Modeling Climate Regulation of Arable Soils in Northern Saxony under the Influence of Climate Change and Management Practices
Round 1
Reviewer 1 Report
I have read the manuscript entitled “Modeling climate regulation of arable soils in northern Saxony under the influence of climate change and management practices”. In this study, the CANDY Carbon Balance (CCB) model was used to determine how different AMPs could affect SOC stocks in a study area in northern Saxony, Germany. Specifically, we used scenarios with different intensities of sustainable AMPs to assess the potential effects of reduced tillage, crop cultivation, fertilizer management as well as the management of crop residues and by products. The analysis was carried out for the simulation period 2020 – 2070, with and without consideration of climate change effects. Generally, I believe the manuscript can interest for the journal. Although, I am satisfied with the overall presentation and writing of the manuscript. I would suggest accept after miner spell check and overlook minor errors in the manuscript.
In my point of paper is good but further few comments are pasted:
I have read the manuscript entitled “Modeling climate regulation of arable soils in northern Saxony under the influence of climate change and management practices”. In this study, the CANDY Carbon Balance (CCB) model was used to determine how different AMPs could affect SOC stocks in a study area in northern Saxony, Germany. Specifically, we used scenarios with different intensities of sustainable AMPs to assess the potential effects of reduced tillage, crop cultivation, fertilizer management as well as the management of crop residues and by products. The analysis was carried out for the simulation period 2020 – 2070, with and without consideration of climate change effects. Generally, I believe the manuscript can interest for the journal. Although, I am satisfied with the overall presentation and writing of the manuscript. I would suggest minor revision addressing with the following comments.’1. Is this model is novel approach regarding the carbon sequestration?
2. Please highlight the review regarding CANDY carbon balance model if literature reviewed present regarding carbon balance, carbon sequestration or climate change.
3. Line 275: In data 6.17 t C ha-1, +6.0%. Is it correct please check or it is ±6.0%.
4. Line 293: What is Crep?
5. Figure 4 Quality is not good, also add error bars if data is present.
Author Response
We would like to thank the reviewer for providing such a helpful review and constructive feedback on our manuscript. We have addressed all comments and believe that the reviewers’ feedback has helped to improve the quality of the manuscript. A detailed response to each comment is provided below.
Comment (1): In my point of paper is good but further few comments are pasted: I have read the manuscript entitled “Modeling climate regulation of arable soils in northern Saxony under the influence of climate change and management practices”. In this study, the CANDY Carbon Balance (CCB) model was used to determine how different AMPs could affect SOC stocks in a study area in northern Saxony, Germany. Specifically, we used scenarios with different intensities of sustainable AMPs to assess the potential effects of reduced tillage, crop cultivation, fertilizer management as well as the management of crop residues and by products. The analysis was carried out for the simulation period 2020 – 2070, with and without consideration of climate change effects. Generally, I believe the manuscript can interest for the journal. Although, I am satisfied with the overall presentation and writing of the manuscript. I would suggest minor revision addressing with the following comments.
Response (1): Thank you very much for your positive feedback and for your time to review the manuscript.
Comment (2): Is this model is novel approach regarding the carbon sequestration?
Response (2): There are a number of models available to assess the impact of agricultural practices on carbon sequestration. In section 1.2 of the manuscript, we write: “To analyze the potential of SCS for different regions, models are used that simulate, in a simplified way, changes in SOC stocks and distributions under different scenarios of agricultural management or climatic conditions [13]. The use of such models allows the identification of critical drivers and approaches for mitigation measures that cannot be determined directly in the field [4,14].”
The CCB model has also been used previously in studies analyzing the carbon sequestration potential of agricultural measures, and we provide several references in section 2.2 of the manuscript: “The CCB model has been validated for various site conditions and cropping systems and has been applied in several German case studies, also close to the model region [7,9,26,29–31].” The model framework as such is therefore not novel per se. However, to the best of our knowledge, our approach of using scenarios with different implementation intensities of measure combinations (reduced tillage, cultivated crops, fertilizer management, management of crop residues and by-products) is innovative, as the effect of single measures on SOC stocks could be enhanced or reduced in combination with other measures. Furthermore, the effectiveness of the measures studied in our manuscript varies across regions and it is the first time that they have been analyzed for our specific case study.
