Study on the Impact of Social Capital on Farmers’ Decision-Making Behavior of Adopting Trusteeship Services

Abstract

The development of sustainable agriculture is an important link in promoting agricultural modernization. As a new sustainable agricultural production model, the promotion effect of agricultural production trust depends on the participation of farmers. Therefore, based on the micro-data of 461 valid questionnaires from farmers in the Heilongjiang Province and the Heckman sample selection model, this study empirically analyzed the impact of social capital on the decision behavior of farmers to adopt custody services from two aspects of adoption behavior and adoption degree. The results showed that social capital had a significant positive impact on the adoption behavior and the degree of adoption of rural households’ trusteeship services. From the perspective of different business scales, for large-scale households, participation in the service promotion activities of agricultural means of production suppliers and trust in the technical information disseminated by the government had a significant positive impact on the adoption of custody services. However, for small farmers, participating in village collective service promotion activities and trusting the service information spread by their neighbors’ relatives and friends had a significant positive impact on trust adoption behavior. Therefore, more attention should be paid to the impact of social capital on farmers’ trust adoption behavior decision making in the future agricultural trust service promotion process to accelerate the promotion of sustainable agricultural development.

1. Introduction

Promoting sustainable agricultural development is the essential stepping stone to constructing agricultural modernization, the crucial assurance of maintaining “clear rivers and green mountains”, and the inevitable trend of encouraging rural revitalization. At present, China’s agricultural operation mode is still small-scale and part-time. These small farmers have inherent weaknesses in economy, information, and resources when applying modern agricultural production technology skills, facilities and equipment compared with the scale operation entities. It is challenging for small farmers to establish modern agriculture on their own, and agricultural production trusteeship services are able to assist them with gaining access to modern agricultural production factors including capital, technology and equipment to promote the sustainable development of agriculture. Therefore, it is crucial to study the behavior of influencing farmers’ agricultural production trusteeship decision making for agricultural sustainable development.

The trusteeship service of agricultural production is a typical “relational” socialized service of agricultural production. This relationship essentially refers to the principal-agent relationship between farmers and service providers. The farmer is the individual who entrusts part or all of the agricultural production to the service organization. The service provider is the agent who manages the agricultural production links entrusted by the farmers. In this relationship, there will be a moral hazard due to opportunistic costs, which will affect farmers’ decision making on agricultural trusteeship. According to a recent study, an “acquaintance relationship” effectively weakened the impact of moral hazard and improved the enthusiasm of farmers for agricultural trusteeship [1]. Maintaining the social relationship between acquaintances would affect farmers’ preferential choice of social services. However, “good relations” were the process of social capital accumulation [2]. The concept of social capital was first clearly put forward and systematically elaborated by Bourdieu [3]. The earliest indicators used to measure social capital were mainly based on the measurement tools used by the World Bank in issuing the comprehensive questionnaire on rural social capital, mainly including the five dimensions of network, organization, trust, solidarity and collective action [4]. Numerous mathematicians agreed that social capital, which included crucial components such as social networks, social confidence, and social involvement, was a key factor in influencing farmers’ behavior after conducting a large number of studies [5,6]. In recent years, more scholars have increasingly enriched their research on the impact of social capital in social network, social trust and social participation on farmers’ production behavior decision making. This was owing to the fact that social capital was crucial in the decision-making process for agricultural production and operation. Social capital was a social structure resource owned by individuals or families. It not only provided farmers with opportunities and resources for interactive learning, cooperation and mutual benefit and access to information [7], but also provided a hidden contract supervision mechanism and implementation mechanism [8], which greatly reduced transaction costs and helped to achieve and perform agricultural production service transactions.

With the development of social progress, social capital cannot remain unchanged. The establishment of rules and the improvement of systems, as well as the effective governance of rural areas and other favorable conditions, would promote the improvement of farmers’ social capital. At the same time, effective policy rules could be accumulated by increasing the social capital of farmers, thus accelerating the healthy development of agricultural productive services; however, the research on this aspect is not in depth. Therefore, it is also necessary to further study the influencing factors of farmers’ decisions on agricultural trusteeship behavior from the perspective of social capital to achieve the goal of sustainable development of agricultural science.

A large number of studies have focused on the influencing factors of agricultural trusteeship decision-making behavior. Tom et al. found that the significant differences in trusteeship were related to the characteristics of farmers’ age, farm scale and institutional environment, as well as the complexity, uncertainty and time requirements of relevant competent departments by analyzing the decision-making behavior of farm trusteeship management in Belgium based on transaction cost economics [9]. Igata et al. compared agricultural production trusteeship in Japan with that in the Netherlands. It was found that factors such as farm scale, lack of labor force, diversity of farm management, ownership of agricultural machinery and farmers’ education level had an impact on agricultural production trusteeship [10]. Baiyegunhi et al. took the small farm in Msinga, KwaZulu Natal province, South Africa as the research object to analyze the factors affecting its adoption of trusteeship services. The results showed that participation in trusteeship promotion projects was affected by factors such as age, education level, membership of farmers and income outside the farm [11]. Cai et al. used the survey data of farmers in the main rice-producing areas of Anhui Province to construct the decision-making behavior model of farmers’ production trusteeship and used a binomial probit model and logit model to analyze the impact of farmers’ characteristics, paddy field characteristics and external environment on farmers’ production trusteeship behavior [12].

Many scholars regarded social capital as an influencing factor to study its impact on farmers’ production decision-making behavior. From the perspective of social network, farmer endowment and social capital, most scholars found that social network, farmer endowment and social capital had a significant positive impact on farmers’ agricultural production decision-making behavior [13,14,15,16]. However, few scholars have studied the trust decision-making behavior of farmers from the perspective of social capital. Based on the above literature review, this study focused on the following questions: Does social capital have an impact on agricultural trusteeship decision-making behavior? What are the methods to make an impact? Analyzing and responding to these two questions has significant practical value to encourage farmers to choose agricultural production trust and improve agricultural sustainability.

In order to solve the problems mentioned above, the impact of social capital on agricultural trusteeship decision-making behavior and the way the impact is translated are investigated. The rest of this article is structured as follows. The 2nd section is the theoretical analysis and research hypothesis of the impact of social capital on the trust behavior of farmers’ cultivated land. The 3rd and 4th sections use the data of Heilongjiang Province in China to empirically estimate the driving factors and summarize the research results. The 5th and 6th sections discuss the policy implications and draw conclusions. The findings of this study will not only be helpful in understanding the factors that affect farmers’ willingness and behavior to participate, but also provide suggestions for agricultural production service promotion policies in China and other similar regions.

