Evaluating the Effectiveness of Different Demonstration Models on Agricultural Climate-Smart Technology Adoption: Evidence from China’s Cotton Farmers

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Dear authors! Your research is designed to solve an important practical problem - assessing the effectiveness of various demonstration models.
The article contains a large number of literary sources on the topic of the study.
The methodology of the study raises big questions. Farmers are commercial structures and the main criterion for their assessment is the possible increase in profit. If it is not there, then no matter how green the proposed technologies are, no one will implement them. Moreover, the authors themselves are aware of this fact when they indicate in the introduction that the technologies are insufficiently developed, risky, and have not reached mature commercialization (72-78).
Therefore, it is incorrect to compare the effectiveness of their implementation with the use of traditional technologies aimed at making a profit.

Author Response

Response: Thank you for your suggestion. It is necessary to clarify that this study focuses on CSTs applied in cotton production that deliver both ecological and economic benefits, including precision fertilization, precision irrigation, conservation tillage, straw incorporation, and dry seeding with wet emergence (as presented in Section 2.1). These technologies have already been applied to varying degrees in cotton production in Xinjiang, and some have been adopted on a very large scale. To clarify that not all CSTs lack economic benefits (lines 72–78), an explanation is provided in lines 76–86. In addition, lines 130–135 indicate that some CSTs applied in cotton production also combine ecological and economic advantages. Furthermore, as this study focuses on technology demonstration for CSTs in Xinjiang, the background of promoting these technologies in the region is specifically elaborated in lines 141–158.

Section 2.1: 2.1. Conceptual Definition

Agricultural CSTs refer to technologies that reduce GHG emissions during agricultural production and enhance soil carbon sequestration. Their primary aim is to im-prove agricultural productivity, mitigate the negative environmental impacts of farming, and promote the harmonious coexistence of agriculture and ecosystems [7]. The prevailing academic consensus holds that CSTs achieve dual goals of emission reduction and carbon sequestration by optimizing agronomic management and resource al-location, reducing the excessive use of high-carbon inputs such as fertilizers and pesticides, improving soil physicochemical properties and water retention, thereby mitigating CH₄ and N₂O emissions from agricultural sources and increasing soil organic carbon storage [41].

 

Considering the specific conditions of cotton production in Xinjiang, the selection of CSTs must balance economic and ecological benefits. On one hand, farmers tend to prioritize yield and economic returns, which necessitates that adopted CSTs provide strong economic incentives. On the other hand, given the scarcity and uneven spatiotemporal distribution of water resources in Xinjiang’s arid regions, CST promotion must integrate water-saving technologies to improve water-use efficiency per unit of resource. Accordingly, this study focuses on five key CSTs in cotton production: precision fertilization, precision irrigation, conservation tillage, straw incorporation, and dry seeding with wet emergence (Table 1).

Table 1. Key Studies Confirming the Positive Contributions of CSTs to Addressing Climate Challenges.

CSTs      definition     references

precision fertilization the optimal ratios and application rates of nutrients such as nitrogen, phosphorus, and potassium based on soil nutrient status and crop requirements   [42-44]

precision irrigation    patch-type drip irrigation  [44-46]

conservation tillage   minimize soil disturbance, maintain surface cover, and preserve soil aggregate struc-ture      [47-49]

straw incorporation    crushing and returning cotton straw to the field       [46,50]

dry seeding with wet emergence    direct sowing without winter or spring irrigation, followed by a single post-sowing watering to promote seedling emergence   [51]

 

Precision fertilization specifically refers to soil testing and formula-based fertilization, which determines the optimal ratios and application rates of nutrients such as nitrogen, phosphorus, and potassium based on soil nutrient status and crop requirements. Its primary function is to reduce excessive nitrogen fertilizer use, decrease nitrogen losses and N₂O emissions, and improve both fertilizer efficiency and crop yield. In Xinjiang cotton production, this practice effectively reduces fertilizer waste caused by blind application, achieving both cost-saving and GHG reduction benefits.

 

Precision irrigation refers to the use of patch-type drip irrigation, an improved version of traditional drip systems. This technique offers better adaptability and clogging resistance, delivering water directly to plant roots. It significantly conserves water and reduces soil denitrification caused by excessive irrigation, thereby lowering N₂O emissions. Moreover, integrated water–fertilizer management improves the rhizo-sphere environment, enhances nutrient uptake efficiency, and achieves both resource conservation and yield gains.

 

Conservation tillage refers to reduced or no-tillage practices that minimize soil disturbance, maintain surface cover, and preserve soil aggregate structure. This approach effectively decreases soil organic carbon mineralization and CO₂ emissions while enhancing long-term soil carbon sequestration. In Xinjiang’s arid cotton-growing regions, conservation tillage also improves soil moisture retention, reduces evaporation losses, and enhances water-use efficiency and drought resistance.

 

Straw incorporation involves crushing and returning cotton straw to the field, where organic matter decomposition increases soil organic matter content and carbon sequestration capacity. Long-term straw incorporation improves soil physicochemical properties, enhances microbial activity, and contributes to soil carbon pool accumulation. Its nutrient-supplying function partially substitutes for chemical fertilizers, indirectly reducing N₂O emissions.

 

Dry seeding with wet emergence refers to direct sowing without winter or spring irrigation, followed by a single post-sowing watering to promote seedling emergence. This method significantly reduces irrigation water use, lowers energy consumption and carbon emissions from pumping, and shortens periods of soil saturation, thereby suppressing denitrification-induced N₂O emissions. It provides both water-saving and emission-reduction benefits.

