Analysis of Cultivated Land Productivity in Southern China: Stability and Drivers

Response to Reviewer 1 Comments

1. Summary

2. Questions for General Evaluation

Reviewer’s Evaluation

Response and Revisions

Does the introduction provide sufficient background and include all relevant references?

Yes/Can be improved/Must be improved/Not applicable

Necessary additions and improvements have been made

Are all the cited references relevant to the research?

Yes/Can be improved/Must be improved/Not applicable

Necessary additions and improvements have been made

Is the research design appropriate?

Yes/Can be improved/Must be improved/Not applicable

The research design was adjusted

Are the methods adequately described?

Yes/Can be improved/Must be improved/Not applicable

The method has been redescribed

Are the results clearly presented?

Yes/Can be improved/Must be improved/Not applicable

The results were reorganized

Are the conclusions supported by the results?

Yes/Can be improved/Must be improved/Not applicable

Results have been strengthened to support the conclusion

3. Point-by-point response to Comments and Suggestions for Authors

Comments 1:

Abstract:Please clarify the novelty of the study by explicitly stating how this work differs from existing research on cultivated land productivity in southern China. For example, mention the unique focus on NPP as a stability indicator or the integration of landscape metrics.Specify the time frame (2001–2022) in the abstract to align with the Methods section.

Response 1:

Thank you for pointing this out. We agree with this comment.

We further clarified the novelty of the study in the abstract and ensured that the time frame of the study was consistent with that of the methods section. The revised abstract is:

Owing to climate change and increasing resource competition, elucidating the control mechanism of cultivated land productivity stability is essential.. Previous research has focused on anthropogenic or climatic factors individually, overlooking their combined effects; therefore, the "climate-anthropogenic" framework was constructed. Net primary productivity (NPP) was employed to measure the cultivated land productivity and investigate the impact of climate change and anthropogenic factors on cultivated land productivity stability in Poyang Lake from 2001 to 2022.. Results revealed that NPP increased but fluctuated significantly and was higher. in southern Poyang Lake  than that in the north. The low spatial stability distribution fluctuation area was concentrated in the periphery of Poyang Lake, the periphery and riverbank comprised the middle and high fluctuation areas, and the Ganjiang River Delta exhibited high fluctuation. Multiple linear regression analysis indicated that the stability of cultivated land productivity was positively impacted by farmland and river proximity, average patch area, and fractal dimension positively affected and negatively impacted by low farmland proximity and average annual precipitation. Stable cultivated land production and improved utilization efficiency requires irrigation and drainage system optimization and improved adaptability to climate change. Moreover, cultivated land fragmentation should be reduced and the resilience of cultivated land to external disturbances should be enhanced. New Line 13-30

Comments 2:

Introduction:The research gap is unclear to readers. The literature review should be improved in an academic way, and explicitly state the research hypotheses or questions (e.g., "How do spatial fragmentation and climate factors interact to influence NPP stability?").

Response 2:

Thank you for pointing this out. We agree with this comment.

Therefore, we have expanded the introduction section in order to provide readers with a more comprehensive overview of the research field. In doing so, we highlight the key developments in the field as well as the major challenges facing it today. In order to further improve the content, we added a literature review on the factors affecting the stability of cultivated land productivity. At the same time, we have a more academic presentation of stability related research to ensure that readers can clearly understand our research background and purpose. Part of the content involved is modified as follows:

Stability is consistently the focus of interdisciplinary complex system, especially ecosystem stability research [7,8]. Traditionally, the evaluation of cultivated land productivity stability relies on microscale indicators, such as soil organic matter (SOM), aggregate stability, and nitrogen (N) content, to detect the microfeedback mechanism between soil and crops [9-11]. However, these data are mostly derived from sample observations and are limited by laboratory measurement conditions and temporal resolution [12]. The rapid progress of satellite remote sensing technology provides an opportunity for largescale, highprecision, and realtime monitoring of regional productivity changes. Vegetation indices, such as the Normalized Vegetation index (NDVI), Enhanced Vegetation Index (EVI), and total primary productivity (GPP), have been widely used in environmental and ecosystem studies [13-16]. Specifically, Chen used GPP to explore ecosystem stability on a global scale, while Liu and Zhou used GPP and NDVI to characterize the productivity of drylands [17,18]. Although these vegetation indices are beneficial in natural ecosystem assessment, their application effects in artificial systems are inconsistent [19]. Therefore, remote sensing indicators used in specific fields are important. Among them, the net primary productivity (NPP), as a superior metric, quantifies the accumulation of photosynthetic organic matter per unit of space and time, effectively avoiding the interference of agricultural structure adjustment, crop variety change, and other factors on crop yield measurement, making it a characteristic index of cultivated land productivity [20].

