Examining the Factors Influencing Agricultural Surface Source Pollution in the Yangtze River Economic Zone from the Perspectives of Government, Enterprise, and Agriculture
Round 1
Reviewer 1 Report
Review of "Analysis of spatial and temporal characteristics and influencing factors of agricultural surface pollution in the Yangtze River Economic Zone--Based on three perspectives of government, enterprise and agriculture". This paper analyzes the spatial and temporal characteristics and influencing factors of agricultural surface source pollution in the Yangtze River Economic Belt from the three perspectives of government, enterprise and agriculture by using the spatial Durbin model and the dynamic GMM method in the period of 2006-2021. The manuscript is written well and the results are promising. The following suggestions and comments must be addressed for possible acceptance of the manuscript.
1. In abstract: expand GMM (is it generalized method of moments)
2. In title: What do you mean by agricultural surface pollution? Is it pollution caused by agricultural runoff? If so redefine it as "agricultural runoff pollution".
3. Line 30: "year-on-year increase of 9.9 percent". Cite the source of data.
4. Line 491: What is meant by "high aggregation area"?
5. How the assumption of exogeneity of the regressors and/or the threshold variable is considered in Hansen's panel threshold model? Cite relevant reference for Hansen's panel threshold model.
6. Moran′ s I scatter plot should be included in the revised manuscript.
7. Why the effect of agricultural surface pollution on groundwater not considered in the study?
8. Include limitations and future scope of work.
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Object: review of the manuscript "Analysis of spatial and temporal characteristics and influencing factors of agricultural surface pollution in the Yangtze River Economic Zone--Based on three perspectives of government, enterprise and agriculture", which has been submitted to the Sustainability journal
Overall commentary
This paper attempts to identify the social and economic determinants of fertilizer use in an major agricultural region of China. It uses a numerical modeling approach based on available government data. While this is a classical approach, it lacks rigor and, in particular, this a lack of a convincing analysis of the uncertainties in the computational results. It also presents an overly econometric and macroscopic vision that lacks a social perspective at the level of farmers and consumers. The manuscript also focuses too much on fertilizers and not on pesticides or fungicides, which are responsible for a large part of soil and water pollution. Formally, the article is too long, locally redundant and sometimes lacks important details. Sentences are often very long and some sections are poorly written.
Overall, I recommend that the article be rejected.
TITLE
The title is long and could be written in a more synthetic way.
KEYWORDS
Add the keyword "Numerical modeling".
ABSTRACT
In the abstract, there is no farmer's or consumer's perspective. The sentences are often too long and could be split in shorter ones. The abstract also lacks numerical data, which makes it rather vague.
1. Introduction
The introduction should be more concise and free of political considerations. To this end, the authors should focus more on numerical data and the bibliography on temporal and spatial trends in Chinese agricultural production and acreage, as well as in the used amounds of phytosanitary products. Health data (e.g. number of cases of cancer among farmers, events of sever air pollution linked to agricultural prctices, etc.) could also be evoked.
L29-30: What's the evolution like over a longer time span (15-20 years)?
L35: Not very clear. What is “main body”? Do you mean "the central government institution or body responsible for managing agricultural pollution"?
L38-42: Provide references and related numerical data.
L48: What does an "example analysis" consist of, and what does or does not it offer in comparison with other types of analyses?
L55: Do you instead mean "small number of farmers" or "small farms"?
L57-67: This part is too vague, politically oriented and does not provide clear scientific information. It can therefore be deleted.
L68: Do you instead mean by "at home" in China ?
L77: Provide some examples and illustrations of the levers available to enforce the environmental regulatory policies, also, what are the policies’ targets.
L83-90: Poorly written. To be rewritten in a more synthetic way and using shorter sentences.
L96: Define the discussed "input factors"
L97-104: Long sentence.
L113-114: Define “market distortion”.
2. MATERIALS AND METHODS
L140: What does the variable "y" represent in equations 3 and 4?
L146-147: Also give the significance of the other idexes.
L154: Why use the logarithmic transform? Why does LnP_it appear on both sides of the equal sign? What is the value of "Wij" in the case of the autocorrelation factor?
L155: What do delta coefficients stand for?
L160: Provide an explanation for these factors and their calculation method.
L162: Quickly remind the endogenicity problem.
L168: The equation number is missing.
L169: How was the lag period set?
L176: I(.) could be confused with Lisa's index
L183: Provide the quantities and nature of fertilizers in a table for each region concerned. Why consider only fertilizers and not also pesticides and fungicides and their degradation products?
A study of sensitivity, or even uncertainty, seems to me essential to assess the significance of data and trends. In the absence of such a study, the article does not seem publishable to me.
L188: How is air pollution taken into account?
