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Based on the survey data of 1707 high-quality farmers in Guangdong and Jiangxi provinces in China, this article uses the DEA-tobit model and propensity score matching method to explore the impact of water-saving technology adoption on agricultural production efficiency from a micro perspective. The research results show that the adoption of water-saving technology by high-quality farmers can significantly improve agricultural production efficiency. By comparing the results with the baseline regression using propensity score matching method, the study findings are found to be robust. The mechanism and results of the study indicate that agricultural subsidy policies play a regulatory role between water-saving technology adoption and the agricultural production efficiency of high-quality farmers. Further analysis reveals that the impact of water-saving technology on the production efficiency of different types of high-quality farmers varies. Specifically, water-saving technology has a greater impact on the production efficiency of farmer cooperatives and agricultural enterprises compared to large-scale farming households and family farms. Therefore, policy recommendations are proposed to improve the water-saving agricultural technology system, among other aspects.
The 20th National Congress of the Communist Party of China emphasized the need to improve total factor productivity and promote high-quality economic development. Among them, the high-quality development of agriculture is essential. The key to promoting high-quality development in agriculture lies in comprehensively improving agricultural production efficiency (
China has a vast territory with diverse climates, terrains, soils, and water resource distributions, which have led to the development of various irrigation systems. The main irrigation systems in China include well-canal combined irrigation system, desert oasis agricultural irrigation system, water wheel pump irrigation system, well irrigation area, rainwater collection and storage utilization projects, and water-saving irrigation for forage fields. With the deepening of the concept of sustainable development, the development of agriculture in China is gradually shifting from extensive production and operation to green and high-quality development, taking into account resource conservation and environmental protection (
Scholars at home and abroad have conducted extensive research on the influencing factors of farmers’ adoption of water-saving technologies. These factors include farmers’ individual water-saving awareness (
It should be noted that there is limited literature on the impact of water-saving technologies on agricultural production efficiency, but there is more research on factors affecting agricultural production efficiency. One factor is the impact of natural factors on agricultural production efficiency. Natural factors mainly include climate, topography, and soil fertility (
Although a significant amount of valuable research has been conducted in the academic community on water-saving technologies and agricultural production efficiency, there is a severe lack of research on the impact of water-saving technology adoption on the agricultural production efficiency of high-quality farmers. High-quality farmers play an important role in agricultural development. Among them, high-quality farmers refer to modern agricultural practitioners who specialize in agriculture, possess advanced professional skills, and earn a certain level of income mainly from agriculture. They mainly include three types: management and operation-oriented, professional production-oriented, and skill service-oriented. The characteristics of high-quality farmers are having cultural knowledge, understanding technology, being good at management, and skilled in operations. Therefore, this article aims to clarify the mechanism through which the adoption of water-saving technologies affects the agricultural production efficiency of high-quality farmers. Furthermore, it evaluates the actual impact of water-saving technology adoption on agricultural production efficiency based on addressing the self-selection problem of high-quality farmers adopting water-saving technologies. By conducting in-depth research on agricultural production efficiency, scientific evidence and policy recommendations can be provided for sustainable agricultural development.
The data used in this study is from a 3-year survey conducted by the research team on high-quality farmers in Jiangxi and Guangdong provinces in China from 2019 to 2021. To ensure data quality, the research team distributed survey questionnaires to high-quality farmers participating in training programs using random sampling method based on the actual situation of each training site. A total of 1800 questionnaires were distributed, and 1750 were collected. After removing samples with outliers, a total of 1707 high-quality farmer samples were used for the study, with a questionnaire utilization rate of 97.54%.
Based on the research focus of this paper, the content of the questionnaire survey mainly includes: 1) village level characteristics of high quality farmers, such as the geographical environment where the village is located, the construction of high standard farmland, the distance from the town government and the geographical distance from the market; 2) individual characteristics of high quality farmers, such as age, gender, education level, employment experience and training experience; 3) the production and operation status of high quality farmers, such as the scale of agricultural production and operation, type of agricultural production and operation, hired labour and agricultural insurance, etc.; 4) the use of digital technology, such as whether and to what extent the Internet is used in the sale of agricultural products, and whether the Internet is used to purchase agricultural production materials.