Improving agricultural productivity and its interannual stability is critical to longterm global food security and environmental sustainability; however, climate change is exacerbating this challenge. While rising temperatures may extend the growing season and promote the northward spread of crops, they may also increase pest and disease occurrences and the threat to food security posed by extreme weather [21]. Rainfall shortages and droughts directly damage food supplies and increase the instability of agricultural production [22]. Moreover, anthropogenic factors, such as land occupation and fragmentation, landscape pattern change, and increased human activity intensity, significantly impact cultivated land productivity [23,24]. Fragmentation, boundary irregularity, and poor spatial connectivity threaten the stability of cultivated land productivity [25]. As a quantitative tool to reflect landscape characteristics, the landscape pattern index is closely related to the stability of cultivated land productivity [26]. However, most studies have focused on climatic or anthropogenic factors individually, and their combined impact remains unclear. New Line 49-81

Comments 3:

Materials and Methods:Provide more details on data preprocessing steps (e.g., how MODIS NPP data were validated or calibrated for agricultural land).

Clarify the selection criteria for landscape metrics (AREA_MN, FRACT, ENN) and their ecological relevance to cultivated land stability.

Response 3:

Thank you for pointing this out. We agree with this comment.

In the data processing section, we have added more detailed validation and calibration information for the MODIS NPP data for agricultural land to clarify this process. In the part of cultivated land landscape pattern measurement, we supplemented the selection basis of landscape indicators with reference to relevant literature, and expounded the ecological correlation between these indicators and cultivated land stability. The data processing part is modified to:

Data processing and downloading was based on the Google Earth Engine (GEE) remote sensing cloud platform. The MODIS/061/MYD17A3HGF dataset of GEE was used to extract NPP data of Poyang Lake region during 2001–2022 at a resolution of 30 m and a geographical coordinate system of WGS1984. MODIS NPP and land use data of the same period were spatially overlaid. Pure cultivated land pixels were extracted using a cultivated land classification code, and mixed pixels and noncultivated land areas were excluded to construct the NPP time series dataset of cultivated land from 2001 to 2022. To verify the ability of NPP data to characterize cultivated land productivity, a trend consistency test was conducted using the grain yield data of the surface cultivated land productivity monitoring station. Pearson correlation coefficient was used to evaluate the correlation between NPP and production, confirming that NPP was a reliable proxy indicator of productivity. New Line 147-158

The landscape pattern measurement part is modified as follows:

The terrain of Poyang Lake region is complex, which affects the utilization efficiency of cultivated land. To evaluate cultivated land use, quantitative index analysis is essential. Patch size (AREA_MN) is a key measure of cultivated land fragmentation, and a large cultivated land area is conducive to structural stability, mechanization, and improving productivity [36]. The scattered cultivated land in hilly areas restricts the mechanization and scale of agriculture. Fractal dimension (FRACT) was introduced to quantify the complexity of cultivated patches. The higher the value, the more complex the shape, which hindered mechanization and scale [37]. The nearest neighbour distance (ENN) reflects the patch isolation degree, reveals the degree of cultivated land intensification, and facilitates cultivated land planning [25]. By comprehensively using these indicators, the present situation of cultivated land utilization in Poyang Lake region was comprehensively analysed. The raster data of cultivated land was divided by county, and the landscape pattern index was calculated using Fragstats 4.2 software [38]. The specific formula and description are shown in Table S1. New Line 187-201

Comments 4:

Results:The results lacks of indepth analysis, and should be improved on the explanations related to a series of Figures and Tables.

Response 4:

Thank you for pointing this out. We agree with this comment. We have thoughtfully considered your suggestions and made corresponding adjustments to the results section.

In the revised version, we have undertaken a more detailed and indepth analysis of the data presented in the figures and tables. Specifically, we have enhanced our explanations by expanding the interpretation for each figure and table, offering clearer contextual and datadriven insights. Additionally, we have strengthened the logical coherence between various analyses, delving deeper into the long-term cultivated land use analysis through the characteristics of spatiotemporal changes in regional cultivated land use. Furthermore, we have refined the visualization of the images and incorporated descriptive text for the figure captions to enhance clarity. New Line 234-259

Comments 5:

Discussion:Discuss why temperature (MAT) was not significant in the regression model. Is this consistent with other studies in similar climatic zones? Compare the findings on NPP stability with studies from other regions (e.g., Yangtze River Delta) to contextualize the uniqueness of the Poyang Lake region.

