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Article

Does Rural Labor Transfer Contribute to the Reduction in Chemical Fertilizer Use? Evidence from China’s Household Finance Survey Data in China

by
Xianhong Qin
1,2,* and
Yongjin Guan
1
1
School of Public Administration, Hohai University, Nanjing 211100, China
2
Institute of Population Science, Hohai University, Nanjing 211100, China
*
Author to whom correspondence should be addressed.
Agriculture 2024, 14(10), 1680; https://doi.org/10.3390/agriculture14101680
Submission received: 27 August 2024 / Revised: 22 September 2024 / Accepted: 23 September 2024 / Published: 26 September 2024
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Abstract

:
In recent years, the Chinese government has been continuously promoting reduced use of chemical fertilizers and enhancing the sustainable development capacity of agriculture. This study uses China’s Household Finance Survey (CHFS) data to explore the impact of rural labor transfer on the intensity of fertilizer use and examines the mediating role of agricultural machinery services. The results show that: (1) rural labor transfer is helpful for reducing chemical fertilizer use, and it has a negative impact on the intensity of chemical fertilizer use; (2) rural labor transfer will enhance the socialization of agricultural machinery services by promoting the adoption of mechanized fertilization and expanding the scale of agricultural land management to promote the reduction of chemical fertilizers use; (3) different topographic conditions have different regulatory effects on the use of socialized agricultural machinery services, and for mountainous areas with poor topographic conditions, socialized agricultural machinery services may be difficult to implement, resulting in poor effects of chemical fertilizer use reduction. These findings provide important evidence for sustainable agriculture development and have significant theoretical and policy implications.

1. Introduction

Since the 20th century, China’s agricultural development has made tremendous breakthroughs, achieving a historic leap from insufficient production to stable balance, and then to a slight surplus in occasional years, laying a solid foundation for the rapid development of China’s economy and society [1]. As is well known, fertilizer has played an important role in increasing grain production. However, the long-term irrational use of fertilizers has intensified and seriously damaged the balance of the ecological system [2,3] and threatened the sustainable development of agriculture [4,5]. In 2015, the Ministry of Agriculture issued the Action Plan for Zero Growth in Fertilizer Use by 2020. Since then, the Chinese government’s No. 1 document has focused on continuously promoting fertilizer reduction and efficiency to enhance the sustainable development capacity of agriculture. Encouraging chemical fertilizer reduction in the future to achieve cleaner outputs is an essential topic that requires immediate attention for the green and sustainable development of Chinese agriculture.
An increasing number of academics believe that, in the transformation process of agricultural management, rural labor transfer and agricultural machinery services are important factors that affect the intensity of fertilizer use [6,7]. Firstly, as a result of the selective migration of the rural labor force in the past few decades, rural surplus labor has gradually shown weak characteristics, such as “aging” and “feminization” [8], which directly lead to changes in the proportion of agricultural human and material capital investment. The rigid constraints of agricultural production on the labor force [9], remittance income from non-agricultural activities [10], and the livelihood of agricultural surplus labor [11] will have different impacts on the intensity of fertilizer use. Second, agricultural machinery services can not only break through the physical limitations of farmers and weaken the negative impact of labor scarcity, but also help farmers break through their cognitive constraints by expanding green agricultural inputs, providing technology, and strengthening the mass production effect, thereby reducing the intensity of fertilizer use [12]. Third, owing to China’s vast territory and complex topography, rural labor transfer and agricultural machinery services often have regional differences, which means that topographic conditions play an important role in reducing the use of chemical fertilizers [13].
Previous studies on chemical fertilizer use reduction often overlooked the factors of rural labor transfer and population outflow, which are directly related to agricultural machinery services, so it is necessary to further explore the impact of rural labor transfer and agricultural machinery services on the intensity of chemical fertilizer use under different topographic conditions, as such information has important theoretical and practical value with regard to reducing chemical fertilizer use. The main contributions of this article are as follows: (1) we construct a theoretical analysis framework by exploring the relationship between rural labor transfer and socialized agricultural machinery services and how they affect the intensity of chemical fertilizer use; (2) we use nationwide large-sample data from CHFS and, employing a mediation effect model, empirically analyze the impact path of agricultural labor transfer on chemical fertilizer reduction; (3) we detect the regulatory effects of topographic conditions and further explore the impact of different terrain conditions on this mechanism.