The productivity of high-quality farmers mainly refers to the maximization of output under the rational input of agricultural production factors, which is the input-output ratio formed by the optimal matching of agricultural production factors. Based on the research of
According to the setting of the survey questionnaire, if high-quality farmers adopt water-saving technologies in the process of agricultural production and management, it is assigned a value of 1 (adopting water-saving technologies = 1); if they do not adopt water-saving technologies, it is assigned a value of 0 (not adopting water-saving technologies = 0).
Policy support refers to the relevant policies or measures formulated by the government to stimulate high-quality farmers to engage in agricultural production and management activities. In this study, “whether to receive agricultural subsidy policy after engaging in agricultural production and management” is used as the proxy indicator for policy support, with a value of 1 for receiving policy support and 0 for not receiving.
Variables are selected mainly from three aspects: individual, family, and village. Specifically, they include age, gender, education level, village geographical location, and other variables. Please refer to
Variable definitions, assignments, and descriptive statistics results.
| Variable name | Variable meaning | Average | Standard deviation | |
|---|---|---|---|---|
| Output Indicator | Agricultural Total Output | Family Annual Income (in ten thousand yuan) | 572.38 | 152.578 |
| Input Indicators | Labor Input | Self-employed labor + Hired labor | 16.86 | 6.325 |
| Fund Input | Actual investment in agricultural production (in ten thousand yuan) | 161.34 | 32.169 | |
| Agricultural Operation Area | Actual operating area (in mu) | 278.21 | 45.263 | |
| Dependent Variable | Production Efficiency | Specific calculated value | 0.124 | 0.188 |
| Key Independent Variable | Water-saving Technology | No = 0; Yes = 1 | 0.318 | 0.466 |
| Moderating Variable | Agricultural Subsidies | No = 0; Yes = 1 | 0.529 | 0.499 |
| Control Variables | Gender | Female = 1; Male = 2 | 1.707 | 0.455 |
| Age | Actual age (in years) | 39.6 | 7.775 | |
| Education Level | Primary school and below = 1; Secondary school or high school = 2; College or above = 3 | 2.344 | 0.687 | |
| Village Cadres | No = 0; Yes = 1 | 0.262 | 0.440 | |
| Years Engaged in Agricultural Production | Time (in years) | 9.156 | 7.875 | |
| Received Agricultural Training | No = 0; Yes = 1 | 0.605 | 0.489 | |
| Total Household Population | Number of people (in individuals) | 5.733 | 2.524 | |
| Stable Agricultural Product Sales Channel | No = 0; Yes = 1 | 0.465 | 0.499 | |
| Purchased Agricultural Insurance | No = 0; Yes = 1 | 0.320 | 0.467 | |
| Number of Agricultural Machinery | Continuous variable (in units) | 4.899 | 19.756 | |
| Geographical Environment of the Village | Plain = 1; Hill = 2; Mountainous = 3 | 2.312 | 0.801 | |
| Whether in Urban Suburbs | No = 0; Yes = 1 | 0.489 | 0.500 | |
| Whether Connected to the Internet | No = 0; Yes = 1 | 0.917 | 0.276 |
After obtaining the efficiency values of decision-making units using the DEA method, in order to further analyze and evaluate the factors that affect the efficiency values and their impact levels, if ordinary least squares regression is directly used, the parameter estimation values will be biased towards zero due to the efficiency values (dependent variable) determined by the DEA model being restricted between 0 and 1. To solve this problem, the Tobit model can be used. Therefore, this study primarily employs the Tobit model to investigate the impact of adopting water-saving technologies on the production efficiency of high-quality farmers. The model formula is shown as Eq.
Here,
Additionally, since both the core independent variable (adopting water-saving technologies) and the moderating variable (agricultural subsidy policy) are categorical variables, a moderation effect model is used to empirically test the relationship between policy support and the adoption of water-saving technologies and the production efficiency of high-quality farmers. Based on this, we construct the following regulatory effect model as shown in Eq.