Comment (3): Please highlight the review regarding CANDY carbon balance model if literature reviewed present regarding carbon balance, carbon sequestration or climate change.
Response (3): Thank you for your comment. We understand that you would like to have a clearer separation in the literature we provide, in particular which of our references on the CANDY carbon balance model address different topics (e.g. carbon sequestration, climate change). We have therefore added additional references on the use of CCB in scientific literature in section 2.2 of the manuscript and reformulated parts of the text to make the topics clearer:
“The CCB model has been validated for various site conditions and cropping systems and has been applied in several German case studies, also close to the model region [7,9,26,29–31]. While some of these applications analyzed and validated the carbon balance and ongoing trends at the field scale [7,26] and considered the carbon sequestration potential of specific measures such as minimum tillage [29] and crop residue management [30], other studies conducted regional scale analyses [9,31], addressed climate change aspects [32], or included CCB in a multi-model ensemble [33].”
Comment (4): Line 275: In data 6.17 t C ha-1, +6.0%. Is it correct please check or it is ±6.0%.
Response (4): The +6.0% refers to the relative increase in SOC stocks within the scenario period (2020-2070) compared to the base year 2020. To improve clarity, we have replaced the “+” with „growth by x%”.
Comment (5): Line 293: What is Crep?
Response (5): Crep is explained in section 2.2 (The CCB model): “…the annual carbon flux from FOM to SOM (Crep) in kg C ha-1 a-1 ..” and again in line 292: “The average annual reproduction flux from FOM to SOM (Crep), and thus the carbon input, (…)”. In our opinion, this explanation is sufficient and we would like to avoid repetition.
Comment (6): Figure 4 Quality is not good, also add error bars if data is present.
Response (6): We have checked the quality of Figure 4 in terms of resolution and dpi, and everything seems to be OK. The same applies to the other figures in the manuscript. As described in section 2.2 (The CCB model), we used the ‘regional mode’ of CCB in our study, and thus evaluated average or total carbon fluxes for the entire study area, rather than for each individual field. This also applies to Figure 4, where we calculated and presented the weighted average annual carbon flux from fresh organic matter to soil organic matter over the entire simulation period. Due to the use of CCBs ‘regional mode’, the analysis and visualization of field-level results could be misleading and was not part of the research question. We have therefore decided not to use box-plots and similar visualizations of distributions (including ‘error bars’) for Figure 4, and hope that you will agree with us.
Reviewer 2 Report
L53, The question should be how to realize this potential and achieve a real increase in carbon stocks.
2.3section: model calibration and validation process and results should be amended, or, we can not know if the performance of the model is ok.
3.2 Analysis of driving factors: should be sensitivity analysis, how to conducted the sensitivity analysis?and which range has been given to these variants? such as N fertilizer amount, changes in cultivated crops, etc.
The comments are very clear, that is, the author needs to supplement the procedures and results of model calibration and verification. Without this calibration and verification, I cannot judge the accuracy of subsequent simulation results. In addition, the third item is about model sensitivity analysis. Now the authors only present the driving factor results, which appear to be model sensitivity analysis, but do not provide details. These are my explanation of the review comments. Please refer to them.
No specific suggestions.
Author Response
We would like to thank the reviewer for providing such a helpful review and constructive feedback on our manuscript. We have addressed all comments and believe that the reviewers’ feedback has helped to improve the quality of the manuscript. A detailed response to each comment is provided below.
Comment (1): L53, The question should be how to realize this potential and achieve a real increase in carbon stocks.
Response (1): Thank you very much for this important specification. We agree and have incorporated your specification into the manuscript.
Comment (2): 2.3 section: model calibration and validation process and results should be amended, or, we can not know if the performance of the model is ok.
