Multifunctional Territorial Differentiation of Rural Production Spaces and Functional Zoning: A Case Study of Western Chongqing

Round 1

Reviewer 1 Report (Previous Reviewer 2)

Comments and Suggestions for Authors

The quality of the work has been significantly enhanced, clearly indicating that the authors possess a proficient grasp of effective methodologies and demonstrate considerable experience in research
The average agricultural production function index is 0.037. The Global Moran’s I is 0.391, with significant clustering characteristics of high and low values but the real values with which the respective coefficients were acquired are missing.

There are no sources indicating the data used to calculate the weights for the evaluation index system of rural production space multifunction.

A very probable source of nonlinearity of the objective function is given by the heterogeneity of the soil quality. Thus, within the same analyzed region and even within the same farm, fertility and soil quality vary greatly. In this way it is very likely that different productions in the same industry will be obtained over short distances.

Additional justification of using regression analysis

I haven't seen any development strategy based on the study,
I also recommend the reformulation of the Conclusions, so as to conclude the main ideas that emerge from the analysis of the data on the analyzed categories.  

Author Response

We are very grateful to the reviewers for their valuable comments on our research paper, and your suggestions and guidance to us will greatly help us to improve the level and quality of the paper. Regarding your suggestions and questions about the content and structure of the paper, we have thought deeply and revised and adjusted them. We will fully explain them item by item below! Sincere thanks again to the reviewers, every suggestion you made to us is valuable and constructive help.The parts modified according to the reviewer's comments will be marked and marked in the appropriate position of the new revised draft.

Thank you for your suggestions and comments on this paper, we have made the following revisions and responses to your suggestions.

1. The overall average of the agricultural production function index is the arithmetic average of the sum of the indices of all research units. The Global Moran index is calculated on the basis of the original index of the agricultural production function according to the spatial measurement method, and we put the parameters of the spatial autocorrelation calculation results in the following table:

2. The multifunctional evaluation index system of rural production space is composed of 25 indicators selected by scientific principles, and this paper introduces the sources of the indicator data in the chapter of "2.2 Data Sources" in lines 202-223, which mainly consists of socio-economic statistical data and spatial data, and labels the corresponding sources of each relevant data. After obtaining all the indicator data to form the indicator data set of this paper, we have gone through the pre-processing such as: the unified magnitude of the polar deviation method of processing, on the basis of standardized data, we weight the 25 indicators according to the entropy weighting method, entropy weighting method is a scientific and objective classic method to obtain the indicator weighting, widely used in many fields of econometrics and other fields, and has already formed a mature technical route and operational process, in this paper is not as the main method to discuss. In this paper, it is not discussed as the main method, which is explained in the chapter of "3.2 Entropy weight method" in lines 300-314.   

3.In this paper, a multivariate linear regression model is constructed to investigate the multifunctionality of the countryside and the corresponding influencing factors, and the influence mechanism of natural and socio-economic factors on the multifunctionality is fully explored, and the results can be seen in Table 2, in which the soil texture or fertility is an important factor affecting the function of agricultural production in the western part of Chongqing, and the composition and quality of the soil on the cultivated land and the garden land of the agricultural production land in different regions are differentiated, which affects the suitability and yield of agricultural production, especially agroforestry planting. The soil composition and quality of agricultural production land in different areas, such as arable land, garden land, etc., are characterized by differentiation, which in turn affects the suitability of agricultural production, especially the suitability and yield of agroforestry planting. The research scale of this paper is the township scale, and the regression model is introduced to reflect the soil-induced differences in agricultural production in different geographical areas (short distances) on the township scale.

Regression analysis is a quantitative method to explore the relationship between the role of dependent and independent variables, which is widely used in econometrics, ecology, geography and other fields, the important research problem of this paper is to study the influencing factors driving the multifunctional geographic differentiation, the multiple linear regression model can be accurately fitted to the calculation of the causal relationship between the variables and the regression coefficient of the numerical form of the performance, can be quantitatively and accurately expressed the strength and direction of the independent variable's effect on the dependent variable. In this paper, the logarithmic deformation form of Cobb-Douglas production function is used to explore the linear relationship between variables, C-D production function is widely used in quantitative analysis of agricultural economic and technical analysis, which can be linearized better and the parameter estimation is more convenient, and the nature of the data in this paper is suitable for the use of linear regression to explore.The significance of each variable in the process of regression analysis in this paper has passed the model In this paper, the significance of each variable in the regression analysis process has passed the test of the model, and there is no interference of factor covariance, and the research data meets the necessary prerequisites and conditions of the regression analysis.The results of the regression analysis in this paper are in line with the common sense and general laws of the social and natural sciences, and the results of the data analysis are scientifically reliable.A detailed description can be found in section "3.4 Multiple regression model" on line 334.

