EN中文

Journal of Resources and Ecology >

2024 , Vol. 15 >Issue 5: 1286 - 1298

DOI: https://doi.org/10.5814/j.issn.1674-764x.2024.05.016

Impact of Human Activities on Ecosystem

Influence of Cooperatives’ Socialized Services on Agricultural Households’ Chemical Fertilizer and Pesticide Use Intensity—Based on the Evidence from Two Counties, Hubei, China

  • DUAN Yuefang , 1, 2 ,
  • CHEN Shaopeng , 2, * ,
  • Brooke WILMSEN 3
Expand
  • 1. Research Center for Reservoir Resettlement, China Three Gorges University, Yichang, Hubei 443002, China
  • 2. School of Economics and Management, China Three Gorges University, Yichang, Hubei 443002, China
  • 3. Department of Social Inquiry, La Trobe University, Melbourne 3086, Australia
* CHEN Shaopeng, E-mail:

DUAN Yuefang, E-mail:

Received date: 2023-09-01

  Accepted date: 2024-02-02

  Online published: 2024-10-09

Supported by

The Australian Research Council(DP180100519)

The National Natural Science Foundation of China(72004116)

The Open Foundation Project of the Reservoir Resettlement Research Center of Hubei Province University Humanities and Social Sciences Key Research Base(2022KFJJ01)

Fold

Abstract

Abuse of chemical fertilizer and pesticide will not only impair the quality of agricultural products, but also damage the agricultural ecological environment. From the perspective of cooperatives’ socialized services, this paper studies agricultural households’ chemical fertilizer and pesticide use behavior, attempting to provide references for the government’s formulation of relevant policies and cooperatives’ adjustment of their operation strategies. The survey data of 518 agricultural households in Zigui County and Badong County, Hubei Province, China are used to examine the influence of cooperatives and their socialized services on agricultural households’ chemical fertilizer and pesticide use intensity via propensity score matching. Research reveals that: (1) Joining cooperatives has a significantly negative influence on agricultural households’ chemical fertilizer and pesticide use intensity, and the average treatment effect is -341.505 yuan mu-1. (2) Agricultural materials supply services and technical support services can significantly bring down agricultural households’ chemical fertilizer and pesticide use intensity, and the average treatment effect is -225.966 yuan mu-1 and -163.580 yuan mu-1, respectively. While the influence of agricultural products sale services on chemical fertilizer and pesticide use intensity is not significant. (3) Grouped investigation is carried out by age, education years and planting scale, and the influence of socialized services on agricultural householders’ chemical fertilizer and pesticide use intensity is obviously varied among different groups. The influence of agricultural materials supply services on agricultural households who are elder, with smaller education years and small planting scale is significant; the influence of technical support services on agricultural households who are younger, with higher education years and small planting scale is significant; the influence of agricultural products sale services on agricultural households who are elder is significant. It is necessary to improve the percentage of agricultural households joining cooperatives, increase the supply level of cooperatives’ socialized services, and make socialized services of cooperatives more targeted. All this can contribute to further reduction of agricultural households’ chemical fertilizer and pesticide use intensity.

Key words： cooperatives; socialized services; chemical fertilizer and pesticide; ecological environment; agricultural non-point source pollution

Cite this article

DUAN Yuefang , CHEN Shaopeng , Brooke WILMSEN . Influence of Cooperatives’ Socialized Services on Agricultural Households’ Chemical Fertilizer and Pesticide Use Intensity—Based on the Evidence from Two Counties, Hubei, China[J]. Journal of Resources and Ecology, 2024 , 15(5) : 1286 -1298 . DOI: 10.5814/j.issn.1674-764x.2024.05.016

1 Introduction

Extensive use of chemical fertilizer and pesticide has greatly contributed to China’s food security. But of special note is that abuse of chemical fertilizer and pesticide still remains. According to the “Zero Growth Action Plan for Chemical Fertilizer Use by 2020” released by the Chinese government, the average chemical fertilizer use for crops in China is 21.9 kg mu-1 (1 mu equals 1/15 ha), which is significantly higher than the world average of 8 kg mu-1, and 2.6 times that of the United States and 2.5 times that of the European Union (MARA, 2015). Statistics show that China’s per unit area pesticide use in 2019 was 13.07 kg ha-1, 3.55 times the average level in Asia (3.68 kg ha-1) and 4.68 times the global average (2.69 kg ha-1) (Zhang et al., 2023). Abuse of chemical fertilizer and pesticide can lead to issues such as agricultural non-point source pollution and land degradation, posing challenges to the sustainable utilization of China’s arable land resources and hindering the improvement of food security level. Farmer specialized cooperatives (referred to as “cooperatives”) are collaborative economic organizations predominantly composed of farmers, which can mobilize more than half of agricultural households in China. The main function of cooperatives is to provide varied socialized services for agricultural households, thus facilitating effective connection between diffused household operation and modernized markets, and increasing the agricultural production efficacy and resource utilization efficiency. Whether cooperatives and their socialized services can reduce Chinese agricultural households’ reliance on chemical fertilizer and pesticide calls for systematic research. This paper proceeds from cooperatives’ socialized services to study chemical fertilizer and pesticide use among Chinese agricultural households. It can help accelerate agricultural green development in China and offer China’s experiences for global agricultural sustainability.

