geochemistry 160.
- Sebag, D., Garcin, Y., Adatte, T., Deschamps, P., Ménot, G., & Verrecchia, E. P. (2018). Correction for the siderite effect on Rock-Eval parameters: application to the sediments of Lake Barombi (southwest Cameroon). Organic Geochemistry 123, 126-135.
Citation: https://doi.org/10.5194/egusphere-2022-1130-AC2
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AC2: 'Reply on RC1', Amicie Delahaie, 07 Feb 2023
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RC2: 'Comment on egusphere-2022-1130', Anonymous Referee #2, 25 Jan 2023
I found this to be a very useful paper highlighting the utility of Rock-eval pyrolysis across a wide geography. I must admit that I was not surprised by any of the results. This was reassuring because it does show that cropland carbon tends to be more stabilised than forest or grassland carbon. I did appreciate the transparent approach to sample curation and that certain samples were removed. I also appreciate the questioning of the partition between organic and inorganic carbon, particularly with respect to the way the S3 curve is segmented. I think this is remains an ongoing question, and does, in part, contribute to the underestimation of organic carbon using this method. I did also appreciate the map of France build with this data, and I suggest below to combine Figures to more easily link land use and Rock eval parameters.
What follows are items that I feel ought to be addressed to improve this work:
For the landcover designations, only crop, grass, forest, and vinyards were addressed in detail. How were wastelands, human-disturbed and gardens grouped? Were they omitted from all analyses?
Table 1, would like to also see a column for T50_HC_PYR
Figure 2, perhaps I missed, are the box widths proportional to n?
Figure 2, letters indicating significance appear incorrect. For example, Fig 2 (a), if they are all significantly different, should read cabd in that ofder. I do have doubts that forests and grasslands in that panel are significantly different. Please check the other panels as well.
Figures 4 and 5: please consider combining the two figures and replacing the histograms in Fig 4 with gradient bars. I found myself wanting to compare land use with the indicators and would prefer them to be adjacent.
Citation: https://doi.org/10.5194/egusphere-2022-1130-RC2 -
AC1: 'Reply on RC2', Amicie Delahaie, 02 Feb 2023
We thank the reviewer for evaluating our manuscript and for their constructive comments. Please find below our answers (plain text) to each specific comment (italics).
I found this to be a very useful paper highlighting the utility of Rock-eval pyrolysis across a wide geography. I must admit that I was not surprised by any of the results. This was reassuring because it does show that cropland carbon tends to be more stabilised than forest or grassland carbon. I did appreciate the transparent approach to sample curation and that certain samples were removed. I also appreciate the questioning of the partition between organic and inorganic carbon, particularly with respect to the way the S3 curve is segmented. I think this is remains an ongoing question, and does, in part, contribute to the underestimation of organic carbon using this method. I did also appreciate the map of France build with this data, and I suggest below to combine Figures to more easily link land use and Rock eval parameters.
What follows are items that I feel ought to be addressed to improve this work:
For the landcover designations, only crop, grass, forest, and vinyards were addressed in detail. How were wastelands, human-disturbed and gardens grouped? Were they omitted from all analyses?
Answer: All the samples available, regardless of their land cover, were analyzed using Rock-Eval thermal analysis. We analyzed 40 samples coming from poorly human-disturbed sites, 14 from wastelands and 3 from gardens. Considering the very small number of samples for wastelands and gardens compared to the whole set, we decided not to include them in our statistical treatment. The number of poorly human-disturbed samples can be considered sufficient for statistical treatment, however they represent a very heterogeneous set of samples (10 miscellaneous subclasses such as peatlands, alpine grasslands, water edge vegetation, heath, dry siliceous meadows, etc.). We did not consider it relevant to analyze them as a whole. We will add a few lines to explain our choice in the revised version.
Table 1, would like to also see a column for T50_HC_PYR
Answer: The column for T50_HC_PYR will be added in the revised version of the manuscript.
