the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Apr 2025
Drivers of soil C quality and stability: Insights from a topsoil dataset at landscape scale in Ontario, Canada
Abstract. Although soil C is a critical component of soil health, studies robustly exploring the agronomic and pedoclimatic effects on soil C are limited, especially at the landscape scale. Therefore, a dataset of 1511 samples from agricultural fields across Ontario was used to evaluate the impacts of agronomic and pedoclimatic factors on eight soil C indicators including chemistry and thermal stability of soil C using the programmed pyrolysis approach. Soil C quality and stability were largely controlled by the inherent soil characteristics such as soil texture. Significant interactive effects of cropping system and tillage intensity on soil C indicators were observed; however, the number of significant effects varied among the three soil textural classes. All soil C indicators were significantly different among the cropping systems for the coarse textured soils, but the cropping system differences decreased under medium and fine textured soils. From the pyrolysis analysis, the hydrogen index (HI) and oxygen index (OI) also confirmed that the soil C chemistry was influenced by the cropping system. For instance, orchard systems had stable pools of soil C whereas vegetable systems were associated with less advanced degree of soil C decomposition. Remaining soil management variables (cover crop use, tillage intensity, and organic amendments) had less influence on soil C indicators in all soil textural classes. Principal component analysis revealed a close association of soil C indicators with the mean annual precipitation (MAP) and cropping system; suggesting that the quantity and quality of soil C inputs associated with different cropping systems and increase in precipitation had a large influence on soil C. Our results confirm the significant effects of agronomic and pedoclimatic variables on chemistry, thermal stability, and composition of soil C pools, which have long-term implications on soil C storage, mitigating global climate change, and improving soil health.
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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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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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Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2025-1055', Anonymous Referee #1, 28 Aug 2025
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AC1: 'Reply on RC1', Inderjot Chahal, 09 Dec 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1055/egusphere-2025-1055-AC1-supplement.pdf
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AC1: 'Reply on RC1', Inderjot Chahal, 09 Dec 2025
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RC2: 'Comment on egusphere-2025-1055', Marie-Liesse Aubertin, 23 Oct 2025
General comments
This study aims to evaluate the impact of agricultural management and environmental variables on soil C indicators, including indicators from thermal analysis. Based on the analysis of 1490 samples, they concluded that soil textured classes and cropping systems had the strongest influence on both quality and stability of soil C indicators, and evidenced interactive effects between cropping systems and tillage intensity. The aim of the subject is very relevant because based on a large dataset at the landscape scale. The experimental approach seems appropriate for answering the scientific questions. I recommend moderate revisions for this paper.
More information should be given about the delimitation of the geographical area. In general, several results warrant further explanation and discussion. The authors should further elaborate on the limitations of the approaches and adopt a more critical perspective towards them. The part concerning the PCA interpretation should be improved.
Detailed comments
Introduction
Line 32-36: “Soil C” à do the authors mean soil C dynamic or turnover or stability or …? Soil C alone is not a parameter. The parameter or process meant in this paragraph should be specified.
Line 34: “Soil organic C (SOC)” content
Line 81-82: I am not sure to understand if the research gap is due to the difficulty to observe any effect of the previously cited factors on C stability, or if it is due to the difficulty to identify the main factors affecting C stability. I suggest reformulating this sentence.
Line 91: again: “soil C “ storage and stability?
Material and method
Line 97: Since the study highlights that the experiment was done at a landscape scale, the authors should specify the geographical area of the project. It may be important to have an idea if the scale of the area (regional, national or multi-national scale?) and if the studied parameters (e.g. cropping systems, tillage intensity, use of organic amendment) are representative of this geographical area.
Line 129: the authors should precise what “QA/QC analysis” refers to.
Line 159: Were the CO emissions also included in the S3?
Line 163: I understand that T50 corresponds to 50% of C emissions under all S1, S2 and S3 signals. If not, this should be specified.
Line 167: if “variance component analysis” are made, the data should follow normal distributions. It would be relevant to precise here if the normality and homoskedasticity were tested for the data. Unless the authors consider that the sample sizes are large enough to override the normality of the data...
Results and discussion
Line 190: it is not clear what “75.2%” refers to.
Table 1: it is surprising that the carbon inputs, such as cover crops (if plant residues remain on the field) and organic amendments, have so low effects on SOC and on the indicators of C stability, when compared to cropping system and soil texture. Indeed, organic matters amendment is expected to increase labile C content and hence, decrease the overall stability of C. These results are, at least to me, very surprising and therefore interesting. I suggest developing a bit more a critical discussion about these results and the approach, and eventually present hypothesis of explanation. An idea to complete this approach, could be to make a partitioning of the variances within each textural class.