2 Literature review

Following the promulgation of the Law of the People’s Republic of China on Specialized Farmers Cooperatives, the legal role of cooperatives has been further clarified. By the end of November 2021, there had been around 2.219 million cooperatives legally registered in China (MARA, 2022). Functions of cooperatives are generally reflected in the following three aspects. First, cooperatives can influence agricultural households’ income. Yuan (2023) empirically analyzed the influence of cooperatives on agricultural households’ income and the heterogeneity of the influence. Zheng et al. (2023) examined the influence of cooperatives on agricultural households’ income from the perspective of livelihood capital heterogeneity. Lu and Zhang (2022) studied the influence of cooperatives on agricultural households’ income in terms of socialized services. Second, cooperatives can influence agricultural households’ capability. Yuan et al. (2023) and Liu et al. (2018) discussed about the influence of cooperatives on agricultural households’ independent development ability. Dong and Mu (2019) empirically analyzed the influence of cooperatives on smallholders’ allocation and management ability of production elements. Lin and Du (2023) measured agricultural households’ livelihood resilience at three dimensions and tested the influence of participation in cooperatives on agricultural households’ livelihood resilience. Third, cooperatives can influence agricultural households’ production behavior. Chen and Ma (2023) and Zhang et al. (2022) utilized the survey data to empirically analyze the influence of cooperatives on agricultural households’ quality control behavior. Li and Lu (2022) studied the treatment effect of cooperatives on agricultural households’ land transfer-in behavior. Xu and Zhang (2022) employed the Probit model to analyze the influence of cooperatives on agricultural households’ new technology adoption.
With the deepening of research, some researchers have started paying attention to the influence of cooperatives on agricultural households’ chemical fertilizer and pesticide use. Feng and Wu (2018) discussed about the influence of cooperatives on agricultural households’ technology adoption of soil testing and formulated fertilization. Zhou et al. (2019) empirically analyzed the influence of cooperative internal management model on agricultural households’ implementation of pesticide use standards. According to the counterfactual analysis framework, Zhang and Liu (2020) calculated the treatment effect of agricultural cooperatives on fruit growers’ biological pesticide technology adoption. Yuan et al. (2018) studied the influence of cooperatives on agricultural households’ chemical fertilizer and pesticide use safety. Cai et al. (2019) and Zhou (2023) estimated the influence of joining cooperatives on agricultural households’ chemical fertilizer and pesticide use reduction. Shen et al. (2022) expounded on the influence of cooperatives that have been obtained agricultural product quality certification on chemical fertilizer use amount and its action mechanism, and also carried out the empirical analysis of panel data of the provincial administrative regions.
The aforesaid research results can lay a solid theoretical foundation for this paper, but the following problems are calling for further discussions. First, the existing literature has only measured the use intensity of chemical fertilizer and pesticide separately, so it has failed to examine chemical fertilizer and pesticide as a whole. Second, the existing research mainly focuses on the influence of cooperative members’ qualification on agricultural households’ chemical fertilizer and pesticide use intensity, so an empirical analysis of the socialized services acquired after joining the cooperatives is lacking. Third, the current research has seldom investigated the influence of agricultural households’ heterogeneity on chemical fertilizer and pesticide use intensity. On that basis, this paper comprehensively examines agricultural households’ chemical fertilizer and pesticide use intensity from the perspective of the total fees of the unit area chemical fertilizer and pesticide input. Propensity score matching can not only measure the influence of cooperative members’ qualification on agricultural households’ chemical fertilizer and pesticide use intensity, but also analyze the influence of cooperatives’ socialized services, such as agricultural materials supply, technical support and agricultural products sale, on agricultural households’ chemical fertilizer and pesticide use intensity. Besides, the influence of cooperatives’ socialized services on agricultural households’ chemical fertilizer and pesticide use intensity under different ages, education years and planting scales is explored, respectively.
The marginal contributions of this paper are reflected as below. First, our discussion of chemical fertilizer and pesticide use is based on the era background of China’s agricultural green development. The research results can provide references for reduction of chemical fertilizer and pesticide use as well as for control of agricultural non-point source pollution. Second, this paper comprehensively examines agricultural households’ chemical fertilizer and pesticide use intensity from a new perspective based on the unit area chemical fertilizer and pesticide input fees, which can enlighten other researchers as well. Third, this paper not only investigates the overall influence of cooperative members’ qualification on agricultural households’ chemical fertilizer and pesticide use intensity, but also studies the functions of different socialized services provided by cooperatives so as to propose more targeted policy suggestions.

3 Theoretical analysis and research hypothesis

3.1 Influence of cooperative members’ qualification on agricultural households’ chemical fertilizer and pesticide use intensity

Driven by the special interest sharing mechanism, cooperatives have the motivation to provide favorable socialized services for agricultural households joining cooperatives. Agricultural households are also more willing to work for common interests of organizations (Liao et al., 2016). With the constant improvement of the socialized service supply level, cooperatives have been critical to guide agricultural households in reasonably using chemical fertilizer, pesticide and other agricultural materials (Mu and Kong, 2019). Besides, cooperatives have a strong information search capacity, which can deliver the latest green agricultural development policies to agricultural households. Thereby, these agricultural households can more easily obtain green agricultural subsidies provided by the government (Zheng et al., 2018). As cooperatives keep expanding, it is foreseeable that cooperatives will play an increasingly important role in promoting the green development of agriculture. Therefore, the following hypothesis is put forward:
H1: Joining cooperatives has a negative influence on agricultural households’ chemical fertilizer and pesticide use intensity.

3.2 Influence of cooperatives’ agricultural materials supply services on agricultural households’ chemical fertilizer and pesticide use intensity

In practice, though agricultural households lack a high cognition of cooperatives, they have a great demand for socialized services provided by cooperatives (Zhang, 2015). Among different kinds of socialized services, agricultural households are crying for agricultural materials supply services. Cooperatives usually help purchase chemical fertilizer, pesticide, seeds and other agricultural materials (Li and Kong, 2010). On the agricultural material market, chemical fertilizer and pesticide are varied in types and specifications. Additionally, the cost is high to acquire information of agricultural materials. Some agricultural households have abused chemical fertilizer and pesticide to avoid risks (Ji et al., 2016). Additionally, individual agricultural households lack the power to negotiate. When they are purchasing chemical fertilizer and pesticide, they are usually in the seller’s market. Seldom can they obtain a competitive price, and worse still, they might buy some inferior products at a high price (Li et al., 2020). Agricultural materials supply services provided by cooperatives can, on the one hand, reduce the information-searching cost and ensure the quality of agricultural materials. On the other hand, by enhancing the price-negotiating ability and reducing the agricultural materials cost, agricultural households can acquire agricultural materials efficiently and at a lower price, which will give them the motivation to reduce the chemical fertilizer and pesticide use intensity. On that basis, the following hypothesis is proposed:
H2: Agricultural materials supply services of cooperatives have a negative influence on agricultural households’ chemical fertilizer and pesticide use intensity.

3.3 Influence of cooperatives’ technical support services on agricultural households’ chemical fertilizer and pesticide use intensity

Technical support services refer to technical guidance and technical training related to agricultural production that are provided by cooperatives (Cai et al., 2019). The influence of technical support services on agricultural households’ chemical fertilizer and pesticide use intensity is mainly reflected in the following two aspects. On the one hand, individual agricultural households, restricted by their cognition, lack the basic ability to judge the match of production elements (Zhu et al., 2022), so they tend to abuse chemical fertilizer and pesticide. Technical support services can effectively modify agricultural households’ cognitive biases, make up their ability defects, and standardize the use frequency and input amount of chemical fertilizer and pesticide to prevent environmental pollution and resource waste resulted from blind input (Cai, 2013; Wan and Cai, 2021). On the other hand, green technologies have a threshold requirement of users’ knowledge of technology and operation experience. If agricultural households are lacking in cultural education, it would be relatively challenging for them to independently grasp green technologies (Zhu et al., 2022). Technical guidance and technical training provided by cooperatives can help agricultural households quickly grasp green technologies. This can improve the quality and efficiency of agricultural production. In this way, reliance of agriculture on chemical fertilizer and pesticide can be alleviated. Based on the above analysis, the following hypothesis is made:
H3: Technical support services of cooperatives have a negative influence on agricultural households’ chemical fertilizer and pesticide use intensity.

