Spatiotemporal Dynamic Evolution Characteristics of Net Carbon Sinks in County-Level Farmland Ecosystems in Hunan Province, China

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This study is very good, and worth of praise to authors. Methods applied here are notably appropriate, especially knowing that there is a deficit in studies dealing with carbon sources and sinks of farmland ecosystems simultaneously, particularly on net carbon sinks in some mainly agricultural provinces of China, such as Hunan. There is, though, a few things in paper that are recommended for improvement:

1) There should be said, in at least one statement in the Introduction section, that there is a lack of studies on net carbon sinks for Hunan province itself, as one of the most important agricultural provinces of PR China, for a low-carbon progress in agriculture and for achieving the “dual carbon” goals. This way, it makes the arguments stronger and more convincing of why particularly this study and chosen methods in it, and specifically in this province, were done, and what the particular importance of the study is.

2) In sub-chapter "2.3. Research methods" (pages 5 - 8), methods cited in references [23] - [33] as well as [36] - [38] should be discussed in more details. It is needed, above all, in order to make it clear to a reader why these methods (given in these references) are chosen. It is recommendable to discuss them within the Chapter 1 (Intro section), before their cited use in Ch. 2.

3) I recommend to authors (but no insisting) to consider a different way of representing data in Figure 2. In my opinion, it seems it might be more informative if the diagram is in a "line"-form, not as histogram, i.e. if each city is represented by a line of specified color, and the net carbon sinks in each city of each year is given by a colored dot, then the dots of same color being interconnected to show a time-dependence of net carbon sinks in each city

Author Response

Dear Reviewer:

Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Spatiotemporal Dynamic Evolution Characteristics of Net Carbon Sinks in County-Level Farmland Ecosystems in Hunan Province, China” (ID: atmosphere-3852476). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked up using the “Highlight” function in the paper. The main corrections in the paper and the responds to the comments are as following:

 

Responses to the comments of Reviewer (C, Comments; R, Response):

 

C: 1. There should be said, in at least one statement in the Introduction section, that there is a lack of studies on net carbon sinks for Hunan province itself, as one of the most important agricultural provinces of PR China, for a low-carbon progress in agriculture and for achieving the “dual carbon” goals. This way, it makes the arguments stronger and more convincing of why particularly this study and chosen methods in it, and specifically in this province, were done, and what the particular importance of the study is.

R: We appreciate the reviewer’s valuable suggestion. We agree that explicitly pointing out the lack of net carbon sink studies focused on Hunan Province strengthens the motivation and significance of our work. In revision, we added a statement in the Introduction emphasizing that, although Hunan is one of the most important agricultural provinces in China and crucial for the “dual carbon” goals, there is still a scarcity of systematic studies on farmland ecosystem carbon sinks at the county level in this region.

 

C: 2. In sub-chapter "2.3. Research methods" (pages 5 - 8), methods cited in references [23] - [33] as well as [36] - [38] should be discussed in more details. It is needed, above all, in order to make it clear to a reader why these methods (given in these references) are chosen. It is recommendable to discuss them within the Chapter 1 (Intro section), before their cited use in Ch. 2.

R: We thank the reviewer for this constructive suggestion. We agree that simply citing methods without adequate discussion may limit clarity for readers. In revision, we have expanded the Introduction to briefly review the methodological approaches covered in references \[23]–\[33], \[36]–\[38], and explained why these methods were suitable and relevant for the present study. This addition clarifies the theoretical basis for our methodological choices.

 

C: 3. I recommend to authors (but no insisting) to consider a different way of representing data in Figure 2. In my opinion, it seems it might be more informative if the diagram is in a "line"-form, not as histogram, i.e. if each city is represented by a line of specified color, and the net carbon sinks in each city of each year is given by a colored dot, then the dots of same color being interconnected to show a time-dependence of net carbon sinks in each city.

