Study on the Mitigation Effect and Promotion Mechanism of Agricultural Digitalization on the Agricultural Land Resource Mismatch

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The submitted paper offers a comprehensive analysis of how digital transformation in agriculture can address issues of land resource mismatch in China. The use of balanced panel data from 29 provinces over a decade provides a robust dataset. The methodology, including empirical analyses of the mitigation effects and mechanisms of agricultural digitalization on land resource misallocation, is well-articulated and thorough.

The paper could benefit from a deeper discussion on the specific challenges and barriers to implementing digital technologies in rural and underdeveloped areas. Thus I suggest to focus on discussion part and better discuss technological implementation challenges. Also the limits of study could be more highlighted in the paper.

I can recommend the paper for publication after these minor changes.

Comments on the Quality of English Language

There are minor grammatical errors and awkward phrasings scattered throughout the text. These do not significantly hinder understanding but could detract from the paper's overall professionalism.

Author Response

Response to Reviewers 1 Comments

Summary

The submitted paper offers a comprehensive analysis of how digital transformation in agriculture can address issues of land resource mismatch in China. The use of balanced panel data from 29 provinces over a decade provides a robust dataset. The methodology, including empirical analyses of the mitigation effects and mechanisms of agricultural digitalization on land resource misallocation, is well-articulated and thorough.

 

Comments1: The paper could benefit from a deeper discussion on the specific challenges and barriers to implementing digital technologies in rural and underdeveloped areas. Thus I suggest to focus on discussion part and better discuss technological implementation challenges. Also the limits of study could be more highlighted in the paper.

Response1: Based on the suggestions of the reviewers, the article provides a more in-depth discussion of the specific challenges and obstacles to the implementation of digital technologies in rural and less developed areas. Specifically, the vast rural areas and less developed areas are generally facing problems such as the shortage of digital talents, weak innovation ability, and imperfect digital infrastructure. At the same time, due to the need to improve the digital literacy of farmers, insufficient social capital investment, the advantages of the digital economy can not be fully reflected. Limitations of the study are also discussed in this paper. For example, due to the availability of data, this paper uses panel data at the provincial level, which cannot further refine the measurement of agricultural digitalization. Heterogeneity analysis may limit the generality of the findings, which, while relevant to China, may not be applicable to other countries.

 

Comments2: There are minor grammatical errors and awkward phrasings scattered throughout the text. These do not significantly hinder understanding but could detract from the paper's overall professionalism.

Response2: Thanks for the reviewer's suggestions, we have read and modified the whole article for many times. Please criticize and correct the inadequacies.

Reviewer 2 Report

Comments and Suggestions for Authors

Summary:  The authors explore the impact of agricultural digital transformation on the misallocation of agricultural land through a static linear panel regression with fixed effects. They find that the impact is significantly negative, suggesting that digital transformation alleviates mismatch issues. The authors also provide robustness checks and trace out the underlying mechanisms. Overall, I find the topic interesting and the paper relatively well-written. Below are my comments, listed in no particular order:

Major comments: 

1. What control variables did you use in your study (e.g., the X in equations (3)–(5))?

2. To explore the indirect (or mediation) effects, why not use a more common method, such as including an interaction term?

3. In Table 2, only the abbreviated names of the variables are provided without any explanations. The authors need to present the full names and thoroughly discuss how they collected or computed the data and the sources.

4. The authors should conduct tests for panel-unit root and panel cointegration.

5. Instead of reporting the R2, report the adjusted R2. Clarify whether the reported t-statistics are based on heteroskedastic-consistent standard errors or clustered heteroskedastic-consistent standard errors.

6. Regarding the endogeneity issue, the authors seem to use lagged digital agriculture as the instrumental variable (IV). However, it could still be the case that expectations about future land misallocation might influence the current level of digital agriculture. In other words, I am concerned about the exogeneity assumption of the IV used. Furthermore, the authors only consider the endogeneity of digital agriculture, but other control variables may also be endogenous.

Minor comments:

1. In the second paragraph, "Combing" should be corrected to "Combining."

2. Equations (4) and (5) use the same notation for fixed effects and errors. These should be distinct.

3. The authors might discuss future research directions. For example, it could be interesting to explore the nonlinear impacts of digital agriculture on land misallocation. It is reasonable to conjecture that there exists a threshold level such that the impact becomes significant or larger if and only if the level of digital agriculture exceeds a certain threshold. The authors might employ a threshold model to investigate this nonlinearity.

Comments on the Quality of English Language

Fine

Author Response

Response to Reviewers 2 Comments

Major comments1: What control variables did you use in your study (e.g., the X in equations (3)–(5))?