3.4 Influence of cooperatives’ agricultural products sale services on agricultural households’ chemical fertilizer and pesticide use intensity

Agricultural products sale is an issue that agricultural households are most concerned about. Therefore, agricultural households have a great demand for agricultural products sale services (Qi, 2016). Agricultural products sale services usually consist of two parts, namely graded acquisition and unified sale (Lu and Zhang, 2022). Agricultural products sale services of cooperatives have exerted an impact on agricultural households’ chemical fertilizer and pesticide use intensity in the following two aspects. First, many cooperatives have established a complete agricultural product quality grading and supervision system. Agricultural households want to upgrade their agricultural products to acquire a higher added value. They must strictly limit the use amount of chemical fertilizer and pesticide in accordance with the requirements of cooperatives (Shi and Fu, 2023). Second, some cooperatives have created their own brands, and made use of brand certification, place of origin tracing and product display, to increase the market recognition and consumer recognition (Zhu et al., 2022). To maintain the brand image and value, cooperatives have a strict set of quality certification standards for agricultural products sold by them. To satisfy the rigid standards, agricultural households must adjust the chemical fertilizer and pesticide use amount. To sum up, the following hypothesis is made:
H4: Agricultural products sale services of cooperatives have a negative influence on agricultural households’ chemical fertilizer and pesticide use intensity.

4 Data sources, research methods and variables selection

4.1 Data sources

Data selected for this research are from the field survey of citrus planters from Zigui County and Badong County, Hubei Province, China. Zigui County belongs to Yichang City, Hubei Province, while Badong County belongs to Enshi Tujia and Miao Autonomous Prefecture, Hubei Province. Both counties are distributed along the river valley of the Yangtze River, whose climate characteristics and soil conditions are highly suitable for the growing of citruses. The research area is located to the west of Hubei Province, and the geographical location is shown in Fig. 1. In this survey, semi-structural interview and questionnaire are combined. We invited citrus planters, specialized cooperatives, processing enterprises, and heads of government departments for semi-structural interviews to learn the overall development of the citrus industry in these two counties and to search for basic data for this questionnaire survey. The questionnaire survey adopted the stratified random sampling to identify sample households. First of all, 4 townships are chosen randomly from each county, and 4 to 5 citrus planting villages are chosen from each township. At last, 15 to 20 citrus planters are randomly selected from every village. The questionnaire covers the household head and family information, livelihood capitals, cooperative participation, citrus planting and agricultural materials use, etc. Specifically, the researcher asks one question and the respondent responds to the question. The researcher is in charge of filling in the print questionnaire. Finally, 584 questionnaires were issued. 518 valid questionnaires were collected, with abnormal value or without responses eliminated, which registered a valid response rate of 88.7%.
Fig. 1 The location of the research area

4.2 Research methods

This paper studies the influence of cooperatives and their socialized services on agricultural households’ chemical fertilizer and pesticide use intensity, and constructs the following econometric model:
y i = α + δ D i + ∑ j = 1 k β j X j i + ε i
where, i denotes the i agricultural household; yi denotes the outcome variable; Di denotes the treatment variable; Xji denotes other explaining variables; δ and βj are parameters for estimation; α is the constant and εi is the disturbing term.
To work out δ, we should focus on thinking about the following two issues: First, whether join cooperatives and whether acquire socialized services do not happen randomly, which are actually self-selection behaviors. The endogenous problems resulted from self-selection can result in deviation of estimation results. Second, the survey can obtain data of the chemical fertilizer and pesticide use intensity of agricultural households that have or have not joined cooperatives and have or have not acquired socialized services, respectively. But the data of agricultural households which did not join (acquire) in the very beginning and joined (acquired) later cannot be observed. “Data missing” can result in the bias of estimation results. So this paper uses propensity score matching (PSM) based on the counterfactual framework to examine the influence of cooperatives and their socialized services on agricultural households’ chemical fertilizer and pesticide use intensity.
First of all, it is necessary to construct a counterfactual analysis framework in which agricultural households join cooperatives or acquire socialized services. It is assumed that the dummy variable is Di={1,0}, which denotes whether agricultural households have joined cooperative and whether they have acquired socialized services. Usually, Di is defined as the treatment variable, while the chemical fertilizer and pesticide use intensity, yi is defined as the outcome variable. This paper focuses on the average treatment effects (ATT). The ATT is actually the net effect of joining cooperatives or acquiring socialized services on agricultural households’ chemical fertilizer and pesticide use intensity. ATT can be specified as below:
A T T = E ( y 1 i | D i = 1 ) − E ( y 0 i | D i = 1 ) = E ( y 1 i − y 0 i | D i = 1 )
where, y1i denotes the chemical fertilizer and pesticide use intensity when agricultural households join cooperatives or acquire socialized services. y0i denotes the chemical fertilizer and pesticide use intensity when agricultural households fail to join cooperatives or acquire socialized services.
Below are the steps to work out ATT using PSM. First, select matching variables. Second, estimate the propensity score. Third, conduct the PSM. Fourth, work out ATT according to samples after matching. Methods for PSM are varied. A method prevailing in academic circles is the simultaneous use of multiple matching methods to prove the validity of samples and the stability of results. So the empirical part of this paper adopts the nearest neighbor matching, caliper matching and kernel matching for the robustness test.