Address the limitation mentioned in Section 4.5 (ignoring crop types) more thoroughly by suggesting how future studies could incorporate cropspecific NPP adjustments.

Response 5:

Thank you for pointing this out. We agree with this comment.

In the discussion part, we added the comparison with the research results of other regions, and found that this was inconsistent with Lu's results on the response of NPP to climate factors in the Yangtze River Economic Belt, and the sensitivity of NPP to temperature change exceeded that of precipitation. It may be due to the more drastic change of precipitation relative to temperature in this study area. It can also be seen from the MK mutation test that the mutation of precipitation relative to temperature is more obvious. New Line 328-336

We have added to the limitations section how to address the impact of crop type on NPP. The sentences added are: To describe the productivity of cultivated land more comprehensively, future studies should analyse multiple vegetation type indices to accurately reflect the impacts of different crop planting types on the productivity of cultivated land [75]. The following two approaches may be adopted: one is to use crop models to predict NPP among different crops and adjust NPP data accordingly[76]. Secondly, NPP of different crops can be accurately calculated by direct measurement of crop biomass and photosynthetic rate through field experiment monitoring[77]. New Line 486-489

Comments 6:

Conclusion:Avoid repeating results verbatim. Instead, synthesize key findings into broader implications (e.g., how reducing fragmentation aligns with national food security policies).Propose specific policy actions (e.g., incentivizing largescale farming) rather than generic recommendations like "rational planning."

Response 6:

Agree. Thank reviewers put forward valuable Suggestions.

We have made the following adjustments to the conclusions to strengthen the policy relevance of our findings and to avoid duplication of findings. The revised conclusion is as follows:

When investigating the interaction between the climate and anthropogenic activities, it is crucial to explore the factors affecting the stability of cultivated land production capacity. This study revealed the influence mechanism of multiple geospatial factors on the stability of cultivated land productivity. Policies should be formulated based on geographical spatial characteristics, such as the distribution of terrain, roads, and water systems to ensure the sustainable use and productive potential of agricultural land. The strategic integration of scattered and irregular cultivated land can improve land use efficiency and promote agricultural scale and mechanization.

Precipitation changes in lakeside areas, such as Poyang Lake Basin, impact the stability of cultivated land productivity more than temperature. Therefore, precipitation factors should be prioritized and irrigation and drainage systems should be optimized to enhance the adaptability of cultivated land to water level fluctuations to manage its adverse effects on agricultural production.

In addition, the change in cultivated land NPP provides a foundation for assessing the resilience of agricultural systems. Despite interannual fluctuations, NPP increased, indicating that the agricultural system was resilient. This provided a reference index for measuring the resilience of cultivated land systems.

The results of this study provide key insights with broad implications for agricultural policy and land management strategies. First, these findings highlighted the importance of land use change and its impact on agricultural systems. The conversion of cultivated land to forest, water, and especially construction land reflected economic development and urbanization pressures and highlighted the need for balanced land use planning. This change was evident around lakes and rivers, suggesting that landuse policies should be stricter in vulnerable areas. New Line 491-514

4. Response to Comments on the Quality of English Language

Point 1:

A thorough proofread for grammar and clarity is recommended, particularly in complex sentences (e.g., "The distance between the field and river affects the irrigation conditions of cultivated land, with impacts on crop yield" could be rephrased for conciseness).

Response 1:

Thank you for your constructive feedback on the manuscript. We have taken note of your suggestion on thorough proofreading, especially focusing on grammar and clarity in complex sentences. We have read the full text, and then check and modify the sentences to make them more concise for reading. In addition, we also introduced abbreviations for some variables involved to further simplify the text. We thank you for your attention to details and will continue to improve the manuscript to ensure its clarity and conciseness.

Response to Reviewer 2 Comments

1. Summary

2. Questions for General Evaluation

Reviewer’s Evaluation

Response and Revisions

Does the introduction provide sufficient background and include all relevant references?

Yes/Can be improved/Must be improved/Not applicable

Necessary additions and improvements have been made

Are all the cited references relevant to the research?