Table 2: is there any reason why annual temperature and mean precipitation are not tested in this table? Furthermore, the results evidence statistical differences but ignore tendencies that may also be interesting to investigate.
Table 3: in the legend, the authors should remind which statistical differences are represented by the letters. As for tables 1 & 2, the authors should add what T50 refers to.
Line 210: “the lowest soil C” content? Stability? Please precise which indicator are referred to.
Lines 210-211: I guess that many types of management exist to produce vegetable and annual grain systems. They are maybe not always intensively managed. I guess that this could be a critical point you could add to the discussion. It would be interesting to have a bit more information about how the fields of the project were managed and if they were all managed the same way within each cropping system.
Line 219: Forage doesn’t have the lowest T50 across all soil textures in table 3; vegetable and perennial have lower values than forage for medium-textured soils, and orchard has lower values for fine-textured soils.
Lines 226-228: This result is interesting, and therefore it would be relevant to develop the discussion about the influence of the texture on thermal stability. There may be a link to do with the higher organo-mineral associations in clay than in sandy soils. It would also be relevant to precise if other studies also found this same result.
Figure 2: In the legend, the authors wrote that the data from the other cropping systems are not shown because of the insufficient number observations. If these number of observations were not a problem for the other tables and figures, why should they be a problem for this figure? It should especially not be a problem when presented as boxplots.
Line 251: I guess you mean “Table 2” instead of “Figure 2”.
Lines 247-264: in this paragraph, there are several mentions of the Table S1. It could be relevant to add this table in the main tables.
Line 269: The HI of vegetable systems doesn’t seem different than those of orchard systems and thus, this figure doesn’t show that the organic matter of vegetable systems mainly consists
Lines 274-287: This paragraph, as well as figures 4 and 5 should be transferred in material and method. In the discussion should only appear how this distribution may affect the results. Figures 4 and 5 could as well be transferred as supplementary materials.
Line 289: The authors should introduce in the main text how the PCA was done and with which indicators and parameters.
Line 290: did the other studies used also the same parameters and indicators? If yes, on which type of data did they use it? It is a bit tricky to compare variances from different datasets…
Lines 294-296: It would be interesting to have an explanation of why the different approaches of variance analysis and PCA do not show the same results concerning the influence of weather (temperature and humidity) on C indicators.
Lines 307-314: In the PCA, the arrows from the soil texture are orthogonal to the arrows from the soil C indicators, thereby showing no correlations. Therefore, I disagree with the results presented in this paragraph.
Line 315: idem, HI and the C indicators are orthogonal to the cover crops and thus, the PCA does not show any correlation between them. In addition, the cover crop reflects a very low contribution in the PCA analysis (small arrow size).
Citation: https://doi.org/10.5194/egusphere-2025-1055-RC2 -
AC2: 'Reply on RC2', Inderjot Chahal, 09 Dec 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1055/egusphere-2025-1055-AC2-supplement.pdf
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AC2: 'Reply on RC2', Inderjot Chahal, 09 Dec 2025
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2025-1055', Anonymous Referee #1, 28 Aug 2025
Dear Editors and Authors,
This manuscript investigates the effects of agronomic practices and pedoclimatic factors on soil carbon (C) quality and stability using a large topsoil dataset (1490 samples post-filtering) from Ontario’s agricultural landscapes. The study’s strength lies in its landscape-scale focus—addressing a critical gap in small-plot-dominated soil C research—and its integration of both traditional (e.g., soil organic carbon [SOC], permanganate oxidizable C [POXC]) and novel (Rock Eval Pyrolysis) C indicators. The findings, particularly regarding the primacy of soil texture and cropping systems as drivers of soil C dynamics, offer relevant implications for sustainable agriculture and climate change mitigation. While the core research question and dataset are valuable, the manuscript requires minor revisions before acceptance.
- Abstract
Line 12: It is better to correct sample size to "a dataset of 1490 topsoil samples" or "a dataset of 1490 post-filtered topsoil samples” here.