4.3 Variables selection

(1) Outcome variable
Chemical fertilizer and pesticide use intensity is the outcome variable of this paper. Different from some scholars (Li and Ma, 2018; Zhu and Wang, 2023) who used the amount of chemical fertilizer and pesticide per unit planting area to reflect the use intensity and separated chemical fertilizer from pesticide for investigation, this paper adopted total fees of the chemical fertilizer and pesticide input to reflect their use intensity. Below are the reasons: First, chemical fertilizer and pesticide are varied in types and specifications. Agricultural households cannot accurately compute their chemical fertilizer and pesticide input amount, but they are clear about their input fees. Second, during the agricultural production process, abuse of chemical fertilizer and pesticide has been a commonplace. Besides, chemical fertilizer and pesticide can do harm to the ecological environment and the human health. Third, chemical fertilizer and pesticide used in the same area are homogeneous, there is no obvious difference of their price. So it is more reasonable to analyze the agricultural households’ chemical fertilizer and pesticide use intensity as a whole using the unit area chemical fertilizer and pesticide input fees.
(2) Treatment variables
The treatment variables include “whether join cooperatives, whether acquire agricultural materials supply services, whether acquire technical support services, and whether acquire agricultural products sale services.” Among them, “whether join cooperatives” is used to examine the integrated effects of cooperative members’ qualification. The variables, “whether acquire agricultural materials supply services, whether acquire technical support services, and whether acquire agricultural products sale services”, are used to measure the specific role of cooperatives’ socialized services.
(3) Matching variables
Referring to relevant research (Dai et al., 2020; Li et al., 2020; Zhu et al., 2022) and combining the practical conditions, this paper selects ten matching variables, including gender, age, education years, whether take a part-time job, total household population, number of farmers in the household, annual household income, planting scale, planting species and irrigation conditions. These ten variables involve characteristics in three aspects, including characteristics of household heads, family characteristics and operation characteristics, respectively.
Table 1 reports explanation and descriptive statistics of variables. The mean of agricultural households’ chemical fertilizer and pesticide use intensity is 1848.639 yuan mu-1. It means that chemical fertilizer and pesticide are indeed abused. There are 518 households investigated. Among them, 338 have joined cooperatives, registering a participation rate of 65.3%. In terms of socialized services, the access of agricultural households to agricultural materials supply services and technical support services is high, reaching 62.4% and 55.0%, respectively. But only 38.5% of agricultural households have access to agricultural products sale services, so the participation rate is relatively low. As to characteristics of household heads, males are in the majority. Most of them are old, have a lower educational degree and a high degree of by-business. These characteristics are consistent with the status of Chinese farmers. The mean of the planting scale is 5.172 mu, which is consistent with the small scale of land production and operation in China.
Table 1 Explanation and descriptive statistics of variables
Items Variables Explanation of variables Sample size Mean Standard deviation
Outcome variable Chemical fertilizer and pesticide use intensity Chemical fertilizer and pesticide input fees (yuan mu-1) 518 1848.639 584.950
Treatment variables Whether join cooperatives Yes=1; No=0 518 0.653 0.477
Whether acquire agricultural materials supply services Yes=1; No=0 338 0.624 0.485
Whether acquire technical support services Yes=1; No=0 338 0.550 0.498
Whether acquire agricultural products sale services Yes=1; No=0 338 0.385 0.487
Matching variables Gender Male=1; Female=0 518 0.627 0.484
Age Specific number (yr) 518 53.888 9.694
Education years Specific number (yr) 518 9.052 2.385
Whether take a part-time job Yes=1; No=0 518 0.654 0.476
Total household population Practical number (person) 518 4.058 1.392
Number of farmers in the household Practical number (person) 518 1.913 0.569
Annual household income Net household income (10000 yuan) 518 7.566 4.308
Planting scale Planting area (mu) 518 5.172 3.115
Planting species Species quantity (specie) 518 2.301 0.887
Irrigation conditions Very poor=1; Relatively poor=2; General=3; Good=4; Very good=5 518 3.183 1.002

Note: Socialized services are open to agricultural households joining cooperatives. Therefore, three variables, including “whether acquire agricultural materials supply services”, “whether acquire technical support services” and “whether acquire agricultural products sale services”, are corresponding to 338 agricultural households joining cooperatives. 1 mu=666.667 m2.

Table 2 displays the mean difference between outcome variable and matching variables in different groups by t-test. Agricultural households joining cooperatives and not joining cooperatives are significantly different in terms of the chemical fertilizer and pesticide use intensity, education years, whether take a part-time job, number of farmers in the household, planting scale, and irrigation conditions, etc. Among them, the chemical fertilizer and pesticide use intensity of agricultural households joining cooperatives is lower than that not joining cooperatives by around 362.217 yuan mu-1. Insignificant differences are observed among different groups whether acquire agricultural materials supply services, whether acquire technical support services, etc. The main differences are reflected as the chemical fertilizer and pesticide use intensity and irrigation conditions. Differences between groups whether acquire agricultural products sale services are also insignificant. The main differences are reflected in three aspects, including gender, number of farmers in the household, and irrigation conditions.
Table 2 Mean difference of samples
Items Variables Mean difference
Whether join cooperatives Whether acquire
agricultural materials
supply services		 Whether acquire technical support services Whether acquire agricultural products sale
services
Outcome variable Chemical fertilizer and pesticide use intensity -362.217*** -286.839*** -183.892*** -59.405
Matching variables Gender 0.051 0.062 -0.083 -0.111**
Age -0.563 1.601 0.254 0.338
Education years 0.744*** -0.108 -0.404 -0.017
Whether take a part-time job -0.095** 0.037 0.041 -0.035
Total household population 0.038 0.177 0.057 0.060
Number of farmers in the household -0.116** 0.036 -0.016 -0.156***
Annual household income 0.522 -0.607 -0.573 -0.733
Planting scale 1.014*** 0.345 -0.042 -0.308
Planting species -0.083 -0.056 0.124 0.095
Irrigation conditions 0.187** -0.207* -0.218** -0.279**

Note: *, ** and *** mean that the variable is significant on the significance level of 10%, 5% and 1%. The same below.

5 Empirical analysis

5.1 Propensity score estimation

The multicollinearity test of various matching variables is conducted. Among them, the maximum variance inflation factor (VIF) is 2.66, which is far smaller than 10. So serious multicollinearity does not exist, and can be analyzed in the following part. This research adopts the nearest neighbor matching (1 to 3 matching). “Whether join cooperatives”, “whether acquire agricultural materials supply services”, “whether acquire technical support services” and “whether acquire agricultural products sale services” as treatment variables for four matchings. Table 3 reports the propensity score estimation results obtained by logit regression. Among them, variables, including age, education years, planting scale and irrigation conditions, are significantly and positively correlated with “whether join cooperatives”. On the contrary, the other two variables, including whether take a part-time job and number of farmers in the household are significantly and negatively correlated with “whether join cooperatives”. Annual household income and irrigation conditions have a significantly negative influence on “whether acquire agricultural materials supply services”. But planting scale has a significantly positive influence on the latter. Regarding “whether acquire technical support services”, only irrigation conditions have a significantly negative influence on it. Variables like gender, whether take a part-time job, number of farmers in the household and irrigation conditions have a significantly negative influence on “whether acquire agricultural products sale services”. Specific matching effects of different dimensions can be judged by the common support region and the equilibrium test.
Table 3 Propensity score estimation results
Variables Whether join cooperatives Whether acquire agricultural materials supply services Whether acquire technical support services Whether acquire agricultural products sale services
Coefficient Standard error Coefficient Standard error Coefficient Standard error Coefficient Standard error
Gender -0.016 0.207 0.273 0.245 -0.320 0.241 -0.531** 0.248
Age 0.033** 0.016 0.020 0.020 -0.012 0.019 0.032 0.020
Education years 0.131*** 0.043 0.006 0.051 -0.070 0.050 0.028 0.052
Whether take a part-time job -0.762** 0.336 0.054 0.413 0.376 0.403 -0.759* 0.425
Total household population 0.035 0.075 0.127 0.087 0.088 0.082 0.101 0.085
Number of farmers in the household -0.501*** 0.181 0.054 0.237 -0.074 0.230 -0.700*** 0.246
Annual household income -0.030 0.028 -0.070** 0.032 -0.043 0.032 -0.046 0.034
Planting scale 0.147*** 0.042 0.082* 0.045 0.029 0.041 -0.002 0.043
Planting species -0.161 0.114 -0.037 0.133 0.176 0.131 0.170 0.136
Irrigation conditions 0.193** 0.098 -0.194* 0.114 -0.203* 0.113 -0.285** 0.118
Constant term -1.736* 1.009 -0.638 1.274 1.712 1.237 0.103 1.269
LR value 43.510 14.530 13.340 26.060
Pseudo R2 0.065 0.033 0.029 0.058
Sample size 518 338 338 338