2. Theoretical Framework

The excessive use of chemical fertilizers has a profound impact on the green development of agriculture, and issues related to fertilizer reduction are the focus of academic and public attention. Related research involves multiple fields, such as Environics [14,15], agriculture [16], economics [17], sociology [18], and psychology [19]. This study reviews the literature on the interaction between rural labor transfer, agricultural machinery services, and the intensity of fertilizer use.
Regarding the relationship between rural labor transfer and fertilizer use intensity, the existing literature mainly focuses on the impact of labor scarcity, aging, and other characteristics on the intensity of farmers’ fertilizer use. Shi (2016) [20] believes that, due to rural labor transfer, the cost of agricultural production labor continues to rise, causing farmers to abandon previous manual farmyard fertilizers and use chemicals as substitutes, resulting in an increase in chemical fertilizer use and environmental degradation. However, Zhang (2021) [21] holds a different opinion in that rural migrant workers can not only enhance farmers’ risk resistance and payment ability, but also enhance their technological acceptance and awareness of sustainable agricultural development, thereby reducing fertilizer use. In addition, intergenerational conceptual differences have a significant impact on farmers’ production behaviors. Li (2020) [22] pointed out that, compared to young farmers, older farmers have a lower willingness to use green agriculture technologies. Tang (2023) [23] believes that the gender of the household head could also affect the intensity of fertilizer use, as female-led households use less fertilizer than male-led households.
Regarding the relationship between agricultural machinery services and the intensity of fertilizer use, the existing literature mostly focuses on the fertilizer reduction effect of agricultural machinery services. Yang (2020) [24] pointed out that agricultural machinery services can not only avoid the irregularity of manual fertilization through mechanized fertilization, but also reduce the intensity of fertilizer use by farmers through green agricultural inputs and technical support provided by suppliers. Zhu (2021) [25] used a mediation effect model to analyze the impact of socialized agricultural machinery services on chemical fertilizer reduction and found that socialized agricultural machinery services promote chemical fertilizer reduction by promoting the adoption of mechanized fertilization, expanding the scale of agricultural land management, and increasing farmers’ income. Zhang (2020) [26] proposed that agricultural machinery services can promote fertilizer reduction by enhancing the size of agricultural plots, operating area, and expanding contiguous scale. However, Han (2019) [27] believes that agricultural machinery services may not necessarily achieve the expected results, as productive services characterized by service scaling often encounter problems, such as high integration costs with small and dispersed farmers, incompatibility between national policies and local regulations, and failure to achieve integration between small farmers and modern agriculture.
Regarding the relationship between rural labor transfer and agricultural machinery services, most literature explores the impact of rural labor transfer on agricultural machinery services. Although this indicates a positive effect of rural labor transfer, there are regional differences in this effect. Qian (2016) [28] introduced the theory of a new migration labor economy and believed that rural households view family members working outside as a risk-sharing strategy. Non-agricultural income and remittances from family members working outside can play a similar role to agricultural insurance, boosting their belief in investing in agriculture and promoting their investment in agricultural machinery services. Fang (2020) [29] confirmed this theory using survey data from fixed observation points in rural China, finding that both out-of-town employment and local non-agricultural employment increased the purchase of agricultural machinery services. Luo (2019) [30] pointed out that the fragmentation and unevenness of arable land will negatively regulate the positive impact of non-agricultural employment on agricultural machinery services, indicating that the supply of agricultural machinery services and the demand for outsourced services will be constrained by topographic conditions.
In summary, most of the existing literature is limited to exploring the pairwise relationship between rural labor transfer, agricultural machinery services, and the intensity of fertilizer use by farmers. Few studies have incorporated the above three factors into a single framework, and there is a lack of in-depth research on the mechanism of the fertilizer reduction effect in rural labor transfer and agricultural machinery services. Moreover, owing to China’s vast territory and diverse topographic conditions, both rural labor transfer and agricultural machinery services are constrained by said topographic conditions, which are closely related to the reduction effect of fertilizer use. Therefore, this study uses topographic conditions as a regulatory variable to explore the key factors affecting the intensity of fertilizer use to improve the path of rural labor transfer affecting fertilizer reduction, providing scientific evidence for eliminating the negative effects of fertilizer reduction, and formulating fertilizer reduction policies, as shown in Figure 1.