L195-197: How was this hypothesis tested? I am not certain about the robustness of the correlation between investment and investment effects. Provide more justification elements or references.
L222: Could you present this equation in a simple way using a matrix formulation? What are the uncertainties on the coefficients in this equation?
L246-247: Is it possible that higher prices can also lead to a qualitative rather than merely quantitative shift in agricultural production? What guides producers towards one production type or the other?
L260: Provide links to sources or, failing that, key figures in an appendix. How is the reliability of this data assessed? How has standardization been carried out? How have outliers been dealt with?
3. RESULTS
L289: “Significance” with respect to what?
L294: Is this map intended for fertilizer use (as indicated in the legend), Moran index (as indicated in the text) or Lisa index (as indicated near the color scale). Check text and figures.
L297: The color scale in the figure 2 is barely readable.
L301: Add the uncertainties surrounding these values. What are the reasons for the large variations from one year to the next?
L305: Do you mean low-high? Check such obvious errors in the text before commenting on the data. Edit the text accordingly. What are the Moran values associated with the "H" and "L" thresholds?
L309-320: Is that also caused by the differences in agricultural crops (which require less fertilizer) or climate/geology in these regions and not just the industrial context?
L328: Recall what this test consists of and why the authors favor "ordinary least square" regression.
Table 2: Do you mean “Spatio-temporal fixed effects” instead of “Temporal fixed effects”.
L342: The econometric observations in this section, while plausible, should be accompanied by sociological analyses and surveys concerning the behavior of farmers/consumers with regard to fertilizer use. Do you have data or bibliography of this nature to support the discussion?
Table 4: Give the formula linking the three types of effect. Check values / signs in this table (especially regarding ER and LM variales).
Table 5: Add the definition of acronyms in all relevant tables.
L452: What would the results have been if other variables had been considered?
L458: Poorly written section to be reformulated.
L465: Why does the word "pollution" appear three times? The article should be corrected for English with the help of an English native speaker.
4. conclusions
The first part of the conclusion is a shorter version of the discussion. It can be removed. This section also helps authors in writing their manuscripts more concisely and clearly.
L525-527: How should local governments coordinate with the central government?
L539-541: On the basis of what results do you write that? This seems to me to be more political rhetoric than genuine science. In what way is the market, in its quest for profit maximization and mass sales, more inclined than a state-supervised mechanism to reduce the use of fertilizers; a kind of products which the market itself promotes.
Several sections and sentences are very long and poorly written. Therefore, the article should be corrected for English with the help of an English native speaker.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 3 Report
This paper analyzed the spatial and temporal characteristics and influencing factors of agricultural surface source pollution in the Yangtze River Economic Belt. Overall, this manuscript is written clearly and of novelty. I think it is suitable for publication in Sustainability after revision and response the following questions.
1. Abstract must be enriched via valuable results which pave the way for understanding the study.
2. What is the most significant finding in this study in comparison to other published works?
3. What specific pollutants are included in agricultural surface pollution? Heavy metals? Microplastics? What are the methods to deal with these pollutes? Supplementation of some nutrients, boron, potassium, sulphur, and so on is a promising strategy. Authors should cite these relevant works to improve this paper (e.g. Environ Sci Pollut Res 2020, 27: 39391–39401; Environ Sci Pollut Res 2021, 28: 52587–52597).
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
The authors have significantly improved the quality of their manuscript, it is of acceptable quality. However, the authors need to address one last query.
Why the factors of salinity and sodicity hazard on agricultural surface pollution is not considered? Include an explanation on this in Introduction section referring https://doi.org/10.3390/agriculture11100983
Author Response
Response to Reviewer 1 Comments
- Summary
Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions/corrections highlighted/in track changes in the re-submitted files.
- Point-by-point response to Comments and Suggestions for Authors
Point 1: Why the factors of salinity and sodicity hazard on agricultural surface pollution is not considered? Include an explanation on this in Introduction section referring https://doi.org/10.3390/agriculture11100983
Response 1: One of the reasons we did not consider salinity and sodicity is that it is not a major source of agricultural surface pollution. According to the Ministry of Agriculture and Rural Affairs of the People's Republic of China [7], chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP) were identified as the main pollution load indicators of agricultural surface source pollution, and existing literature[8,9] analyzed the dominant sources of agricultural surface source pollution through cluster analysis methods, and concluded that China's agricultural surface source pollutants TN and TP were mainly originated from livestock manure and farmland cultivation, and that although the provincial Although the pollution situation varies from province to province, the sum of these two pollutants accounts for most of the pollution, more than 90%. The second reason we did not consider is that the salinity and sodium data of each province and city are difficult to obtain.
We've added this part to the introduction.