5.2 Common support region and equilibrium test

To ensure the matching quality between the treated group and the controlled group, it is necessary to test the common support region. The wider the common support region is, the fewer the samples will be lost, and the better the matching effects will be achieved (Zhu et al., 2022). Figure 2 shows all common support regions that are matched. In the matching process of variables, such as “whether join cooperatives”, “whether acquire agricultural materials supply services”, “whether acquire technical support services”, and “whether acquire agricultural products sale services”, a majority of observed values fall into the same value range. The matching process will result in the loss of a few samples. The matching effects are favorable.
Fig. 2 Common support region chart
The purpose of the equilibrium test is to examine whether the matching variables between the treated group and the control group achieve a favorable equilibrium before and after matching. Generally, the standard deviation is smaller than 10%, which means that the matching effects are favorable (Niu et al., 2022). Test results are shown in Table 4. As one can notice, Pseudo R2, LR value and standard deviation of treatment variables, including “whether join cooperatives”, “whether acquire agricultural materials supply services”, “whether acquire technical support services”, and “whether acquire agricultural products sale services” all decline significantly. The standard deviation after matching is all smaller than 10%, meaning that the treatment variables have passed the equilibrium test.
Table 4 Equilibrium test results
Statistics Whether join cooperatives Whether acquire agricultural materials supply services Whether acquire technical support services Whether acquire agricultural products sale services
Before matching After matching Before matching After matching Before matching After matching Before matching After matching
Pseudo R2 0.066 0.011 0.033 0.007 0.029 0.024 0.057 0.028
LR value 43.830 9.290 14.590 3.890 13.340 12.120 25.770 9.930
Standard deviation (%) 16.5 6.8 11.2 5.3 10.3 7.9 13.4 8.9

5.3 Analysis of average treatment effects

Table 5 describes the average treatment effects of the influence of cooperatives and their socialized services on agricultural households’ chemical fertilizer and pesticide use intensity. Results show that joining cooperatives has a significantly negative influence on agricultural households’ chemical fertilizer and pesticide use intensity. The average treatment effect is -341.505 yuan mu-1, and is significant at the significance level of 1%. This means that H1 is verified. A possible explanation is that, after agricultural households joining the cooperatives, they are required to standardize their chemical fertilizer and pesticide use behavior, being driven by the demonstration and unified production requirements of the cooperatives. Thereby, they will reduce their chemical fertilizer and pesticide use amount.
Table 5 Average treatment effects
Treatment variables Mean of treated group
(yuan mu-1)		 Mean of controlled group
(yuan mu-1)		 ATT
(yuan mu-1)		 Standard error
(yuan mu-1)		 t-value
Whether join cooperatives 1746.318 2087.823 -341.505*** 64.747 -5.270
Whether acquire agricultural materials supply services 1617.053 1843.019 -225.966*** 85.344 -2.650
Whether acquire technical support services 1643.152 1806.732 -163.580** 74.708 -2.190
Whether acquire agricultural products sale services 1665.315 1745.329 -80.014 82.672 -0.970
In terms of socialized services, the average treatment effect of the influence of agricultural materials supply services on the agricultural households’ chemical fertilizer and pesticide use intensity is -225.966 yuan mu-1, and is significant on the significance level of 1%. Therefore, H2 is verified. A possible explanation lies in that agricultural materials supply services can help ensure the quality of agricultural materials, and reduce cost of agricultural materials. To avoid risks, agricultural households abuse chemical fertilizer and pesticide less and less. The average treatment effect of the influence of technical support services on agricultural households’ chemical fertilizer and pesticide use intensity is -163.580 yuan mu-1 and is significant on the significance level of 5%. Therefore, H3 is established. This is probably because technical support services can improve agricultural households’ cognition of scientific chemical fertilizer and pesticide use. With a better cognition of chemical fertilizer and pesticide, agricultural households can get rid of the misconception of “more chemical fertilizer and pesticide, higher output”. On the other hand, technical support services can help agricultural households better adopt green technology, thus reducing the reliance of agricultural production on traditional chemical fertilizer and pesticide. Matching results suggest that the influence of agricultural products sale services on agricultural households’ chemical fertilizer and pesticide use intensity is insignificant. Therefore, H4 is not verified. As to the reasons behind, only 38.5% of agricultural households joining cooperatives have access to agricultural products sale services. A low participation causes a limited influence of agricultural products sale services on agricultural households’ planting behavior. Second, the graded acquisition system of cooperatives is incomplete. Some agricultural households tend to glorify agricultural products beyond quality standards, which has seriously dampened the enthusiasm of other agricultural households. Third, the brand effect of cooperatives is insignificant, which can result in a poor sales performance. Consequently, agricultural households lack confidence in the brand premium.