 

Lines 76-86: However, not all CSTs are economically unviable. Some, such as precision agriculture, conservation tillage, and straw management, have already been widely ap-plied in agricultural production and demonstrated tangible economic as well as environmental benefits. Nevertheless, due to limited technical knowledge and information asymmetries, farmers often misperceive certain economically viable CSTs as lacking profitability. Therefore, compared with institutional or service-oriented reforms on the supply side, a more feasible and policy-oriented approach in the short term is to focus on the demand side, address farmers' cognitive bias towards CSTs, and release farmers' demand for CSTs.

 

Lines 130-135: Most farmers perceive the relationship between ecological and economic benefits as a “zero-sum game,” neglecting the long-term environmental and economic gains that climate-smart technologies can deliver, which results in a generally low willingness to adopt such technologies. In fact, some cotton farmers have already achieved cost re-ductions and increased efficiency through the adoption of CSTs, such as soil testing and fertilization, while also reducing GHG emissions and soil pollution in cotton pro-duction.

 

Lines 141-158: To address this issue, the Chinese government has considered Xinjiang’s natural endowments and production realities in recent years to actively promote trials and demonstrations of CSTs—such as precision fertilization, precision irrigation, and dry seeding with wet emergence—that offer both yield-enhancing and carbon-reducing benefits. A series of demonstration and extension programs focused on Xinjiang have been implemented through policy incentives and project support, encouraging research institutes, technical service organizations, and grassroots extension personnel to conduct field-based research and technology dissemination. These efforts aim to enhance farmers’ awareness and adoption willingness through a “visible, learnable, and applicable” demonstration mechanism. Numerous agricultural enterprises have actively participated in promoting CSTs, establishing high-standard demonstration fields and experimental sites across Xinjiang to improve both the accessibility and the perceived economic benefits of these technologies through enterprise-led demonstrations. Some emerging agricultural entities, such as large-scale farmers, cooperatives, and agricultural service organizations, have taken the lead in piloting CSTs. These early adopters have achieved notable emission-reduction outcomes and economic returns, gradually exerting a “pioneering demonstration–technology diffusion” effect. As a result, several CSTs have been widely adopted in cotton production practices.

 

 

Thanks again for you all valuable suggestions!

Reviewer 2 Report

Comments and Suggestions for Authors

Dear authors,

The aim of the manuscript “Evaluating the Effectiveness of Different Demonstration Models on Agricultural Climate-Smart Technology Adoption: Evidence from China’s Cotton Farmers” is proposing a multi model able to calculate the rate of digitalization in agriculture.

This is an interesting idea, and the results could be valuable for researcher, authorities and environmental engineer to calculate and reduce the carbon fingerprint in agriculture.

In this form, the manuscript couldn’t be suitable for publication in the Sustainability Journal, because the approach is very abstract. This manuscript does not explain the connection between factors in charge with carbon emission or which are the most important factors which can increase the implementation of CST in cotton cultivation.

 

Specific comments
Regarding the aspects which need to be clarify:

The concept uses relatively simple mathematical models to determine the empirical cognitive level (equation 1) and the adoption level of CST (equation 2) as a direct effect of technology demonstration intensity and the mediating role of technology cognition. The authors introduce a new equation (equation 3) to determine the interaction mechanism between the demonstration models. This approach has several serious shortcomings to be considered valid:

1) It is not specified what the physical signification of the coefficients of the equations are.

2) The source of these models is not stipulated.

3) It is not specified how the mathematical approach was validated.

4) The CST includes a wide range of instruments. Some are presented (page 7 lines 298-300), but many are missing, such as: energy, utility and human resource management instruments, waste management instruments, including plant waste, etc. In the manuscript, the reference to the CST is abstract without measuring the weight of each component in reducing the carbon footprint.

5) The variables considered are quantified by numerical evaluation. For example, „…demonstration intensity is recorded as 4” (line 308), ”...government-led = 2, enterprise-led = 2, peer driven = 1” (line 315), ”…farmer reports being familiar with (range: 0–5)” (line 319 ) etc. Moreover, there are quantified characteristics such as: gender, ethnicity, age, education, health status, household size etc.

Based on which reasoning were these values established?

Author Response

1. It is not specified what the physical signification of the coefficients of the equations are.

Response: Thank you very much for finding this error. The authors provided detailed interpretations of the coefficients in the econometric models to clarify their practical implications.

Lines 462-466: “A significantly positive  indicates that higher demonstration intensity enhances farmers’ cognition of CSTs. Conversely, a negative or insignificant  may suggest that overly intensive demonstrations could trigger skepticism or resistance among farmers, thereby reducing their cognition levels.”

and Lines 472-480“while  denotes the farmer’s latent (unobservable) true adoption level. The relationship between  and  is specified in Equation (3), where  , , and  are threshold cut-points.. Here,  captures the direct effect of demonstration intensity on CSTs adoption. A significantly positive  indicates that higher demonstration intensity directly promotes farmers’ adoption of CSTs.  measures the effect of technology cognition on CSTs adoption. A significantly positive  suggests that higher levels of CSTs cognition are associated with greater adoption, indicating that cognition plays a positive role in promoting adoption behavior”

 

2. The source of these models is not stipulated.

Response: We appreciate the reviewer’s comments regarding the insufficient explanation of model selection. To address this issue, the authors have provided a detailed rationale for adopting the ordered logit and mediation effect models, along with an explanation of their applicability

Lines 462-466: “The ordered logit model is one of the most widely used classical econometric models in studies of technology adoption behavior. It is particularly suitable for situations where the dependent variable is ordinal with multiple categories and non-equidistant intervals. This model effectively characterizes the behavioral process in which a latent continuous propensity is transformed into discrete ordered outcomes. The model aligns well with the decision-making logic underlying farmers’ gradual transformation from latent willingness to actual adoption behavior and has been extensively validated in agricultural technology adoption research [52-53]. In this study, the dependent variable is an ordered categorical variable, which satisfies the basic assumption of the ordered logit model regarding the ordinal nature of the dependent variable. Furthermore, the proportional odds assumption—a key requirement of the ordered logit model—was tested using the Wald test, a mainstream method for this purpose. The results showed that p-values exceeded 0.05, indicating that the null hypothesis could not be rejected. Thus, the proportional odds assumption holds, confirming the appropriateness of the ordered logit model for this study. Therefore, the ordered logit model was employed for hypothesis testing in this study.