Response (2): The CCB model has been validated in previous publications using a dataset of 40 long-term experiments located in Central Europe, including 391 treatments with a total number of 4794 Corg observations. The relevant reference is given in section 2.2 of the manuscript:
Franko, U., Kolbe, H., Thiel, E., Ließ, E., 2011. Multi-site validation of a soil organic matter model for arable fields based on generally available input data. Geoderma 166, 119–134. https://doi.org/10.1016/j.geoderma.2011.07.019
We have added additional references on the use of CCB in scientific literature (in section 2.2) and tried to make it clearer that CCB is not a new development, but has been successfully applied for various research questions before:
“The CCB model has been validated for various site conditions and cropping systems and has been applied in several German case studies, also close to the model region [7,9,26,29–31]. While some of these applications analyzed and validated the carbon balance and ongoing trends at the field scale [7,26] and considered the carbon sequestration potential of specific measures such as minimum tillage [29] and crop residue management [30], other studies conducted regional scale analyses [9,31], addressed climate change aspects [32], or included CCB in a multi-model ensemble [33].”
Of course, it would be optimal to have an additional, case study specific calibration/validation procedure. However, due to the limited availability of monitoring data and the regional scale of the assessment, this could not be done and is also uncommon for regional modelling of SOC. To ensure a proper initialization of case study specific SOC levels in the CCB model, we used SOC measurements from samples collected at 82 sites in and close to the study area between 2014 and 2017. To further clarify that we could not include an additional, case-study specific calibration/validation procedure in our study, we have added some additional text on this aspect in section 4.4. (Limitations and outlook): “This limited availability of monitoring data and the regional scale of the assessment also prevented a case study specific calibration and validation procedure of the CCB model.“
Comment (3): 3.2 Analysis of driving factors: should be sensitivity analysis, how to conducted the sensitivity analysis?
Response (3): We agree, that our analysis of the driving factors is a sensitivity analysis, but in a simplified form. Consequently; we did not previously mark it as such, but have made changes in response to your comment: We have renamed chapter 3.2 to „Sensitivity of driving factors” and have made several changes in chapter 2.5 to add the term sensitivity analysis, e.g. in the first sentence of the section: “We conducted a sensitivity analysis by determining the impact of the five different driving factors on simulated SOC stocks between 2020 and 2070 (…)”. For more details on how we conducted the sensitivity analysis, see section 2.5 (“Analysis of driving factors”). Here we describe:
- How we ran the CCB model five times, changing only one of the driving factors (agricultural parameters + climate change) each time. The BaUnoCC scenario was used as the basis for these runs and each driving factor was changed to its maximum implementation intensity as defined by scenario Sc3 (the values can be found in detail in Table S4 in the Supplementary Material).
- We then compared the results with the original BaUnoCC results:
- Since the BaUnoCC scenario itself also led to an increase in regional SOC stocks, we subtracted the effect of the BaUnoCC on the CSOM values for each field.
- We analysed the difference in the change in SOC stocks caused by the driving factors between the first year (2020) and the last year of the simulation period (2070).
We have made several modifications to our description in section 2.5 (please see track-change version of the manuscript) and hope that this has improved the clarity of our procedure.
Comment (4): and which range has been given to these variants? such as N fertilizer amount, changes in cultivated crops, etc.
Response (4): Thank you for pointing out that this needs to be clarified. The values and ranges of the agricultural driving factors (cultivated crops, reduced tillage, management of crop residues and by-products, fertilizer management) can be found in Table S4 in the Supplementary Material. Here, the parameterizations of the BaU and Sc3 scenarios are presented, which were used in the ‘sensitivity analysis’ as described in section 2.5. The values for the driving factor climate change can be found in section 2.4 of the manuscript. To improve understanding, we have added the following text in section 2.5 (line 260): “(for values and ranges of the agricultural driving factors see Table S4 in the Supplementary Material, for values of climate see section 2.4.)”.