4.This paper puts forward the corresponding development strategy and rural revitalization suggestions for different types of functional zones in the chapter of "4.2.2 Rural functional zoning and revitalisation strategy" in rows 604-703. This paper uses a clustering model combined with the multifunctional geographical characteristics of the rural areas in the western part of Chongqing to divide the western part of Chongqing into four types of functional zones: balanced development type、Lagging development-ecological recreation type 、Urban development type 、Modern agricultural type. The detailed distribution and zoning can be seen in Figure 5：“Rural functional zones spatial distribution pattern in Western Chongqing”.In "4.2.2 Rural functional zoning and revitalization strategy", this paper gives a detailed overview of the four types of functional zones, and proposes a specific, differentiated and operable development strategy. Details can be found in the description of the development approach in rows 604-703 and in Figure 6：“Revitalizing suggestions for rural functional zones in Western Chongqing.”.

①Balanced development type development model: 1. adhere to the integration of agriculture, culture and tourism development strategy, use of agricultural resources and tourism resources to synergize the development of agriculture and tourism 2. villages and towns to hold groups to build thematic agriculture and tourism project cluster development area, to create a special agriculture and tourism brand 3. focus on organizing diversified vocational training of farmers and residents, and to further expand the channels of income

②Township development type: 1. expand the scale of township enterprises, further improve public infrastructure 2. play its own role as a strong industrial town, the radiation of characteristic industries, drive the surrounding villages and towns to diversify the development of industry 3. take its own small town as the center, rationally configure the public service infrastructure, guide the public services to the surrounding villages to comprehensively cover the urban and rural areas to narrow the gap between public basic services.

③Modern agriculture type : 1. Continuously invest in the scientific construction of modern agricultural parks, and drive the modernization and industrialization of agriculture with the advanced production and operation mode of agricultural parks. 2. Create characteristic brands of agricultural products, and drive farmers to actively participate in the process of agricultural industrialization of "one village, one product" through the form of production and marketing cooperation with large-scale agribusiness enterprises as a leader, so as to increase the agricultural output and farmers' income. Agricultural production and farmers' income

④Lagging development-ecological recreation type : 1. according to the environmental constraints of the mountainous backward areas, actively adjust the agricultural structure, actively develop mountain cash crop planting and special farming, and improve the income of the primary industry. 2. merging villages and residences, relocating and merging villages with remote and inconvenient living conditions, scientifically integrating dispersed and fragmented agricultural land, and centrally transforming and improving quality.

5.We thank the reviewers for their suggestions on the conclusion of this paper. At the suggestion of the reviewers, we have adjusted the conclusion to include a summary of our thoughts on the proposed development strategies for each type of functional area, which can be found on lines 801-840 of the new revised draft.

Reviewer 2 Report (New Reviewer)

Comments and Suggestions for Authors

When I read the article, I saw that it was a revised version of the article, showing that the authors had made changes in line with previous reviews. I find that the content of the article is fulfilled. Before publishing the article, I suggest one small change. My concern is mainly with the methodological approach, which is very complex and I fear it will confuse the reader. The authors describe various statistical methodological approaches, but the essential research question of the article is lost. I suggest that at least in section 3.4, where the authors describe the regression model, it is explained that it is a transformation of the so-called Cobb-Douglas function. I suggest that at this point it should be explained why the logarithmic form was chosen, since we also know the other functional forms. I have no further comments on the article.

Author Response

We are very grateful to the reviewers for their valuable comments on our research paper, and your suggestions and guidance to us will greatly help us to improve the level and quality of the paper. Regarding your suggestions and questions about the content and structure of the paper, we have thought deeply and revised and adjusted them. We will fully explain them item by item below! Sincere thanks again to the reviewers, every suggestion you made to us is valuable and constructive help.The parts modified according to the reviewer's comments will be marked and marked in the appropriate position of the new revised draft.