2. Theoretical Analysis and Research Hypothesis

Lin et al. believed that social capital was “embedded in a specific social structure, and individuals obtain the resources they want through targeted activities and exchanges” [17]. Krishna et al. defined social capital as “the ability of individuals to obtain resources in social networks or broader social structures” [18]. These definitions revealed that the significance of social capital in acquiring resources had been universally acknowledged in previous research. In this study, social capital referred to the process of establishing the relationship between the individual and the family through the network. As for the measurement of social capital, Paldam et al. (2004) measured it with structural social capital and cognitive social capital. Structural social capital mainly referred to the tangible part, including networks, norms and organizations that affected people’s behavior; cognitive social capital mainly referred to the intangible part, including trust, values and social reputation [19]. Although scholars had different ways to measure social capital, in general, the social capital was carried out around the three aspects of “social network”, “social participation” and “social trust”. Therefore, according to the existing scholars’ measurement of social capital and the content of this study, we mainly measured farmers’ social capital from three aspects of “social network”, “social participation” and “social trust” [20,21,22].

Generally speaking, social capital affected farmers’ adoption of outsourced services through the following two ways: one was to reduce the cost of information acquisition and enrich the sources of information. Farmers’ mastery of agricultural information was largely affected by their neighbors since the grass-roots agricultural technology extension system is not ideal [23]. Farmers with social capital relations exchange and transmit information such as technical services to each other, which not only brought farmers access to a wider range of information, but also overcame the issue of difficulty in understanding, low applicability and the cost of farmers’ specialized learning technical knowledge of grass-roots agricultural technology promotion [24]. The other was to speed up the dissemination of information and promote the sharing of technical information. In rural areas, social capital was considered to be a relatively reliable and sustainable social resource, which was directly used by farmers and affected the behavior orientation of farmers. Moreover, social capital based on geographical proximity made communication and learning between farmers more convenient, which not only improved the efficiency of agricultural technology diffusion, but also promoted the dissemination of agricultural information [25], which had a positive impact on farmers’ decision-making behavior, and agricultural trusteeship decision making belongs to agricultural production decision-making behavior. Based on this, hypothesis H1 was proposed.

Hypothesis 1 (H1).

Social capital has a significant positive impact on farmers’ decision-making behavior of adopting trusteeship services.

Social network was a relatively stable social system formed by the interaction between individual members of society. It emphasized the interaction and connection between people [26]. China’s rural areas were in the social environment of complex local relations, and the impact of farmers’ social network on their family production and agricultural management decision-making behavior was more prominent [27]. Lv et al. studied the impact of relationship network on the selection bias of the agricultural production trusteeship service of corn growers. Lv’s research showed that the relationship network had a positive impact on farmers’ selection of production trusteeship service [28]. In this study, social networks were divided into network width and network depth. The network width reflected the number and scale of social network personnel contacted by farmers, expanded the breadth of farmers’ access to information, and increased the possibility of prompting farmers to choose agricultural trusteeship [29]. Network depth reflected the social depth of farmers and their relatives and friends. Through social networks with different depths, a variety of trusteeship information was obtained, which also promoted farmers’ choice of agricultural trusteeship [30]. Based on this, hypothesis H2 was proposed.

Hypothesis 2 (H2).

Social network has a significant positive impact on farmers’ decision-making behavior of adopting trusteeship services.

Social participation referred to the degree of farmers’ understanding, attention and investment in agricultural production and operation information [23]. Social participation was divided into two forms: special participation and general participation [31]. The former referred to farmers’ participation in agricultural information promotion and other activities carried out by the government and village collectives [32]. The latter referred to farmers’ participation in information publicity and other activities carried out by agricultural means of production suppliers [33]. The higher the degree of social participation of farmers, the more they can strengthen social interaction, reduce moral hazard, and then strengthen the communication and cooperation between farmers and service organizations, so as to promote farmers to choose agricultural trusteeship. Based on this, hypothesis H3 was proposed.

Hypothesis 3 (H3).

Social participation has a significant positive impact on farmers’ decision-making behavior of adopting trusteeship services.

Social trust was a kind of behavior norm of mutual recognition and interdependence between people, as well as between people and organizations, based on social relationship network. Social trust was divided into two types: special trust and general trust. The former was mainly manifested in the trust in the dissemination of information by relatives, neighbors and villagers in the village, The latter was mainly reflected in the degree of trust in the government’s dissemination of service information [34]. This trust determined whether farmers were willing to pay credit or rely on the advice of others to carry out their own agricultural production activities [35]. Based on this, hypothesis H4 was proposed.

Hypothesis 4 (H4).

Social trust has a significant positive impact on farmers’ decision-making behavior of adopting trusteeship services. The theoretical analysis framework of this study was shown in Figure 1.

3. Materials and Methods

3.1. Variable Selection

3.1.1. Dependent Variables

The dependent variables of this study were “adoption or not” and “degree of adoption” of farmers’ trusteeship services. For the measurement of “adoption or not” of farmers’ trusteeship services, the value of farmers’ adoption of trusteeship services was 1, and vice versa was 0. The measurement of the “adoption degree” of farmers’ outsourced services was carried out using the proportion of the land area adopting outsourced services in the total area of household cultivated land, and the proportion was assigned as 1 and 5 in [0, 0.2], [0.2, 0.4], [0.4, 0.6], [0.6, 0.8], [0.8, 1] [36].

3.1.2. Key Independent Variables

The key independent variables were mainly the independent variable of social capital, including social network, social participation and social trust. According to the previous research results, this study divided the social network into network width and network depth, which was expressed by the number of contacts in the farmers’ mobile phone address book and the number of gatherings with relatives and friends every year [37]. Social participation was divided into special participation and general participation. Special participation referred to participation in agricultural information, technology and service promotion activities carried out by the government and village collectives. General participation referred to farmers’ participation in information publicity and promotion activities carried out by agricultural means of production suppliers. Social trust was divided into the same special trust and general trust. Special trust referred to the trust in the dissemination of information by surrounding relatives, neighbors and villagers in the village. General trust mainly referred to the trust in the dissemination of service information by the government [38]. Among them, social trust and social participation were assigned with a five-level scale and hierarchical method respectively. Meanings and descriptive statistical analysis of main variables are shown in Table 1.

3.1.3. Control Variables

Existing literature research showed that farmers’ personal characteristics and family characteristics affect farmers’ decision-making behavior in the process of agricultural production and management. Based on the existing research, this study divided the control variables into five aspects: individual characteristics of farmers, family characteristics, cultivated land characteristics, environmental characteristics and identification variables. The individual characteristics of farmers included farmers’ gender, age, education level and farmers’ risk aversion. The family characteristics included the number of household labor force, part-time degree, annual household income and the proportion of household planting income. The characteristics of cultivated land included the area of farmers’ management land and the degree of fragmentation of management land. The environmental characteristics included the price of agricultural trusteeship, the overall condition of tractor roads in the village, and the distance between villages and towns and counties. The identified variables included farmers’ experience in technical promotion training related to agricultural production and whether farmers had received government promotion [39,40,41].