Here, Y represents the variable of agricultural production efficiency for high-quality farmers, X1 represents the variable of adopting water-saving technologies, X2 represents the variable of policy support, X1*X2 represents the interaction term between water-saving technologies and policy support, and X1*X3 represents the interaction term between water-saving technologies and industrial structure adjustment. If the coefficients β2 and β3 are significant, it indicates the presence of a moderation effect.
Using DEAP2.1 software, the production efficiency of 1707 high-quality farmers was measured, as shown in
Statistical analysis results of production efficiency values for high-quality farmers.
| Efficiency value interval | Overall efficiency | Technical efficiency | Scale efficiency | |||
|---|---|---|---|---|---|---|
| Number (units) | Percentage (%) | Number (units) | Percentage (%) | Number (units) | Percentage (%) | |
| 0 ≤ E < 0.4 | 1,496 | 87.64 | 1,171 | 68.60 | 1,099 | 64.34 |
| 0.4 ≤ E < 0.7 | 161 | 9.43 | 355 | 20.80 | 366 | 21.44 |
| 0.7 ≤ E < 0.9 | 35 | 2.05 | 48 | 2.81 | 145 | 8.49 |
| 0.9 ≤ E ≤ 1 | 15 | 0.88 | 133 | 7.79 | 97 | 5.68 |
| Mean | 0.124 | 0.323 | 0.345 | |||
According to the statistical analysis results of the high-quality farmers’ production efficiency values in
Regression results of the impact of water-saving technologies on the productivity of high-quality farmers.
| I | II | |||
|---|---|---|---|---|
| Coefficient | Standard error | Coefficient | Standard error | |
| Tobit model | Tobit model | |||
| Water-saving Technologies | 0.063*** | 0.009 | 0.054*** | 0.009 |
| Gender | --- | --- | 0.003 | 0.009 |
| Age | --- | --- | 0.001 | 0.001 |
| Education Level | --- | --- | 0.017** | 0.007 |
| Village Cadres | --- | --- | 0.004 | 0.010 |
| Years of Agricultural Production | --- | --- | 0.001* | 0.0007 |
| Agricultural Training | --- | --- | 0.037*** | 0.009 |
| Total Family Population | --- | --- | 0.007*** | 0.001 |
| Stable Agricultural Sales Channels | --- | --- | 0.009 | 0.009 |
| Purchase of Agricultural Insurance | --- | --- | 0.021** | 0.009 |
| Number of Agricultural Machinery | --- | --- | 0.0005** | 0.0002 |
| Geographical Environment of the Village | --- | --- | −0.013*** | 0.005 |
| Urban or Suburban Area | --- | --- | 0.019** | 0.016 |
| Internet Access | --- | --- | 0.015 | 0.016 |
| R2 | −0.0480 | −0.1458 | ||
| Sample Size | 1707 | 1707 | ||
Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
Among the control variables, education level, years of agricultural production, and the number of agricultural machinery have varying degrees of impact on the agricultural production efficiency of high-quality farmers, while the remaining control variables did not pass the significance test. Specifically, the education level of high-quality farmers is statistically significant at the 5% level, indicating that educational attainment contributes to improving individual agricultural production efficiency. By receiving education, high-quality farmers acquire more knowledge and skills, enabling them to better apply scientific and technological advancements and modern agricultural management methods to improve agricultural production efficiency. The variable of years of agricultural production is statistically significant at the 10% level, suggesting that with an increase in years of agricultural production, farmers accumulate more knowledge related to agricultural production, which is beneficial for improving agricultural production efficiency. The variable of agricultural training is statistically significant at the 1% level, indicating that agricultural training can enhance individual human capital, helping farmers improve their skill levels and better cope with challenges in agricultural production, thereby achieving sustainable agricultural development. The variable of total household population is statistically significant at the 1% level, suggesting that population size can increase the material and human capital stock of the household, leading to increased agricultural investment and improved agricultural production efficiency. Among the external environmental variables, the geographic location of the village is statistically significant at the 1% level, but with a negative direction, indicating that as the geographic type changes from plain areas to hilly and mountainous areas, the agricultural production efficiency of farmers decreases. In hilly and mountainous areas, agricultural production faces more challenges and difficulties due to changes in topography and soil conditions. In hilly mountainous areas, the variations in terrain and soil conditions can have an impact on agricultural production. Due to the higher elevation, the soil in hilly mountainous areas is usually more infertile, with limited supply of water and nutrients. This can restrict the growth of crops, and farmers need to put in more effort in soil improvement and water resource management to enhance agricultural productivity. Additionally, the variations in terrain in hilly mountainous areas also pose challenges to agricultural production. The changes in topography make the layout and management of farmland more complex, and farmers need to take appropriate measures to address issues related to slope agriculture, such as preventing soil erosion and utilizing water resources efficiently. Therefore, farmers in hilly mountainous areas need to face more challenges and difficulties, and they need to adopt suitable measures to improve agricultural productivity, such as selecting appropriate crop varieties, soil improvement, and rational utilization of water resources.