Line 23: The statement "Remaining soil management variables (cover crop use, tillage intensity, and organic amendments) had less influence" is vague (e.g., "less influence" relative to what?) and conflicts with two key sources. Table 1 shows tillage intensity explains 18.0% of variance in Cmin-96h and 22.3% in ACE; Table 2 further demonstrates tillage significantly affects POXC in fine-textured soils (P=0.0210). The Introduction cites Sun et al. (2023) and Balota et al. (2004), who show reduced tillage and cover crops increase SOC and C mineralization.
I suggest revising vague "less influence" to "weaker influence than texture/cropping systems"
- Introduction
Line 41: The Introduction cites Weil et al. (2003) for POXC, while Section 2.2 references Moebius-Clune et al. (2016) for POXC methods. The former is the original POXC protocol, the latter is the revised method? Please reconcile the two citations.
- Materials and Methods
Lines 101–102: The original description "Soil samples were collected …… terminated at 30 cm" is ambiguous. Confirm and revise to: "Soil samples were collected from the Ap horizon (agricultural tilled layer). To ensure all samples were restricted to this horizon, sampling was terminated at 30 cm if the Ap horizon exceeded this depth; the median thickness of the sampled Ap horizons across all sites was 25 cm."
Line 112: Overlapping soil texture ranges
The current texture classifications (coarse: 52–94% sand; medium: 2–78% sand; fine: 1–45% sand) have overlapping ranges, leading to ambiguous categorization. Why did the Ref. Moebius-Clune et al., 2016 do like this?
Line 130: Explain ACE (n=151) was measured only on the 151 pyrolysis subsamples (representative of full dataset), tell the reader they are the same 151 samples.
Line 153-162: Programmed pyrolysis (Section 2.2) is central to the study’s novel insights, but key parameters are omitted: helium flow rate (critical for hydrocarbon capture), sample weight precision (±0.1 mg?), and calibration standards used for S1/S2/S3 quantification.
Lines 163–164: the definition of T50 ("temperature at which 50% of the SOC was pyrolyzed") conflicts with the cited reference (Gregorich et al., 2015), which defines T50 for pyrolyzable C (not total SOC).
- Results and Discussion
Soil texture explains 60.5% of SOC variance (Table 1)—the largest driver—but the manuscript only attributes this to "inherent soil characteristics" without linking it to specific processes (e.g., clay mineral adsorption, aggregate protection of C). I suggest expanding discussions to: "Soil texture was the strongest driver of SOC (explaining 60.5% of variance; Table 1), primarily due to texture-mediated C stabilization processes: fine-textured soils (high clay content) enhance C retention via clay mineral adsorption (e.g., smectite and illite bind organic molecules) and aggregate protection (microaggregates physically isolate SOC from microbial decomposition). This aligns with Figure 1B, where fine-textured soils had the highest T50 (greater thermal stability), likely due to increased clay-C binding capacity compared to coarse/medium-textured soils."
Table 1: "Model R²" values (0.16–0.37) are low, indicating unaccounted variance? Other potential variables (e.g., crop residue input rates, microbial community composition, or historical land use,……)? The manuscript does not address this, leaving readers to question if key drivers were omitted.
Line 193: "Tillage intensity was not found to be an important predictor" contradicts Table 2, where tillage affects POXC in fine-textured soils (P=0.0210). Qualify this statement.
Line 201: Perennials and forages had greatest SOC aligns with Table 3, but Table 3 shows coarse-textured perennial SOC (19.4ab) is not statistically distinct from annual grain (16.9ab). Soften the claim to "greater or comparable concentrations."
Line 213: "Diversification of cropping systems... critical for building soil C"—no data on residue inputs to support "quantity/quality of C inputs" as the mechanism. Add a discussion linking cropping systems to measured C inputs (if available) or cite literature on residue differences.
Line 253: "In coarse-textured soils, Cmin-96h, POXC... were significantly impacted by tillage"—Figure 2 (boxplots) for these indicators shows overlapping ranges between tillage intensities, weakening the "significant" claim. Discuss effect size alongside statistical significance.
Line 260: "Medium and fine textured soils had more interactions than coarse"—no explanation for this pattern (e.g., texture-mediated microbial activity). Add a mechanistic hypothesis.
Line 262: Inconsistent terminology: "tillage intensity" vs. "tillage treatments" (Line 262). Standardize to "tillage intensity."
Line 268: "Vegetable systems... less advanced decomposition" is supported by Figure 3 (high HI, low OI), but Table 3 shows vegetable OI in coarse soils (147) is lower than orchard (203) but not statistically distinct from annual grain (168). Clarify this nuance.
