Encouraging farmers to adopt environmentally friendly technology through the rational use of social learning and agricultural technology extension is an effective way to overcome the bottleneck caused by the slow diffusion of environmentally friendly technology. Based on expanding the existing objects of research on farmers’ technology adoption behavior, this paper examines the influence of social learning and agricultural technology extension on farmers’ environmentally friendly technology adoption behavior from a dynamic perspective. In doing so, it enriches theoretical and empirical research on farmers’ technology adoption behavior. Specifically, this paper takes fertigation technology as an example, constructs a dynamic analysis framework that is independent of the case study, and finds that social learning and agricultural technology extension, as the main channels for farmers to obtain technical information, can shorten the duration from awareness to the adoption of fertigation technology. Then, based on survey data, this paper uses the discrete-time cloglog model to conduct an empirical test. The empirical analysis supports the theoretical analysis results, and there is a complementary effect between social learning and traditional and new agricultural technology extension. Heterogeneity analysis shows that social learning and new agricultural technology extension have a greater marginal improvement effect on farmers’ fertigation technology adoption behavior in the middle-aged to young group, middle and high education degree group and above median land scale group. This paper provides not only new empirical evidence to explain farmers' technology adoption behavior under the background of the internet revolution but also a decision-making reference for how to accelerate the construction of multivariate complementary, collaborative and efficient agricultural socialized service systems.

Based on field survey data from 443 family farms in Shandong and Henan Provinces, the green control techniques(GCT) adoption behavior of family farms was measured in terms of adoption or non-adoption. Based on the global Moran’s I test, the Bayesian spatial Durbin probit model(BSDPM) was constructed, the appropriate spatial weight matrix was set, the optimal model for parameter estimation was selected, and the direct and spatial spillover effects of family farm characteristics on GCTs adoption behavior of family farms were decomposed by means of the partial differential method. The results show that the GCTs adoption behaviors of adjacent family farmers are spatially correlated and strongest when they are within 2.0 km of each other. Farm leaders’ educational level, degree of risk preference, financial status, number of laborers, understanding of GCTs and of the dangers of chemical pesticides, knowledge of other GCT adopters, frequency of communication with neighbors, participation in technical training and the strength of media publicity have significantly positive effects on the GCT adoption behaviors of family farms, which are mainly influenced by the direct effects of characteristic variables. However, the spatial spillover effects of neighboring family farmers’ participation in technical training, number of laborers, and financial status cannot be ignored. This result provides not only theoretical support for the demonstration and extension of the effectiveness of GCTs but also a reference for the selection of family farms as model households.

Green control techniques are helpful for ensuring the safety of the ecological environment. Their application offers important support for clean production among farmers, but their application level is not high because farmers are risk-averse. To solve this dilemma, farmers’ degree of risk aversion was measured through an experimental economics approach applied to the results of a survey of 385 vegetable farmers in Shandong Province. This study adopts an endogenous switching probit model to reveal the impact of vegetable farmers’ risk aversion and cooperative membership on their adoption of green control techniques. Furthermore, it examines whether cooperative membership helps alleviate the inhibitory effect of risk aversion on the adoption of green control techniques among vegetable farmers. The results show that vegetable farmers’ degree of risk aversion has a significant and positive impact on their cooperative membership and a significant and negative impact on their adoption of green control techniques, while their participation in cooperatives may not only promote their adoption of green control techniques but also alleviate the inhibitory effect of risk aversion on such adoption. To promote cleaner production by farmers, policymakers should reduce the risk of adopting green control techniques for farmers, increase support for cooperatives and improve the internal conditions and external environment to promote the adoption of green control techniques among farmers.

With the development of Internet information technology and portable mobile terminals, a new agricultural technology extension mode has emerged that uses new media such as WeChat public accounts and apps. However, empirical studies on the effectiveness of the new agricultural technology extension mode have not been reported. To compensate for the shortage of existing research, this article uses survey data from 759 peasant households in Shandong Province and Henan Province to measure soil fertilizer technology, water-saving irrigation techniques and the prevention and control of plant diseases and insect pests through green technology. A score matching method is used to explore new agricultural technology extension modes for farmers’ direct effects and spillover effects from technology adoption behavior as well as distribution effects. A robustness inspection instrumental variable method is used to identify the effectiveness of the new agricultural technology extension mode and provide extensive information to evaluate the effectiveness of the analytical framework. The study finds that the new agricultural technology extension mode improves the technology adoption level of farmers to a certain extent with a partial spillover effect, and farmers of different ages and with different sizes of farmland benefit differently. When guiding farmers to use the new agricultural technology extension mode, it is important to consider the information diffusion among farmers who have already adopted this mode and to disseminate this information to elderly and small-scale farmers.

In China, land tenure security refers to the stability of land management rights in the context of the Three Rights Separation Policy, according to which rural land ownership rights, land contract rights, and land management rights can be separated and land management rights can be freely transferred. We apply endogenous switching probit models to a dataset of 443 family farms in Shandong and Henan Provinces to investigate the influence of social capital and land tenure security on family farms’ adoption of green control techniques (GCTs). We develop simplified equations to verify whether social capital strengthens the positive effect of land tenure security on family farms’ GCT adoption. Specifically, we focus on the different effects of embedded and disembedded social capital. Our findings show that both social capital and land tenure security have significant positive effects on family farms’ adoption of GCTs. Furthermore, social capital strengthens the positive effect of land tenure security on GCT adoption, but the effects of embedded and disembedded social capital are different. Government policies should strengthen the stability of land management rights and cultivate the social capital of family farms, especially disembedded social capital.

Using survey data of 375 family farms in five provinces of the Huang-Huai-Hai Plain, this paper conducts a comprehensive measurement of family farms’ welfare within the framework of the capability approach theory. Furthermore, using an endogenous switching regression model and a multinomial treatment effects model, this paper evaluates the impact of the adoption or non-adoption of green control techniques on family farms’ welfare and estimate the welfare effects of the degree and timing of adoption. This research finds that the average treatment effect on family farm welfare with and without adopting green control techniques is significant, at 0.084 and 0.046, respectively. Therefore, green control techniques help to improve the welfare level of family farms. Compared with family farms that do not adopt green control techniques, the welfare level of family farms adopting a high or low degree of green control techniques increases by 22.63% and 16.42%, respectively, and the welfare level of family farms given the early or late adoption of green control techniques increases by 5.87% and 7.57%, respectively. Therefore, the welfare effect of a high degree of adoption on family farms is greater, and the welfare level of family farms with late adoption is higher.

Based on field survey data of 366 traditional households (THs) and 364 family farms (FFs) from Huang-Huai-Hai Plain, a discrete-time cloglog model for parameter estimation was constructed to reveal factors that affect the two types of farms’ duration from the awareness to the adoption of green control techniques (GCTs). Differences in the influencing factors affecting the duration of the two types of farmers were also discussed. The research results are as follows. First, the duration from awareness to adoption of GCTs is significantly shorter in FFs than that in THs. Second, a higher degree of education, risk preference, family financial status, perceived ease of use and usefulness of the technique, and extension of media and supervision of agricultural technique extension departments of local governments significantly reduce the duration from awareness to adoption of GCTs by THs and FFs, whereas a male head of household prolongs the duration. Third, the age, farm size, and number of laborers exert different impacts on the duration from awareness to adoption of GCTs by THs and FFs.