3. Materials and Methods

3.1. Research Samples

The research data are from the 2019 China Household Finance Survey, which surveyed 29 provincial-level regions in China, with a total of 37,289 samples; this survey has good representativeness and ensures the scientific validity of this research’s conclusions. The household questionnaire included household production and operation projects and fixed content. Subsequently, the samples were screened and cleaned. Because this study focused on the intensity of fertilizer use by farmers, only rural household samples engaged in grain cultivation were retained. After the missing values of the key variables were removed, a total of 5090 samples were obtained.

3.2. Variable Description

The dependent variable was the intensity of fertilizer use and the average fertilizer input per mu was used as a measurement indicator. However, considering the limitations of the data structure itself, reference [31] was used to measure the fertilizer input per unit area; that is, the average fertilizer expenditure per mu was used as a proxy dependent variable, and this variable appeared in logarithmic form during the estimation process.
The core independent variable is the transfer of the agricultural labor force, which is measured by the proportion of migrant workers in rural households comprising the labor force. It is a continuous variable with variable values in (0,1), calculated based on the following question from the household questionnaire: “How many people in your household work or worked in agriculture last year?” and another from the personal questionnaire: “What was the nature of your first/second job?”
The mediating variable is the use of agricultural machinery services, which was mainly measured by the question “How much did your household spend on renting agricultural machinery and agricultural transportation vehicles (including equipment operators) last year?” (units: CNY). Expenditures above zero are classified as evidence of using agricultural machinery services, and expenditure of zero is classed as not using agricultural machinery services.
The regulatory variable was topographic conditions. Referring to the method used by Wang (2021) [32], the household questionnaire item “Is this land suitable for large-scale mechanical cultivation” was used as a measuring tool. If the answer is 1, it means that the farmer’s arable land is suitable for mechanical cultivation; if the answer is 0, it means that the farmer’s arable land is not suitable for mechanical cultivation.
To make our conclusions more robust and credible, this study also referred to the existing literature [33,34,35,36] and used four moderating variables: individual characteristics, household characteristics, land characteristics, and regional characteristics. Individual characteristics mainly include the gender, age, and educational level (years of education) of the household head; household characteristics mainly include whether land ownership is confirmed, the per capita household income, and the amount of agricultural labor force; land characteristics include variables such as farm size, road accessibility, irrigation facilities, and land quality; the regional characteristics are divided into three categories based on the location of the household: the eastern region, the central region, and the western region, and their assigned values (Table 1).