Therefore, measures need to be taken to improve the production efficiency of high-quality farmers, such as improving soil quality, introducing crop varieties adapted to mountainous environments, and strengthening agricultural technical training.
PSM estimation results of water-saving technology on the production efficiency of high-quality farmers.
| Matching method | Experimental group | Control group | ATT | Standard error | T-value | ||
|---|---|---|---|---|---|---|---|
| Water-saving Technology | Nearest Neighbor Matching | Before Matching | 0.141 | 0.097 | 0.035 | 0.009 | 3.78 |
| After Matching | 0.135 | 0.106 | 0.027 | 0.100 | 2.76 | ||
| Radius Matching | Before Matching | 0.141 | 0.097 | 0.035 | 0.009 | 3.78 | |
| After Matching | 0.133 | 0.104 | 0.029 | 0.010 | 2.91 | ||
| Kernel Matching | Before Matching | 0.141 | 0.097 | 0.035 | 0.009 | 3.78 | |
| After Matching | 0.135 | 0.103 | 0.032 | 0.102 | 3.32 |
This study uses matching methods such as k-nearest neighbor matching, radius matching, and kernel matching to match the treatment group (adopting water-saving technologies) and the control group (not adopting water-saving technologies) based on propensity scores. The results show that the three matching results are similar, and all pass the significance test at the 1% level, with consistent effect direction and significance level. In addition, after matching using the three methods, the average treatment effect on the treated (ATT) is slightly reduced, with a small decrease in magnitude and consistent sign. Overall, adopting water-saving technologies can improve the production efficiency of high-quality farmers. That is, after using the propensity score matching method to address endogeneity issues, the impact of adopting water-saving technologies on the production efficiency of high-quality farmers still has a significant promoting effect. This also indicates that the research results obtained in this study have not changed due to different matching methods, verifying the robustness of the empirical results on the impact of adopting water-saving technologies on the production efficiency of high-quality farmers.
The Ordinary Least Squares (OLS) method is a commonly used statistical method for estimating the linear relationship between variables. It determines the best-fitting line by minimizing the difference between observed values and estimated values. In this study, we use the OLS method to re-estimate the impact of water-saving technology adoption on the agricultural production efficiency of high-quality farmers.
Impact of water-saving technologies on agricultural production efficiency of high-quality farmers (substitution estimation method).
| Variable | (1) | |
|---|---|---|
| Coefficient | Standard Error | |
| OLS Model | ||
| Water-saving Technologies | 0.057*** | 0.216 |
| Control Variables | Controlled | |
| cons | 2.341 | |
| Sample Size | 1705 | |
Note: *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively.