Figure 2, perhaps I missed, are the box widths proportional to n?
Answer: The box width is proportional to the square root of n; we will specify this in the caption in the revised version.
Figure 2, letters indicating significance appear incorrect. For example, Fig 2 (a), if they are all significantly different, should read cabd in that ofder. I do have doubts that forests and grasslands in that panel are significantly different. Please check the other panels as well.
Answer: Indeed, we did not ordinate the letters according the mean value; this way does not seem to be unusual, to our knowledge and according to Piepho, H. P. (2018). Letters in mean comparisons: what they do and don’t mean. Agronomy Journal 110(2), 431-434.
The significant differences in the means for forests and grasslands come from the large number of samples in each category (respectively n=526 and n=481). We verified all the panels as you suggested and all are correct. As an example, the detailed result of the Wilcoxon test in R for the T90_HC_PYR is as follow:
Pairwise comparisons using Wilcoxon rank sum test with continuity correction
data: Smalldata$T90_HC_PYR and Smalldata$nom_occupation
croplands forests grasslands
forests < 2e-16 - -
grasslands < 2e-16 1.4e-07 -
vineyards & orchards 1.6e-08 < 2e-16 < 2e-16
P value adjustment method: holm
Figures 4 and 5: please consider combining the two figures and replacing the histograms in Fig 4 with gradient bars. I found myself wanting to compare land use with the indicators and would prefer them to be adjacent.
Answer: The figure will be modified according to your suggestion in the revised version.
Citation: https://doi.org/10.5194/egusphere-2022-1130-AC1
-
AC1: 'Reply on RC2', Amicie Delahaie, 02 Feb 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2022-1130', Anonymous Referee #1, 10 Jan 2023
Dear Author and Editor,
The manuscript "egusphere-2022-1130" brings a massive sampling and posterior massive thermal analysis, which support great interpretations about soil organic matter properties and its stability. However, I would like to observe a few comments about:
- Are there some thermal analysis limitations? for instance, how is the impact of the mineralogical composition on SOM thermal behavior?
- Extrapolations, such as: "Are French results interpretations able to describe soil management around the World? (e.g., land use change, Climate change)
Additionally, It is interesting a last review of the text in order to avoid non-formal expressions, such as, "mess up" (L.387)).
Citation: https://doi.org/10.5194/egusphere-2022-1130-RC1 -
AC2: 'Reply on RC1', Amicie Delahaie, 07 Feb 2023
We thank the reviewer for evaluating our manuscript and for their constructive comments. Please find below our answers (plain text) to each specific comment (italics).
Dear Author and Editor,
The manuscript "egusphere-2022-1130" brings a massive sampling and posterior massive thermal analysis, which support great interpretations about soil organic matter properties and its stability. However, I would like to observe a few comments about:
- Are there some thermal analysis limitations? for instance, how is the impact of the mineralogical composition on SOM thermal behavior?
Answer: The Rock-Eval thermal analysis shows very few limitations. One of them is the presence of carbonate mineral cracking at low temperature, such as siderite (FeCO3). Some solutions exist to correct this problem (Sebag et al., 2018). However, in the geographical context of mainland France, the presence of siderite in soils in highly unlikely. Another potential limitation lies in the fact that we have little insight in the study of andosols with this technique. Once again, the fact that our study focuses on mainland France strongly limits this potential issue.
On top of these specific cases, more generally, clays can retain pyrolysis effluents (Espitalié et al., 1980). At first order, the effect of clays seems, in our study, pretty limited as we observe a small positive correlation between clay content and S2 (n=1834, r²=0.03, p-value < 10-10). However, the retention of pyrolysis effluents may have played on the shape of the S2 signal. One can speculate that the retention of pyrolysis effluents may delay its output which would slightly increase T parameters measured on the S2 signal. This may contribute to the observed positive correlation between T90_HC_PYR and clay contents. However, the retention does not impact CO and CO2 signals (Kanari et al., 2021). We therefore consider, that if any, the matrix effect does not influence significantly our results and conclusions.