3.2. Data Source

The survey data used in this study were collected from the cities of Harbin, Suihua, Daqing and Qiqihar in Songnen Plain of Heilongjiang Province in 2021. The gross domestic product (GDP) of these cities in 2022 was CNY 635.5 billion, CNY 268 billion, CNY 138.43 billion and CNY 135.32 billion, respectively. The proportion of rural population in these areas to the total local population was 29.4%, 46.4%, 27.5% and 56.3%, respectively. In addition, these regions had maize as their primary crop, and they were among the initial to establish agricultural trusteeship [42]. Among them, Suihua City will achieve the entrusted land area of 6 million mu (4 × 10⁸ hectares), and the entrusted land area of Daqing City will reach 1.4 million mu (9.33 × 10⁷ hectares). The selected study sites were representative.

We adopted stratified sampling and simple lottery methods to conduct household questionnaire survey on farmers. The first step was to classify all urban areas into four categories according to their development level of agricultural trusteeship services. In the 2nd step, we used the lottery method to randomly select one–two counties from each city in each classification. In the 3rd step, we used the simple lottery method to select two–three towns from the selected counties. In the 4th step, we selected three–five villages from the selected towns by drawing lots. In the last step, we randomly surveyed 15–30 households in selected villages by drawing lots. See Table S1 for the names and geographical locations of specific villages. A total of 517 questionnaires were obtained. After eliminating the invalid questionnaires with incomplete or contradictory information, there were 461 valid questionnaires, the effective rate of which was 89.17% for the willingness to accept agricultural trusteeship, including the degree of acceptance, and the cognition and view of agricultural trusteeship.

We used SPSS 26.0 software (SPSS, IBM, Chicago, IL, USA) to collect the survey data from three dimensions of social network, social participation and social trust, and select Cronbach’s α model to check the reliability of the questionnaires. The obtained α = 0.791 > 0.7, indicating that the reliability of the questionnaire is good. Next, we carried out the validity test of the questionnaires, and carried out exploratory factor analysis through the steps of “analysis-dimensionality reduction-factor”. The cumulative variance contribution rate of the principal component was 76.524%. This contribution rate was an ideal result for exploratory factor analysis, indicating that the structural validity of the questionnaire was good. The overall scale’s reliability was good according to the questionnaire’s reliability and validity tests.

3.3. Model Setting

Since the trusteeship of agricultural production services was mostly attributed to the two choices of “trusteeship or not”, the more commonly used econometric analysis methods were the logistic regression model and the probit regression model. In terms of empirical analysis, whether the service trusteeship model was adopted or not by farmers was investigated. The Heckman sample selection mode established in this study is as follows.

(1)$p_i=\frac{1}{1+e^{-(\alpha +{\displaystyle \sum _{i=1}^n{\beta }_i{x}_i)}}}=\frac{e^{\alpha +{\displaystyle \sum _{i=1}^n{\beta }_i{x}_i}}}{1+e^{\alpha +{\displaystyle \sum _{i=1}^n{\beta }_i{x}_i}}},$

By comparing the probability of adopting trusteeship services, the linear expression of the logistics model after logarithmic transformation was obtained as follows.

(2)$\ln (\frac{p_i}{1-p_i})={\beta }_0+{\beta }_1{x}_1+{\beta }_2{x}_2+\dots +{\beta }_i{x}_i+\epsilon ,$

where $p_i$ represents the probability of farmers adopting trusteeship services, $1-p_i$ represents the probability of farmers not adopting trusteeship services, and $x$ is the ith influencing factor of farmers adopting trusteeship services, ${\beta }_0$ is the regression constant term, ${\beta }_i$ is the regression coefficient, $\epsilon$ is a random error term.

On the basis of farmers’ adoption of trusteeship services, the value was assigned 1~5 according to their adoption degree, and the ordered probability model was selected to analyze the adoption degree of farmers’ trusteeship services. The specific form of the model was as follows.

(3)$Y=\alpha +{\displaystyle \sum _{i=1}^n{b}_i{x}_i}+\mu ,$

where $y$ is the degree of farmers’ adoption of trusteeship services, $x_i$ is the ith influencing factor of farmers’ adoption, and a is a constant term, ${\beta }_i$ is the regression coefficient of the ith influencing factor, and $\mu$ is the random error term.

4. Results and Analysis

The study adopted the variance expansion factor method (VIF) to conduct multiple collinearity tests on all independent variables prior to model estimation, considering that the key variables of social capital and various control variables might have collinearity problems. The test results, VIF_max = 1.6 and VIF_avg = 1.31, meant there was no multicollinearity between the variables, as both the maximum value of variance expansion factor and the mean value of variance expansion factor were far lower than 10. Stata15.0 software (Stata Inc., College Station, TX, USA) was used for empirical analysis, and the regression results are shown in Table 2, Table 3 and Table 4.

4.1. The Impact of Social Capital on the Adoption Behavior of Farmers’ Trusteeship Services

The impact of social capital on farmers’ adoption of trusteeship services is shown in Table 2. To ensure the reliability of the empirical results, the stepwise regression method was used for analysis. Among them, model (1) mainly analyzed the impact of comprehensive indicators of social capital and control variables on the adoption behavior of rural households’ trusteeship services. Models (2)~(4) respectively analyzed the impact of social network, social participation, social trust and control variables on the adoption behavior of rural households’ trusteeship services.

Table 2

Impact of social capital on farmers’ adoption of trusteeship services.