3.3. Model Design

This study draws on the testing steps for moderating effects described by Baeron (1986) [37] and Wen (2006) [38] and designs eight models, as follows:
Fertilizeri = α0 + α2 Xi + δi
Fertilizeri = α0 + α1Offfarmi + α2 Xi + δi
Fertilizeri = α0 + α1 Servicei + α2 Xi + δi
Servicei = β0 + β1 Offfarmi + β3 Xi + υi
Fertilizeri = α0 + α1 Offfarmi + α2Servicei + α3 Xi + δi
Servicei = β0 + β1 Offfarmi + β2 Typei + β3 Xi + υi
Servicei = β0 + β1Offfarmi + β2Typei + β3Offfarm × Typei + β4 Xi + υi
Fertilizeri = α0 + α1 Servicei + α2 typei + α3 Xi + δi
Fertilizeri = α0 + α1 Servicei + α2Typei + α3 Servicei × Typei + α4 Xi + δi
where i is a simple number, Fertilizeri is the intensity of fertilizer use by farmers, Offfarmi is rural labor transfer, Servicei represents agricultural machinery services, Typei represents the topographic conditions, Xi represents all moderating variables, and δi and υi are random factors.
Model (1) explores the main effects of moderating variables on reducing the intensity of farmers’ fertilizer use, while Models (2) and (3) explore the main effects of rural labor transfer and agricultural machinery services on the intensity of fertilizer use.
Models (4) and (5) examine the mediating effects of agricultural machinery services. According to the testing approach of stepwise regression, the existence of the mediating effect needs to meet the following conditions: first, the estimated coefficient of rural labor transfer on fertilizer use intensity in Model (2) is significant; second, the coefficient of the impact of rural labor transfer on agricultural machinery services in Model (4) is significant; third, after agricultural machinery services are incorporated into Model (5), if the coefficient of the impact on intensity is significant, and the estimated coefficient of the impact of rural labor transfer on the intensity of fertilizer use decreases or becomes insignificant, then agricultural machinery services play a partial or complete mediating role.
As for Models (6)–(9), their purpose is to test the existence of regulatory effects, which also need to meet the following conditions: first, the coefficient of the independent variable in the main effects test is significant; second, after incorporating the moderating variables into the model, both the estimated coefficients of the independent and mediator variables on the intensity of fertilizer use are significant; and third, when the independent variable, mediator variable, and interaction term are included in the unified model, if the estimated coefficient of the interaction term is significant, this is considered to be evidence of the existence of the moderating effect.

4. Results and Discussion

4.1. Main Effect Analysis

As shown in Table 2, Model (1) examines only the moderator variables. Among the individual characteristics, only the age of the household head has a significant reducing effect on the intensity of fertilizer use, while the other variables are not significant; among household characteristics, the impact of per capita income on the intensity of fertilizer use is significantly negative, while the impact of labor force is significantly positive; among land characteristics, agricultural land area, irrigation facilities, and land quality all have a significant negative impact on the intensity of fertilizer use by farmers; among regional characteristics, the estimated coefficients show that the intensity of fertilizer use in the eastern and central regions is significantly higher than that in the western region.
Models (2) and (3) tested the main effects of the independent and mediating variables, and the results showed that they both passed the 5% significance test. Their estimated coefficients are all negative, indicating that both rural labor transfers and agricultural machinery services have significant fertilizer reduction effects. This indicates that in China, which urgently needs to promote agricultural fertilizer reduction, promoting rural labor transfer while vigorously developing agricultural machinery services is an important way to solve excessive fertilizer use problems. The income effect brought about by non-agricultural employment of household members alleviates the liquidity constraints that farmers may face in agricultural production, thereby increasing their ability to purchase agricultural machinery services, which, among other factors, promotes the adoption of scientific and green production technologies in farm settings.

4.2. Mediator Effect Test

After exploring the main effects of the independent and mediating variables on the dependent variable, the mediating effects of the agricultural machinery services were tested. However, considering the issue of low efficacy in stepwise regression analysis, the bootstrap method in the coefficient product test was used. Five thousand repeated samples were used to construct a confidence interval with a 95% confidence level. If the interval is not zero, the coefficient product is significant, indicating the presence of a mediating effect [39]. The results are presented in Table 3. It shows that, at a 95% confidence interval, the indirect effect of rural labor transfer on reducing the intensity of fertilizer use by enhancing the purchasing agricultural machinery services is −0.011, with confidence intervals of [−0.017, −0.002], and 0 is not within the confidence interval, indicating the existence of the mediating effect of agricultural machinery services. The direct effect of rural labor transfer on the intensity of fertilizer use is −0.129; the confidence interval is [−0.250, −0.123], which also does not include 0; and indirect effect accounts for 7.71%. After testing for the mediating variable of agricultural machinery services, the independent variable still had a significant impact on the dependent variable, indicating that agricultural machinery services partially mediated the relationship between rural labor transfer and the intensity of fertilizer use.