R: We sincerely thank the reviewer for this constructive suggestion. We did attempt to represent Figure 2 in a line-chart form during the drafting stage. However, from the perspective of visual clarity, the line chart appeared more cluttered due to the large number of counties. In addition, the line form emphasizes individual city-level changes over time, while our research objective in Figure 2 was to highlight the overall temporal variation of net carbon sinks across the province. Considering both aesthetics and the intended focus of the results, we finally chose the histogram format, which we believe better conveys the aggregated changes.

 

We would like to thank the reviewers again for their deliberation and help. We think these suggestions are very meaningful!

 

With best regards,

Yours sincerely,

Lin Li

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

The authors use statistical methods to examine how greenhouse gas emissions and sinks of the agricultural sector change in space and in time throughout Hunan Province from 2005 to 2020. In general, the manuscript is well-written, and the methods and results are presented clearly. I have a few minor questions and concerns for the authors to consider.

One item missing from your analysis is non-human animal contributions. Creatures such as cows, pigs, goats, etc., can be significant sources of methane and other greenhouse gas emissions. Are these types of animals farmed in Hunan province? If their contribution to overall GHG emissions is considered minimal in your study, please state that.

Figure 1 typo – the grey color key says “unused lande”

Reference 23 by Weng et al (2018) is listed as DOI10.5555/20183188018, but looking up this article in the proper journal, the doi is noted as 10.11975/j.issn.1002-6819.2018.06.030. Maybe the standard is different, but please check once more to make sure you are referencing the article you intend.

Line 246-7: the sentence starting with “Basic connotations…” seems incomplete.

Line 420 and again on 435: the references to Rey et al. need a Reference number matching the end of the manuscript (#41?), like it appears on Line 250, unless this is a different Rey et al. paper.

Line 437-8: you state here that Type I indicates no spatiotemporal transition, but earlier in Line 257 you say that Type I indicates that only the subject county transitioned but its neighbors did not, while Type IV indicates no transition of the county or its neighbors. The statement from line 437-8 occurs again on Line 483. Please clarify the language so these three sections of the manuscript are consistent.

Section 5’s policy recommendations are not strongly related to the research study, except for the portions discussing the protection of agricultural lands during urbanization development. Also, you are missing a discussion of the paper’s potential shortcomings and open questions for future research efforts. For instance, how many of the correlation factors and coefficients listed in Tables 1 and 2 come directly from agricultural practices in Hunan province? You repeatedly state (Line 433 for instance) that county-level decisions and techniques can greatly influence carbon emissions and sinks. Could a future research project improve the results presented here by examining how those coefficients may change by Province or even by county?

Author Response

Dear Reviewer:

Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Spatiotemporal Dynamic Evolution Characteristics of Net Carbon Sinks in County-Level Farmland Ecosystems in Hunan Province, China” (ID: atmosphere-3852476). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked up using the “Highlight” function in the paper. The main corrections in the paper and the responds to the comments are as following:

 

 

Responses to the comments of Reviewer (C, Comments; R, Response):

 

C: 1. One item missing from your analysis is non-human animal contributions. Creatures such as cows, pigs, goats, etc., can be significant sources of methane and other greenhouse gas emissions. Are these types of animals farmed in Hunan province? If their contribution to overall GHG emissions is considered minimal in your study, please state that.

R: We sincerely thank you for your close attention to our research project. When calculating agricultural carbon emissions, domestic scholars primarily consider emissions from agricultural land use, agricultural energy consumption, and livestock farming. However, the boundary between classifying livestock farming emissions as part of the farmland ecosystem or agricultural production remains somewhat ambiguous. Therefore, this study does not directly classify livestock and poultry farming carbon emissions as part of the agricultural ecosystem. Once clear boundaries for livestock and poultry farming carbon emissions are established, we will incorporate them into our research scope in the future.

 

C: 2. Figure 1 typo – the grey color key says “unused lande”.

R: We sincerely appreciate your valuable feedback. We have already corrected it in the article. See Figure 1.