Variable Name	Model (1)		Model (2)		Model (3)		Model (4)
	Regression Coefficient	p	Regression Coefficient	p	Regression Coefficient	p	Regression Coefficient	p
Social capital	0.102 **	0.048
Network width			0.011	0.105
Network depth			0.006	0.139
Special participation					0.058 **	0.021
General participation					0.041 *	0.056
Special trust							0.156 ***	0.006
General trust							0.098	0.115
Gender	0.065	0.020	0.039	0.051	0.281	0.081	0.069	0.021
Age	−0.039 *	0.004	0.041	0.002	0.061	0.004	0.351	0.039
Education level	0.027	0.039	0.021 *	0.008	0.005	0.002	0.104 *	0.063
Risk aversion status	−0.002	0.024	0.026	0.074	0.061	0.100	0.006	0.035
Number of household labor force	0.034	0.027	−0.019 **	0.000	0.091 ***	0.026	−0.016 **	0.076
Concurrent degree	0.164 **	0.006	0.093	0.019	0.261	0.003	−0.027 **	0.012
Total household income	0.021	0.058	0.164 *	0.028	0.051 *	0.009	0.287 **	0.005
Proportion of household planting income	0.163	0.067	0.061	0.059	0.089	0.052	0.162	0.016
Operating land area	0.059 **	0.001	−0.043 **	0.026	−0.015 *	0.11	0.028	0.063
Degree of land fragmentation	0.029	0.081	0.008	0.024	−0.056 **	0.012	−0.019 *	0.002
Agricultural trusteeship service price	0.016	0.575	0.018	0.001	−0.009 **	0.056	0.008	0.06
Tractor road condition	0.051	0.003	−0.009 *	0.006	0.165	0.013	−0.014 ***	0.003
Distance from village to town and county	0.023	0.073	0.053	0.075	0.037	0.089	0.019	0.056
Technical promotion and training experience	0.005 ***	0.043	0.019	0.036	0.049	0.028	0.127	0.024
Whether to accept government technology promotion	0.016 **	0.058	0.024	0.01	0.006	0.165	0.703	0.652
LR chi square value	0.217		0.156		0.372		0.278
Wald chi square value	168.290 ***		147.372 ***		171.254 ***		200.000 ***
Log Likelihood	−333.961		−345.178		−332.76		−319.259

Note: *, ** and ***, respectively, indicate that they have passed the significance test at the statistical level of 10%, 5% and 1%.

4.1.1. Impact of Social Capital

According to model (1), social capital had a positive impact on farmers’ trusteeship service adoption, with an impact coefficient of 0.102, which was significant at the statistical level of 5%. That is, the higher the level of social capital, the greater the possibility of farmers’ trusteeship service adoption behavior. This result preliminarily verified hypothesis H1, indicating that social capital played a positive role in promoting farmers’ trusteeship service adoption behavior.

4.1.2. Impact of Social Networks

According to model (2), neither the regression coefficient of network width nor the network depth passed the significance level of 10% in the regression results. The social network had no significant effect on farmers’ trusteeship adoption behavior, therefore, the hypothesis H2 was not verified. A possible reason was that compared with formal agricultural technology training, although farmers’ access to technical information through social networks was low-cost and efficient, which solved the uncertainty in technology application, the social network only solved the basic production practice of farmers and did not reach the best production practice. While formal agricultural technology training was highly targeted and scientific, by organizing farmers to participate in technical training, the transformation from basic production practice to optimal production practice was realized.

4.1.3. Impact of Social Participation

According to model (3), the regression coefficient of special participation was significant at the statistical level of 5%, with the coefficient of 0.058, indicating that farmers’ participation in village collective service promotion activities had a positive role in promoting their adoption of agricultural trusteeship services. The general participation passed the significance test of 10%, and the coefficient was also positive, indicating that farmers’ participation in the service promotion activities of agricultural means of production suppliers also positively affected their adoption of agricultural trusteeship services, which showed that social participation had a significant positive impact on farmers’ adoption of trusteeship services. This showed that the improvement of farmers’ social participation promoted their adoption of trusteeship services. This result preliminarily verified hypothesis H3. The reason for this may be that the more farmers participate in society, the more extensive information they will get, and the more detailed knowledge they will possess about agricultural trusteeship services, which would help and play a role in farmers’ choice of agricultural trusteeship services.

4.1.4. Impact of Social Trust

According to model (4), the special trust was significant at the statistical level of 5%, and the coefficient was positive, indicating that farmers trust the service information transmitted by farmers in the same industry, which had a positive impact on their adoption of trusteeship services. The increase of social trust had a positive impact on farmers’ adoption of trusteeship services. However, general trust, that is, the degree of trust of farmers in government communication service information, had no significant effect on farmers’ trusteeship service adoption behavior. Therefore, hypothesis H4 was only partially verified. There were two possible reasons. On the one hand, although the service information mastered by the government was highly targeted and professional, there were still some problems such as an imperfect management system, which made the extension service personnel unable to transmit the service information to farmers in a timely and comprehensive manner, resulting in a certain “gap” between the service information and farmers’ real needs, which makes the impact effect not significant. On the other hand, during the field investigation, the study group found that in the early stage of the promotion of producer services, due to the great “uncertainty” of the application results of the service, and in order to avoid this “uncertainty” risk, farmers prefer to learn from the reliable “acquaintances” around them and ask them for solutions when they encounter difficulties.

In models (1)~(4), the age of household had a significant negative impact on farmers’ trusteeship service adoption behavior, which was significant at the statistical level of 10%. That is, with the increase of the age of the head of household, the trusteeship adoption behavior would decrease. This may be that compared with the young labor force, the elderly farmers were in a weak position when adopting new technologies and learning new information. In addition, the elderly labor force had lower enthusiasm for the adoption of new agricultural technologies, because the technology benefit time of the elderly labor force was shorter under the same technology learning cost. Therefore, the adoption enthusiasm was low. The degree of part-time employment had passed the significance test of 5%, which showed that the higher the degree of part-time employment, the easier it was for farmers to choose agricultural trusteeship services. This regression result was well-explained. This was mainly because farmers choose agricultural trusteeship services because their own labor force was not enough to complete their own family agricultural labor during part-time employment. The existing cultivated land area had a significant positive impact on farmers’ technology adoption behavior, and had passed the 5% statistical level test, that is, the larger the existing cultivated land area of farmers, the stronger the promotion effect on their trusteeship service adoption behavior.

4.2. The Impact of Social Capital on the Adoption of Rural Households’ Trusteeship Services

If the farmers who did not adopt trusteeship services were excluded and only the farmers who adopted trusteeship services were selected as the samples, it might lead to the problem of sample selectivity error, because if only the farmers who adopted trusteeship services were analyzed, the self-selected samples were used instead of random samples. In order to correct the possible sample selectivity error in the regression model, this study made an empirical analysis using the Heckman two-step method. The explanatory variables in Table 3 were the degree of farmers’ adoption of trusteeship services. The explanatory variables were consistent with Table 2. In the selection equation, in addition to the factors affecting farmers’ technology adoption, an identification variable meeting the exclusive conditions was also required, that is, this variable affected farmers’ trusteeship choice, but did not directly affect farmers’ trusteeship adoption. Finally, refer to the existing studies, technology promotion training experience and government technology promotion were selected as identification variables. The impact of social capital on farmers’ adoption degree of trusteeship services is shown in Table 3.

Table 3

Impact of social capital on farmers’ adoption degree of trusteeship services.

