Cropland Expansion Mitigates the Supply and Demand Deficit for Carbon Sequestration Service under Different Scenarios in the Future—The Case of Xinjiang

Round 1

Reviewer 1 Report

The paper I reviewed was written according to the rules and is nice in style. In fact, I saw similarities with the paper, Future Impacts of Land Use Change on Ecosystem Services under Different Scenarios in the Ecological Conservation Area, Beijing, China, by Zuzheng Li, Xiaoqin Cheng, and Hairong Han, published in your journal Forests 2020, 11( 5), 584; https://doi.org/10.3390/f11050584 The fact is that this paper used the same base methodology as in the 2020 paper, but I do not see the plagiarism because the methodology is more complex than in the 2020 paper mentioned above. In this paper, we have modeling with 4 scenarios (BAU, RED, ELP and SD scenarios) applied to the PLUS model + GMOP model, while the authors in the 2020 paper give 3 scenarios (BAU, RED and ELP scenarios). So my overall opinion is that the paper is well written and although I am not a native English speaker, the language and writing style are fine in my opinion. If the paper is written according to the rules and methodologically correct, if the results, the discussion and the resulting conclusion are clearly written, as in this paper, then there is waste of words to say that something is other than written correctly. As well as if a paper is poorly written, and/or because the methodology is wrong or wrongly applied or described, the entire scientific paper is wrong and flawed... Though in such a case there is room to suggest style or methodological improval... Unfortunately, there are not many well-written papers, but since I am not just reviewing for this journal, I have noticed that the papers sent to higher ranked journals (Q1) are already pretty good in their composition - very well written, in the lower ranked journals there are more papers that require corrections or should be rejected entirely

 

Author Response

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Reviewer 2 Report

This article is mainly focused on the simulation of four scenarios of carbon storage by soil and vegetation according to the type of land and land changes in a region of China for the year 2030. Most of the references are relevant with a high portion of recent articles.  And there is consistency in the reference format.

 

  * Fig. 1 has figures that are a little odd.  It has a mistake at least: It reads “Corpland” instead of “Cropland.”

            * The same for Fig. 3.

 * The circles of Fig. 3 are not easy to understand at first sight.  They are not even explained in the main text.

 * You cannot easily see and follow the changes of the 4 scenarios through time in Fig. 4.  I suggest rearranging them to follow such changes.  For example, you could arrange the LULC and the four land types in rows, assigning the four 2025 scenarios in the first row and the 2030 scenarios in the second row of each type.  There would be 4 columns and 10 rows.  In this way, you can visualize the differences of the scenarios among themselves and along time in pairs of rows.

 * The authors might write down a few words to explain what gray multi-objective optimization means, the GMOP-PLUS model.  Some references about the model might be useful to reinforce the technical background knowledge.

 * Moreover, the authors bothered to say the shortcomings of other tools or models, such as the field survey method and empirical biogeochemical modeling.  However, the did not justify the use of the GMOP-PLUS model.  Not even what it is.

 * Why the quotations in:

            The landscape of Xinjiang is made up of "three mountains and two basins."?

* Aren’t the “mountains” rather mountain ranges?

 * The acronym DEM is not spelled out.

 * In “we focus on terrestrial ecosystems, which echoes SDGs 15.3,” it not clear what SDGs 15.3.  By the way, SDGs and SDG are apparently used indistinctly, which might be confusing or bothering for the reader.

 * A section with the abbreviations might be handy.

 * In “We used a random forest technique that incorporates environmental factors in our approach to assess changes in the landscape pattern of LULC-induced carbon sequestration service in terrestrial ecosystems in Xinjiang under different scenarios from 2020 to 2030 (Figure 5),” what is this “random forest technique”?

 

 

Author Response

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Reviewer 3 Report

1. The major contribution is not clear. Please clarify. 

2. Please add more related references.

3. The improvement / originality in the conclusion is not clear. Please clarify.

4. Please clarify how local government can benefit from this article by implementing some suggestions in the conclusion

Author Response

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Reviewer 4 Report

This paper demonstrates how spatial and temporal variation of terrestrial ecosystem carbon sequestration can be derived under different landuse land cover (LULC) change scenarios in Xinjiang, China. The LULC change was modelled using a machine learning simulation called Gray multi-objective optimization – Patch level land use simulation (GMOP-PLUS). The author anticipates that this method is useful to understand supply and demand of carbon sequestration in the study area

In general, the paper confuses readers by how the estimation of carbon stock and sequestration comes from. The drawback of many existing methodologies, including IPCC AFOLU [1] is that every land use class has the same carbon value, while in reality the spatial variability is significant due to differences in land use, species, Leaf Area Index, age of vegetation and more. Nayak et al. (2019) [2]already provides a very good overview on this problem.  The source of carbon density, hence, an important input data to this study is not clearly explained. The use of machine learning based model like GMOP is not new and has been and currently used earlier in landuse/land cover change simulation in many studies.

I suggest to clarify the approach and provide a clear ratification on the novelty of the manuscript. The authors should clarify how the pixel-dependent carbon stock can be estimated and how carbon assimilates into above/below ground and soil organic. An explanation and more references on GMOP-’s pros and cons and why GMOP-PLUS was chosen for this type of analysis is also missing.

My last general comment is related to the conclusion of the study. The (first) conclusion that LULC changes result in a change in carbon stock, including demand and supply is a known phenomenon and not a new finding.

Furthermore, the following concerns need to be addressed prior to publication:

Abstract: Lacking of presentation of approach.

Section 1: Besides look-up table and ecology/eco-hydrological models, there is new emerging methodologies to quantify carbon stock is remote sensing approach for Net Primary Production (NPP). The authors should provide a review on this method as well.

Section 2: Fully explain in following sections the use of data presented in 2.2, i.e. temperature, precipitation, socio-economic data etc.

Figure 3: Does not clearly provide the information on landuse changes. The graphs should be enhanced for visualization purpose.

Table 2: Explain how the level “accuracy” or confidence is achieved.

My feeling is that the graph-work and English needs extensive editing. It both lacks of description, details and does not help to deliver the message of the study.

[1] IPCC. 2006. Volume 4: Agriculture, Forestry and Other Land Use.

[2] Nayak AK, Rahman MM, Naidu R, Dhal B, Swain CK, Nayak AD, Tripathi R, Shahid M, Islam MR, Pathak H. Current and emerging methodologies for estimating carbon sequestration in agricultural soils: A review. Sci Total Environ. 2019 May 15;665:890-912. doi: 10.1016/j.scitotenv.2019.02.125. Epub 2019 Feb 11. PMID: 30790762.

Author Response

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Round 2

Reviewer 3 Report

Accept in present form

Reviewer 4 Report

I see that the author(s) has done further work to improve the paper, in responding to the reviewer’s comments. I found the demonstrated novelty, scientific approach and presentation of the revised manuscript sufficient and ready for publication.