Yes/Can be improved/Must be improved/Not applicable

Necessary additions and improvements have been made

Is the research design appropriate?

Yes/Can be improved/Must be improved/Not applicable

The research design was adjusted

Are the methods adequately described?

Yes/Can be improved/Must be improved/Not applicable

The method has been redescribed

Are the results clearly presented?

Yes/Can be improved/Must be improved/Not applicable

The results were reorganized

Are the conclusions supported by the results?

Yes/Can be improved/Must be improved/Not applicable

Results have been strengthened to support the conclusion

3. Point-by-point response to Comments and Suggestions for Authors

Comments 1:

Line 135 Title of Figure 1: It is recommended to add details about the panels (a, b and c) in the figure title, in order to make it easier for the reader to understand.

Response 1:

Thank you for pointing this out. We agree with this comment. Therefore, we have added a caption chart note.

Figure (a) is a schematic diagram showing the location of the Poyang Lake region in China and within Jiangxi Province.Figure (b) is a remote sensing image of the Poyang Lake region.  Figure (c) illustrates the distribution of farmland in the Poyang Lake region in 2022, as well as the spatial distribution of lakes and rivers. New Line 136-139

The new headings now clearly specify the content of each panel, making it easier for readers to navigate and understand the graphics.

Comments 2:

Line 148 Statistical Analysis:It is necessary to specify the computing environment (e.g., R, Python) and the statistical packages used to carry out the time trend analyses (Theil Sen Median Trend Analysis and Mann-Kendall Significance Test). In addition, we suggest including a brief discussion of the possible presence of autocorrelation in the data and, if there is any, mentioning whether the modification proposed by Hamed and Rao (1998) for the Mann-Kendall test was applied. The inclusion of classic references that support the choice of these tests is also recommended.

Response 2:

Thank you for your thorough review and valuable suggestions regarding the statistical analysis section. We fully agree with the need to specify the computing environment and statistical packages used for the time trend analyses.. Therefore, we have revised the content of Theil Sen Median Trend Analysis and Mann Kendall Significance Test. The computing environment (R language version 4.3.1), the statistical packages used (zyp for Theil-Sen median trend analysis and Kendall for Mann-Kendall significance test) and the methods for dealing with potential autocorrelations in the data are defined. Meanwhile, the Mann-Kendall significance test was performed. Considering that long time series data may be affected by autocorrelation, such as lag effects caused by interannual climate fluctuations, we adopt the variance correction method proposed by Hamed and Rao. The revised text is as follows:

Theil Sen Median trend analysis is a nonparametric method that requires no specific sample distribution and is unaffected by outliers. It is often used to analyse the trend in long-term series changes and is calculated as follows:

    (1)

where ρ is the median value of cultivated land NPP to slope. When ρ>0, the vegetation change increases, and when ρ<0, the vegetation change decreases. xj and xi are the values of years j and i in the cultivated land NPP time series, respectively, and the median was used [34]. The calculation was applied using by the zyp package (version 0.1-1) in R language (version 4.3.1), called the zyp.sen function for median slope estimation.

The Mann–Kendall significance test is a nonparametric method which supplements Theil Sen Median slope estimation to test the significance of a time series. Considering that long-term series data may exhibit autocorrelations (such as lag effects caused by interannual climate fluctuations), we adopted the variance correction method proposed by Hamed and Rao to reduce the risk of Type I errors by adjusting the effective sample size [35]. The modified test was implemented in R language using the MannKendall function of the Kendall package (version 2.2.1), and the effective sample size was calculated with autocorrelation correction. MK significance test formula is:

     (2)

Where, Xi and Xj are the observed values corresponding to time series i and j respectively, and i<j, sgn() is a symbolic function. New Line 160-180

34. Xiao, Q.; Wang, Y. Spatial-temporal Differentiation Characteristics and Influencing Factors ofCultivated Land NPP in Major Grain Producing Areas(Henan Province). Environmental Science 2024, 1-15, doi:10.13227/j.hjkx.202406053.

35.    Hamed, K.H.; Ramachandra Rao, A. A modified Mann-Kendall trend test for autocorrelated data. J Hydrol 1998, 204, 182-196, https://doi.org/10.1016/S0022-1694(97)00125-X.

Comments 3:

Suggested reference:Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for autocorrelated data. Journal of Hydrology, 204(1-4), 182-196. DOI: 10.1016/S0022-1694(97)00125-X.