 

The mediation effect model, in contrast, is an econometric method designed to examine how independent variables indirectly influence dependent variables through mediating variables. Its core function lies in revealing the underlying causal mechanisms, and it has been widely applied in economics and behavioral decision-making research. To test the mediating role of technology cognition in the relationship between demonstration and adoption, this study employed a mediation effect model to analyze the underlying mechanisms.”

 

3. It is not specified how the mathematical approach was validated.

Response: Thank you for your advice. Before conducting the empirical regressions, the proportional odds assumption was verified using the Wald test (lines 444–447). The results confirmed that the ordered logit model was appropriate, applicable, and scientifically justified. In addition, to ensure the robustness of the results, three methods were adopted: model substitution, sample adjustment, and variable reclassification. The description of these robustness checks was moved from the Results section to Section 2.4 (lines 508–515) for better methodological clarity.

Lines 444–447: “Furthermore, the proportional odds assumption—a key requirement of the ordered logit model—was tested using the Wald test, a mainstream method for this purpose. The results showed that p-values exceeded 0.05, indicating that the null hypothesis could not be rejected.”

Lines 508-515: “To ensure the robustness of the results, a series of robustness checks were con-ducted using alternative models, sample trimming, and variable transformation. First, the ordered logit model was replaced with an ordered probit model to test consistency under different distributional assumptions. Second, observations with extreme values in household income were removed to reduce potential outlier bias. Third, the five-level adoption variable was reclassified into three categories (Scores 1-2 as “low adoption”, Score 3 as “medium adoption”, Scores 4-5 as “high adoption”), which al-lowed for validation of whether results are sensitive to the scaling of the dependent variable.”

 

4. The CST includes a wide range of instruments. Some are presented (page 7 lines 298-300), but many are missing, such as: energy, utility and human resource management instruments, waste management instruments, including plant waste, etc. In the manuscript, the reference to the CST is abstract without measuring the weight of each component in reducing the carbon footprint.

Response: Thank you for your advice. This study primarily focuses on CSTs with both ecological and economic benefits in the cotton production process, analyzing how demonstrations by different actors affect farmers’ adoption decisions. Although numerous CSTs exist, this research specifically targets those with active demonstration and adoption practices. The five selected CSTs—precision fertilization, precision irrigation, conservation tillage, straw incorporation, and dry seeding with wet emergence—are all technologies combining economic and ecological benefits, and several have been widely applied in Xinjiang. To further clarify the definition of CSTs and explain their role in reducing carbon footprints, Section 2.1 (“Conceptual Definition”) was added, detailing how these CSTs help cotton farmers reduce emissions and improve efficiency in different production stages. This highlights that adopting CSTs not only generates positive environmental effects but also increases farmers’ economic profits.

Lines 221-273: “2.1. Conceptual Definition

Agricultural CSTs refer to technologies that reduce GHG emissions during agricultural production and enhance soil carbon sequestration. Their primary aim is to improve agricultural productivity, mitigate the negative environmental impacts of farming, and promote the harmonious coexistence of agriculture and ecosystems [7]. The prevailing academic consensus holds that CSTs achieve dual goals of emission reduction and carbon sequestration by optimizing agronomic management and resource al-location, reducing the excessive use of high-carbon inputs such as fertilizers and pesticides, improving soil physicochemical properties and water retention, thereby mitigating CH₄ and N₂O emissions from agricultural sources and increasing soil organic carbon storage [41].

Considering the specific conditions of cotton production in Xinjiang, the selection of CSTs must balance economic and ecological benefits. On one hand, farmers tend to prioritize yield and economic returns, which necessitates that adopted CSTs provide strong economic incentives. On the other hand, given the scarcity and uneven spatiotemporal distribution of water resources in Xinjiang’s arid regions, CST promotion must integrate water-saving technologies to improve water-use efficiency per unit of resource. Accordingly, this study focuses on five key CSTs in cotton production: precision fertilization, precision irrigation, conservation tillage, straw incorporation, and dry seeding with wet emergence (Table 1).

CSTs	definition	references
precision fertilization	the optimal ratios and application rates of nutrients such as nitrogen, phosphorus, and potassium based on soil nutrient status and crop requirements	[42-44]
precision irrigation	patch-type drip irrigation	[44-46]
conservation tillage	minimize soil disturbance, maintain surface cover, and preserve soil aggregate structure	[47-49]
straw incorporation	crushing and returning cotton straw to the field	[46,50]
dry seeding with wet emergence	direct sowing without winter or spring irrigation, followed by a single post-sowing watering to promote seedling emergence	[51]

Table 1. Key Studies Confirming the Positive Contributions of CSTs to Addressing Climate Challenges.