- Extrapolations, such as: "Are French results interpretations able to describe soil management around the World? (e.g., land use change, Climate change)
Answer: Our interpretations can be extended to similar pedoclimatic conditions only. As mentioned above, certain soil types such as andosols have not been studied enough yet using Rock-Eval thermal analysis to ensure accuracy of the analysis; however, the range of environmental conditions explored using Rock-Eval thermal analysis is constantly growing. As a result, we can expect a global coverage of RE analyses in the coming years.
Additionally, It is interesting a last review of the text in order to avoid non-formal expressions, such as, "mess up" (L.387)).
Answer: We will replace “mess up” and operate a final careful check to avoid such expressions in the revised version.
Cited References
- Espitalie, J., Madec, M., Tissot, B., 1980. Role of Mineral Matrix in Kerogen Pyrolysis : Influence on Petroleum Generation and Migration. The American Association of Petroleum Geologists 64, 59–66.
- Kanari, E., Barré, P., Baudin, F., Berthelot, A., Bouton, N., Gosselin, F., Soucémarianadin, L., Savignac, L., & Cecillon, L. (2021). Predicting Rock Eval® thermal analysis parameters of a soil layer based on samples from its sublayers; an experimental study on forest soils. Organic Geochemistry 160.
- Sebag, D., Garcin, Y., Adatte, T., Deschamps, P., Ménot, G., & Verrecchia, E. P. (2018). Correction for the siderite effect on Rock-Eval parameters: application to the sediments of Lake Barombi (southwest Cameroon). Organic Geochemistry 123, 126-135.
Citation: https://doi.org/10.5194/egusphere-2022-1130-AC2
-
AC2: 'Reply on RC1', Amicie Delahaie, 07 Feb 2023
-
RC2: 'Comment on egusphere-2022-1130', Anonymous Referee #2, 25 Jan 2023
I found this to be a very useful paper highlighting the utility of Rock-eval pyrolysis across a wide geography. I must admit that I was not surprised by any of the results. This was reassuring because it does show that cropland carbon tends to be more stabilised than forest or grassland carbon. I did appreciate the transparent approach to sample curation and that certain samples were removed. I also appreciate the questioning of the partition between organic and inorganic carbon, particularly with respect to the way the S3 curve is segmented. I think this is remains an ongoing question, and does, in part, contribute to the underestimation of organic carbon using this method. I did also appreciate the map of France build with this data, and I suggest below to combine Figures to more easily link land use and Rock eval parameters.
What follows are items that I feel ought to be addressed to improve this work:
For the landcover designations, only crop, grass, forest, and vinyards were addressed in detail. How were wastelands, human-disturbed and gardens grouped? Were they omitted from all analyses?
Table 1, would like to also see a column for T50_HC_PYR
Figure 2, perhaps I missed, are the box widths proportional to n?
Figure 2, letters indicating significance appear incorrect. For example, Fig 2 (a), if they are all significantly different, should read cabd in that ofder. I do have doubts that forests and grasslands in that panel are significantly different. Please check the other panels as well.
Figures 4 and 5: please consider combining the two figures and replacing the histograms in Fig 4 with gradient bars. I found myself wanting to compare land use with the indicators and would prefer them to be adjacent.
Citation: https://doi.org/10.5194/egusphere-2022-1130-RC2 -
AC1: 'Reply on RC2', Amicie Delahaie, 02 Feb 2023
We thank the reviewer for evaluating our manuscript and for their constructive comments. Please find below our answers (plain text) to each specific comment (italics).