4.3. Regulatory Effect Test

This study completed a test of the main effect of the independent variable on the dependent variable, and the result was a significant negative effect. Table 4 reports the results of the last two steps in the regulatory effect test. In Models (6) and (8), topographic condition variables were included, and the coefficients of rural labor transfer, agricultural machinery services, and topographic condition variables were significant. Meanwhile, in the regression results of Model (7), the interaction term between topographic conditions and rural labor transfer is significant and positive, indicating that topographic conditions play a significant positive regulatory role in the first half of the adjustment process. In Model (9), although the estimated coefficient of the interaction term between agricultural machinery services and topographic conditions was negative, it did not pass the significance test, indicating that topographic conditions did not play a regulatory role in the second half of the process.
When topographic conditions are flatter and more suitable for mechanized operations, rural labor transfer has a stronger promoting effect on farmers’ investment in agricultural machinery services. For farmers who purchase agricultural machinery services, these services can significantly inhibit the intensity of fertilizer use, but this cannot change because of the quality of topographic conditions. The regulatory effect of topographic conditions mainly affects the first half of the process of rural labor transfer, which encourages farmers to purchase agricultural machinery services and achieve fertilizer reduction. That is to say, the process of “going out for work -> agricultural machinery services,” and has no effect on the stage of “agricultural machinery services -> fertilizer reduction” Thus far, the Chinese government has taken a series of measures, such as promoting rural land transfer, high-quality farmland projects, and the use of agricultural machinery in mountainous areas [40]. This can overcome the “small and scattered” management characteristics of Chinese farmland and the negative impact of unfavorable topographic conditions, improving the supply and application level of socialized agricultural machinery services, creating good external conditions for modern and large-scale agricultural management, and providing strong guarantees for promoting reduced use of agricultural fertilizers.

5. Conclusions

This study empirically analyzed the impact and mechanism of agricultural labor force transfer and agricultural machinery services on the intensity of fertilizer use using data from the 2015 China Household Finance Survey. The results show that (1) rural labor transfer is helpful for reducing chemical fertilizer use, and it has a negative impact on the intensity of chemical fertilizer use; (2) rural labor transfer will enhance the socialization of agricultural machinery services by promoting the adoption of mechanized fertilization, expanding the scale of agricultural land management, and increasing farmers’ income to promote the reduction in chemical fertilizers use; (3) different topographic conditions have different regulatory effects on the use of socialized agricultural machinery services, and for mountainous areas with poor topographic conditions, socialized agricultural machinery services may be difficult, resulting in poor effects of chemical fertilizer use reduction. It should be noted that these findings are based on CHFS data in China, which are related to the geographic specificity and data quality. If applied to different agricultural systems in other countries, there are possible variations in results. Nevertheless, it also has significant theoretical and policy implications for sustainable development, which provides important evidence for China’s precise agriculture.
Under the backdrop of rural labor transfer, precise agriculture is a necessary pathway for high-quality agricultural development, as well as an important measure for achieving sustainable agricultural development, comprehensive rural revitalization, and responding to global climate change. Based on the above conclusion, the following policy suggestions can be implicated: first, as the practical subject of agricultural production and operational decision-making, the importance of human capital of farmers for agricultural development is self-evident. Therefore, it is necessary to train new professional farm workers, further strengthen the application of precise agriculture technologies, and reduce non-point source pollution. Second, we need to expand the supply of socialized agricultural machinery services, introduce cleaner production technologies into agricultural machinery services, increase subsidies for the purchase of green agricultural inputs, and encourage agricultural machinery service companies to promote green production technologies. Third, we should strengthen the invention of small- and medium-sized agricultural machinery suitable for operation in hilly and mountainous areas, promoting agricultural mechanization in the western regions of China. This not only helps to avoid non-standard manual fertilization, but also helps to break the constraints of labor shortage, providing a practical foundation for the use of socialized agricultural machinery services.