 

Precision fertilization specifically refers to soil testing and formula-based fertilization, which determines the optimal ratios and application rates of nutrients such as nitrogen, phosphorus, and potassium based on soil nutrient status and crop requirements. Its primary function is to reduce excessive nitrogen fertilizer use, decrease nitrogen losses and N₂O emissions, and improve both fertilizer efficiency and crop yield. In Xinjiang cotton production, this practice effectively reduces fertilizer waste caused by blind application, achieving both cost-saving and GHG reduction benefits.

Precision irrigation refers to the use of patch-type drip irrigation, an improved version of traditional drip systems. This technique offers better adaptability and clog-ging resistance, delivering water directly to plant roots. It significantly conserves water and reduces soil denitrification caused by excessive irrigation, thereby lowering N₂O emissions. Moreover, integrated water–fertilizer management improves the rhizo-sphere environment, enhances nutrient uptake efficiency, and achieves both resource conservation and yield gains.

Conservation tillage refers to reduced or no-tillage practices that minimize soil disturbance, maintain surface cover, and preserve soil aggregate structure. This approach effectively decreases soil organic carbon mineralization and CO₂ emissions while enhancing long-term soil carbon sequestration. In Xinjiang’s arid cotton-growing regions, conservation tillage also improves soil moisture retention, reduces evaporation losses, and enhances water-use efficiency and drought resistance.

Straw incorporation involves crushing and returning cotton straw to the field, where organic matter decomposition increases soil organic matter content and carbon sequestration capacity. Long-term straw incorporation improves soil physicochemical properties, enhances microbial activity, and contributes to soil carbon pool accumulation. Its nutrient-supplying function partially substitutes for chemical fertilizers, indirectly reducing N₂O emissions.

Dry seeding with wet emergence refers to direct sowing without winter or spring irrigation, followed by a single post-sowing watering to promote seedling emergence. This method significantly reduces irrigation water use, lowers energy consumption and carbon emissions from pumping, and shortens periods of soil saturation, thereby suppressing denitrification-induced N₂O emissions. It provides both water-saving and emission-reduction benefits.”

 

5. The variables considered are quantified by numerical evaluation. For example, „…demonstration intensity is recorded as 4” (line 308), ”...government-led = 2, enterprise-led = 2, peer driven = 1” (line 315), ”…farmer reports being familiar with (range: 0–5)” (line 319 ) etc. Moreover, there are quantified characteristics such as: gender, ethnicity, age, education, health status, household size etc.

Based on which reasoning were these values established?

The authors further elaborated on the selection, definition, and measurement of the dependent, independent, mediating, and control variables in Section 2.3 (“Variable Selection”). Detailed explanations were provided on how the key variables—“technology adoption level,” “demonstration intensity,” and “technology cognition level”—were constructed by summing relevant indicators. Several tables were included to facilitate readers’ understanding.”

Lines 350-359:“Dependent Variable. The dependent variable in this study is the level of technology adoption, which measures the actual adoption level of CSTs by cotton farmers. Following established methods in the literature and focusing on five key CSTs, the number of CSTs adopted by each farmer is used as an indicator of adoption level. A greater variety of adopted technologies indicates a higher level of CST adoption, whereas fewer adopted technologies reflect a lower level of adoption. According to the number of CSTs adopted by cotton farmers, their degree of technology adoption is as-signed a value of 1 to 5 (there is no zero adoption in the sample), where "1" represents the lowest degree of adoption and "5" represents the highest degree of adoption. The increasing values from 1 to 5 reflect the improvement of the degree of technology adoption.”

Lines 360-382: “Independent Variable. The independent variable is technology demonstration in-tensity, which measures the breadth of technology promotion received by farmers. As shown in Table 2, this variable is constructed by summing the number of CSTs demonstrations a farmer has attended. During the survey, farmers were asked five bi-nary questions: “Have you ever attended a demonstration of soil testing and formula fertilization?”, “...of patch-type drip irrigation?”, “...of conservation tillage?”, “...of straw incorporation?”, and “...of dry seeding with wet emergence?”. Each affirmative response was coded as 1, and negative as 0. The total sum yields an ordinal variable representing demonstration intensity. For example, if a farmer had attended four of the five demonstrations, their demonstration intensity was coded as “4”.

 

To explore the heterogeneous effects of different demonstration modes, this variable is further disaggregated into three sub-variables: government-led demonstration intensity, enterprise-led demonstration intensity, and peer-driven demonstration in-tensity. The measurement method mirrors that of overall demonstration intensity. Farmers were additionally asked whether they had attended each technology demonstration under these three modes. For instance, if a farmer had attended two government-led, one enterprise-led, and one jointly enterprise- and peer-driven demonstration (for four technologies in total), their demonstration intensities for the three modes would be 2, 2, and 1, respectively.

Table 2. Technology Demonstration Intensity Measurement Method. The cotton farmers interviewed will be asked five questions about climate-smart technology demonstration in turn. The answer "yes" is assigned a value of "1" and "no" is assigned a value of "0", and the five answers are then added together to form a total.

 

CSTs	is demonstration	government-led	enterprise-led	peer-driven
precision fertilization	1	0	1	0
precision irrigation	1	1	0	0
conservation tillage	1	1	0	0
straw incorporation	1	0	1	1
dry seeding with wet emergence	0	0	0	0
intensity	4	2	2	1

 

Lines 383-403: “Mediating variable. To examine the mechanism through which demonstration in-fluences adoption, farmers’ technology cognition is introduced as a mediating variable. Similar to demonstration intensity, cognition level is measured by summing responses to five yes/no questions (Table 3): “Do you know about precision fertilization?”, “...precision irrigation?”, “...conservation tillage?”, “...straw incorporation?”, and “...dry seeding with wet emergence?”. Each affirmative response was coded as 1, and negative as 0. For example, if a farmer reported knowledge of three technologies, their technology cognition level was coded as “3”.