Based on the results in
Test results for regulatory effect mechanism.
| Variable | I | II | III |
|---|---|---|---|
| Productivity | Productivity | Productivity | |
| Tobit | Tobit | Tobit | |
| Coefficient (Standard error) | Coefficient (Standard error) | Coefficient (Standard error) | |
| Water-saving technology | 0.042**(0.018) | 0.021 (0.013) | 0.024 (0.016) |
| Policy support | 0.063**(0.029) | 0.056*(0.031) | --- |
| Water-saving technology * Policy support | --- | 0.072**(0.034) | --- |
| Control variables | Control | Control | Control |
| R2 | −0.172 | −0.163 | −0.168 |
| Sample size | 1707 | 1707 | 1707 |
Note: *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively.
Secondly, policy support also has a significant positive impact on production efficiency. In Model I, the coefficient of policy support is 0.063, with a standard error of 0.029, and a significance level of 1%. In Model II, the coefficient of policy support is 0.056, with a standard error of 0.031, and a significance level of 5%. In Model III, the coefficient of policy support is 0.072, with a standard error of 0.034, and a significance level of 1%. This indicates that policy support is also crucial for improving productivity. Agricultural subsidies can provide financial support to help high-quality farmers purchase the necessary materials for agricultural production, such as seeds, fertilizers, and pesticides, thereby solving the problem of financial shortage and improving farmers’ production capacity (
Thirdly, the interaction between water-saving technology and policy support also has a significant positive impact on production efficiency. In Model II, the coefficient of the interaction between water-saving technology and policy support is 0.072, with a standard error of 0.034, and a significance level of 1%. With the intensification of global climate change and water scarcity, water conservation in agriculture has become a hot topic of global concern. In order to promote sustainable agricultural development, governments have implemented a series of policies to encourage farmers to adopt water-saving technologies. Agricultural subsidies play an important role in promoting the adoption of high-quality water-saving technologies by farmers. The regulatory role of agricultural subsidies can stimulate farmers to adopt high-quality water-saving technologies by providing economic incentives. Firstly, agricultural subsidies can provide economic support and reduce the cost of adopting water-saving technologies for high-quality farmers. The introduction and application of water-saving technologies require certain investments, including the purchase of water-saving equipment and the renovation of irrigation systems, etc., Agricultural subsidies can help farmers alleviate these cost pressures and encourage them to adopt water-saving technologies. Secondly, agricultural subsidies can provide market protection and increase the motivation for high-quality farmers to adopt water-saving technologies. High-quality farmers usually have strong market awareness and marketing capabilities, making it easier for them to apply adopted water-saving technologies to agricultural production and obtain better economic benefits through market sales. Agricultural subsidies can provide market support and sales channels to help high-quality farmers better promote and sell water-saving agricultural products, further motivating them to adopt water-saving technologies. In this way, high-quality farmers can not only save water but also increase income and improve economic efficiency when adopting water-saving technologies.
Overall, water-saving technology, policy support, and the interaction between water-saving technology and policy support all have a positive impact on production efficiency. These results indicate that adopting water-saving technology and receiving policy support can improve production efficiency.
As analyzed earlier, overall, water-saving technologies are beneficial for improving the production efficiency of high-quality farmers. However, is there a difference in the impact of water-saving technologies on different types of high-quality farmers? Based on this, this section explores the impact of water-saving technologies on the production efficiency of different types of high-quality farmers. Based on the definition of high-quality farmers, the types of high-quality farmers are further divided, and the impact of water-saving technologies on the production efficiency of different types of high-quality farmers is explored. The research subjects in columns I to IV are large-scale farmers, family farms, farmer cooperatives, and agricultural enterprises, respectively.
Regression results of the impact of water-saving technologies on the productivity of different types of high-quality farmers.
| I | II | III | IV | |||||
|---|---|---|---|---|---|---|---|---|
| Large-scale farms | Family farms | Farmer cooperatives | Agricultural enterprises | |||||
| Coefficient | Standard Error | Coefficient | Standard Error | Coefficient | Standard Error | Coefficient | Standard Error | |
| Water-saving Technologies | 0.047** | 0.023 | 0.036** | 0.016 | 0.071*** | 0.030 | 0.069*** | 0.022 |
| Control Variables | Control | Control | Control | Control | ||||
| R2 | −0.131 | −0.143 | −0.162 | −0.675 | ||||
| Sample Size | 330 | 482 | 444 | 451 | ||||
Note: *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively.