Response1: According to the existing research on the misallocation of agricultural land resources, the paper selected the following control variables: ①urbanization rate, ②industrial structure, ③industrialization level, ④financial support for agriculture, ⑤income distribution. In part 2.3 of this paper, the name and specific measurement method of the control variable are explained, and in part 2.5, the source of the control variable is introduced.

Major comments2: To explore the indirect (or mediation) effects, why not use a more common method, such as including an interaction term?

Response2: In order to explore the intermediary influence, we selected two mechanism variables, land scale management and agricultural socialization service, and used the intermediary effect model to illustrate that agricultural digitalization can promote land scale management and improve the level of agricultural socialization service. Furthermore, it shows that agricultural digitalization can alleviate the misallocation of agricultural land resources by promoting land scale management and improving the level of agricultural socialized service. The purpose is to explore the mechanism of mediating effect, so the interaction term is not selected.

 

Major comments3: In Table 2, only the abbreviated names of the variables are provided without any explanations. The authors need to present the full names and thoroughly discuss how they collected or computed the data and the sources.

Response3: In Table 2, we added the full name of each variable, detailed the measurement method and selection basis of each variable in part 2.3, and introduced the data source of each variable in part 2.5.

 

Major comments4: The authors should conduct tests for panel-unit root and panel cointegration.

Response4: According to the reviewer's suggestion, we conducted the panel unit root test, and the results of the LLC test showed a P value of 0.0000, which indicated that the panel data had stationarity characteristics. We also learned that the purpose of panel cointegration test is to determine whether a set of linear combinations of non-stationary sequences have a stable equilibrium relationship. According to the results of panel unit root test, the data has the characteristics of stationarity, so the panel cointegration test is not conducted again.

 

Major comments5: Instead of reporting the R2, report the adjusted R2. Clarify whether the reported t-statistics are based on heteroskedastic-consistent standard errors or clustered heteroskedastic-consistent standard errors.

Response5: According to the regression results of the data, R2 from Table (3) to Table (6) was replaced by adjusted R2, and it was explained in the paper that the t-statistic in the table was presented based on heteroskedastic-consistent standard errors.

 

Major comments6: Regarding the endogeneity issue, the authors seem to use lagged digital agriculture as the instrumental variable (IV). However, it could still be the case that expectations about future land misallocation might influence the current level of digital agriculture. In other words, I am concerned about the exogeneity assumption of the IV used. Furthermore, the authors only consider the endogeneity of digital agriculture, but other control variables may also be endogenous.

Response6: In order to avoid the endogeneity problems that may be caused by taking the digitization level of agriculture one stage behind as an instrumental variable, we use the interaction between the number of landlines per 100 people in 1984 and the national Internet investment in the previous year as a new instrumental variable. In addition, as for the selection of control variables, after reading a large number of literatures, we selected the relevant variables affecting the misallocation of agricultural land resources as control variables, Control variables that may have endogeneity problems are removed from the regression. Therefore, we believe that the selection of control variables is reliable.

 

Minor comments1: In the second paragraph, "Combing" should be corrected to "Combining."

Response1: The "Combing" in the article has been corrected to "Combining"

 

Minor comments2: Equations (4) and (5) use the same notation for fixed effects and errors. These should be distinct.

Response2: According to the reviewer's suggestion, we distinguish the symbols representing fixed effects and errors in equation (4) and equation (5). In formula (5), the individual fixed effect symbol m is replaced by q, the time fixed effect symbol h is replaced by z, and the error term symbol e is replaced by z.

 

Comments3: The authors might discuss future research directions. For example, it could be interesting to explore the nonlinear impacts of digital agriculture on land misallocation. It is reasonable to conjecture that there exists a threshold level such that the impact becomes significant or larger if and only if the level of digital agriculture exceeds a certain threshold. The authors might employ a threshold model to investigate this nonlinearity.

Response3: According to the reviewer's suggestion, we added a future outlook part to the paper, for example, using a threshold model to explore the nonlinear impact of agricultural digitalization on agricultural land resource misallocation.

Reviewer 3 Report

Comments and Suggestions for Authors

The paper is clear, and the research questions are well addressed. It provides valuable insights into the role of agricultural digital transformation in mitigating land resource misallocation. However, some limitations have been identified regarding on the one hand the capacity of the models to address the complexity and regional diversity of agricultural systems and on the other hand the validity and applicability of the findings.

Data sources and data quality are very important to determine the reliability of the conclusions. Regarding the endogeneity issue, please explain why certain methodological choices were made (omitted variable bias can rise significant challenges). These arguments should be included in the paper,  better outlining the method used.

Another important point is related to regional differences: The effectiveness of digital transformation might vary significantly across regions due to differences in infrastructure, economic development, and local policies. While heterogeneity tests address this to some extent, regional disparities could still limit the generalizability of the findings. The findings, while relevant to China, might not be directly applicable to other countries with different agricultural structures and levels of digital adoption.