Author Contributions

Conceptualization, X.Q.; methodology, X.Q.; software, Y.G.; validation, X.Q.; formal analysis, Y.G.; investigation, X.Q.; resources, X.Q.; data curation, Y.G.; writing—original draft preparation, X.Q.; writing—review and editing, X.Q.; visualization, Y.G.; supervision, X.Q.; project administration, X.Q.; funding acquisition, X.Q. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (No. 4247010799), Humanities and Social Sciences Foundation of the Ministry of Education in China (No. 17YJC630115), and Fundamental Research Funds for the Central Universities in China (No. B230207026).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Data will be made available on request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Agriculture 14 01680 g001
Figure 1. Theoretical framework.
Figure 1. Theoretical framework.
Agriculture 14 01680 g001
Table 1. Variable definition and descriptive statistics.
Table 1. Variable definition and descriptive statistics.
VariablesDefinition and AssignmentMeanVariance
Dependent variableFertilizer use behaviorAverage fertilizer input per land: CNY/mu (logarithmic)5.3611.004
Independent variableRural labor transferRural migrant workers outside the county/number of household labor force0.3440.37
Mediator variablesAgricultural machinery servicesWhether agricultural machinery services are used: Yes = 1; No = 00.4340.496
Regulatory variableTopographic conditionsIs it suitable for mechanical farming? Yes = 1; No = 00.5630.005
Individual characteristicsAgeThe age of the household head in years55.5311.74
GenderMale = 1; Female = 00.8950.306
Education levelIlliterate = 1; Primary school = 2; Junior high school = 3; High school = 4; Vocational school = 5; College = 6; Undergraduate = 72.670.962
Household characteristicsLand rightsHas a certificate of land ownership? Yes = 1; No = 00.4310.009
Household laborsNumber of agriculture labors1.9570.871
Household incomePer capita income of household members: CNY (logarithmic)3.8333.017
Land characteristicsFarm sizeLand owned by household: mu8.5932.94
Road accessibilityWhether the arable land is adjacent to the machine-cultivated road: Yes = 1; No = 01.4390.496
Soil qualityThe level of arable land: Good = 1; Better = 2; Averay = 3; Poor = 4; Very poor = 51.5690.495
Irrigation facilitiesAre there irrigation facilities available? Yes = 1; No = 02.6521.013
Regional characteristicsWestern regionIs it in the western region? Yes = 1; No = 00.3120.463
Central regionIs it in the central region? Yes = 1; No = 00.3350.472
Eastern regionIs it in the eastern region? Yes = 1; No = 00.3530.478
Note: Eastern region (including 10 provinces, such as Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong and Hainan), central region (including 6 provinces, such as Shanxi, Anhui, Jiangxi, Henan, Hubei and Hunan), and western region (including 12 provinces, such as Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Xizang, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang); mu is a widely used unit area in China, with 1 mu = 1/1500 km2.
Table 2. The impact of rural labor transfer and agricultural machinery services on the intensity of fertilizer use.
Table 2. The impact of rural labor transfer and agricultural machinery services on the intensity of fertilizer use.
Independent VariableThe Intensity of Fertilizer Use
Model (1)Model (2)Model (3)
The proportion of rural migrant workers −0.140 ** (−2.39)
Agricultural machinery services −0.104 *** (−3.71)
Age of household head−0.007 *** (−5.47)−0.008 *** (−5.81)−0.007 *** (−5.39)