 

To further analyze the mechanisms of different demonstration modes, the mediating variable was decomposed into three dimensions: economic utility cognition, eco-logical utility cognition, and operational utility cognition. A five-point Likert scale was used to evaluate farmers’ perceptions of each technology in three aspects: perceived economic benefits (1 = very low, 5 = very high), perceived ecological benefits (1 = very low, 5 = very high), and perceived adoption difficulty (reverse-coded: 1 = very difficult, 5 = very easy). Scores across all technologies were summed to generate the three cognition indices. For example, if a farmer knew three technologies and rated them as (5,4,3), (4,4,5), and (3,5,4) across the three dimensions, their economic utility, ecological utility, and operational utility cognition scores would be 12, 13, and 12, respectively.

Table 3. Technology Awareness Level Measurement Method. The cotton farmers interviewed will be asked five questions about climate-smart technology demonstration in turn, and the answers will be summed up according to the respondents.

CSTs	is cognition	economic utility	ecological utility	operational utility
precision fertilization	1	5	4	3
precision irrigation	1	4	4	5
conservation tillage	0	0	0	0
straw incorporation	1	3	5	4
dry seeding with wet emergence	0	0	0	0
level	3	12	13	12

 

Thanks again for you all valuable suggestions!

Reviewer 3 Report

Comments and Suggestions for Authors

The manuscript offers valuable insights into the impact of different agricultural demonstration models on the adoption of climate-smart technologies in cotton farming in China. A further reflection on the relevance and limitations of the findings in different contexts (e.g. other crops or regions) would be beneficial, as it could broaden the applicability of the conclusions.

From a methodological standpoint, it is advisable to clarify how key variables such as risk preference, education level, and technology adoption were defined and measured. A more detailed description of the construction of the counterfactual group and the matching procedures used would enhance transparency and allow for a more accurate assessment of the analytical rigor.

The presentation of tables and figures would benefit from the addition of clear explanatory notes to aid comprehension, particularly for readers less familiar with the statistical techniques employed.

The discussion of results should include an analysis of the feasibility of scaling up the effective demonstration models, with attention to budgetary implications and their sustainability under resource-constrained conditions.

The literature review could be strengthened by incorporating relevant international and comparative studies, thus better situating the research within a global context.

Author Response

1. From a methodological standpoint, it is advisable to clarify how key variables such as risk preference, education level, and technology adoption were defined and measured. A more detailed description of the construction of the counterfactual group and the matching procedures used would enhance transparency and allow for a more accurate assessment of the analytical rigor.

Response: Thank you very much for this suggestion. The authors further elaborated on the selection, definition, and measurement of the dependent, independent, mediating, and control variables in Section 2.3 (“Variable Selection”). Detailed explanations were provided on how the key variables—“technology adoption level,” “demonstration intensity,” and “technology cognition level”—were constructed by summing relevant indicators. Several tables were included to facilitate readers’ understanding.”

Lines 350-359:“Dependent Variable. The dependent variable in this study is the level of technology adoption, which measures the actual adoption level of CSTs by cotton farmers. Following established methods in the literature and focusing on five key CSTs, the number of CSTs adopted by each farmer is used as an indicator of adoption level. A greater variety of adopted technologies indicates a higher level of CST adoption, whereas fewer adopted technologies reflect a lower level of adoption. According to the number of CSTs adopted by cotton farmers, their degree of technology adoption is as-signed a value of 1 to 5 (there is no zero adoption in the sample), where "1" represents the lowest degree of adoption and "5" represents the highest degree of adoption. The increasing values from 1 to 5 reflect the improvement of the degree of technology adoption.”

Lines 360-382: “Independent Variable. The independent variable is technology demonstration in-tensity, which measures the breadth of technology promotion received by farmers. As shown in Table 2, this variable is constructed by summing the number of CSTs demonstrations a farmer has attended. During the survey, farmers were asked five bi-nary questions: “Have you ever attended a demonstration of soil testing and formula fertilization?”, “...of patch-type drip irrigation?”, “...of conservation tillage?”, “...of straw incorporation?”, and “...of dry seeding with wet emergence?”. Each affirmative response was coded as 1, and negative as 0. The total sum yields an ordinal variable representing demonstration intensity. For example, if a farmer had attended four of the five demonstrations, their demonstration intensity was coded as “4”.

 

To explore the heterogeneous effects of different demonstration modes, this variable is further disaggregated into three sub-variables: government-led demonstration intensity, enterprise-led demonstration intensity, and peer-driven demonstration in-tensity. The measurement method mirrors that of overall demonstration intensity. Farmers were additionally asked whether they had attended each technology demonstration under these three modes. For instance, if a farmer had attended two government-led, one enterprise-led, and one jointly enterprise- and peer-driven demonstration (for four technologies in total), their demonstration intensities for the three modes would be 2, 2, and 1, respectively.

Table 2. Technology Demonstration Intensity Measurement Method. The cotton farmers interviewed will be asked five questions about climate-smart technology demonstration in turn. The answer "yes" is assigned a value of "1" and "no" is assigned a value of "0", and the five answers are then added together to form a total.