Comment (5): The comments are very clear, that is, the author needs to supplement the procedures and results of model calibration and verification. Without this calibration and verification, I cannot judge the accuracy of subsequent simulation results. In addition, the third item is about model sensitivity analysis. Now the authors only present the driving factor results, which appear to be model sensitivity analysis, but do not provide details. These are my explanation of the review comments. Please refer to them.
Response (5): Thank you again for your helpful review and constructive feedback. We hope that we have addressed all comments according to your satisfaction. We explained that the CCB model has already been validated in a previous publication considering a dataset of 40 long-term experiments located in Central Europe and provided the reference. Furthermore, we explained that we used monitoring data from 82 sites to ensure a proper initialization of case study specific SOC levels in the CCB model. We also added some additional discussion on the model calibration/verification aspect in section 4.4. (Limitations and outlook). We have clarified section 2.5 on the sensitivity of driving factors to ensure that readers understand the results presented in section 3.2. Furthermore, we added additional references to the supplementary materials, where the specific settings for each scenario are provided.
Reviewer 3 Report
Your limitations might be not considering the use of different crop species, since their C:N ratio and biomass varies, time of plantings that dependent on soil temperature and moisture along with biological activities, land use patterns etc.
The conclusion seems quite eye opening. Reduced tillage and reduced doses of chemical fertilizers seems to be effective in SCS. However, the area under reduced tillage can not be increased significantly due to many reasons. Similarly, the worlds soil’s fertility has been declining, therefore there is challenge to increase crop yields, how to trade-off it is a challenge for the scientific communities. Therefore, our future thrust must be towards increasing area under zero/minimum tillage and nutrient management.
Author Response
We would like to thank the reviewer for providing such a helpful review and constructive feedback on our manuscript. We have addressed all comments and believe that the reviewers’ feedback has helped to improve the quality of the manuscript. A detailed response to each comment is provided below.
Comment (1): Your limitations might be not considering the use of different crop species, since their C:N ratio and biomass varies, time of plantings that dependent on soil temperature and moisture along with biological activities, land use patterns etc.
Response (1): Thank you for your input on this topic! The CCB model considers different crop species (e.g. winter wheat, barley, maize, etc.) and parametrizes their properties in a plant parameter database (including e.g. C:N ratio). Furthermore, crop yields and management factors (e.g. residue management) are input variables to the CCB model. For more details on this please refer to the CCB manual and the following publication, which has been referenced to in section 2.5:
Franko, U., Kolbe, H., Thiel, E., Ließ, E., 2011. Multi-site validation of a soil organic matter model for arable fields based on generally available input data. Geoderma 166, 119–134. https://doi.org/10.1016/j.geoderma.2011.07.019
We were also partly able to consider different crop species (e.g. winter wheat and summer wheat) in our case study (for more information please refer to Table S1 in the Supplementary Materials). To address your point and to improve clarity, we integrated this as follows in the section 4.4 (Limitations and outlook): “Furthermore, the effect of different cultivated crop species and crop variants on SOC stocks, as well as the influence of their respective C:N ratio, biomass, time of planting and other factors, could be the subject of future studies.”. We agree that this is a very interesting point for further studies. However, we don’t see it as a strong limitation of our study because detailed attributes of the cultivated crops (as mentioned above) are parametrized in CCB (and therefore influence our results).
Comment (2): The conclusion seems quite eye opening. Reduced tillage and reduced doses of chemical fertilizers seems to be effective in SCS. However, the area under reduced tillage can not be increased significantly due to many reasons. Similarly, the worlds soil’s fertility has been declining, therefore there is challenge to increase crop yields, how to trade-off it is a challenge for the scientific communities. Therefore, our future thrust must be towards increasing area under zero/minimum tillage and nutrient management.
Response (2): Thank you for your motivating comment. You are right that there is often a trade-off between increasing crop yields and preserving soils and their fertility on the long term. Sustainable agricultural management practices such as reduced tillage may not increase crop yields in the short term, but they will stabilize yields and reduce risks in the long term and contribute to climate change mitigation and adaptation.