Variable Name	Model (5)	Model (6)	Model (7)	Model (8)	Model (9)	Model (10)	Model (11)	Model (12)
Social capital	0.073 *	0.105 *
Network width			0.002	0.004
Network depth			0.016	0.009
Special participation					0.013 **	0.132 *
General participation					0.011 *	0.098 *
Special trust							0.206 **	0.197 *
General trust							0.211	0.109
Gender	0.025	0.070	0.039	0.001	0.201	0.046	0.079	0.021
Age	−0.079 *	0.014	0.041	0.012	0.021	0.002	0.331	0.039
Education level	0.020 *	0.009	0.021 *	0.018	0.015	0.003	0.102 *	0.063
Risk aversion status	−0.001 *	0.014	0.026	0.084	0.091	0.200	0.005	0.035
Number of household labor force	0.065	0.077	−0.019 **	0.010	0.011 ***	0.029	−0.011 **	0.076
Concurrent degree	0.133 **	0.026	0.093	0.009	0.661	0.004	−0.057 **	0.012
Total household income	0.011	0.078	0.164 *	0.008	0.091 *	0.008	0.227 **	0.005
Proportion of household planting income	0.103	0.057	0.061	0.049	0.019	0.053	0.062	0.016
Operating land area	0.019	0.011	−0.043 **	0.036	−0.055 *	0.170	0.128	0.063
Degree of land fragmentation	0.009	0.001	0.008	0.074	−0.051 **	0.062	−0.219 *	0.002
Agricultural trusteeship service price	0.066	0.505	0.018	0.011	−0.008 **	0.046	0.108	0.06
Tractor road condition	0.091	0.013	−0.009 *	0.026	0.175	0.003	−0.024 ***	0.003
Distance from village to town and county	0.093	0.063	0.053	0.065	0.027	0.049	0.119	0.056
Technical promotion and training experience	0.002 ***	0.033	0.019	0.026	0.099	0.018	0.147	0.024
Whether to accept government technology promotion	0.013 **	0.048	0.024	0.09	0.016	0.105	0.713	0.652
LR chi square value	0.117		0.056		0.172		0.189
Wald chi square value	176.230 ***		117.372 ***		161.254 ***		209.000 ***
Log Likelihood	−212.961		−295.178		−332.76		−309.529

Note: *, ** and ***, respectively, indicate that they have passed the significance test at the statistical level of 10%, 5% and 1%.

Table 3 shows the results of the impact of social capital on the adoption of farmers’ trusteeship services. Model (5) indicated that the comprehensive index of social capital had a significant positive impact on the degree of farmers’ trusteeship services, which was significant at the statistical level of 10%, which was consistent with the regression results of model (1). Hypothesis H1 was further verified, which showed that social capital had a positive impact on farmers’ trusteeship adoption; however, the impact had decreased. If we only analyzed the impact of social capital on farmers’ trusteeship service adoption, we might exaggerate its impact. According to model (7), the effect of social network on farmers’ technology adoption was not significant, which was consistent with the regression result of model (2), and the hypothesis H2 has not been verified. Model (9) indicated that both special participation and general participation had a significant positive impact on the adoption of farmers’ trusteeship services, which was consistent with the statistical results of model (3). Assuming that H3 was further verified, it further showed that the more active farmers participate in service promotion activities, the more conducive they were to obtain comprehensive service information and market information and the more favorable it was for its trusteeship service adoption decision. The regression results of model (11) showed that special trust in social trust had a positive impact on farmers’ adoption of trusteeship services, which was significant at the statistical level of 5%; however, the general participation still fails to pass the significance test of 10%, indicating that it has no significant impact on farmers’ adoption of trusteeship services. Hypothesis H4 was still partially verified.

4.3. Analysis of the Impact of Social Capital on the Decision of Trusteeship of Different Scales Farmers

At present, based on different standards, different scholars have different definitions of small-scale. On the whole, it is a common standard to define small-scale growers using land-management scale. For example, Shi et al. classified the land below 0.67 ha as small-scale land when investigating 1040 farmers in four provinces [43]. Chen and Tang defined grain farmers with less than 2 ha of cultivated land as small farmers, which was consistent with the definition of small-scale farmers by the World Bank. This study comprehensively considered the existing research and combined with the actual situation of the investigated area: the farmers with a planting area of less than 2 ha were called small farmers, and the farmers with a planting area of more than 2 ha were defined as large-scale households. Finally, 188 large-scale households and 392 small-scale farmers were obtained [44]. This study was enriched by analyzing the impact of social capital on the adoption behavior and degree of trusteeship services of farmers of different sizes. The impact of social capital on trusteeship service adoption decisions of farmers of different sizes was shown in Table 4.

Table 4

Impact of social capital on trusteeship service adoption decisions of farmers of different sizes.

Variable Name	Small Farmers		Scale Household
	Adoption Behavior	Degree of Adoption	Adoption Behavior	Degree of Adoption
Social capital	0.102 ***	0.048 *	0.099 **	0.032 *
Network width	0.065	0.071	0.097	0.032
Network depth	0.020	0.012	0.091	0.027
Special participation	0.051 ***	0.039 *	0.046	0.056
General participation	0.060	0.051	0.081 **	0.021 **
Special trust	0.097 **	0.047 **	0.281	0.069
General trust	0.077	0.067	0.074 *	0.061 *
LR chi square value	0.217	0.156	0.372	0.278
Wald chi square value	168.290 ***	147.372 ***	171.254 ***	200.000 ***
Log Likelihood	−333.961	−345.178	−332.76	−319.259

Note: *, ** and ***, respectively, indicate that they have passed the significance test at the statistical level of 10%, 5% and 1%.

Table 4 showed that the comprehensive index of social capital had a significant positive impact on the adoption of trusteeship services by small-scale farmers and large-scale households; the higher the level of social capital, the greater the possibility of adopting trusteeship services. Farmers were embodied in social participation and social trust. Special participation, i.e., participation in village collective service promotion activities, only had a significant positive impact on the trusteeship adoption behavior of small farmers, which showed that small farmers use the opportunity of village collective service promotion to exchange information and transfer technical experience with other farmers, which was conducive to mastering the information of agricultural trusteeship services, and then affects their trusteeship adoption behavior. However, general participation, that is, participation in the service promotion activities of agricultural means of production suppliers, only had a significant positive impact on the large-scale outdoor package service adoption behavior, and had no significant impact on the small-scale farmers’ outdoor package adoption behavior. A possible reason is that although the service promotion of agricultural materials suppliers was the key force to support the development of agricultural trusteeship services, which also faces the problem of huge promotion expenditure, this made the service extension personnel unable to provide specific guidance services to all farmers. Therefore, the service organization will select large-scale households as the promotion service object first, form a certain demonstration and leading role in the adjacent village, and then further spread to the surrounding small farmers. However, the problem was that large-scale households were more likely to be invited to supplier service promotion activities because they had a wide range of social capital relations. At the same time, in order to make the service promotion effect better, the service promotion personnel of suppliers were also more inclined to promote the service information to large-scale households, so that the service promotion activities had a significant impact on the adoption behavior of large-scale households. Special trust was to trust the service information transmitted by surrounding farmers, which had a significant positive impact on the trusteeship service adoption behavior of small farmers, but had no significant impact on the trusteeship adoption behavior of large-scale households. Small farmers have established a mutual trust relationship with each other through close communication with familiar farmers in adjacent regions, so that the information exchanged with each other was more transparent. In addition, the mutual dissemination of service information among farmers has the characteristics of fast information dissemination and low cost, which was more conducive for small farmers to obtain agricultural trusteeship service information. In summary, the ways in which impact and effects of social capital are translated on their trusteeship service adoption behavior were different.