 

CSTs	is demonstration	government-led	enterprise-led	peer-driven
precision fertilization	1	0	1	0
precision irrigation	1	1	0	0
conservation tillage	1	1	0	0
straw incorporation	1	0	1	1
dry seeding with wet emergence	0	0	0	0
intensity	4	2	2	1

 

Lines 383-403: “Mediating variable. To examine the mechanism through which demonstration in-fluences adoption, farmers’ technology cognition is introduced as a mediating variable. Similar to demonstration intensity, cognition level is measured by summing responses to five yes/no questions (Table 3): “Do you know about precision fertilization?”, “...precision irrigation?”, “...conservation tillage?”, “...straw incorporation?”, and “...dry seeding with wet emergence?”. Each affirmative response was coded as 1, and negative as 0. For example, if a farmer reported knowledge of three technologies, their technology cognition level was coded as “3”.

 

To further analyze the mechanisms of different demonstration modes, the mediating variable was decomposed into three dimensions: economic utility cognition, eco-logical utility cognition, and operational utility cognition. A five-point Likert scale was used to evaluate farmers’ perceptions of each technology in three aspects: perceived economic benefits (1 = very low, 5 = very high), perceived ecological benefits (1 = very low, 5 = very high), and perceived adoption difficulty (reverse-coded: 1 = very difficult, 5 = very easy). Scores across all technologies were summed to generate the three cognition indices. For example, if a farmer knew three technologies and rated them as (5,4,3), (4,4,5), and (3,5,4) across the three dimensions, their economic utility, ecological utility, and operational utility cognition scores would be 12, 13, and 12, respectively.

Table 3. Technology Awareness Level Measurement Method. The cotton farmers interviewed will be asked five questions about climate-smart technology demonstration in turn, and the answers will be summed up according to the respondents.

CSTs	is cognition	economic utility	ecological utility	operational utility
precision fertilization	1	5	4	3
precision irrigation	1	4	4	5
conservation tillage	0	0	0	0
straw incorporation	1	3	5	4
dry seeding with wet emergence	0	0	0	0
level	3	12	13	12

 

2. The presentation of tables and figures would benefit from the addition of clear explanatory notes to aid comprehension, particularly for readers less familiar with the statistical techniques employed.

Response: Thank you for your advice. The authors added annotations to several figures and tables that involve complex concepts or multiple regression methods to facilitate readers’ understanding.

3. The discussion of results should include an analysis of the feasibility of scaling up the effective demonstration models, with attention to budgetary implications and their sustainability under resource-constrained conditions.

Response: Thank you for your advice. In the Discussion section, a standalone “4.3 Feasibility Analysis” was added to assess the feasibility of the proposed policy recommendations derived from the study’s findings (lines 921–968).

Lines 921-968:“4.3. Feasibility Analysis

Building on the technology extension strategies proposed earlier from the per-spective of the technology promotion chain, the demonstration of CSTs must strike a balance between feasibility and sustainability under real-world constraints of limited budgets and restricted resources. Government-led demonstrations, as the upstream component of the promotion chain, play a critical role in shaping farmers’ technology cognition. However, due to their high organizational costs and fiscal dependence, large-scale or full-sector implementation is difficult. Under constraints of water, land, and fiscal resources, a strategy of concentrated pilot projects and targeted break-throughs should be adopted. A set of CSTs with both economic and ecological benefits should be prioritized for government-led demonstrations in core areas with high adoption potential and strong spillover effects. A “small-scale, high-standard” pilot approach can then diffuse to surrounding regions, minimizing redundant investment and improving the marginal efficiency of promotion.

Enterprise-led demonstrations, as the midstream component of the promotion chain, hold considerable potential. Compared with government-led demonstrations, enterprises possess stronger financial mobilization capacity and greater market sensitivity, with lower marginal fiscal costs of promotion. However, the demonstration effect of agricultural enterprises is currently limited by deficiencies in brand credibility and service standardization. To balance budget savings with promotion efficiency, a credit record system for enterprise technology promotion should be established. “Reputable enterprises” could be granted policy subsidies and demonstration incentives, while government–enterprise cooperation should define clear technical standards and information disclosure mechanisms to avoid excessive sales-driven bias that could undermine farmers’ trust. Such measures would reduce direct government spending in mid-term promotion while encouraging enterprises to develop a self-sustaining pro-motion mechanism through market-based incentives.

Peer-driven demonstrations, as the downstream component of the promotion chain, have the advantages of low cost and high psychological acceptance. They are particularly suitable as a supplementary and diffusion mechanism to government and enterprise demonstrations under resource-constrained conditions. However, their effectiveness depends on the quality of the demonstration source and peers’ demonstration capability. Thus, a “model farmers + demonstration households” cultivation mechanism should be established, providing small-scale subsidies and technical training to high-quality peer demonstrators within budgetary limits. This would foster sustained reputation effects within social networks, achieving large-scale diffusion with relatively low investment. Considering farmers’ heterogeneity in resource endowment and adoption willingness, targeted matching and tiered promotion strategies are recommended under budget constraints. For instance, limited fiscal and technical resources should be concentrated on large-scale farmers with high sensitivity to eco-nomic returns and strong adoption potential to maximize demonstration efficiency, whereas smallholders should primarily rely on peer demonstrations and simplified, easy-to-operate technologies to reduce promotion costs.

Overall, the effectiveness of demonstration models depends not merely on the intensity of investment in individual components but on the organic integration of government-led demonstrations for “efficient leadership,” enterprise-led demonstrations for “market-driven promotion,” and peer-driven demonstrations for “low-cost diffusion.” Such integration is essential to achieving sustainable and large-scale CSTs promotion under budget constraints.”

4. The literature review could be strengthened by incorporating relevant international and comparative studies, thus better situating the research within a global context.