 

 

 

C: 3. Reference 23 by Weng et al (2018) is listed as DOI10.5555/20183188018, but looking up this article in the proper journal, the doi is noted as 10.11975/j.issn.1002-6819.2018.06.030. Maybe the standard is different, but please check once more to make sure you are referencing the article you intend.

R: Thank you for bringing this to our attention. Upon verification, the DOI for this document is indeed 10.11975/j.issn.1002-6819.2018.06.030. I have corrected the DOI for this document. See Reference 23.

 

 

C: 4. Line 246-7: the sentence starting with “Basic connotations…” seems incomplete.

R: We sincerely appreciate your valuable feedback. We have revised this statement to read: The fundamental concepts and specific calculations of these two indices draw upon Yang Qiang's research.

 

 

C: 5. Line 420 and again on 435: the references to Rey et al. need a Reference number matching the end of the manuscript (#41?), like it appears on Line 250, unless this is a different Rey et al. paper.

R: We sincerely appreciate your close attention to the references. We have made the modifications as per your suggestions, as follows:

 

 

 

C: 6. Line 437-8: you state here that Type I indicates no spatiotemporal transition, but earlier in Line 257 you say that Type I indicates that only the subject county transitioned but its neighbors did not, while Type IV indicates no transition of the county or its neighbors. The statement from line 437-8 occurs again on Line 483. Please clarify the language so these three sections of the manuscript are consistent.

R: We sincerely appreciate your thoughtful and valuable feedback. We have revised the content on Line 257 to ensure consistent wording across these three sections.

 

 

C: 7. Section 5’s policy recommendations are not strongly related to the research study, except for the portions discussing the protection of agricultural lands during urbanization development. Also, you are missing a discussion of the paper’s potential shortcomings and open questions for future research efforts. For instance, how many of the correlation factors and coefficients listed in Tables 1 and 2 come directly from agricultural practices in Hunan province? You repeatedly state (Line 433 for instance) that county-level decisions and techniques can greatly influence carbon emissions and sinks. Could a future research project improve the results presented here by examining how those coefficients may change by Province or even by county?

R: We sincerely appreciate your valuable feedback. We have optimized the policy recommendations section to make it more specific and practical, ensuring it fully reflects its close relevance to the research. Additionally, we have included a discussion of the paper's limitations and directions for future improvement at the end of the conclusion.

 

 

 

 

We would like to thank the reviewers again for their deliberation and help. We think these suggestions are very meaningful!

 

With best regards,

Yours sincerely,

Lin Li

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

The present paper identifies three research gaps: (1) Limited studies focus specifically on net carbon sinks (absorption minus emissions); (2) Most research targets large spatial scales; county-level analyses are rare despite the counties’ direct role in agricultural emission reduction; (3) Insufficient attention to spatial linkages and dynamic evolution patterns.

Methodologically, the paper estimates carbon absorption for nine major crops (e.g., paddy, corn, wheat) using crop-specific coefficients for carbon absorption rate, economic coefficient, moisture content, and root-to-shoot ratio. This is a tier 2 methodology, based on carbon absorption correlation coefficients taken from prior research (Hui et al., Han et al., Gu et al.). Carbon emissions were considered for farmland inputs (fertilizers, pesticides, agricultural film, diesel, tillage, irrigation, and crop residues (coefficients from IPCC and other studies)), open burning of crop residues calculated using residue-to-product ratios, burning fractions, combustion efficiencies, and emission coefficients, food consumption based on 400 kg per capita annual consumption and emission intensity of 0.27 kg CO₂/kg food, and soil respiration which was fixed at 0.710 t C·hm⁻²·a⁻¹ for Hunan’s paddy-dominated farmland. The net carbon sink calculation was based on the subtraction of emissions from absorption.