Figure 6: "Cover crop" is negatively associated with C indicators, but Table 1 shows cover crops explain <5% variance. Discuss why this association is weak (small sample size: n=55?).
Table 4: "HI vs. ACE (r=0.59***)"—ACE is a labile N/C proxy, but no discussion of why hydrogen-rich labile C correlates with protein. Link this to microbial metabolism of fresh organic matter?
- Conclusion
It is better to explicitly add methodological limitations, e.g. (1) Tillage intensity categories (e.g., 'light disturbance,' 'no disturbance') relied on producer self-reporting of descriptive terms, with no objective metrics (e.g., plowing depth, number of tillage passes) to standardize classification—introducing bias, as 'light disturbance' may vary by grower interpretation; (2) The small sample size for cover crops (n=55) and orchards (n=33) limits the generalizability of results for these management systems. Future studies should adopt objective tillage measurements and balance sample sizes across management categories to strengthen conclusions.
Best regards
Dr. Chao Song
Citation: https://doi.org/10.5194/egusphere-2025-1055-RC1 -
AC1: 'Reply on RC1', Inderjot Chahal, 09 Dec 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1055/egusphere-2025-1055-AC1-supplement.pdf
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RC2: 'Comment on egusphere-2025-1055', Marie-Liesse Aubertin, 23 Oct 2025
General comments
This study aims to evaluate the impact of agricultural management and environmental variables on soil C indicators, including indicators from thermal analysis. Based on the analysis of 1490 samples, they concluded that soil textured classes and cropping systems had the strongest influence on both quality and stability of soil C indicators, and evidenced interactive effects between cropping systems and tillage intensity. The aim of the subject is very relevant because based on a large dataset at the landscape scale. The experimental approach seems appropriate for answering the scientific questions. I recommend moderate revisions for this paper.
More information should be given about the delimitation of the geographical area. In general, several results warrant further explanation and discussion. The authors should further elaborate on the limitations of the approaches and adopt a more critical perspective towards them. The part concerning the PCA interpretation should be improved.
Detailed comments
Introduction
Line 32-36: “Soil C” à do the authors mean soil C dynamic or turnover or stability or …? Soil C alone is not a parameter. The parameter or process meant in this paragraph should be specified.
Line 34: “Soil organic C (SOC)” content
Line 81-82: I am not sure to understand if the research gap is due to the difficulty to observe any effect of the previously cited factors on C stability, or if it is due to the difficulty to identify the main factors affecting C stability. I suggest reformulating this sentence.
Line 91: again: “soil C “ storage and stability?
Material and method
Line 97: Since the study highlights that the experiment was done at a landscape scale, the authors should specify the geographical area of the project. It may be important to have an idea if the scale of the area (regional, national or multi-national scale?) and if the studied parameters (e.g. cropping systems, tillage intensity, use of organic amendment) are representative of this geographical area.
Line 129: the authors should precise what “QA/QC analysis” refers to.
Line 159: Were the CO emissions also included in the S3?
Line 163: I understand that T50 corresponds to 50% of C emissions under all S1, S2 and S3 signals. If not, this should be specified.
Line 167: if “variance component analysis” are made, the data should follow normal distributions. It would be relevant to precise here if the normality and homoskedasticity were tested for the data. Unless the authors consider that the sample sizes are large enough to override the normality of the data...
Results and discussion
Line 190: it is not clear what “75.2%” refers to.
Table 1: it is surprising that the carbon inputs, such as cover crops (if plant residues remain on the field) and organic amendments, have so low effects on SOC and on the indicators of C stability, when compared to cropping system and soil texture. Indeed, organic matters amendment is expected to increase labile C content and hence, decrease the overall stability of C. These results are, at least to me, very surprising and therefore interesting. I suggest developing a bit more a critical discussion about these results and the approach, and eventually present hypothesis of explanation. An idea to complete this approach, could be to make a partitioning of the variances within each textural class.
Table 2: is there any reason why annual temperature and mean precipitation are not tested in this table? Furthermore, the results evidence statistical differences but ignore tendencies that may also be interesting to investigate.
Table 3: in the legend, the authors should remind which statistical differences are represented by the letters. As for tables 1 & 2, the authors should add what T50 refers to.
Line 210: “the lowest soil C” content? Stability? Please precise which indicator are referred to.
Lines 210-211: I guess that many types of management exist to produce vegetable and annual grain systems. They are maybe not always intensively managed. I guess that this could be a critical point you could add to the discussion. It would be interesting to have a bit more information about how the fields of the project were managed and if they were all managed the same way within each cropping system.