5. Discussion

5.1. Factors Influencing the Adoption Decision of Rural Households’ Trusteeship Services

Social capital had a significant positive impact on the adoption behavior and the degree of adoption of rural households’ trusteeship services. Among them, social participation and social trust have a significant positive impact on the adoption behavior and degree of farmers’ trusteeship services; however, the impact of social network on the adoption behavior and degree of farmers’ trusteeship services was not significant, which reached a consensus with the research conclusions of other scholars [20]. According to the research results, the conclusion of this study was that social capital plays an important role in farmers’ adoption of decision-making behavior. On the one hand, it was through farmers’ participation in activities such as social services promoted by village collectives and suppliers of agricultural means of production. On the other hand, it was the trust foundation that farmers built for farmers and suppliers in the same industry in the vicinity. Social capital was an important “soft power” of farmers, and the improvement of social capital level would further expand the resource pool of farmers, which would be more conducive to their adoption of agricultural trust services, thus promoting the sustainable development of agriculture.

5.2. The Driving Factors of the Adoption Decision of Farmers’ Trusteeship Services

In this study, we found that the main factors driving farmers to adopt production custody services included social participation and social trust in social capital, which also reached a consensus with the research conclusions of other scholars [22]. Therefore, the government departments should start with these two driving factors and give full play to the incentive role of social capital for farmers to adopt custody services. On the one hand, the government should make a thorough investigation before the technical publicity, effectively screen the publicity objects and understand the demand characteristics of different characteristics of farmers’ trusteeship services. The government should take measures to improve the enthusiasm of farmers to participate in the promotion activities of productive services such as agricultural means of production suppliers. For example, the trusteeship service extension personnel should observe the field and achieve the trusteeship service guidance work that varies from time to place and from person to person, so as to make the guidance content more targeted. On the other hand, government staff should pay attention to the openness and transparency of daily work and strengthen communication with farmers to increase farmers’ trust in the government. For large-scale households, the government should focus on improving their trust in the village-level government, establishing stable communication channels with large-scale households, regularly communicating service application information, and improving mutual trust in communication, so as to achieve efficient information dissemination and promote more large-scale households to choose agricultural trust services.

5.3. Limitations of This Study

On the one hand, this study should not be limited to the farmers who only plant a certain crop or a certain type of crops. The farmers should be classified according to different crop combinations according to the current situation, so as to better understand the diversified needs of heterogeneous farmers for agricultural productive services. On the other hand, in the later research, we should also expand the selection of survey sites and survey samples, not only select representative agricultural cities and counties, but also expand to the whole province and even the whole country, and study the impact of agricultural productive services on farmers’ operating efficiency on the national farmers’ survey samples, which is more conducive to the development of agricultural productive services and improves the construction of agricultural socialized service system.

6. Conclusions

Social capital positively promoted farmers to choose trusteeship services and further promoted the sustainable development of agricultural trusteeship. The social network, social participation and social trust included in social capital have promoted the sustainable development of agricultural trusteeship services to varying degrees. In addition to social factors, economic factors also promoted the sustainable development of agricultural trusteeship services. For example, the income of farmers and the scale of land input and production also affected the sustainable development of agricultural trusteeship services to varying degrees. In the process of China’s agricultural modernization, agricultural production trust, as a sustainable agricultural production model, has shown advantages and adaptability. At present, China’s imperfect agricultural trusteeship service system, unbalanced trusteeship organization and chaotic trust market affect farmers to adopt agricultural production trusteeship services. Developing typical farmland trust organizations and strengthening their active publicity can help farmers feel the benefits of farmland trust participation. Organizations with good system construction and standardized operation can become pilots, gradually spread successful experience, and distinguish different crops and different agricultural resource endowments. On the other hand, it helps to improve farmers’ recognition and trust in farmland trust organizations. Government departments can provide support to strengthen the education and training of farmers, help them understand the trusteeship system and operation mechanism, and gain the ability to access and use services. These practices can be realized and applied through the establishment and accumulation of farmers’ social capital.

The case study of Chinese farmers in this study also provides experience and lessons for other developing countries, especially in areas where the vast majority of underdeveloped economic countries are moving towards agricultural modernization, especially in those countries where farmers’ sustainable agricultural production trusteeship service mode and corresponding behavior are very common. This study confirmed the necessity of trusteeship services for agricultural production. It also reminds farmers to recognize the development stage of agricultural modernization and choose the appropriate agricultural production trusteeship service mode. In addition, it has certain reference significance for improving and increasing farmers’ recognition and participation in trust services, supporting and improving agricultural sustainable production service organizations and promoting agricultural sustainable development.

In this study, the research object is limited to farmers who grow corn. For further studies, a larger sample size with a variety of crops will be required for the subsequent research to investigate the effect of social capital on farmers’ decision making when adopting trustee services.

Author Contributions

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

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

Ma Zhiyuan, working at Jingxing Chaoyue Agricultural Cooperative in Longjiang County, Heilongjiang Province, China, accepted the interview and provided many useful materials. The author thanks Ma Zhiyuan for his warm help. We thank the anonymous reviewers and the editors for their suggestions which substantially improved the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figure and Tables

Figure 1. Theoretical analysis framework.

Table 1

Meanings and descriptive statistical analysis of main variables.