Thank you for your suggestions and comments on this paper, we have made the following revisions and responses to your suggestions

  1.Regarding the methodology of this paper, i.e. the research logical framework system, we have made a detailed and clear explanation in "2.3 Methodology" and the whole Chapter 3: "Research Methods", and drawn a research technology framework diagram to illustrate the technical route and research ideas of this paper. The technical route and research ideas of this paper are visualized and matched. In the Methodology and Framework section, a large part of the paper is devoted to explaining the idea of the paper, aiming to clearly illustrate for readers and reviewers the connection between the research methodology and the corresponding problem to be solved. The section "2.3 Methodology" provides an overall demonstration of the research process and the research questions addressed by each methodology, and the technical description of each methodology in Chapter 3 highlights the problem it addresses and the role it plays in the research. The methodology of this paper is more complex, with more methods, so a large number of illustrations are used in the paper, hoping to provide readers with a clear technical reference to the maximum extent possible.

    2.The Cobb-Douglas production function, also known as the C-D production function, was first used to reveal the complex relationship between production, output and inputs in regional industrial systems, and was later widely used in econometrics, spatial econometrics and other fields, and is also generally applicable in agricultural techno-economic research. This paper adopts the logarithmic form of the Cobb-Douglas production function to conduct regression analysis on the influencing factors of rural multifunctionality, because the logarithmic form of the C-D production function is easier to be linearized and has the essential characteristics of a linear function, so that it can clearly reveal the linear or causal relationship between variables, and the calculation of the regression coefficients can quantitatively detect the precise degree of influence and form of the effect between variables.C-D production function is widely used in agricultural economic and technical quantitative analysis, which can be better linearized and parameter estimation is more convenient. This part of the note we added to lines 371-380 of the new revised draft.

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1. This manuscript has the potential to become a valuable paper because it examines issues and approaches that are not very common. However, unfortunately, the manuscript is prepared with an unclear statement of research objectives (see the end of the abstract) between lines 121-125. Manuscripts need to have clear research objectives that readers can understand.

 2. In the first chapter, the authors explain that the determination of Western Chongqing as a research location was motivated by the national demonstration zones for integrated rural-urban development. However, there is insufficient information about the intended concept of integrated rural-urban development. Discussion of research results and conclusions should be linked to this concept.

 3. The manuscript is also structured using an unusual, ineffective, and somewhat confusing system, especially regarding chapter 2. At the end of Chapter 2, the authors have presented several analysis results. Usually, research results (lines 319 to 330) are written separately from the methods chapter.

 4. The authors do not provide sufficient information for readers outside China regarding the hierarchy of administrative units in the study area. There should be an explanation regarding the relationship and hierarchical order of administrative areas such as cities, municipalities, districts, counties, townships, and urban streets. What is the definition used in this research regarding urban and rural areas? What are the implications of the definition used in determining the study area? Is the study area for this research limited to rural areas or outside the city area?

 5. The systematics of chapter 2 should be in line with the research objectives. Each research objective must be obvious technically about the research method. There needs to be a clear and consistent sequence between the order of research objectives and the order of explanation of methods.

 6. Who is meant by principles in line 178? How are principles determined? 

 7. Table 1 not only explains the operational variables of the research but also contains research results. What is the meaning of the numbers listed in the first column? The weight column contains the weight value of which research results? Some variables show units per capita or person. Who is meant by persons here? Are they residents of the region or just farmers? What is meant by the direction column? What is the basis for determining it?

 8. There is no conceptual explanation regarding the relationship between the methods in Chapter 2.

 9. Research equations should be numbered. In the equation in line 229, what does a variable i mean? How many are i and j? Is there a difference between Wij in equation lines 229 and wij in lines 242 and 298?

 10. Regarding the regression equation in line 268, what is meant by i? How many regression equations are used? Explain each dependent and independent variable in each research regression equation in more detail.

 11. Due to the confusing systematics, I had difficulty connecting the description of the analysis results and conclusions in chapters 3, 4, and 5 with the research objectives and methods in the previous chapter.

Author Response

We are very grateful to the reviewers for their valuable comments on our research paper, and your suggestions and guidance to us will greatly help us to improve the level and quality of the paper. Regarding your suggestions and questions about the content and structure of the paper, we have thought deeply and revised and adjusted them. We will fully explain them item by item below! Sincere thanks again to the reviewers, every suggestion you made to us is valuable and constructive help.The parts modified according to the reviewer's comments will be marked and marked in the appropriate position of the revised draft.