Line 219: Forage doesn’t have the lowest T50 across all soil textures in table 3; vegetable and perennial have lower values than forage for medium-textured soils, and orchard has lower values for fine-textured soils.
Lines 226-228: This result is interesting, and therefore it would be relevant to develop the discussion about the influence of the texture on thermal stability. There may be a link to do with the higher organo-mineral associations in clay than in sandy soils. It would also be relevant to precise if other studies also found this same result.
Figure 2: In the legend, the authors wrote that the data from the other cropping systems are not shown because of the insufficient number observations. If these number of observations were not a problem for the other tables and figures, why should they be a problem for this figure? It should especially not be a problem when presented as boxplots.
Line 251: I guess you mean “Table 2” instead of “Figure 2”.
Lines 247-264: in this paragraph, there are several mentions of the Table S1. It could be relevant to add this table in the main tables.
Line 269: The HI of vegetable systems doesn’t seem different than those of orchard systems and thus, this figure doesn’t show that the organic matter of vegetable systems mainly consists
Lines 274-287: This paragraph, as well as figures 4 and 5 should be transferred in material and method. In the discussion should only appear how this distribution may affect the results. Figures 4 and 5 could as well be transferred as supplementary materials.
Line 289: The authors should introduce in the main text how the PCA was done and with which indicators and parameters.
Line 290: did the other studies used also the same parameters and indicators? If yes, on which type of data did they use it? It is a bit tricky to compare variances from different datasets…
Lines 294-296: It would be interesting to have an explanation of why the different approaches of variance analysis and PCA do not show the same results concerning the influence of weather (temperature and humidity) on C indicators.
Lines 307-314: In the PCA, the arrows from the soil texture are orthogonal to the arrows from the soil C indicators, thereby showing no correlations. Therefore, I disagree with the results presented in this paragraph.
Line 315: idem, HI and the C indicators are orthogonal to the cover crops and thus, the PCA does not show any correlation between them. In addition, the cover crop reflects a very low contribution in the PCA analysis (small arrow size).
Citation: https://doi.org/10.5194/egusphere-2025-1055-RC2 -
AC2: 'Reply on RC2', Inderjot Chahal, 09 Dec 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1055/egusphere-2025-1055-AC2-supplement.pdf
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AC2: 'Reply on RC2', Inderjot Chahal, 09 Dec 2025
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Adam W. Gillespie
Daniel D. Saurette
Laura L. Van Eerd
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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- Final revised paper
Dear Editors and Authors,
This manuscript investigates the effects of agronomic practices and pedoclimatic factors on soil carbon (C) quality and stability using a large topsoil dataset (1490 samples post-filtering) from Ontario’s agricultural landscapes. The study’s strength lies in its landscape-scale focus—addressing a critical gap in small-plot-dominated soil C research—and its integration of both traditional (e.g., soil organic carbon [SOC], permanganate oxidizable C [POXC]) and novel (Rock Eval Pyrolysis) C indicators. The findings, particularly regarding the primacy of soil texture and cropping systems as drivers of soil C dynamics, offer relevant implications for sustainable agriculture and climate change mitigation. While the core research question and dataset are valuable, the manuscript requires minor revisions before acceptance.
Line 12: It is better to correct sample size to "a dataset of 1490 topsoil samples" or "a dataset of 1490 post-filtered topsoil samples” here.
Line 23: The statement "Remaining soil management variables (cover crop use, tillage intensity, and organic amendments) had less influence" is vague (e.g., "less influence" relative to what?) and conflicts with two key sources. Table 1 shows tillage intensity explains 18.0% of variance in Cmin-96h and 22.3% in ACE; Table 2 further demonstrates tillage significantly affects POXC in fine-textured soils (P=0.0210). The Introduction cites Sun et al. (2023) and Balota et al. (2004), who show reduced tillage and cover crops increase SOC and C mineralization.
I suggest revising vague "less influence" to "weaker influence than texture/cropping systems"
Line 41: The Introduction cites Weil et al. (2003) for POXC, while Section 2.2 references Moebius-Clune et al. (2016) for POXC methods. The former is the original POXC protocol, the latter is the revised method? Please reconcile the two citations.
Lines 101–102: The original description "Soil samples were collected …… terminated at 30 cm" is ambiguous. Confirm and revise to: "Soil samples were collected from the Ap horizon (agricultural tilled layer). To ensure all samples were restricted to this horizon, sampling was terminated at 30 cm if the Ap horizon exceeded this depth; the median thickness of the sampled Ap horizons across all sites was 25 cm."