Response 3:

Thank you for pointing this out. We agree with this comment. This reference is indeed highly relevant to our study, as we employ the modified Mann-Kendall trend test proposed by Hamed and Rao to examine autocorrelation in time series data. We've already added.

We've already added. That can also be seen in Response 2. New Line 175

Comments 4:

Line 167 Landscape metrics:It is suggested to specify the software or environment used to calculate the landscape metrics, in order to guarantee the reproducibility of the study.

Response 4:

Thank you for pointing this out. We agree with this comment.

We adhere to very specific guidelines regarding the software used for our analysis, and in this context, Fragstats 4.2 software has been explicitly designated for the purpose of calculating landscape metrics. New Line 200

Comments 5:

Line 182 Methodological Flowchart:It is recommended to include, at the end of the Material and Methods section, a flowchart that clearly and concisely illustrates the methodological steps adopted in the study.

Response 5:

Thank you for pointing this out. We agree with this comment.

Based on this recommendation, we have developed a detailed flow chart outlining the entire methodological process. This flowchart provides a visual representation of the sequence of steps we follow, from data collection and preprocessing to analysis and interpretation. New Line 216

Methodological flowchart for the stability cultivated land productivity

Comments 6:

Line 235-249 and Figure 6:The text referring to Figure 6 should be revised to follow the order in which the panels are presented. As for the figure, we suggest reorganizing it as follows: First row: Theil Sen Slope (Figure 6a) and Mann-Kendall Test (Figure 6b), arranged in two columns.Second row: Spatial trend map (Figure 6c), with greater visual prominence.

In addition, we recommend increasing the size of the legends for the Theil Sen Slope and Mann-Kendall Test maps, which are currently difficult to read. The panel subtitles should be included in the figure title.

Response 6:

Thank you for pointing this out. We agree with this comment.

Therefore, we have modified the text shown in Figure 6 by increasing the size of the Theil-Sen slope and Mann-Kendall test legend to ensure that they are now easy to read.These changes can be found in new line 285 of the revised manuscript, where the updated text and figure are shown.

Comments 7:

Line 267 Figure 7:It is recommended to add the subheadings (a, b, c and d) in the figure caption to improve organization and clarity.

Response 7:

Thank you for pointing this out. We agree with this comment.Therefore, we have added a caption chart note.

Figure (a) shows the spatial distribution of the coefficient of variation of cultivated land in the Poyang Lake region, while Figure (b), Figure (c) and Figure (d) show local magnification of the areas with high fluctuation, respectively. New Line 295-297

Comments 8:

Line 291-292 Tables 1 and 2:It is suggested to merge Tables 1 and 2 into a single table, keeping the original structure, to make it easier to compare data and optimize space.

Response 8:

Thank you for pointing this out. We agree with this comment.

We carefully merged Tables 1 and 2 into a unified table while maintaining the original structure of the data. This merging allows easier side-by-side comparison of the information provided and makes efficient use of the available space in the manuscript. New Line 316

Comments 9:

Line 299 Pettitt test:To increase the robustness of the conclusions, it is recommended to apply the Pettitt test to identify possible change points in the analyzed time series.

Response 9:

Thank you once again for the thoughtful suggestion to apply the Pettitt test in our analysis. We truly appreciate the consideration you have given to our study and the value of exploring multiple statistical methods in time series analysis. After careful evaluation, we have decided to maintain our focus on the Mann-Kendall trend test for the current study, and here is our rationale.

Our research aims to analyze long-term trends in net primary productivity (NPP) as a proxy for cultivated land productivity. The Mann-Kendall test is particularly suited to our needs because it effectively detects monotonic trends, is robust against outliers, and accommodates various data distributions. This aligns well with our objective of understanding the gradual changes in cultivated land productivity over extended periods.Furthermore, the results obtained from the Mann-Kendall (MK) test are robust and adequately support our conclusions. The analysis has revealed clear trends and change points (though the primary focus is on trends, the test also incidentally highlights points of change) that align with our hypotheses and provide valuable insights into the behavior of the time series data. These findings reinforce our research conclusions and offer a solid foundation for our understanding of the long-term changes in cultivated land productivity.