“According to the Ministry of Agriculture and Rural Affairs of the People's Republic of China [7], chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) have been identified as the main pollution load indicators of agricultural non-point source pollution. Existing literature [8,9] analyzed the main sources of agricultural non-point source pollution through clustering analysis, and concluded that the main sources of agricultural non-point source pollutants TN and TP in China come from livestock and poultry manure and farmland cultivation, although the pollution situation varies from province to province, The total of these two pollutants accounts for the majority of the pollution, exceeding 90%.”
Thanks for your review!
Reviewer 2 Report
Object: 2nd review of the manuscript "Examining the Factors Influencing Agricultural Surface Source Pollution in the Yangtze River Economic Zone from the Perspectives of Government, Enterprise, and Agriculture" (revised title), which has been submitted to the Sustainability journal
Overall commentary
The article has been extensively revised and enriched with more explanations and details. It has also been written more concisely, with sections/paragraphs reduced in length and/or readability improved. Nevertheless, I remain skeptical of the results due to the absence of any thorough assessment of the uncertainties on the inputs and outputs used in the calculations. Failing that, a sensitivity study could be carried out to try and identify the most decisive factors. The results of this study could be presented in an appendix. Finally, some editing or referencing errors persist and should be resolved before acceptation.
On the whole, I recommend that the article be subject to moderate revisions.
SPECIC COMMENTS
Reply to authors’ responses in the summary: What are the procedures and elements for validating the robustness of source data and the accuracy of calculated values?
Reply to authors’ response 23: Could you give more details on this model validation step in the appendices or in the text of the article? How does this address uncertainty issue, and what uncertainties are considered?
Why are pesticides/fungicides not taken into account as well? Provide an explanation along with relevant figures (e.g. used quantities).
Reply to authors’ response 26: I am skeptical about the absence of uncertainty in the beta coefficients and, moreover, in the variables and parameters used as model inputs. If factors are given as fixed values, it would be important to at least estimate uncertainties around them using the literature or, failing that, common sense.
Reply to authors’ response 31: How can you tell whether Moran index values are significant in the absence of associated uncertainties? For example, is 0.189 +/- 0.15 significant in terms of autocorrelation?
Reply to authors’ response 36: This information could also be mentioned in the discussion of the manuscript.
The English has been improved and the article has been proofread by an English speaker. Minor errors persist and could be corrected
Author Response
Response to Reviewer 2 Comments
- Summary
Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions/corrections highlighted/in track changes in the re-submitted files.
We appreciate for time and energy the reviewer committed and the valuable comments.
- Point-by-point response to Comments and Suggestions for Authors
Reply to authors’ responses in the summary: What are the procedures and elements for validating the robustness of source data and the accuracy of calculated values?
Response 1: The source data were all downloaded from the official website by our research group itself, which is a reliable source of data. And the data were preprocessed and cleaned, including missing values and outliers, to ensure the integrity and consistency of the data. Once again, the data were standardized, and the data from different sources were transformed into a unified format for easy comparison and analysis. Finally, the results of data analysis were validated, and the robustness was verified in the article with two commonly used methods for studying the influencing factors of agricultural surface pollution, including spatial Durbin model and dynamic GMM methods.
Reply to authors’ response 23: Could you give more details on this model validation step in the appendices or in the text of the article? How does this address uncertainty issue, and what uncertainties are considered?
Response 2: We have added to the article detailing the validation steps of this model. The uncertainty of measuring agricultural surface pollution in a single way has been resolved, and the inventory analysis method has been used to quantitatively measure rural agricultural surface pollution in the absence of direct statistical data. This has been the method with the highest level of rigor for current scholars to study the measurement of agricultural face source pollution.
“Comprehensively comparing the existing literature on the measurement methods of agricultural surface source pollution, this paper adopts the unit investigation and assessment method based on inventory analysis to account for the pollutant emissions from agricultural surface sources. Inventory analysis method is to determine the pollutant production unit, based on the pollution production unit of the production coefficient with the help of the accounting formula to measure the amount of environmental pollution, can effectively establish the relationship between the amount of pollutants produced and pollutant emissions. Specific ideas are as follows: through the identification of rural water environment pollution unit, determine the pollution coefficient and accounting formula to measure the pollution of rural water environment of chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP) occurrence. This paper refers to the practice of Chen Minpeng et al [25] slightly optimized, according to the availability of data, agricultural surface pollution is identified as fertilizer application and farmland solid waste two pollution units, as shown in Table 1, taking into account that China's Yangtze River Basin, hemp, tobacco, medicinal herbs and other crops planted in the area of the small, incomplete statistical data, this paper selects the eight kinds of cereals, legumes, yams, oilseeds, cotton, sugar, vegetables, fruits and so on. agricultural solid waste of crops to account for pollutant emissions.”