Line 112: Overlapping soil texture ranges
The current texture classifications (coarse: 52–94% sand; medium: 2–78% sand; fine: 1–45% sand) have overlapping ranges, leading to ambiguous categorization. Why did the Ref. Moebius-Clune et al., 2016 do like this?
Line 130: Explain ACE (n=151) was measured only on the 151 pyrolysis subsamples (representative of full dataset), tell the reader they are the same 151 samples.
Line 153-162: Programmed pyrolysis (Section 2.2) is central to the study’s novel insights, but key parameters are omitted: helium flow rate (critical for hydrocarbon capture), sample weight precision (±0.1 mg?), and calibration standards used for S1/S2/S3 quantification.
Lines 163–164: the definition of T50 ("temperature at which 50% of the SOC was pyrolyzed") conflicts with the cited reference (Gregorich et al., 2015), which defines T50 for pyrolyzable C (not total SOC).
Soil texture explains 60.5% of SOC variance (Table 1)—the largest driver—but the manuscript only attributes this to "inherent soil characteristics" without linking it to specific processes (e.g., clay mineral adsorption, aggregate protection of C). I suggest expanding discussions to: "Soil texture was the strongest driver of SOC (explaining 60.5% of variance; Table 1), primarily due to texture-mediated C stabilization processes: fine-textured soils (high clay content) enhance C retention via clay mineral adsorption (e.g., smectite and illite bind organic molecules) and aggregate protection (microaggregates physically isolate SOC from microbial decomposition). This aligns with Figure 1B, where fine-textured soils had the highest T50 (greater thermal stability), likely due to increased clay-C binding capacity compared to coarse/medium-textured soils."
Table 1: "Model R²" values (0.16–0.37) are low, indicating unaccounted variance? Other potential variables (e.g., crop residue input rates, microbial community composition, or historical land use,……)? The manuscript does not address this, leaving readers to question if key drivers were omitted.
Line 193: "Tillage intensity was not found to be an important predictor" contradicts Table 2, where tillage affects POXC in fine-textured soils (P=0.0210). Qualify this statement.
Line 201: Perennials and forages had greatest SOC aligns with Table 3, but Table 3 shows coarse-textured perennial SOC (19.4ab) is not statistically distinct from annual grain (16.9ab). Soften the claim to "greater or comparable concentrations."
Line 213: "Diversification of cropping systems... critical for building soil C"—no data on residue inputs to support "quantity/quality of C inputs" as the mechanism. Add a discussion linking cropping systems to measured C inputs (if available) or cite literature on residue differences.
Line 253: "In coarse-textured soils, Cmin-96h, POXC... were significantly impacted by tillage"—Figure 2 (boxplots) for these indicators shows overlapping ranges between tillage intensities, weakening the "significant" claim. Discuss effect size alongside statistical significance.
Line 260: "Medium and fine textured soils had more interactions than coarse"—no explanation for this pattern (e.g., texture-mediated microbial activity). Add a mechanistic hypothesis.
Line 262: Inconsistent terminology: "tillage intensity" vs. "tillage treatments" (Line 262). Standardize to "tillage intensity."
Line 268: "Vegetable systems... less advanced decomposition" is supported by Figure 3 (high HI, low OI), but Table 3 shows vegetable OI in coarse soils (147) is lower than orchard (203) but not statistically distinct from annual grain (168). Clarify this nuance.
Figure 6: "Cover crop" is negatively associated with C indicators, but Table 1 shows cover crops explain <5% variance. Discuss why this association is weak (small sample size: n=55?).
Table 4: "HI vs. ACE (r=0.59***)"—ACE is a labile N/C proxy, but no discussion of why hydrogen-rich labile C correlates with protein. Link this to microbial metabolism of fresh organic matter?
It is better to explicitly add methodological limitations, e.g. (1) Tillage intensity categories (e.g., 'light disturbance,' 'no disturbance') relied on producer self-reporting of descriptive terms, with no objective metrics (e.g., plowing depth, number of tillage passes) to standardize classification—introducing bias, as 'light disturbance' may vary by grower interpretation; (2) The small sample size for cover crops (n=55) and orchards (n=33) limits the generalizability of results for these management systems. Future studies should adopt objective tillage measurements and balance sample sizes across management categories to strengthen conclusions.
Best regards
Dr. Chao Song