While we recognize the strengths of the Pettitt test, especially its ability to detect abrupt changes without relying on distributional assumptions, our study's emphasis is on the overall trend rather than specific change points. The Mann-Kendall test's focus on monotonic trends and its robustness to outliers make it a suitable choice for our dataset, given that our initial results using this method are statistically significant and support our conclusions.In light of our research question and the characteristics of our data, we believe that the Mann-Kendall trend test is the most appropriate method for our current study. However, we are grateful for your suggestion and acknowledge the potential of the Pettitt test in other contexts or in future studies where detecting specific change points may be more relevant. We will certainly consider incorporating the Pettitt test in our future research endeavors where it may provide additional insights.

Thank you once more for your insightful feedback. We hope this explanation clarifies our decision,which has prompted us to think more deeply about the applicability of different statistical methods in our research.

Response to Reviewer 3 Comments

1. Summary

2. Questions for General Evaluation

Reviewer’s Evaluation

Response and Revisions

Does the introduction provide sufficient background and include all relevant references?

Yes/Can be improved/Must be improved/Not applicable

Necessary additions and improvements have been made

Are all the cited references relevant to the research?

Yes/Can be improved/Must be improved/Not applicable

Necessary additions and improvements have been made

Is the research design appropriate?

Yes/Can be improved/Must be improved/Not applicable

The research design was adjusted

Are the methods adequately described?

Yes/Can be improved/Must be improved/Not applicable

The method has been redescribed

Are the results clearly presented?

Yes/Can be improved/Must be improved/Not applicable

The results were reorganized

Are the conclusions supported by the results?

Yes/Can be improved/Must be improved/Not applicable

Results have been strengthened to support the conclusion

3. Point-by-point response to Comments and Suggestions for Authors

Comments 1:

Line 79:According to UN (World Population Prospects 2024), the country with the largest population is India (1.45 billion), while China has 1.42 billion inhabitants.

Response 1:

Thank you for pointing this out. We agree with this comment. Therefore, we have updated the data and the statement to read:

China is the second most populous country globally, with 1.42 billion people, and ensuring China's food security is imperative. New Line 82-83

Comments 2:

Line 182:The influencing factors that were included in the multiple linear regression should be enumerated here

Response 2:

Thank you for pointing this out. We agree with this comment. Therefore, We added the variables involved in the multiple linear regression used in the analysis of factors affecting the stability of cultivated land productivity. Added in the text of the content as follows: Mean precipitation (MAP), mean air temperature (MAT), field road distance, average patch size (AREA-MN), distance between the field and the road ( field-road distance), distance between the field and the river (field-river distance), nearest neighbour distance (ENN), and fractal dimension (FRACT) were considered as factors that may influence cultivated land productivity. New Line 207-212

Comments 3:

Figure 3:The maps are almost unreadable; the resolution is very low, please make them clearer or do not include them at all

Response 3:

Thank you for pointing this out. We agree with this comment.

We have redrawn the picture to improve the clarity of the picture and the legend size. The modified figure

Comments 4:

Figure 6 (b) and (c):The same observation: maps almost unreadable, please make them clearer

Response 4:

Thank you for pointing this out. We agree with this comment.

We have redrawn the picture and made a new layout according to the reviewer's requirements to improve the clarity of the picture and the size of the legend. The modified figure

Comments 5:

Line 208-209:“… periphery of the Poyang Lake region was the main area where cultivated land was transformed into forest into cultivated land…” ??? Please correct and rephrase.

Response 5:

Thank you for pointing this out. We agree with this comment. Therefore, we have revised it for clarity and accuracy. We found that it was a mistake in language expression, and it was around Poyang Lake rather than around the research area. The corrected sentence is:Specifically, the Poyang Lake plain emerged as the primary region where cultivated land was transformed into construction land, while the outermost area of the Poyang Lake was the main location for cultivated land conversion to forest. New Line 238-240

Comments 6:

Line 432: Please do not start a phrase with “[62] pointed out that….”. You may write “Some authors [62] pointed out that….”

Response 6:

Thank you for pointing this out. We agree with this comment. We will revise the sentence accordingly. On line 432, instead of starting with "[62] pointed out that...", we will write "Yu et al.[70] pointed out that...".  This adjustment will ensure that the phrase is introduced in a more appropriate manner, following your guidance. New Line 452

Comments 7:

Line 503: Please replace “we should” by impersonal writing (such as “one should…”)

Response 7:

Agree. Thank you for your suggestion.

I have revised the text accordingly, replacing 'we should' with 'one should' to adopt a more impersonal writing style. To make the sentences more consistent with the impersonal style of writing."