1.In response to the reviewer's comment that in the second half of the introduction, the research objectives of this paper were not clear. We have reorganized the research objectives of this paper, which are explained as follows:

Research Objective 1: That is, to take the Western Chongqing as an example, to construct a model of evaluation index system to evaluate and measure the multifunctionality of rural production space.

Research Objective 2: On the basis of the evaluation results, explore the geographical differentiation pattern of multifunctional functions such as agricultural production function, leisure and tourism function, as well as the driving factors and influencing mechanisms of their formation.

Research Objective 3: Identify the types of rural development on the basis of the study of multifunctional differentiation patterns, and put forward corresponding suggestions and guidance for rural revitalization and development.

2.The driving factor for choosing the Western Chongqing as the study area is that it is a "national urban-rural integrated development demonstration area" that receives key support and attention from the state, a concept that is not sufficiently explained in this paper. We will explain it in detail here. The so-called national-level urban-rural integrated development demonstration zones are national-level policy guarantees for regions across the country that have made certain achievements in urban-rural integrated development. This concept was put forward in 2019 by the relevant departments of the central government with the aim of selecting a number of regions with a good background in urban-rural synergistic development as pilot demonstration zones for urban-rural development policy reform.

The criteria for the selection of national-level urban-rural integrated development demonstration zones are: 1. areas with a reform background of integrated urban-rural synergistic development, which have already done a series of measures to improve the rural socio-economic outlook and urban-rural disparity in rural areas, such as industrial and economic policy reforms, industrial structural adjustments and adjustments to the rural land and homestead system, and which have a certain amount of experience in urban-rural integrated development reforms. 2. areas that are in central cities, megacities Within the metropolitan area where the center city or mega-city is located, there are a number of contiguous counties in the suburbs that are radiated and driven by the big cities. The Western Chongqing fully meets these conditions. The central and local government departments at all levels will provide funds, talents, policy and institutional guarantees as well as professional guidance to the national urban-rural integrated development demonstration zones in order to help the villages in these areas to realize the transformation, improve the income and living standards of the residents, improve the rural living environment and narrow the gap between the urban and rural development. On this basis, this paper explores the multifunctional status of the villages in the western part of Chongqing, analyzes the reasons behind the functional differentiation, and classifies the area into zones. Using the key support of national policies and the characteristics of each type of functional area, it puts forward corresponding development suggestions for each type of functional area, so as to transform the policy advantages of the National Urban-Rural Integration and Development Demonstration Area into the driving efficacy of promoting the modernization of agriculture and rural areas in Western Chongqing, as well as the transformation and revitalization of the countryside.

3.The reviewer said that the chapter system of this paper is rather confusing, and the analysis of results appeared at the end part of Chapter 2: Introduction to Research Methods. We are sorry that this may be an error in the layout of the paper, we have mistakenly put the tables and figures of the result analysis at the end of Chapter 2, which is really inappropriate, and it looks like the overall structure of the article is rather messy. We will adjust this system properly to make the structure of the article look more reasonable.

4.Regarding the reviewer's comment that the article lacks a description of the relationship between the various levels of administrative units in China, we will explain this in further detail. First of all, according to the current Chinese administrative system, China's administrative units are currently divided into five levels: provincial (including provinces, autonomous regions, and municipalities directly under the central government), municipal, district (county), township (street), and administrative village (community). Chongqing is a municipality directly under the central government, with 38 district (county) administrative units under its jurisdiction. The "district" level units in this article are under the direct jurisdiction of the "city" level units, and the district (county) level units include the central or main urban areas with a high level of urbanization, as well as the "districts" and "counties" with a low level of urbanization and which are still predominantly rural. The district (county) level units include central or main urban areas with a high level of urbanization, as well as "districts" and "counties" that are less urbanized and still predominantly rural, the latter of which are included in the study area. The next level of administrative units at the district (county) level are townships, towns and streets, with townships, towns and streets being at the same level. "Streets" are generally the more urbanized parts of the districts or counties, which have a low proportion of rural population, rural area and agricultural industry, i.e., they are the central townships of the districts or counties, and are classified as cities at the statistical level and the economic level, which are outside the scope of the countryside study. Townships, on the other hand, are generally administrative units with agriculture as the key industry, a high share of agriculture, low urbanization, and a predominantly rural population. The scale of this paper is the township-level unit, and the object of study is rural multifunctionality, so it excludes streets with high urbanization because these parts belong to cities, and takes predominantly rural townships and a few streets with low urbanization (which are also rural-type townships by nature) as the object of study. In this paper, rural areas are defined as areas with a predominantly rural population (urban and rural populations as distinguished in China's demographics), where agriculture is the main or key industry, and where agricultural production and the provision of agricultural products are the main functions. Objectively speaking, it is difficult to completely separate the concepts of rural areas and towns at the level of existing administrative divisions, and this paper takes rural areas as the main body of the study of rural development in townships, focusing on the geographical scope of the vast rural areas.