5.4 Robustness test

In the previous contest, the nearest neighbor matching calculates the average treatment effects of cooperatives and their socialized services. To test the robustness of the aforesaid results, caliper matching and kernel matching (bandwidth 0.06) are used to calculate the average treatment effects. Results suggest (Table 6) that, in terms of cooperative members’ qualification, the average treatment effects of caliper matching and kernel matching are all significant at the significance level of 1%. The numerical value and the nearest neighbor matching have approximate results. As to agricultural materials supply services, the results of two matching methods are both significant at the significance level of 1%. The numerical value and the nearest neighbor matching are approximate to each other. Regarding technical support services, the results of two matching methods are both significant at the significance level of 5%. The numerical value and the nearest neighbor matching results are close to each other. Concerning agricultural products sale services, the results of two matching methods are insignificant, which are similar to the nearest neighbor matching. To sum up, results of caliper matching and kernel matching are generally consistent with the nearest neighbor matching in terms of significance and numerical value. This proves that results of the nearest neighbor matching are stable and reliable.
Table 6 Robustness test results
Treatment variables Caliper matching Kernel matching
ATT
(yuan mu-1)		 Standard error
(yuan mu-1)		 t-value ATT
(yuan mu-1)		 Standard error
(yuan mu-1)		 t-value
Whether join cooperatives -343.378*** 56.431 -6.080 -349.659*** 56.120 -6.230
Whether acquire agricultural materials supply services -236.345*** 71.026 -3.330 -237.663*** 70.548 -3.370
Whether acquire technical support services -158.778** 65.096 -2.440 -161.756** 64.084 -2.520
Whether acquire agricultural products sale services -82.086 70.848 -1.160 -82.110 70.458 -1.170

5.5 Analysis of group difference

Referring to relevant literature (Niu et al., 2022; Zeng et al., 2023), this research groups the research samples by age, education years and planting scale, respectively, to further examine the influence of socialized services on the chemical fertilizer and pesticide use intensity of agricultural households with different characteristics. The nearest neighbor matching is adopted as the research method. The results are shown in Table 7.
Table 7 Average treatment effects of different groups
Grouped variables Whether acquire agricultural
materials supply services		 Whether acquire technical
support services		 Whether acquire agricultural
products sale services
ATT
(yuan mu-1)		 Standard error
(yuan mu-1)		 t-value ATT
(yuan mu-1)		 Standard error
(yuan mu-1)		 t-value ATT
(yuan mu-1)		 Standard error
(yuan mu-1)		 t-value
Age Larger than mean -267.687** 123.553 -2.170 -47.835 110.551 -0.430 -225.997** 109.559 -2.060
Smaller than mean -160.982 118.970 -1.350 -200.178* 104.662 -1.910 27.989 111.859 0.250
Education years Larger than mean -208.670 145.187 -1.440 -357.781** 175.898 -2.030 79.531 133.977 0.590
Smaller than mean -250.464** 111.093 -2.250 -133.776 97.166 -1.380 -47.231 104.347 -0.450
Planting scale Larger than mean 56.179 100.580 0.560 -43.264 112.149 -0.390 -25.712 145.945 -0.180
Smaller than mean -242.102** 115.226 -2.100 -227.248** 101.692 -2.230 -74.610 116.367 -0.640
In terms of age, the average age of agricultural households joining cooperatives is 53.692 years. The influence of agricultural materials supply services on agricultural households whose age is higher than the average is significant. The average treatment effect is -267.687 yuan mu-1, registering a significance level of 5%. A possible reasons is that agricultural households who are elder are disadvantaged in acquiring information related to agricultural materials, and that they are more reliant on agricultural materials supply services. Significant influence of technical support services on agricultural households who are younger than the average age is observed. The average treatment effect is -200.178 yuan mu-1, which is significant at the significance level of 10%. A possible reason is that agricultural households who are younger are more ready to accept new technology. The influence of agricultural product sale services on agricultural households who are elder is significant. The average treatment effect is -225.997 yuan mu-1, and the significance level is 5%. A possible reason is that agricultural households who are older than the average age are not good at marketing and are more inclined to sell agricultural products through cooperatives.
Regarding education years, average education years of agricultural households joining cooperatives is 9.311 years. The influence of agricultural materials supply services on agricultural households whose education years is smaller than the mean is significant, with the average treatment effect being -250.464 yuan mu-1, and the significance level reaches 5%. This is probably because agricultural households with smaller education years have a weak cognition of agricultural materials, who are more reliant on cooperatives to help them purchase agricultural materials. The influence of technical support services on agricultural households whose education years is higher than the average is significant, and the average treatment effect can reach -357.781 yuan mu-1, which is significant at the significance level of 5%. A possible explanation is that agricultural households with higher education years are better at learning, so they are more open to green technologies. But no significant influence of agricultural products sale services is observed on these two groups of agricultural households. Probably, the percentage of two groups of agricultural households selling agricultural products through cooperatives is not high, so agricultural products sale services cannot play their due role.
In terms of planting scale, the average planting scale of agricultural households joining cooperatives is 5.524 mu. The influence of agricultural materials supply services on agricultural households whose planting scale is smaller than the mean is significant, whose average treatment effect is -242.102 yuan mu-1, and the significance level is 5%. A possible reason is that agricultural households with small planting scale have a small quantity of agricultural materials, and that their bargaining power is relatively weak while purchasing agricultural materials, thereby they are more reliant on agricultural materials provided by cooperatives. The influence of technical support services on agricultural households whose planting scale is smaller than the mean is significant, and the average treatment effect can reach -227.248 yuan mu-1, which is significant on the significance level of 5%. A possible explanation is that agricultural households with small planting scale have fewer species, so they need to acquire simple knowledge of planting technologies and their knowledge acquisition cost is lower. They are more willing to accept technical support services. The influence of agricultural products sale services on two groups of agricultural households is insignificant. This is probably because that agricultural households have access to more optional sales channels, and that agricultural products sale services of cooperatives are less appealing.

6 Discussion

Cooperatives have varied functions, but they regard providing socialized services for its members as its top priority. Over a period of time, small-holding farmers took up the majority of agricultural operators in China. Therefore, to develop cooperatives’ socialized services on the basis of scattered small-holding farmer operation is an effective way. Set against the backdrop of vigorously promoting green development of agriculture, this research focuses on the influence of cooperatives and their socialized services on agricultural households’ chemical fertilizer and pesticide use intensity. Research reveals that joining cooperatives significantly and negatively influences agricultural households’ chemical fertilizer and pesticide use intensity, which is consistent with the research results of Cai et al. (2019) and Zhou (2023). Cooperatives are organizations with farmers as the main participants. The role of cooperatives in reducing agricultural households’ chemical fertilizer and pesticide use should be affirmed. According to the existing research, we examine the influence of different socialized services of cooperatives on agricultural households’ chemical fertilizer and pesticide use intensity. Results shed light on that agricultural materials supply services and technical support services can both significantly bring down agricultural households’ chemical fertilizer and agricultural use intensity. Nevertheless, the influence of agricultural products sale services is insignificant. During the survey process, we have observed that many cooperative members show a low degree of participation in socialized services provided by cooperatives. Consequently, the influence of cooperatives on these cooperative members is limited. Hence, if we only consider the influence of cooperative members’ qualification on agricultural households’ chemical fertilizer and pesticide use intensity, the role of cooperatives might be exaggerated. Agricultural product sale services involve members’ core interests, but why its influence on chemical fertilizer and pesticide use intensity is insignificant requires further investigation.
This paper also has some limitations. First, the survey area is limited to Hubei Province, so the samples are not representative enough. The follow-up research can consider expanding the survey area. Second, the data are limited to the same period of time, thus making it impossible to analyze the dynamic influence of cooperatives’ socialized services on agricultural households’ chemical fertilizer and pesticide use intensity. In the future, follow-up survey can be carried out to make up this research limitation.