The authors use Standard Deviational Ellipse (SDE) which captures spatial distribution trends and center of gravity shifts. The authors use the SDE method to analyse the spatial distribution patterns and directional trends of net carbon sinks in county-level farmland ecosystems in Hunan Province. They also use Spatial Autocorrelation and Global Moran’s I (overall clustering) to measure the overall spatial agglomeration of net carbon sinks, i.e., whether counties with similar values tend to cluster together in space, and Local Moran’s I (LISA clusters), i.e., HH (High–High), HL (High–Low), LH (Low–High), and LL (Low–Low) types. The authors finally adopt the LISA time path method within the ESTDA framework to capture changes in local spatial association patterns over time. They explain that the LISA time path analysis allows them to examine the dynamics of the local spatial structure of net carbon sinks and the volatility in the spatial dependence direction. The classification of spatiotemporal transition types (I–IV), indicate whether changes occur in the county itself, its neighbours, both, or neither.

The key takeaways show that urbanization is the main driver of net carbon sink decline, spatial clustering is intensifying, but regional disparities are significant, policy differentiation by region can better align agricultural productivity with carbon sequestration goals. Especially for Hunan, conclusions show that to meet “dual carbon” goals, technological innovation, land-use planning, and farmer engagement are critical.

 

Recommendations to the authors concerning methodological issues and the estimation source/sink carbon:

1. The study uses fixed carbon coefficients without any uncertainty analysis or sensitivity analysis on the results that use these coefficients (SDE and global and local spatial autocorrelation). The estimation of carbon absorption and emissions relies on fixed constants (e.g., carbon absorption rates, moisture content, residue-to-product ratios, emission factors) largely drawn from prior literature or different regions. There is no sensitivity analysis or uncertainty quantification, even though these coefficients can vary significantly by microclimate, soil type, crop variety, and management practices. This may affect the accuracy and robustness of results, especially SDE and global Moran’s I.

Recommendation: The authors should discuss the potential implications of the coefficients on the results derived by their methodology. There are two ways: Either argue that the coefficients used are very precise or argue that the potential uncertainty range of the coefficients does not really affect the estimations of distribution trends and/o the center of gravity shifts.

2. Omission of certain carbon fluxes – The methodology focuses on crop photosynthesis, farmland inputs, residue burning, food consumption, and soil respiration. Relevant fluxes in farmland systems including (a) carbon emissions from livestock in mixed farming systems; (b) methane (CH₄) and nitrous oxide (N₂O) fluxes from paddy soils and fertilization (important in Hunan’s rice-based systems) and (c) changes in soil organic carbon stocks from land-use change or conservation tillage are omitted and thus the study may provide only a partial carbon budget. What is the potential implication of this omission? Especially the methane fluxes may be important.

Recommendation: The authors should discuss the potential implications of omitting these carbon fluxes and justify why the estimates are still valid.

3. There is, potentially, a Modifiable Areal Unit Problem (MAUP) because the analysis uses counties as the spatial unit, merging urban districts into county units 4. This aggregation may distort spatial autocorrelation measures (Moran’s I, LISA), and results could differ with finer or different zoning. The spatial autocorrelation and LISA analyses appear to use a single spatial weight matrix over the whole period, despite possible changes in spatial relationships over 15 years (e.g., due to administrative boundary changes or urban expansion).

Recommendation: Please explain and provide some plausible arguments of why these issues do not distort your results and influence your conclusions.

4. The paper interprets high Type I spatiotemporal stability (82.51%) as "weak spatial linkage" (pages 15–16), but this could also result from strong local path dependence or from the choice of spatial weights. The causal interpretation may be overstated.

Recommendation: Please re-visit your explanation to make sure that holds true.

5. Land use data span only 4 time points (obtained for 2005, 2010, 2015, and 2020), but the study reports annual net carbon sink estimates. The paper does not clearly explain how land use data were interpolated between these years, which could introduce error. Crop yield and energy use data sources come from statistical yearbooks, which may have reporting errors or inconsistencies, especially at the county level. There is no discussion of data quality or potential biases.

Recommendation: Please provide a very short clarification of the land use interpolation between years and the reporting errors ar the county level.