Response: Thank you for your advice. The Literature Review section was reorganized to incorporate an international perspective, reviewing global studies on technology adoption and technology demonstration. Several international research findings were added to place the study within a broader global context (lines 159–172).

Lines 921-968: “The adoption of CSTs has emerged as a key topic in global social science research and has attracted increasing attention from the international academic community in recent years. Guided by classical theories such as the TAM, the TRA, and the TPB, numerous studies have demonstrated that farmers’ adoption decisions regarding CSTs are shaped by both traditional and emerging factors. On the one hand, traditional fac-tors—including farm size, education level, policy incentives, and regulatory frame-works—consistently serve as significant drivers across regions [23-25]. On the other hand, emerging factors—such as climatic conditions and disaster shocks, the opportunity costs of farmland abandonment for reforestation, the development and accessibility of green finance, individual environmental awareness, and social pressure—have also been shown to significantly influence adoption behavior [26-29]. In addition, scholars have examined cross-country and regional variations in CST adoption to explore differentiated strategies for promoting CSTs in diverse economic contexts and to identify effective approaches for contributing to global climate governance [12,29,30]. “

Reviewer 4 Report

Comments and Suggestions for Authors

First of all, I would like to congratulate the authors for their vision and hard work.
The article addresses an extremely relevant and current research topic and proposes an innovative approach and presents numerous strong points (which I will not detail).
In order to increase the value of the article, I consider it useful to:
• more explicit clarification of how the various types correlate with the general variable of "intensity";
• nuance, by deepening the discussions regarding the implications of the variations related to Table 8, where the robustness checks are presented, the mention appears that "some coefficient significance levels vary";
• develop the discussions about the limitations of the study.
In conclusion, I consider that the article still needs some small discussions before publication

Author Response

1. More explicit clarification of how the various types correlate with the general variable of "intensity.

Response: Thank you very much for this suggestion. The authors further elaborated on the selection, definition, and measurement of the dependent, independent, mediating, and control variables in Section 2.3 (“Variable Selection”). Detailed explanations were provided on how the key variables—“technology adoption level,” “demonstration intensity,” and “technology cognition level”—were constructed by summing relevant indicators. Several tables were included to facilitate readers’ understanding.”

Lines 350-359:“Dependent Variable. The dependent variable in this study is the level of technology adoption, which measures the actual adoption level of CSTs by cotton farmers. Following established methods in the literature and focusing on five key CSTs, the number of CSTs adopted by each farmer is used as an indicator of adoption level. A greater variety of adopted technologies indicates a higher level of CST adoption, whereas fewer adopted technologies reflect a lower level of adoption. According to the number of CSTs adopted by cotton farmers, their degree of technology adoption is as-signed a value of 1 to 5 (there is no zero adoption in the sample), where "1" represents the lowest degree of adoption and "5" represents the highest degree of adoption. The increasing values from 1 to 5 reflect the improvement of the degree of technology adoption.”

Lines 360-382: “Independent Variable. The independent variable is technology demonstration in-tensity, which measures the breadth of technology promotion received by farmers. As shown in Table 2, this variable is constructed by summing the number of CSTs demonstrations a farmer has attended. During the survey, farmers were asked five bi-nary questions: “Have you ever attended a demonstration of soil testing and formula fertilization?”, “...of patch-type drip irrigation?”, “...of conservation tillage?”, “...of straw incorporation?”, and “...of dry seeding with wet emergence?”. Each affirmative response was coded as 1, and negative as 0. The total sum yields an ordinal variable representing demonstration intensity. For example, if a farmer had attended four of the five demonstrations, their demonstration intensity was coded as “4”.

 

To explore the heterogeneous effects of different demonstration modes, this variable is further disaggregated into three sub-variables: government-led demonstration intensity, enterprise-led demonstration intensity, and peer-driven demonstration in-tensity. The measurement method mirrors that of overall demonstration intensity. Farmers were additionally asked whether they had attended each technology demonstration under these three modes. For instance, if a farmer had attended two government-led, one enterprise-led, and one jointly enterprise- and peer-driven demonstration (for four technologies in total), their demonstration intensities for the three modes would be 2, 2, and 1, respectively.

Table 2. Technology Demonstration Intensity Measurement Method. The cotton farmers interviewed will be asked five questions about climate-smart technology demonstration in turn. The answer "yes" is assigned a value of "1" and "no" is assigned a value of "0", and the five answers are then added together to form a total.

CSTs	is demonstration	government-led	enterprise-led	peer-driven
precision fertilization	1	0	1	0
precision irrigation	1	1	0	0
conservation tillage	1	1	0	0
straw incorporation	1	0	1	1
dry seeding with wet emergence	0	0	0	0
intensity	4	2	2	1

 

Lines 383-403: “Mediating variable. To examine the mechanism through which demonstration in-fluences adoption, farmers’ technology cognition is introduced as a mediating variable. Similar to demonstration intensity, cognition level is measured by summing responses to five yes/no questions (Table 3): “Do you know about precision fertilization?”, “...precision irrigation?”, “...conservation tillage?”, “...straw incorporation?”, and “...dry seeding with wet emergence?”. Each affirmative response was coded as 1, and negative as 0. For example, if a farmer reported knowledge of three technologies, their technology cognition level was coded as “3”.

 

To further analyze the mechanisms of different demonstration modes, the mediating variable was decomposed into three dimensions: economic utility cognition, eco-logical utility cognition, and operational utility cognition. A five-point Likert scale was used to evaluate farmers’ perceptions of each technology in three aspects: perceived economic benefits (1 = very low, 5 = very high), perceived ecological benefits (1 = very low, 5 = very high), and perceived adoption difficulty (reverse-coded: 1 = very difficult, 5 = very easy). Scores across all technologies were summed to generate the three cognition indices. For example, if a farmer knew three technologies and rated them as (5,4,3), (4,4,5), and (3,5,4) across the three dimensions, their economic utility, ecological utility, and operational utility cognition scores would be 12, 13, and 12, respectively.