I found this to be a very useful paper highlighting the utility of Rock-eval pyrolysis across a wide geography. I must admit that I was not surprised by any of the results. This was reassuring because it does show that cropland carbon tends to be more stabilised than forest or grassland carbon. I did appreciate the transparent approach to sample curation and that certain samples were removed. I also appreciate the questioning of the partition between organic and inorganic carbon, particularly with respect to the way the S3 curve is segmented. I think this is remains an ongoing question, and does, in part, contribute to the underestimation of organic carbon using this method. I did also appreciate the map of France build with this data, and I suggest below to combine Figures to more easily link land use and Rock eval parameters.
What follows are items that I feel ought to be addressed to improve this work:
For the landcover designations, only crop, grass, forest, and vinyards were addressed in detail. How were wastelands, human-disturbed and gardens grouped? Were they omitted from all analyses?
Answer: All the samples available, regardless of their land cover, were analyzed using Rock-Eval thermal analysis. We analyzed 40 samples coming from poorly human-disturbed sites, 14 from wastelands and 3 from gardens. Considering the very small number of samples for wastelands and gardens compared to the whole set, we decided not to include them in our statistical treatment. The number of poorly human-disturbed samples can be considered sufficient for statistical treatment, however they represent a very heterogeneous set of samples (10 miscellaneous subclasses such as peatlands, alpine grasslands, water edge vegetation, heath, dry siliceous meadows, etc.). We did not consider it relevant to analyze them as a whole. We will add a few lines to explain our choice in the revised version.
Table 1, would like to also see a column for T50_HC_PYR
Answer: The column for T50_HC_PYR will be added in the revised version of the manuscript.
Figure 2, perhaps I missed, are the box widths proportional to n?
Answer: The box width is proportional to the square root of n; we will specify this in the caption in the revised version.
Figure 2, letters indicating significance appear incorrect. For example, Fig 2 (a), if they are all significantly different, should read cabd in that ofder. I do have doubts that forests and grasslands in that panel are significantly different. Please check the other panels as well.
Answer: Indeed, we did not ordinate the letters according the mean value; this way does not seem to be unusual, to our knowledge and according to Piepho, H. P. (2018). Letters in mean comparisons: what they do and don’t mean. Agronomy Journal 110(2), 431-434.
The significant differences in the means for forests and grasslands come from the large number of samples in each category (respectively n=526 and n=481). We verified all the panels as you suggested and all are correct. As an example, the detailed result of the Wilcoxon test in R for the T90_HC_PYR is as follow:
Pairwise comparisons using Wilcoxon rank sum test with continuity correction
data: Smalldata$T90_HC_PYR and Smalldata$nom_occupation
croplands forests grasslands
forests < 2e-16 - -
grasslands < 2e-16 1.4e-07 -
vineyards & orchards 1.6e-08 < 2e-16 < 2e-16
P value adjustment method: holm
Figures 4 and 5: please consider combining the two figures and replacing the histograms in Fig 4 with gradient bars. I found myself wanting to compare land use with the indicators and would prefer them to be adjacent.
Answer: The figure will be modified according to your suggestion in the revised version.
Citation: https://doi.org/10.5194/egusphere-2022-1130-AC1
-
AC1: 'Reply on RC2', Amicie Delahaie, 02 Feb 2023
Peer review completion
Journal article(s) based on this preprint
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| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 340 | 144 | 13 | 497 | 4 | 5 |
- HTML: 340
- PDF: 144
- XML: 13
- Total: 497
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Viewed (geographical distribution)
| Country | # | Views | % |
|---|---|---|---|
| France | 1 | 150 | 30 |
| United States of America | 2 | 107 | 21 |
| Germany | 3 | 67 | 13 |
| China | 4 | 33 | 6 |
| Canada | 5 | 16 | 3 |
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
- 150
Amicie A. Delahaie
François Baudin
Dominique Arrouays
Antonio Bispo
Line Boulonne
Claire Chenu
Claudy Jolivet
Manuel P. Martin
Céline Ratié
Nicolas P. A. Saby
Florence Savignac
Lauric Cécillon
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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