6. Some conceptual issues related to the net carbon sink definition and issues with policy recommendation may be re-considered. The study subtracts emissions from absorption to get “net carbon sinks”, but the emissions include processes (soil respiration, residue burning) that are partly natural and partly management-induced. This makes it ambiguous whether the figure reflects anthropogenic mitigation potential or total ecosystem balance. In addition the calculation are not validated against independent measurements of carbon flux measurements (e.g., eddy covariance, soil sampling), so the accuracy of the modelled values remains untested.

Recommendation: The authors can devote one or two sentences just referring to the definitional issues and the triangulation of measurements.  

7. Policy recommendations are not directly tied to quantified drivers. While the conclusion suggests differentiated policies for eastern, central, northern, and southern Hunan, the analysis does not present a quantitative driver model linking socioeconomic or land-use variables to the spatial patterns. This weakens the causal basis for the recommendations and reduces the paper’s usefulness to policy makers and practitioners at the regional and local levels.

Recommendation: Please be more specific with your policy recommendation and especially their linkage with land-use variables which can immediately and directly influence net carbon evolution in the region.

Author Response

Dear Reviewer:

Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Spatiotemporal Dynamic Evolution Characteristics of Net Carbon Sinks in County-Level Farmland Ecosystems in Hunan Province, China” (ID: atmosphere-3852476). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked up using the “Highlight” function in the paper. The main corrections in the paper and the responds to the comments are as following:

 

 

Responses to the comments of Reviewer (C, Comments; R, Response):

 

C: 1. The study uses fixed carbon coefficients without any uncertainty analysis or sensitivity analysis on the results that use these coefficients (SDE and global and local spatial autocorrelation). The estimation of carbon absorption and emissions relies on fixed constants (e.g., carbon absorption rates, moisture content, residue-to-product ratios, emission factors) largely drawn from prior literature or different regions. There is no sensitivity analysis or uncertainty quantification, even though these coefficients can vary significantly by microclimate, soil type, crop variety, and management practices. This may affect the accuracy and robustness of results, especially SDE and global Moran’s I. The authors should discuss the potential implications of the coefficients on the results derived by their methodology. There are two ways: Either argue that the coefficients used are very precise or argue that the potential uncertainty range of the coefficients does not really affect the estimations of distribution trends and/o the center of gravity shifts.

R: We sincerely appreciate your valuable feedback. We have added a section discussing the limitations of this study and potential directions for future research. This study does have several limitations. The reference values for carbon emission coefficients were derived from existing research literature. However, to mitigate the negative impact of single errors on the findings, we selected research results from Hunan Province and its surrounding comparable regions, employing the average value method for calculation. This approach has been demonstrated in other scholars' studies and possesses a degree of validity. The potential uncertainty of the coefficients has a limited impact on the trends of center-of-gravity shifts and spatial distribution.

 

C: 2. Omission of certain carbon fluxes - The methodology focuses on crop photosynthesis, farmland inputs, residue burning, food consumption, and soil respiration. Relevant fluxes in farmland systems including (a) carbon emissions from livestock in mixed farming systems; (b) methane (CH₄) and nitrous oxide (N₂O) fluxes from paddy soils and fertilization (important in Hunan’s rice-based systems) and (c) changes in soil organic carbon stocks from land-use change or conservation tillage are omitted and thus the study may provide only a partial carbon budget. What is the potential implication of this omission? Especially the methane fluxes may be important. The authors should discuss the potential implications of omitting these carbon fluxes and justify why the estimates are still valid.