5.The issue of the need for a clear sequence of correspondence between research methods and research objectives as stated by the reviewer. In this paper, when describing the research methods in Chapter 2, the description of each method includes the purpose of using the method, i.e., which problem is being solved and which objective is being reached by using the method. It is possible that our descriptions were not in place, leading to some confusion between the structures. We will improve this issue in the follow-up by describing the research methods in relation to the corresponding research objectives in the corresponding sections.

6.The "principle" in line 178 of this paper is the scientific principle of indicator selection and design, which is the premise of this paper to build a multifunctional evaluation index system of rural production space. It mainly includes the following aspects: （1）The selection of indicators must be in line with the definition and scientific theory of multifunctionality of rural production space, and can fully and effectively characterize the research object of this paper - multifunctionality of rural production space.（2）The selection of indicators should be concise and clear, and based on the basis of the indicators, and should not exist arbitrarily. （3）The selection of indicators should not be overly The selection of indicators should not favor one aspect or one function, but should take into account the overall situation and the whole, and fully consider all aspects of the research object（4）The research object of this paper is the multifunctionality of rural production space, and from the scientific connotation and definition of multifunctionality, five sub-functional systems of agricultural production function, industrial and trade economic function, leisure and sightseeing function, ecological preservation function, and life protection function have been selected. The evaluation system is constructed by selecting indicators from social, economic and ecological aspects, covering almost all levels of multifunctional system.（5） When selecting indicators, the statistical caliber and calculation method of indicators should also be unified to ensure the accessibility, reliability and credibility of the data and information, and try to obtain the authoritative data from the official statistics department or the scientific data provided by the experts and institutions in the field.（6）Not only should be in line with the scientific theory, but also need to be operable, can be put into practice, in the process of practice kind of verification of the practicality of the indicators.

7.Table 1 reflects the evaluation index system of "multifunctionality of rural production space", which is the object of this paper. The figures in the first column refer to the final weight coefficients of the sub-functional system of the index system, i.e., the functions, which can reflect the importance of the sub-functions in the evaluation of multifunctionality of rural production space. The column "Indicator weight" in Table 1 contains the weights of all the secondary indicators in the indicator system, i.e. X1...X24. The weights of the primary indicators (i.e., subfunctions) and the secondary indicators should be stated independently of each other, and we will improve this in the future.

The reviewer mentions the indicator "per capita", where "people" is specified in the column of the corresponding indicator calculation method, i.e. the total rural population. It is further explained that this paper focuses on the study of rural development, and the scope of the statistics is also "rural" as the ultimate goal and focus. The total rural population here refers to all rural inhabitants, i.e. those who are registered in the countryside, who live in the countryside for a long time, and who produce and live in the countryside. The group of rural inhabitants is mainly farmers or agricultural households.

The direction of the indicator is determined by the nature of the role of the indicator on the object of study, the indicator plays a positive positive role on the object of study is positive "+", the indicator plays a negative negative role on the object of study is negative "-", this link is important for the normalization of processing indicator This link is very important to the normalization of the indicator data, if the lack of this link to determine the direction of the indicator, it will lead to serious deviations in the final calculation results.

8.The issue of linkages and sequencing between the research methods in chapter 2. Can this issue be interpreted as a similar aspect to question 5, i.e. the lack of clarity in the link between the description of the research methodology and the research objectives. We explain in detail here that the research objectives of this paper and the corresponding research methods used are as follows:

Research Objective 1: To construct a model of the evaluation index system for the multifunctionality of rural production space by taking the national-level urban-rural integrated development pilot area -the Western Chongqing as an example of the multifunctionality of rural production space. The corresponding research methods are “2.3 Evaluation Index System Construction” and “2.4 Functional exponent Measurement Model ”of the revised draft text, which are used to construct the evaluation index system and calculate the index value of each function on this basis.