Variable Name		Variable Meaning and Assignment	Median	Quartile
Dependent variable	Adoption behavior	Whether to adopt trusteeship services: Yes = 1, No = 0	1	0
	Degree of adoption	The proportion of the area of trusteeship services in the total area of household cultivated land: [0, 0.2) = 1, [0.2, 0.4) = 2; [0.4, 0.6) = 3;[0.6, 0.8) = 4; [0.8, 1] = 5	4	4
	Network width	Number of contacts in mobile phone address book (person)	75	118
	Network depth	Number of gatherings with relatives and friends every year (times)	7	12
Social participation	Special participation	Annual average number of participation in village collective service promotion activities	4	7
	General participation	Annual average number of service promotion activities of suppliers of agricultural means of production	3	9
Social trust	Special trust	Trust the service information spread by neighbors, relatives and friends: Great distrust = 1,Distrust = 2,General trust = 3, Trust = 4, Great trust = 5.	3	5
	General trust	Trust the service information transmitted by the government: Great distrust = 1, Distrust = 2, General trust = 3, Trust = 4, Great trust = 5	2	4
Control variables	Gender	Male = 1, Female = 2	1	2
	Age	Actual age of farmers (years)	49	56
	Education level	Education years of head of household (years)	6	9
	Risk aversion status	Risk-averse = 1, Other = 0	0	0
	Number of householdlabor force	Actual labor force ofhouseholds surveyed (person)	3	4
	Concurrent degree	None = 1, Occasional part-time = 2, Seasonal part-time = 3, Long term part-time = 4	3	4
	Total household income	Logarithm of total household income (CNY)in 2020	15,006	69,613
	Proportion of household planting income	Proportion of farmers’ agricultural incomein total income in 2020 (%)	46	53
	Operating land area	Actual operating land area of sample farmers (ha)	62	157
	Degree of land fragmentation	Number of cultivated land/cultivated land area (block/ha)	4.5	6.6
	Agricultural trusteeship service price	Average trusteeship cost of each link (CNY/ha)	96	123
	Tractor road condition	Village agricultural land has inorganic farming road:Yes = 1, None = 0	0	1
	Distance from village totown and county	Logarithm of actual distance (km)	32	117
	Technical promotion and training experience	Training experience or not: Yes = 1, None = 0	0	1
	Whether to accept government technology promotion	Whether the farmers have received the technology promoted by the government in 2021: Yes = 1, No = 0	1	1

Note: The reason for the high age of farmers is that the majority of young and middle-aged farmers choose to work in cities.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su15065343/s1, Table S1: Locations of survey samples and household numbers.

References

1. Huan, M.L.; Hou, Y.X. Research on service quality control contract in agricultural production trusteeship. J. Agric. For. Econ. Manag.; 2020; 19, pp. 288-296. (In Chinese)

2. Narayan, C. A dimensional approach to measuring social capital: Development and validation of a social capital inventory. Curr. Sociol.; 2001; 49, pp. 59-102. [DOI: https://dx.doi.org/10.1177/0011392101049002006]

3. Bourdieu, P. Handbook of Theory and Research for the Sociology of Education; Greenwood Press: New York, NY, USA, 1986.

4. Grootaert, C.N.; Bastelaer, T.V. The Role of Social Capital in Development: An Empirical Assessment; Cambridge University Press: Cambridge, UK, 2002; pp. 43-55.

5. Putnam, R.D. Making Democracy Work: Civic Traditions in Modern Italy; Princeton University Press: Princeton, NJ, USA, 1993.

6. Ostrom, E. Analyzing Collective Action. Agric. Econ.; 2010; 41, (Suppl. 1), pp. 155-166. [DOI: https://dx.doi.org/10.1111/j.1574-0862.2010.00497.x]

7. Yang, Z.H. Aging, social networks and farmers’ green production technology adoption behavior: Verification of farmers’ data from six provinces in the Yangtze River basin. China Rural. Obs.; 2018; 4, pp. 44-58. (In Chinese)

8. Li, H.; Xia, X.L.; Cai, J. Research on relationship network, farmer differentiation and farmland transfer behavior. Agric. Mod. Res.; 2020; 41, pp. 255-264. (In Chinese)

9. Tom, V.; Verbeke, W.; van Huylenbroeck, G. Transaction cost analysis of outsourcing farm administration by Belgian farmers. Eur. Rev. Agric. Econ.; 2000; 27, pp. 325-345.

10. Igata, H.H. Agricultural Outsourcing: Comparison between the Netherlands and Japan. Appl. Stud. Agribus. Commer.; 2008; 2, pp. 29-33. [DOI: https://dx.doi.org/10.19041/APSTRACT/2008/1-2/4]

11. Baiyegunhi, M.F. Impact of outsourced agricultural extension program on smallholder farmers’net farm income in Msinga, KwaZulu-Natal, South Africa. Technol. Soc.; 2019; 57, pp. 1-7. [DOI: https://dx.doi.org/10.1016/j.techsoc.2018.11.003]

12. Cai, R.; Cai, S. An empirical study on Agricultural Production trusteeship: Based on the investigation of the main rice producing areas in Anhui Province. Agric. Technol. Econ.; 2014; 4, pp. 34-42. (In Chinese)

13. Ren, J.; Lei, H.; Ren, H. Livelihood Capital, Ecological Cognition, and Farmers’ Green Production Behavior. Sustainability; 2022; 14, 16671. [DOI: https://dx.doi.org/10.3390/su142416671]

14. Wang, W.; Zhang, S. The Impact of Internet Use on Rural Women’s Off-Farm Work Participation: Empirical Evidence from China. Sustainability; 2022; 14, 16972. [DOI: https://dx.doi.org/10.3390/su142416972]

15. Geng, Y.; Zheng, S.F.; Lu, Q. Effects of economic incentives and social networks on Farmers’ adoption of green prevention and control technology: Evidence from main Kiwi producing areas in Shanxi. J. Huazhong Agric. Univ. Soc. Sci. Ed.; 2017; 6, pp. 59-69. (In Chinese)

16. Yao, K.Y.; Chen, L.G.; Liu, Z.Z. Effects of farmers’ endowment, policy factors and crop types on the adoption decision of straw returning technology. Agric. Technol. Econ.; 2018; 12, pp. 64-75. (In Chinese)

17. Lin, N. Building a network theory of social capital. Connections; 1999; 22, pp. 28-51.

18. Krishna, A.; Shrader, E. Social capital assessment tool. Proceedings of the Conference on Social Capital and Poverty Reduction; Washington, DC, USA, 22–24 June 1999.