R: We are very grateful to the reviewers for raising this critical question. Indeed, the accounting framework of this study primarily considers crop photosynthesis, agricultural inputs, straw burning, grain consumption, and soil respiration, while livestock emissions, CH₄/N₂O fluxes from paddy fields, and changes in soil organic carbon (SOC) are not included. We recognize the importance of these fluxes. However, livestock emissions primarily occur within mixed agricultural systems and account for a relatively small proportion of agricultural output at the county level in Hunan Province (according to provincial statistics, less than 8% of total agricultural carbon emissions). When calculating agricultural carbon emissions, domestic scholars primarily consider emissions from agricultural land use, agricultural energy consumption, and livestock farming. However, the boundary between classifying livestock farming emissions as part of the farmland ecosystem or agricultural production remains somewhat ambiguous. Therefore, this study does not directly classify livestock and poultry farming carbon emissions as part of the agricultural ecosystem. Once clear boundaries for livestock and poultry farming carbon emissions are established, we will incorporate them into our research scope in the future. Additionally, rice paddy methane constitutes a significant emission source in Hunan. Although this study did not explicitly account for CH₄, its omission reduces the strength of the net carbon sink, particularly in double-cropping rice areas. However, since emissions are primarily concentrated in eastern and central Hunan, the impact of this omission is more related to the magnitude of the values than to the evolution of spatiotemporal distribution patterns. Finally, changes in soil organic carbon are influenced by tillage practices and land-use changes, exhibit longer cycles, and are difficult to obtain with consistent data at the county statistical level. Excluding this factor implies that the estimates in this paper are more oriented toward short- to medium-term net fluxes rather than long-term carbon pool changes.

Therefore, the results of this study should be interpreted as a partial carbon budget, primarily covering crop- and input-related fluxes that can be reliably measured through statistical yearbooks. It is important to note that, given the study's focus on relative spatiotemporal evolution characteristics (such as SDE, Moran's I, and LISA clustering), the omission of these fluxes may lead to an overestimation of the absolute value of net carbon sequestration. However, this omission does not compromise the robustness of conclusions regarding spatial patterns. We have added supplementary information to the main text.

 

C: 3. There is, potentially, a Modifiable Areal Unit Problem (MAUP) because the analysis uses counties as the spatial unit, merging urban districts into county units 4. This aggregation may distort spatial autocorrelation measures (Moran’s I, LISA), and results could differ with finer or different zoning. The spatial autocorrelation and LISA analyses appear to use a single spatial weight matrix over the whole period, despite possible changes in spatial relationships over 15 years (e.g., due to administrative boundary changes or urban expansion). Please explain and provide some plausible arguments of why these issues do not distort your results and influence your conclusions.

R: We appreciate the reviewers' attention to the issue of variable spatial units (MAUP) and fixed spatial weight matrices. Indeed, merging urban districts into county-level units may introduce certain statistical biases, and administrative boundary adjustments or urban expansion over the 15-year period could alter spatial relationships. However, in China, counties represent the most fundamental administrative and statistical units. Agricultural policies and land use statistics are implemented and published at this level, ensuring that analyses at this scale maintain policy relevance and data consistency. Furthermore, although MAUP may alter the absolute value of Moran's I or the local distribution of LISA clusters under different partitioning conditions, existing research indicates that the overall spatial aggregation trend—characterized by enhanced clustering and stable differences between eastern and western regions—remains robust across various scales.

For example, Jelinski and Wu (1996) found that scale effects and partitioning effects influence statistical values, yet spatial autocorrelation patterns (such as hotspots) remain identifiable across multiple scales [1]. Altaweel (2018) proposed evaluating autocorrelation stability across scales through Bayesian spatial modeling, demonstrating that robust spatial relationships persist even when regional boundaries are adjusted [2]. Collectively, these studies indicate that while MAUP may alter the absolute values of Moran’s I or LISA clustering, the observed trends of enhanced spatial aggregation and stable regional disparities between eastern and western regions at the county level remain credible. We have added supplementary information to the main text.

1. Jelinski, D. E., & Wu, J. The modifiable areal unit problem and implications for landscape ecology. Landscape ecology, 1996, 11(3), 129-140.
2. Tuson, M., Yap, M., Kok, M. R., Murray, K., Turlach, B., & Whyatt, D. Incorporating geography into a new generalized theoretical and statistical framework addressing the modifiable areal unit problem. International journal of health geographics, 2019, 18(1), 6.