Research Objective 2: To analyze the geographical differentiation pattern and characteristics of the multifunctionality of rural production space in the Western Chongqing, and to explore the influence mechanism of the formation of the differentiation pattern. The corresponding research methods are “2.5 global spatial autocorrelation analysis” and “2.6 stepwise regression”.“ 2. 5global spatial autocorrelation analysis” is used to analyze the spatial agglomeration of each function and its characteristics, and“ 2.6 stepwise regression ”is used to analyze the linear regression relationship between each function and its influences. “2.6 The stepwise regression method ”is used to analyze and explore the linear regression relationship between each function and the influencing factors to reveal the causal relationship between multifunctionality and the influencing factors, and explore the constraints of multifunctionality from these relationships.

Research Objective 3: To conduct the identification of rural development types on the basis of the study of multifunctional geographic differentiation patterns, and to put forward corresponding suggestions and guidance for rural revitalization and transformational development. The corresponding research method is “2.7 fuzzy clustering model”. In this paper, the improved weighted clustering model is used to deal with high-dimensional data, and all the research units in the study area are clustered. The analysis results of the clustering model are used to determine the best classification scheme for optimal functional zoning.

Regarding this issue, we have made corresponding structural adjustments and explanations on the basis of the original manuscript.

9.We will number and adjust the formula section of the research methodology as requested by the reviewers. The formula for weighted summation and synthesis calculation (where i=1,2...5) is involved in line 229. In the formula and parameter description in the text, there is a clear explanation, namely: the ith function, the research object of this paper contains a total of five functions, namely, agricultural production function, industrial and trade economic function, leisure and tourism function, ecological conservation function and life protection function, and each of these functions is characterized by a corresponding index. j represents the characterization index of each function (j=1,2...m). According to the evaluation index system constructed in this paper, different functions have different numbers of indicators. For example, the characterization indicators of the agricultural production function are X1, X2, X3, X4 totaling four indicators, and the characterization indicators of the livelihood security function are X19...X24 totaling six indicators.where, S_i is the primary functional value of a township, Z_ij is the standardized value of the j th index corresponding to the functional value, and W_j is the weight of the j th index.

W_j in the equation in line 229, where W_j is the weight coefficient of each indicator calculated according to the entropy weighting method. W_ij in the equation describing the global Moran index in line 242 is the spatial weight matrix used in the calculation of the global Moran index. The W_k in line 298 refers to the weighting coefficients of the composite indicators that have been processed on the basis of the original indicator system. These three parameters involving weights are fundamentally different.

10.The reviewer is referring to "i" in the regression equation in line 268, which is not in the regression equation in this paper, and the reviewer should be asking about "k"（k = 1...7）, which refers to the influencing factors, and this paper finally selects seven influencing factors to establish a regression relationship with the functions, in order to explore the driving mechanism and constraints of multi-functional geographic variation. This paper explores the causal relationship between multifunctionality and influencing factors through the regression model, which involves a total of five sub-functional systems, such as agricultural production function, leisure and tourism function, etc., and carries out independent regression analysis for each of the five kinds of functions, so this paper uses five regression equations, and each regression equation corresponds to one kind of function as a dependent variable, and the independent variables are 7 selected influencing factors. We will further add the significance of each parameter in the description of the research methodology.

11.With regard to the reviewer's comment that the chapter structure of this paper is somewhat confusing, resulting in a lack of a reasonably clear logical sequence between the description of the research objectives and the research methodology. In particular, it is difficult to correspond between Chapter 3, 4 and 5 and the previous chapters. We would like to add that Chapter 1 of this paper is the introductory part, and the research objectives of this paper are stated at the end of the chapter. Chapter 2 of this paper is an introduction to the research materials (including an overview of the study area and the sources of research data) and the research methodology. Chapter 3 of this paper is an investigation of the pattern of multifunctional geographical differentiation of rural production space and its influence mechanism, using the methods “2.5: global spatial autocorrelation analysis” and “2.6: regression analysis”. Chapter 4 of this paper is a proposal of functional zoning and rural revitalization and transformation development paths based on the results of multifunctional geographical differentiation in Chapter 3. The corresponding method is “2.7fuzzy clustering model”. Chapter 5 is the discussion part of this paper, which mainly explains the innovations and shortcomings of this paper. Chapter 6 is the conclusion part of this paper, which further reveals the findings of this paper. We will also further improve the logical structure between the chapters in the article by adding the correspondence between the research methodology at the technical level and the research objectives in each chapter.