Why are pesticides/fungicides not taken into account as well? Provide an explanation along with relevant figures (e.g. used quantities).
Response 3: One reason we did not take insecticides/fungicides into account is that it is not the main source of agricultural surface pollution. According to the State Ministry of Agriculture and Rural Affairs of the People's Republic of China [7], it is determined that chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP) are the main pollution load indicators of agricultural surface pollution, and there are already some literatures [8,9] analyzed the dominant sources of agricultural surface pollution by cluster analysis method, and it is concluded that China's agricultural surface pollutants TN, TP are mainly from agricultural fertilizers and farmland planting, and although the provincial pollution is different, but the sum of the two accounted for most of the more than 90%.
The second reason why we did not take pesticides/fungicides into account is that data from provinces and cities are difficult to obtain. According to the China Rural Statistical Yearbook, the total data on the use of insecticides nationwide in 2021 is 1.23 million tons, and in 2006 it was 1.08 million tons; while the magnitude of fertilizers is 51.913 million tons in 2021 and 47.662 million tons in 2006.
We've added this part to the introduction.
“According to the Ministry of Agriculture and Rural Affairs of the People's Republic of China [7], chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) have been identified as the main pollution load indicators of agricultural non-point source pollution. Existing literature [8,9] analyzed the main sources of agricultural non-point source pollution through clustering analysis, and concluded that the main sources of agricultural non-point source pollutants TN and TP in China come from livestock and poultry manure and farmland cultivation, although the pollution situation varies from province to province, The total of these two pollutants accounts for the majority of the pollution, exceeding 90%.”
Reply to authors’ response 26: I am skeptical about the absence of uncertainty in the beta coefficients and, moreover, in the variables and parameters used as model inputs. If factors are given as fixed values, it would be important to at least estimate uncertainties around them using the literature or, failing that, common sense.
Response 4: The Translog production function is based on the principle of outputting β0, βm, βl, βd, βk, and βg by inputting into the model the gross agricultural product Yit, the sown area Mit, the labor transfer Lit, the fertilizer application Dit, the total power of the agricultural machinery Kit, and the effective irrigated area Git. The robustness and accuracy of the data sources for which the inputs are used for the variables and the parameters have been shown in Response 1. For the point that the β coefficient is a fixed value, we also add a citation of the relevant literature [1,2] to support this point.
[1] Qijun J,Mengmeng W,Dongyong Z. Evidence of the Contribution of the Technological Progress on Aquaculture Production for Economic Development in China—Research Based on the Transcendental Logarithmic Production Function Method[J]. Agriculture,2023,13(3).
[2] LIU Li,LIU Jing. Analysis of the elasticity of substitution between organic fertilizer and chemical fertilizer based on beyond logarithmic production function - A survey on the fertilizer application behavior of fruit growers from the main apple producing areas in Bohai Bay[J]. Agricultural Technology and Economics,2022(08):69-82.
Reply to authors’ response 31: How can you tell whether Moran index values are significant in the absence of associated uncertainties? For example, is 0.189 +/- 0.15 significant in terms of autocorrelation?
Response 5: The significance of Moran's index is determined by the p-value, when the p-value is less than 0.05 (by 95% confidence test), and the Z-score exceeds the critical value of 1.65, the Moran's index is significant and autocorrelation is present. Values like 0.189 +/- 0.15 and their signs are not significant as far as autocorrelation is concerned.
Reply to authors’ response 36: This information could also be mentioned in the discussion of the manuscript.
Response6: We have added this section to the introductory part of the text.
“At the same time, our group in the book "Research on Lake Protection Strategies in Northern Jiangsu" [6] had taken farmers in Jinhu County, Huai'an City, Jiangsu Province, China as the survey object, 240 questionnaires were distributed, 238 were recovered, excluding duplicates and incomplete invalid questionnaires, 162 questionnaires could be used as valid questionnaires, and the questionnaire validity rate was 68%. The results of the questionnaire showed that 8% of the farmers chose to rely very much on fertilizers, 33% chose to rely generally on fertilizers, 24% chose to rely on fertilizers, 19% chose to rely slightly on fertilizers, and 16% chose not to rely on fertilizers during planting, which indicates that farmers in the region still rely on fertilizers to a high degree at this stage in order to obtain high output and high yields.”
Comments on the Quality of English Language
The English has been improved and the article has been proofread by an English speaker. Minor errors persist and could be corrected
Response7: We had another scholar majoring in English proofread the entire revised text and added the proofreading marks to the article.
Thanks for your review!
Reviewer 3 Report
No more comment.
Author Response
Thank you very much for taking the time to review this manuscript !