 

C: 4. The paper interprets high Type I spatiotemporal stability (82.51%) as "weak spatial linkage" (pages 15-16), but this could also result from strong local path dependence or from the choice of spatial weights. The causal interpretation may be overstated. Please re-visit your explanation to make sure that holds true.

R: We sincerely appreciate your valuable feedback. We have re-examined this interpretation and made revisions to ensure the wording is more reasonable and accurate.

 

C: 5. Land use data span only 4 time points (obtained for 2005, 2010, 2015, and 2020), but the study reports annual net carbon sink estimates. The paper does not clearly explain how land use data were interpolated between these years, which could introduce error. Crop yield and energy use data sources come from statistical yearbooks, which may have reporting errors or inconsistencies, especially at the county level. There is no discussion of data quality or potential biases. Please provide a very short clarification of the land use interpolation between years and the reporting errors at the county level.

R: We sincerely appreciate your valuable feedback. We calculated the data for each year from 2005 to 2020, as detailed in the attached data in Appendix 1. We have supplemented the text with explanations regarding the interpolation methods for cross-year land use data, as well as potential errors in county-level data.

 

C: 6. Some conceptual issues related to the net carbon sink definition and issues with policy recommendation may be re-considered. The study subtracts emissions from absorption to get “net carbon sinks”, but the emissions include processes (soil respiration, residue burning) that are partly natural and partly management-induced. This makes it ambiguous whether the figure reflects anthropogenic mitigation potential or total ecosystem balance. In addition the calculation are not validated against independent measurements of carbon flux measurements (e.g., eddy covariance, soil sampling), so the accuracy of the modelled values remains untested. The authors can devote one or two sentences just referring to the definitional issues and the triangulation of measurements.

R: We appreciate the reviewer's important point. We acknowledge that the definition of net carbon sink encompasses both natural and anthropogenic processes, making it more suitable as an indicator of ecological equilibrium rather than a direct measure of emission reduction potential. This study employs the metric primarily to analyze spatiotemporal evolution patterns at the county level in Hunan Province.

The reference values for carbon emission coefficients in this paper are derived from existing research literature. To mitigate the negative impact of individual errors on the research findings, we selected studies from Hunan Province and its surrounding comparable regions, employing the average value method for calculation. This methodology has been demonstrated in the research of other scholars [1] [2]. We have added this clarification in the main text and indicated that future studies will validate the results using independent carbon flux observation data (e.g., eddy covariance monitoring, soil sampling).

1. Zhou, S. Y., Xi, F. M., Yin, Y., Bing, L. F., Wang, J. Y., Ma, M. J., & Zhang, W. F. Accounting and drivers of carbon emission from cultivated land utilization in Northeast China. Ying Yong Sheng tai xue bao= The Journal of Applied Ecology, 2021, 32(11), 3865-3871.
2. Shi, H., Mu, X. M., Zhang, Y., & Lu, M. Q. Effects of different land use patterns on carbon emission in Guangyuan city of Sichuan province. Bull. Soil Water Conserv, 2012, 32(3), 101-106.

 

C: 7. Policy recommendations are not directly tied to quantified drivers. While the conclusion suggests differentiated policies for eastern, central, northern, and southern Hunan, the analysis does not present a quantitative driver model linking socioeconomic or land-use variables to the spatial patterns. This weakens the causal basis for the recommendations and reduces the paper’s usefulness to policy makers and practitioners at the regional and local levels. Please be more specific with your policy recommendation and especially their linkage with land-use variables which can immediately and directly influence net carbon evolution in the region.

R: We sincerely appreciate your valuable feedback. We have optimized the policy recommendations section to make it more targeted and practical, ensuring it fully reflects its close connection to land use variables.

 

We would like to thank the reviewers again for their deliberation and help. We think these suggestions are very meaningful!

 

With best regards,

Yours sincerely,

Lin Li

Author Response File: Author Response.docx

Reviewer 4 Report

Comments and Suggestions for Authors

Review of the scientific article " Spatiotemporal Dynamic Evolution Characteristics of Net Carbon Sinks in County-Level Farmland Ecosystems in Hunan Province, China" by Huangling Gu and co-authors.