Reviewer 2 Report

Comments and Suggestions for Authors

In line 176 it is not clear the three aspects you wanted to combine of rural characteristics and relevant policies in Western Chongqing,

In the line 334 and 335 the author explain that: "the average agricultural production function index of 0.041" . In this case, he suggests that, on average, agricultural production in the studied region is positively influenced by the input factors being considered. This index value indicates that for each unit increase in the input factors, there is a corresponding 0.041 unit increase in agricultural production, assuming a linear relationship.

On the other hand, the Global Moran's I value of 0.515 is a measure of spatial autocorrelation, indicating whether there is clustering or dispersion of similar values across the study area. A value of 0.515 indicates that there is a significant clustering pattern in the data. In this context, it means that areas with high agricultural production are clustered together, and similarly, areas with low agricultural production are also clustered together.

This clustering of high and low agricultural production values can have important implications for regional planning and policy decisions. It suggests that there might be certain spatial factors or geographic influences that are driving these patterns. For example, it could be due to variations in soil quality, climate, or other local conditions. Understanding these clustering characteristics is crucial for optimizing resource allocation, improving agricultural practices, and addressing regional disparities in agricultural productivity.

In summary, the combination of a positive average agricultural production function index and a significant Global Moran's I value of 0.515 indicates that there are spatial patterns of high and low agricultural production in the region, and further investigation is needed to identify the underlying factors contributing to these patterns.

The system of indices for evaluating the multifunctional rural production space is your approach and calculations or taken from statistics?

Why was the data not taken into account in their evolution over 3 years (2017-2020 for example), and the results were much more conclusive?

Please additional justification of using regression analysis!

Author Response

We are very grateful to the reviewers for their valuable comments on our research paper, and your suggestions and guidance to us will greatly help us to improve the level and quality of the paper. Regarding your suggestions and questions about the content and structure of the paper, we have thought deeply and revised and adjusted them. We will fully explain them item by item below! Sincere thanks again to the reviewers, every suggestion you made to us is valuable and constructive help.The parts modified according to the reviewer's comments will be marked and marked in the appropriate position of the revised draft.

1.In line 178, this paper builds an evaluation index system model and selects indicators based on the combination of existing research results, the characteristics of the regional environment of the Western Chongqing (including the characteristics of the humanistic environment and the characteristics of the natural environment), and the relevant policies. In the second half of this paragraph, in lines 188-222, the paper clearly explains which indicators are selected based on the existing research results, and which indicators are designed and selected by combining the geographical characteristics of the Western Chongqing, such as low terrain, high level of agricultural mechanization, uneven distribution of public service resources for social development, and so on. Which indicators are designed and selected according to the major policies of government departments at all levels. This paper will be further described and explained in the text.We will provide a detailed supplementary explanation on the basis of indicator selection in “2.3 Construction of evaluation index system ”of the revised draft.

2.The average value of agricultural production function in rows 334-335 as stated by the reviewer is 0.041, which is calculated by the research method:“2.4 Functional exponent evaluation model”

, and is the average result of dividing the value of agricultural production function of each unit by the total number of research units on the basis of calculating the value of agricultural production function of each unit, which is the overall average of all the units of the whole Western  Chongqing, reflecting the overall average level of agricultural production function in the research area. function overall average level. Not related to the subsequent regression equations, the research method “2.6: Stepwise regression modeling”studies the causal relationship between each function and the influencing factors to reveal the mechanisms that influence/constrain the formation and development of the function. The results of the analysis on the regression model are presented specifically and in detail in Table 3: Regression results table.