7 Conclusions

In this paper, survey data of 518 agricultural households in Zigui County and Badong County, Hubei Province, China are used to empirically analyze the influence of cooperatives and their socialized services on agricultural households’ chemical fertilizer and pesticide use intensity via PSM. This research reaches the following conclusions:
(1) In terms of cooperatives, joining cooperatives has a significantly negative influence on agricultural households’ chemical fertilizer and pesticide use intensity, with the average treatment effect being -341.505 yuan mu-1. This means that cooperatives have played a positive role in reducing chemical fertilizer and pesticide use.
(2) As to socialized services, agricultural materials supply services and technical support services have a significantly negative influence on agricultural households’ chemical fertilizer and pesticide use intensity, with the average treatment effect being -225.966 yuan mu-1 and -163.580 yuan mu-1, respectively. However, the influence of agricultural products sale services on chemical fertilizer and pesticide use intensity is insignificant. This means that the influence of different socialized services on agricultural households’ chemical fertilizer and pesticide use intensity is varied.
(3) From the perspective of agricultural households’ heterogeneity, the influence of agricultural materials supply services on agricultural households who are elder, with smaller education years and small planting scale is significant; the influence of technical support services on agricultural households who are younger, with higher education years and small planting scale is significant; the influence of agricultural products sale services on agricultural households who are elder is significant. This means that socialized services have varied influence on chemical fertilizer and pesticide use intensity of agricultural households with different characteristics.
Based on the aforesaid conclusions, this paper comes up with the following inspirations:
(1) Improve the participation percentage of agricultural households in cooperatives and take more agricultural households to alleviate chemical fertilizer and pesticide use amount. The government should enhance daily supervision of cooperatives by eliminating “hollow cooperatives” and “alienated cooperatives” immediately, strengthening the development vigor and motivation of cooperatives, and encouraging more agricultural households to join cooperatives. Additionally, cooperatives themselves should speed up their system construction, improve qualities of management personnel and lower the threshold to attract more agricultural households to join cooperatives.
(2) Upgrade the supply level of cooperatives’ socialized services, and prompt more agricultural households standardize their chemical fertilizer and pesticide use behavior. Governments at all levels should combine practical conditions to encourage and support cooperatives to provide more socialized services through items, capital, policies, etc. Cooperatives should keep on optimizing supply of services, such as agricultural materials supply, technical support and agricultural product sale, in accordance with agricultural households’ practical needs. Their focus should be on agricultural products sale services. Cooperatives should vigorously advocate their agricultural products sale services and guide agricultural households to participate in agricultural product standardization and brand construction. Only in this way can agricultural products sold by cooperatives can obtain higher market gains. Meanwhile, agricultural households will also be more active in acquiring agricultural products sale services.
(3) Make socialized services of cooperatives more targeted and lead agricultural households further reduce the chemical fertilizer and pesticide use amount. Agricultural households who are elder should have priority access to agricultural materials supply services and agricultural products sale services, while younger agricultural households should receive prioritized technical support services. Priority technical support services should be granted to agricultural households with higher education years, while those with smaller education years should have priority access to agricultural materials supply services. Meanwhile, priority in agricultural materials supply services and technical support services should be provided to agricultural households with small planting scale. Cooperatives should constantly adjust focuses on different groups based on practical situations, ensuring that service content can better satisfy agricultural households’ diverse demands. Thereby, socialized services can play a bigger role.

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
Cai R. 2013. Contractual arrangement of agricultural cooperatives: Allocation of production decision rights—Based on the perspective of farmers. Chinese Rural Economy, (4): 60-70, 78. (in Chinese)

[2]
Cai R, Wang Z Y, Qian L, et al. 2019. Do cooperatives promote family farms to choose environmental-friendly production practices?—An empirical analysis of fertilizer and pesticide reduction. China Rural Survey, (1): 51-65. (in Chinese)

[3]
Chen W Q, Ma P C. 2023. Cooperative support and quality control behavior of smallholder farmers: Theoretical perspective and test. Journal of Huazhong Agricultural University (Social Sciences Edition), (1): 82-92. (in Chinese)

[4]
Dai L, Yu L H, Lan Q G. 2020. An empirical test on the income effect of farmers’ participation in land joint-stock cooperatives—Based on the survey data of Chaoyang City, Liaoning Province. Journal of Arid Land Resources and Environment, 34(6): 42-47. (in Chinese)

[5]
Dong Y, Mu Y Y. 2019. Research on the role of cooperatives in production factor allocation and management ability of small farmers—Empirical analysis based on PSM-SFA model. Journal of Agrotechnical Economics, (10): 64-73. (in Chinese)

[6]
Feng Y, Wu J F. 2018. Cooperative organization, planting scale and farmer’s soil testing formula adoption behavior: A survey of rice growers based on Taihu Lake and Chaohu Lake Basin. Journal of Nanjing Tech University (Social Science Edition), 17(6): 28-37. (in Chinese)

[7]
Ji Y Q, Zhang H, Lu W Y, et al. 2016. Differentiation, incomplete information and farmers’ overuse of chemical fertilizer. Journal of Agrotechnical Economics, (2): 14-22. (in Chinese)

[8]
Li J H, Lu Q. 2022. Influence of joining cooperatives on farmers’ land transfer-in behavior. Resources Science, 44(6): 1181-1195. (in Chinese)

[9]
Li K Q, Ma D D. 2018. On the investment behavior and decision mechanism of farmers’ fertilizer reduction in ecologically vulnerable regions—Based on the survey data of 421 farmers in four counties of Shanxi Province. Journal of Nanjing Agricultural University (Social Sciences Edition), 18(5): 138-145, 159-160. (in Chinese)

[10]
Li S J, Kong X Z. 2010. Research on the priority of farmers’ technology demand and the main body of effective supply. Xinjiang State Farms Economy, (5): 11-16. (in Chinese)

[11]
Li X J, Chen Z, Liu F, et al. 2020. Does participating in ecommerce promote the adoption of green production technologies by kiwifruit growers?—A counterfactual estimation based on propensity score matching method. Chinese Rural Economy, (3): 118-135. (in Chinese)

[12]
Liao X J, Ying R Y, Deng H S, et al. 2016. The income effect and benefit distribution: Analysis on the effect of farmers’ cooperation. China Soft Science, (5): 30-42. (in Chinese)

[13]
Lin Z Y, Du C. 2023. Can cooperative participation improve farmers’ livelihood resilience?—Based on samples from Tibetan-inhabited areas in Sichuan. Rural Economy, (8): 122-133. (in Chinese)