The presented work is devoted to the topical topic of studying the carbon cycle in agricultural ecosystems. The study of the spatial and temporal dynamics of carbon sinks is important for developing strategies for sustainable agricultural development and achieving goals to reduce greenhouse gas emissions.

The authors used a comprehensive approach that includes methods of standard deviation ellipse, spatial autocorrelation, and exploratory spatiotemporal data analysis. This approach makes it possible to comprehensively assess the dynamics of the carbon balance in the region under study. The study was conducted at the county level, which provides a more detailed picture of the distribution of carbon sinks. The manuscript is well structured and contains a detailed analysis of the results obtained. The work is written at a high scientific level, the methodology is well-founded, the results are reliable and have practical significance; the manuscript contains relevant literary sources.

Remarks:

         In table 2, the carbon emission coefficient is presented in various units: kg/hm2, kg/kg, t/t. Is it true that this coefficient is not a dimensionless quantity?

         The caption to Figure 2 indicates: from 2000 to 2020. However, the figure itself shows data for 2005, 2010, 2015, and 2020. Please add a correct caption to the figure.

         In Fig.3, please indicate which area of the left part of the drawing the right parts belong to. Please use the letters (a, b) to explain what the left and right sides of the picture mean. Explain the meaning of the symbols in the upper-left part of the drawing: __.__

Figure 5 contains Chinese characters. Please use the letters (a, b) to explain what the left and right sides of the picture mean.

         Please explain for Table 5 what they mean: Type I, II, III, IV.

In my opinion, the manuscript corresponds to the Atmosphere theme and can be published after making copyright edits.

Author Response

Dear Reviewer:

Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Spatiotemporal Dynamic Evolution Characteristics of Net Carbon Sinks in County-Level Farmland Ecosystems in Hunan Province, China” (ID: atmosphere-3852476). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked up using the “Highlight” function in the paper. The main corrections in the paper and the responds to the comments are as following:

 

Responses to the comments of Reviewer (C, Comments; R, Response):

 

C: 1. In table 2, the carbon emission coefficient is presented in various units: kg/hm², kg/kg, t/t. Is it true that this coefficient is not a dimensionless quantity?

R: We greatly appreciate your attention to the carbon emission coefficient. This coefficient is not a dimensionless quantity, and we have adjusted its finer measurement units in accordance with IPCC standards. kgCO₂/hm² represents emissions per unit area of land, kgCO₂/kg represents emissions per unit mass of material, and tCO₂/t represents emissions per unit mass of product. See Table 2.

 

C: 2. The caption to Figure 2 indicates: from 2000 to 2020. However, the figure itself shows data for 2005, 2010, 2015, and 2020. Please add a correct caption to the figure.

R: We sincerely appreciate your thoughtful and valuable feedback. We have added the correct title. See Figure 2.

 

C: 3. In Fig.3, please indicate which area of the left part of the drawing the right parts belong to. Please use the letters (a, b) to explain what the left and right sides of the picture mean. Explain the meaning of the symbols in the upper-left part of the drawing: __.__

R: We sincerely appreciate your thoughtful and valuable feedback. We have modified the figure caption. See Figure 3.

 

C: 4. Figure 5 contains Chinese characters. Please use the letters (a, b) to explain what the left and right sides of the picture mean.

R: We sincerely appreciate your close attention to our visual expressions. We have corrected the figure. See Figure 5.

 

C: 5. Please explain for Table 5 what they mean: Type I, II, III, IV.

R: We greatly appreciate your close attention to our spatiotemporal migration analysis. We have described these four types in the text and have added annotations below Table 5. See Table 5.

 

We would like to thank the reviewers again for their deliberation and help. We think these suggestions are very meaningful!

 

With best regards,

Yours sincerely,

Lin Li

Author Response File: Author Response.docx