3.The value of global Moran index of agricultural production function is 0.515, indicating that the agricultural production function in the study area as a whole has a significant high - low agglomeration characteristic trend. In Chapter 3, Section 1: "3.1 Agricultural production function", there is a detailed explanation and description of the geographical differentiation pattern of agricultural production function. And the research method “2.6: stepwise regression model ”was used to carry out in-depth analysis of the influencing factors, to determine the linear and causal relationship between the agricultural production function and the natural and socio-geographical factors. Stepwise regression model is a method of stepwise regression between multiple alternative independent variables and dependent variables, which can eliminate the non-significant and less significant variable factors and get the optimized regression model. In this paper, several independent variables were selected before the regression analysis: including precipitation, temperature, topography, slope, location and other factors. In the significance test of the stepwise regression method the p-value of the significance coefficients of the precipitation and temperature factors failed the significance test. On the whole, the eight districts and county-level units under the jurisdiction of the Western Chongqing have a small geographic scope and area depth, small climatic differences, and belong to the same regional climatic natural area, with small differences in precipitation and temperature. The key to influencing the emergence and divergence of high and low clusters of agricultural production functions is local topographic factors. The terrain of low-level valleys and low hills and plains basin terrain area has more cultivated land and most of them are plain cultivated land and gently sloping cultivated land, which is easier to develop and utilize and carry out modernized and large-scale high-yield production mode, and the agricultural production function is higher. Cultivated land in the mountainous ridge and valley terrain area is mostly sloping cultivated land, with serious fragmentation of land parcels, which is difficult to be utilized and adapted to modern agricultural production mode on a large scale, resulting in a weaker agricultural production function. This significant influence factor is also explained in detail in "3.1 Agricultural production function".

4.The question of the source basis of the evaluation indicator system, as stated by the reviewer, is specified in the text. There are detailed explanations in the abstract and introduction of this paper and in “2.3 Construction of evaluation index system”of the revised draft. The indicator system of this paper is based on the scientific connotation of the multifunctional theory of rural production space, and under the guidance of the scientific theory, the indicator system is selected and constructed by referring to the consensus indicators in the existing research results in the professional field, and combining with the regional characteristics of the Western Chongqing as well as the relevant policies. The data of each indicator is obtained from the statistics and acquisition of multiple sources of data, and is used after our scientific processing. Referring to the indicators with high consensus in the existing high-level research results in the specialized fields ensures to a certain extent that the indicators are scientific and universal, and can be operated in practice. The rural production space system is limited by its own geographical environment (including humanistic and natural environment), and then driven by internal human society's diversified development needs, such as group needs and individual needs, forming a relationship system with a two-way interaction between people and land. This system is also disturbed by external policy conditions and economic behavior. Therefore, the selection and construction of the indicator system combined with the regional characteristics of the Western  Chongqing and related policies highlights the regional nature of the system as a whole. The indicators selected in this paper are supported by scientific basis and data, which is an improvement and innovation of the previous research.

5.The issue of data changes for 2017-2020 as stated by the reviewer. The main point here is the relative stability of the indicator data over a certain period of time. The research node of this paper is 2020, because 2020 is the time node of Chinese grass-roots level, especially in rural areas, to achieve the goal of poverty eradication, China has basically eliminated the problem of absolute poverty in rural areas, and the level of socio-economic development in rural areas has been significantly improved, and the analysis of data and information in 2020 can fully reflect the current rural development status. In the 2017-2019 time before this, on the one hand, due to the objective reasons of the epidemic, the rural socio-economic development and construction has slowed down, and the overall level of change is relatively small. On the other hand, there are great difficulties in the statistics of data and information, and a large number of rural grassroots data and information have not been updated and counted in a timely manner. These reasons have led to the relative stability of the data in the study area and small changes, so they are not taken as the focus of this paper for in-depth research.

6.Regression analysis is a scientific model for studying causal relationships between things, and is often used extensively in research in both the social and natural science fields.（1） Regression analysis utilizes the powerful ability of a fitted model to calculate linear and precise mathematical relationships between variables. It can visualize the overall causal relationship between variables. The regression model can quantitatively reveal that when the input of the independent variable increases/decreases by a certain value, the dependent variable correspondingly increases/decreases by a certain value.（2）This paper investigates the driving mechanism between multifunctionality and various influencing factors, and explores the key factors affecting the formation of the functional geographic differentiation, and the regression analysis is well suited for this paper's research objectives.（3）The significance of each variable in the process of regression analysis in this paper has passed the model In this paper, the significance of each variable in the regression analysis process has passed the test of the model, and there is no interference of factor covariance, and the research data meets the necessary prerequisites and conditions of the regression analysis.（4）The results of the regression analysis in this paper are in line with the common sense and general laws of the social and natural sciences, and the results of the data analysis are scientifically reliable.