19. Paldam, S. Creation and returns of social capital. Social Capital and Economics; Routledge: London, UK, 2004; 178.

20. Lv, M.; Sun, Y.; Shi, B. Impact of Introversion-Extraversion Personality Traits on Knowledge-Sharing Intention in Online Health Communities: A Multi-Group Analysis. Sustainability; 2023; 15, 417. [DOI: https://dx.doi.org/10.3390/su15010417]

21. Rudito, B.; Famiola, M.; Anggahegari, P. Corporate Social Responsibility and Social Capital: Journey of Community Engagement toward Community Empowerment Program in Developing Country. Sustainability; 2023; 15, 466. [DOI: https://dx.doi.org/10.3390/su15010466]

22. Wang, D.; Li, S. Innovation of Contemporary Chinese Urban Community Governance under the Perspective of Social Capital: Participation of Multiple Subjects Based on Community Proposals. Sustainability; 2023; 15, 93. [DOI: https://dx.doi.org/10.3390/su15010093]

23. Calha, I.; Oliveira, M.D.F.; Reis, P. Weed Management Challenges in Rice Cultivation in the Context of Pesticide Use Reduction: A Survey Approach. Sustainability; 2023; 15, 244. [DOI: https://dx.doi.org/10.3390/su15010244]

24. Qian, L.; Qian, W.R. Does social capital affect farmers’ land transfer behavior: Empirical test based on CFPS. J. Nanjing Agric. Univ. Soc. Sci. Ed.; 2017; 17, pp. 88-99. (In Chinese)

25. Xu, X.L.; Zhou, L.L.; Chen, X. Heterogeneity of social capital and health status of landless farmers under the background of urbanization. China Rural. Obs.; 2017; 5, pp. 74-86. (In Chinese)

26. Brown, T.F. Theoretical Perspectives on Social Capital; Working Paper Lamar University: Beaumont, TX, USA, 1997.

27. Jiang, W.J.; Yan, T.W.; Jiang, X.; Zhang, J.B. Effects of social network and ecological cognition on Farmers’ willingness to return straw to the field. J. China Agric. Univ.; 2019; 24, pp. 203-216. (In Chinese)

28. Lv, J.; Xue, Y.; Han, X. Risk aversion, relationship network and the choice bias of agricultural production trusteeship service: An analysis based on the hypothesis of bounded rationality. Rural. Econ.; 2020; 3, pp. 118-126. (In Chinese)

29. Mwanri, L.; Miller, E.; Walsh, M.; Baak, M.; Ziersch, A. Social Capital and Rural Health for Refugee Communities in Australia. Int. J. Environ. Res. Public Health; 2023; 20, 2378. [DOI: https://dx.doi.org/10.3390/ijerph20032378]

30. Hrabéczy, A.; Ceglédi, T.; Bacskai, K.; Pusztai, G. How Can Social Capital Become a Facilitator of Inclusion?. Educ. Sci.; 2023; 13, 109. [DOI: https://dx.doi.org/10.3390/educsci13020109]

31. Yu, H.; Zhang, W.; Pang, S. Exploring the Role of Land Transfer and Social Capital in Improving Agricultural Income under the Background of Rural Revitalization. Int. J. Environ. Res. Public Health; 2022; 19, 17077. [DOI: https://dx.doi.org/10.3390/ijerph192417077]

32. Ren, Z.; Yue, G.; Xiao, W.; Fan, Q. The Influence of Subjective Socioeconomic Status on Life Satisfaction: The Chain Mediating Role of Social Equity and Social Trust. Int. J. Environ. Res. Public Health; 2022; 19, 15652. [DOI: https://dx.doi.org/10.3390/ijerph192315652] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36497727]

33. Teng, Y.; Lin, P. Research on Behavioral Decision-Making of Subjects on Cultivated Land Conservation under the Goal of Carbon Neutrality. Land; 2022; 11, 1664. [DOI: https://dx.doi.org/10.3390/land11101664]

34. Li, X.; Guo, X. Can Policy Promote Agricultural Service Outsourcing? Quasi-Natural Experimental Evidence from China. Sustainability; 2023; 15, 1009. [DOI: https://dx.doi.org/10.3390/su15021009]

35. Li, R.; Yu, Y. Impacts of Green Production Behaviors on the Income Effect of Rice Farmers from the Perspective of Outsourcing Services: Evidence from the Rice Region in Northwest China. Agriculture; 2022; 12, 1682. [DOI: https://dx.doi.org/10.3390/agriculture12101682]

36. Min, S.; Ding, Y.; Wang, X.; Wang, Y. The Demand of Agricultural Socialized Services in Small holders’ Farming: Evidence from the Survey of A Hundred of Townships and Ten Thousands of Households. J. Agro-For. Econ. Manag.; 2019; 18, pp. 795-802. (In Chinese)

37. Genius, M.; Koundouri, P.; Nauges, C. Information Transmission in Irrigation Technology Adoption and Diffusion: Social Learning, Extension Services and Spatial Effects. Am. J. Agric. Econ.; 2014; 96, pp. 328-344. [DOI: https://dx.doi.org/10.1093/ajae/aat054]

38. Hu, R.F.; Cai, Y.Q.; Chen, K.Z.; Huang, J.K. Effects of Inclusive Public Agricultural Extension Service: Results from A Policy Reform Experiment in Western China. China Econ. Rev.; 2012; 23, pp. 962-974. [DOI: https://dx.doi.org/10.1016/j.chieco.2012.04.014]

39. Qu, X.; Kojima, D.; Wu, L.; Ando, M. Do Farming Scale and Mechanization Affect Moral Hazard in Rice Harvest Outsourcing Service in China?. Agriculture; 2022; 12, 1205. [DOI: https://dx.doi.org/10.3390/agriculture12081205]

40. Xie, L.; Luo, B.; Zhong, W. How Are Smallholder Farmers Involved in Digital Agriculture in Developing Countries: A Case Study from China. Land; 2021; 10, 245. [DOI: https://dx.doi.org/10.3390/land10030245]

41. Zhang, Q.; Yan, B.; Huo, X. What Are the Effects of Participation in Production Outsourcing? Evidence from Chinese Apple Farmers. Sustainability; 2018; 10, 4525. [DOI: https://dx.doi.org/10.3390/su10124525]

42. Nie, T.; Huang, J.; Zhang, Z.; Chen, P.; Li, T.; Dai, C. The inhibitory effect of a water-saving irrigation regime on CH₄ emission in Mollisols under straw incorporation for 5 consecutive years. Agric. Water Manag.; 2023; 278, 108163. [DOI: https://dx.doi.org/10.1016/j.agwat.2023.108163]

43. Shi, H.B.; Lv, K.Y.; Luan, J.D. Land endowment, supporting policies and the expansion of farmers’ business scale: An analysis based on the survey data of 1040 farmers in four provinces. J. Northwest Univ. Agric. For. Sci. Technol. Soc. Sci. Ed.; 2019; 19, pp. 142-151. (In Chinese)

44. Chen, C.; Tang, R.D. The impact of rice production trusteeship services on Farmers’ land transfer: From the perspective of farmers’ scale differentiation. J. Nanjing Agric. Univ. Soc. Sci. Ed.; 2020; 20, pp. 156-166. (In Chinese)