Gender of household head0.062 (1.34)0.066 (1.37)0.067 (1.44)
Education level of household head−0.018 (−0.79)−0.024 (−1.05)−0.016 (−0.70)
Per capita income of household members−0.043 *** (−11.20)−0.043 *** (−10.82)−0.043 *** (−11.23)
Land owned by household−0.002 (−0.06)−0.002 (−0.06)−0.007 (−0.26)
Household labors0.101 *** (−6.42)0.092 *** (5.55)0.099 *** (6.32)
Arable land area−0.355 *** (−25.35)−0.357 *** (−24.77)−0.356 *** (−25.41)
Irrigation facilities−0.124 *** (−4.30)−0.129 *** (−4.39)−0.134 *** (−4.62)
Road conditions−0.021 (−0.72)−0.013 (−0.45)−0.038 (−1.28)
Land quality−0.034 ** (−2.42)−0.035 ** (−2.46)−0.035 ** (−2.48)
Western region as a reference
Central region0.310 *** (9.23)0.312 *** (9.18)0.325 *** (9.62)
Eastern region0.179 *** (5.04)0.183 *** (5.08)0.188 *** (5.30)
Intercept6.373 *** (51.35)6.465 *** (49.95)6.428 *** (51.48)
Note: “**”, “***” represent significant at the 5% and 1% levels, respectively. The numbers in parentheses are the t-statistics of the variable.
Table 3. Bootstrap analysis results of the intermediary effect of agricultural machinery services.
Table 3. Bootstrap analysis results of the intermediary effect of agricultural machinery services.
TypesCoefficientStandard ErrorZP95% Confidence Interval
Indirect effect−0.0110.004−2.550.011[−0.017, −0.002]
Direct effect−0.1290.032−5.750[−0.250, −0.123]
Proportion of/Indirect effect/%7.71%
Table 4. Regression results of the regulating effect of topographic conditions.
Table 4. Regression results of the regulating effect of topographic conditions.
VariablesModel (6)Model (7)Model (8)Model (9)
Agricultural
Machinery Services		Agricultural
Machinery Services		The Intensity of
Fertilizer Use		The Intensity of
Fertilizer Use
Rural labor transfer0.108 *** (3.34)0.107 *** (4.85)
Topographic conditions0.391 *** (24.50)0.404 *** (28.41)−0.060 *** (−2.49)−0.056 *** (−2.35)
Agricultural machinery services −0.125 *** (−5.68)−0.133 *** (−5.99)
Rural labor transfer × Topographic conditions 0.094 ** (2.20)
Agricultural machinery services × Topographic conditions −0.132 (−0.28)
Individual characteristicsModeratingModeratingModeratingModerating
Household characteristicsModeratingModeratingModeratingModerating
Land characteristicsModeratingModeratingModeratingModerating
Regional characteristicsModeratingModeratingModeratingModerating
Note: “**”, “***” represent significant at the 5% and 1% levels, respectively. The numbers in parentheses are the t-statistics of the variable.
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Qin, X.; Guan, Y. Does Rural Labor Transfer Contribute to the Reduction in Chemical Fertilizer Use? Evidence from China’s Household Finance Survey Data in China. Agriculture 2024, 14, 1680. https://doi.org/10.3390/agriculture14101680

AMA Style

Qin X, Guan Y. Does Rural Labor Transfer Contribute to the Reduction in Chemical Fertilizer Use? Evidence from China’s Household Finance Survey Data in China. Agriculture. 2024; 14(10):1680. https://doi.org/10.3390/agriculture14101680

Chicago/Turabian Style

Qin, Xianhong, and Yongjin Guan. 2024. "Does Rural Labor Transfer Contribute to the Reduction in Chemical Fertilizer Use? Evidence from China’s Household Finance Survey Data in China" Agriculture 14, no. 10: 1680. https://doi.org/10.3390/agriculture14101680

APA Style

Qin, X., & Guan, Y. (2024). Does Rural Labor Transfer Contribute to the Reduction in Chemical Fertilizer Use? Evidence from China’s Household Finance Survey Data in China. Agriculture, 14(10), 1680. https://doi.org/10.3390/agriculture14101680

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