Furthermore, the paper focuses everything on quantitative analysis, completely leaving out qualitative and contextual aspects that I think would be important to consider and that are complementary. As known, agriculture is influenced by numerous factors including climate, market conditions, and labor dynamics. The study focuses on digital transformation and might not fully capture the interplay of these various elements. T

The Discussion section is missing.

As far as the Conclusions are concerned, I think that the points described are very sharp and presented as absolute truths. The policy implications are interesting, but any concrete actions are described to address them How to obtain results from this purposes? Are there any financial support/funding available on these issues ate the regional level? Are there any projects that have addressed these issues in the past? Previous or current projects? How might these policies could be better tailored on the western regions than others?

 

2.3 Variable selection: Please explain the formula 1 and 2. The PLA perpetual inventory method should be briefly outlined, as well as the entropy method.

Table 1: Please describe weights and signs (direction). Figure 4: which are the data under the picture? The data sources should be clear.

2.4 Model setting. Theoretical references are missing. I suggest to briefly motivate the methodological choice you made.

Author Response

Response to Reviewers 3 Comments

Comments1: Data sources and data quality are very important to determine the reliability of the conclusions. Regarding the endogeneity issue, please explain why certain methodological choices were made (omitted variable bias can rise significant challenges). These arguments should be included in the paper, better outlining the method used.

Response1: In order to solve the endogeneity problem, we choose to delay the core explanatory variable by one stage and the instrumental variable method. The reason is that there is no correlation between the core explanatory variable lagging one period and the current error term, while the instrumental variable method can effectively identify the causal relationship between variables and improve the accuracy and reliability of research results under the circumstances of limited data, and is a commonly used method to solve the endogeneity problem.

 

Comments2: Another important point is related to regional differences: The effectiveness of digital transformation might vary significantly across regions due to differences in infrastructure, economic development, and local policies. While heterogeneity tests address this to some extent, regional disparities could still limit the generalizability of the findings. The findings, while relevant to China, might not be directly applicable to other countries with different agricultural structures and levels of digital adoption.

Response2: Thanks for the reviewer's suggestion, this is indeed the research limitation of the article. We will explain this problem in the research limitation part of the article, in order to provide ideas for future related research.

 

Comments3: The Discussion section is missing.

Response3: According to the reviewer's suggestion, we have added a discussion section in 2.1, which includes the data, model, research purpose, research limitations, research prospects and marginal contributions of this paper.

 

Comments4: 2.3 Variable selection: Please explain the formula 1 and 2. The PLA perpetual inventory method should be briefly outlined, as well as the entropy method.

Response4: The explanation of Formula 1 and Formula 2 is explained in part (1) of 2.3. And  represents the amount of land input in Province i as a percentage of the total amount of land input in the economy,  is the proportion of the total agricultural output value of province i in the total agricultural output value of the whole country,  represents the contribution of the land factor of Province i to the total agricultural output of the whole economy,  represents the elasticity of land output. In order to empirical analysis, the article converts the relative distortion coefficient of land resources into the land resource mismatch index, and the conversion formula is shown in formula (2). The introduction of perpetual inventory method and entropy method is also added in the paper. The essence of the perpetual inventory method is to adjust and convert the capital flow in different periods year by year to accumulate the capital stock with the same meaning. Entropy method is an objective weighting method, which calculates the information entropy of indicators and determines the weights of indicators according to the influence of the relative change degree of indicators on the whole system, so as to obtain the corresponding weights of each indicator.

 

Comments5: Table 1: Please describe weights and signs (direction). Figure 4: which are the data under the picture? The data sources should be clear.

Response5: As can be seen from Table 1, Indicators with relatively large weights are electricity for rural use, People working in the information transmission, software and information technology services, Rural broadband access users, while indicators with relatively little weight include Fertilizer use, Plastic film usage. Only Fertilizer use, Pesticide use and Plastic film usage are negative indicators, and the rest are positive indicators. In Figure 4, the data below the picture refers to the year, where the data of agricultural digitization level is measured by the entropy method above.

 

Comments6: 2.4 Model setting. Theoretical references are missing. I suggest to briefly motivate the methodological choice you made.

Response6: According to the reviewer's suggestion, we added references, the title is impact of digital village construction on agricultural green total factor productivity and its mechanisms. Meanwhile, the paper uses baseline regression model and intermediary effect model for empirical analysis. Among them, the core of baseline regression analysis is to establish a linear model, which is usually used for predictive analysis and to find causality between variables, and to understand the degree of influence of independent variables on dependent variables. And the mediating effect model describes the process by which independent variables influence dependent variables through mediating variables.

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

I have no further comments.