Table 3. Technology Awareness Level Measurement Method. The cotton farmers interviewed will be asked five questions about climate-smart technology demonstration in turn, and the answers will be summed up according to the respondents.

CSTs	is cognition	economic utility	ecological utility	operational utility
precision fertilization	1	5	4	3
precision irrigation	1	4	4	5
conservation tillage	0	0	0	0
straw incorporation	1	3	5	4
dry seeding with wet emergence	0	0	0	0
level	3	12	13	12

2. Nuance, by deepening the discussions regarding the implications of the variations related to Table 8, where the robustness checks are presented, the mention appears that "some coefficient significance levels vary";

Response: Thank you for your advice. The authors conducted an in-depth analysis of the slight differences in coefficient significance between the robustness checks and the baseline regression for Hypothesis 3 and explained why such minor deviations do not compromise the robustness of the findings (lines 737–751).

Lines 737-751: “Some variables show minor differences in statistical significance compared with the baseline regression, but the overall direction of the coefficients remains consistent. These differences may partly stem from changes in sample structure and variance distribution caused by excluding extreme observations and recategorizing the dependent variable. Compressing the five-level adoption variable into three levels merges some intermediate categories, which may weaken or amplify marginal effects across different groups. Another possible reason is that the influence of control variables and measurement errors may vary across sample structures, with significance levels more likely to weaken in smaller samples or when category boundaries are less distinct. n addition, some differences in significance may reflect genuine heterogeneity among farmers, as high-adoption and low-adoption groups may respond fundamentally differently to various demonstration modes. Therefore, despite fluctuations in significance levels under different robustness settings, the consistency of coefficient directions and the underlying causal logic further confirm the robustness of the core conclusions.”

3. Develop the discussions about the limitations of the study.

Response: Thank you for your advice. The authors further expanded the discussion of this study’s limitations, analyzing them in depth from five perspectives and proposing suggestions and directions for future research. Details are provided in Section 4.4 “Limitations and Future Directions” (lines 969–1021).

Lines 969-1021:

“4.4. Limitations and Future Directions

First, the sample size and representativeness are limited. Due to constraints in research resources and sample accessibility, the data are mainly drawn from major cotton-producing areas in Xinjiang, with a relatively small sample size. In subgroup regressions and heterogeneity analyses, some subsamples are particularly small, potentially resulting in wider confidence intervals and limited statistical power. Moreover, as the sample is region-specific, regional particularities in agricultural policies, market conditions, and social network structures may limit the generalizability of the findings to other regions or crops. Future studies should conduct cross-regional and multi-wave tracking surveys to build panel or longitudinal datasets, which would not only enhance model robustness but also help uncover the dynamic evolution of technology adoption.

 

Second, causal identification strategies require further improvement. This study employs ordered logit and mediation effect models based on cross-sectional data to estimate the effects of demonstration and cognition on adoption behavior; however, the design cannot fully address potential endogeneity issues. Demonstration participation is unlikely to be completely random, and farmers’ willingness to adopt may, in turn, influence their probability of participating in demonstrations, introducing potential selection bias. Furthermore, unobserved individual traits such as risk preference and innovativeness may jointly affect technology cognition and adoption behavior, leading to omitted variable bias. Future research could adopt more rigorous causal identification strategies, such as instrumental variable approaches, propensity score matching, difference-in-differences, or quasi-natural experiments, to enhance the causal interpretability of the findings.

 

Third, the measurement precision of key variables can be improved. Technology cognition, as a core mediating variable, provides important insights into mechanisms but remains subject to non-negligible measurement errors due to its subjective nature. On one hand, farmers’ understanding of CSTs is highly heterogeneous, and their responses on cognition may be influenced by education level, information sources, and social desirability bias. On the other hand, the wording of cognition questions in surveys may also be affected by farmers’ language comprehension and social desirability, causing potential discrepancies between reported and actual cognition levels. Future research could adopt quasi-experimental methods to cross-validate the formation mechanisms of cognition from multiple dimensions, thereby strengthening both theoretical explanatory power and empirical validity.

 

Fourth, the measurement of demonstration modes remains insufficiently refined. Although this study categorizes demonstration modes mainly based on the attributes of demonstration actors, which is logically distinguishable, it does not fully capture the more critical procedural elements of “how demonstrations are implemented.” Factors such as demonstration frequency, interaction depth, and technology maturity can profoundly influence farmers’ willingness and behavioral decisions. Future research should develop a multi-dimensional index system for demonstration intensity to analyze the dynamic coupling among “demonstration content–cognition change–behavioral response,” providing an empirical basis for more targeted and differentiated promotion strategies.

 

Finally, external effects under resource constraints have not been fully explored. Although this study discusses feasible promotion strategies and resource allocation, it does not quantitatively analyze the optimal promotion pathways under different fiscal budgets and resource constraints, nor does it adequately consider potential externalities or diminishing marginal effects of large-scale promotion. Future research could integrate agricultural extension economics and policy simulation frameworks, combining cost–benefit analysis with scenario simulations to explore optimal combinations of promotion models under fiscal constraints and assess their long-term sustainability.”

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

Dear authors,

I consider that after the revision the quality of manuscript was improved. Now, its more easy to read and can be published.

Author Response

Thank you.