[14]
Liu Y Y, Wang X J, Fu X H. 2018. Impact of agricultural cooperatives on the self-development ability of small household—The empirical evidence from Qinlin-Daba mountain region in Sichuan. Journal of Sichuan Agricultural University, 36(3): 413-420. (in Chinese)

[15]
Lu Q Z, Zhang Y F. 2022. New means of increasing farmers’ income evaluation on the influential effect of cooperative’s diverse socialized services—A “counterfactual” estimate based on the survey data of farmers in Shandong Province. Journal of Northwest A&F University (Social Science Edition), 22(1): 129-140. (in Chinese)

[16]
MARA (Ministry of Agriculture and Rural Affairs, PRC). 2015. Zero growth action plan for chemical fertilizer and pesticide use by 2020. http://www.moa.gov.cn/ztzl/mywrfz/gzgh/201509/t20150914_4827907.htm. Viewed on 2023-11-10 in Chinese)

[17]
MARA (Ministry of Agriculture and Rural Affairs, PRC). 2022. Research report on the development index (2020) of the national farmers’ cooperatives was released in Beijing. http://www.moa.gov.cn/xw/zwdt/202201/t20220122_6387449.htm. Viewed on 2023-11-10 in Chinese)

[18]
Mu N N, Kong X Z. 2019. Evolution logic of agricultural socialized services of cooperatives: A case study on Renfa cooperative. Finance and Trade Research, 30(8): 64-75. (in Chinese)

[19]
Niu W H, Shen S H, Cai M Y, et al. 2022. Can the agricultural industrial organization influence the farmers’ safe production behavior?—Evidence from 500 kiwifruit rural households in Mei County, Shaanxi Province. Journal of Agrotechnical Economics, (1): 114-128. (in Chinese)

[20]
Qi Q Y. 2016. Study on farmer professional cooperatives service function demand from farmers and its influencing factors. Diss., Wuhan, China: Huazhong Agricultural University. (in Chinese)

[21]
Shen X, Ao R J, Gong S S, et al. 2022. Theoretical mechanism and empirical analysis on the influence of agricultural chemical fertilizer input by cooperatives with agricultural product quality certification. Journal of Natural Resources, 37(12): 3267-3281. (in Chinese)

DOI

[22]
Shi Z H, Fu Y. 2023. Influence of socialized service on farmers pesticide reduction behavior—Investigation based on the dimension of service specialization. Chinese Journal of Agricultural Resources and Regional Planning, 44(3): 130-142. (in Chinese)

[23]
Wan L X, Cai H L. 2021. Study on the impact of cooperative’s participation on farmers’ adoption of testing soil for formulated fertilization technology—Analysis based on the perspective of standardized production. Journal of Agrotechnical Economics, (3): 63-77. (in Chinese)

[24]
Xu Q H, Zhang G S. 2022. Effects of joining cooperative on farmers’ adopting new agricultural technology: Based on the data of “ 100 villages, 1000 households” in Liaoning Province. Journal of Hunan Agricultural University (Social Sciences), 23(1): 26-32, 71. (in Chinese)

[25]
Yuan J L. 2023. Farmers’ income effect and heterogeneity in poverty reduction by farmers’ cooperatives—Empirical analysis based on endogenous switching regression model. Chinese Journal of Agricultural Resources and Regional Planning, 44(5): 198-210. (in Chinese)

[26]
Yuan J L, Zhao Y L, Wei H. 2023. Can farmers’ cooperatives enhance the independent development ability of farmers?—Evidence from underdeveloped rural areas in Western China. World Agriculture, (1): 99-114. (in Chinese)

[27]
Yuan X P, Liu T J, Yan B B. 2018. Influence of farmers’ participation in cooperatives on their safe production behavior—A case study of farmers in main apple producing areas in China. Journal of Northwest A&F University (Social Science Edition), 18(6): 97-106. (in Chinese)

[28]
Zeng Y, Zhao X, Duan Y F. 2023. Study on the impact of e-commerce value chain renewal on rural reservoir migrants’ income growth: A case study of Zigui County. Issues in Agricultural Economy, (1): 131-144. (in Chinese)

[29]
Zhang H L. 2015. The research on agricultural social service of the farmers’ professional cooperatives from the perspective of farmers—Based on a case study in Yingshan County, Hubei Province. Diss., Wuhan, China: Huazhong Agricultural University. (in Chinese)

[30]
Zhang M L, Weng Z L, Gao X P. 2023. Research on the impact of high-standard farmland and socialized agricultural services on pesticide inputs: Based on the survey data of 605 rice farmers in Jiangxi Province. China Land Science, 37(9): 106-116. (in Chinese)

[31]
Zhang Y F, Lv C C, Lu Q Z. 2022. Fruit and vegetable cooperative agri-food branding and farmers’ agri-food quality control behavior: Impact mechanism and effects assessment. Journal of China Agricultural University, 27(7): 283-298. (in Chinese)

[32]
Zhang Z H, Liu Y T. 2020. The effect of joining cooperatives on the adoption of new bio-pesticide technology by farmers. Forestry Economics, 42(12): 20-26, 92. (in Chinese)

[33]
Zheng S, Chen Q M, Wang Z G. 2018. Scale of land, enrollment of agricultural cooperatives and adoption of unmannered aerial vehicle —Evidence from Jilin Province. Journal of Agrotechnical Economics, (6): 92-105. (in Chinese)

[34]
Zheng S Y, Zhou M J, Feng Q. 2023. Research on the income-increasing effect of cooperatives under the heterogeneity of households’ livelihood capital. Study and Practice, (1): 84-93. (in Chinese)

[35]
Zhou J. 2023. Study on the effect of farmers’ cooperatives in promoting the reduction of fertilizer use by farmers. Journal of Northwest A&F University (Social Science Edition), 23(5): 116-128. (in Chinese)

[36]
Zhou J H, Yang Z Y, Liang Q. 2019. Internal management and performance of quality safety in cooperatives: From the perspective of farmers’ pre-harvest interval practices. Journal of Zhejiang University (Humanities and Social Sciences), 49(1): 37-50. (in Chinese)

[37]
Zhu P, Zheng J, Zhang M Y, et al. 2022. Effect of joining cooperatives on grain grower’s behavior of green production technology adoption and driving mechanism. Journal of Arid Land Resources and Environment, 36(10): 67-75. (in Chinese)

[38]
Zhu W, Wang R M. 2023. Land management scale, number of plots, land transfer and pesticide reduction. Chinese Journal of Agricultural Resources and Regional Planning, 44(5): 91-100. (in Chinese)

Options
Outlines

/

〈 〉