The impact of water-saving technologies on the production efficiency of different types of high-quality farmers varies. Firstly, the impact of water-saving technologies on the production efficiency of large-scale farmers is significant at the 5% level, with a coefficient of 0.047. Secondly, the impact of water-saving technologies on the production efficiency of family farms is significant at the 5% level, with a coefficient of 0.036. Thirdly, the impact of water-saving technologies on the production efficiency of farmer cooperatives is significant at the 1% level, with a coefficient of 0.071. Finally, the impact of water-saving technologies on the production efficiency of agricultural enterprises is significant at the 1% level, with a coefficient of 0.069. This indicates that the impact of water-saving technologies on the production efficiency of farmer cooperatives and agricultural enterprises is higher than that of large-scale farmers and family farms. This research result demonstrates that the adoption of water-saving technologies can effectively improve the production efficiency of farmers and is of great significance for sustainable agricultural development.
Based on the survey data of 1707 high-quality farmers from Guangdong and Jiangxi provinces, this study used the DEA-tobit model to explore the impact of water-saving technology adoption on agricultural production efficiency from a micro perspective. The following conclusions were drawn. Firstly, the adoption of water-saving technology by high-quality farmers can improve agricultural production efficiency. Even after controlling for other variables, this impact remains significant. Secondly, robustness tests using propensity score matching (PSM) and substitution estimation models show that the promotion of water-saving technology adoption among high-quality farmers is robust in improving agricultural production efficiency. Thirdly, the results of the mechanism study indicate that government policies can further strengthen farmers’ adoption of water-saving technology, thereby enhancing agricultural production efficiency. Finally, the analysis of the impact of water-saving technology on the production efficiency of different types of high-quality farmers reveals differences. According to the significance level, the impact of water-saving technology on the production efficiency of farmer cooperatives and agricultural enterprises is higher than that of large-scale farming households and family farms. Based on these findings, the following implications can be drawn: continuously improve the water-saving agricultural technology system. Firstly, enhance the research and development and promotion of water-saving agricultural technology. Increase investment in the research and development of water-saving agricultural technology, promote collaboration between research institutions, agricultural enterprises, and farmers in the research and experimentation of water-saving agricultural technology. At the same time, strengthen the publicity and promotion of water-saving agricultural technology, improve farmers’ awareness and acceptance of water-saving agricultural technology. Secondly, strengthen the construction of farmland water conservancy facilities. Improve farmland water conservancy facilities, including irrigation systems and water conservancy projects, to improve the efficiency of water resource utilization. Finally, promote efficient water-saving agricultural technologies such as precision irrigation, mulching cultivation, and straw returning. Through scientific and reasonable irrigation management, reduce irrigation water volume and improve irrigation water utilization efficiency. Mulching cultivation can reduce soil water evaporation and improve soil water retention capacity. Strengthen policy support and incentive mechanism construction. Firstly, formulate relevant policies to encourage and support farmers in adopting water-saving agricultural technology. For example, provide financial subsidies for the research, promotion, and application of water-saving equipment and technology, reduce the economic burden on enterprises and individuals, and promote the popularization and application of water-saving technology. Secondly, establish incentive mechanisms to provide rewards and subsidies to farmers who adopt water-saving agricultural technology. At the same time, strengthen the supervision and evaluation of water-saving agricultural technology to ensure the effective implementation and promotion of the technology.
The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.
Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the [patients/ participants OR patients/participants legal guardian/next of kin] was not required to participate in this study in accordance with the national legislation and the institutional requirements.
XL: Conceptualization, Writing–original draft. DY: Data curation, Formal Analysis, Writing–review and editing.
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was funded by the National Social Science Foundation of China (grant no. 20BJY141) and the Henan Province Soft Science Project (grant no. 232400411094).
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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