Abstract
In recent years, as an important implementation measure to realize agricultural green development and rural revitalization, organic agriculture faces both opportunities and challenges and needs public policy support. Farmers are the direct production decision-maker, clarifying the influencing mechanism of farmers’ organic agriculture adoption is reasonable in allowing the design of effective extension policies to promote the development of organic agriculture for the public sector. On the basis of questionnaire survey and on-the-spot interview data of 516 farmers in seven cities in Xinjiang Uygur Autonomous Region, this study constructed a spatial Durbin Probit model to explore the influencing factors and their spatial effects of farmers’ adoption of organic agriculture. Direct effects and spatial spillover effects of characteristic variables on farmers’ organic agriculture adoption were determined using the partial differential method. The main findings revealed that first, 59.3% of the farmers adopted organic agriculture, with income expectation being the key influencing factor. In addition, more organic farmers were located in southern Xinjiang and less in northern Xinjiang, indicating that poor ecological suitability does not constitute an obstacle to the development of organic agriculture. Compared with conventional farmers, organic farmers had a positive understanding of organic agriculture, were more willing to obtain relevant information about organic agriculture through social networks, realized interactive learning and mutually beneficial support, joining cooperative organizations more actively, and had a higher degree of social trust among similar farmers. Second, the demonstration area, cooperative organizations, social networks, social norms, contract guarantees, social trust, risk preference, guiding policy, incentive policy, number of laborers, farmers’ cognition degree of organic agriculture, and age had significant, positive, and independent effects on the adoption of organic agriculture. The total effects of these factors decreased following the above order. It is worth noting that there were differences in the role of social networks in different dimensions, more specifically, the positive effect of the industrial organization network on farmers’ adoption of organic agriculture was greater than that of the neighborhood network. From the perspective of government policies, there were differences in the impact of guidance policies and incentive policies on organic and conventional farmers, but there was no significant difference in restraint policies. Third, the adoption of organic agriculture by neighboring farmers had a positive spatial correlation. Farmers’ organic agriculture adoption was mainly influenced by the direct effects of the influencing factors. However, the neighbors’ spatial spillover effects cannot be ignored, especially regarding their participation in industrial organizations and the organic product certification demonstration area. The public sector can shift organic agriculture support policy toward the demonstration operators and promote farmers’ adoption of organic agriculture by increasing publicity, financial and technical support, cultivating and developing cooperation organizations such as agricultural leading enterprises and farmers’ cooperative organizations, establishing a national organic product certification demonstration (creation) area, and improving farmers’ awareness of organic agriculture. This study made the following contributions: first, it has investigated the influencing factors and spatial effects of farmers’ organic agriculture adoption based on survey data, helping to understand the spatial mechanism of farmers’ organic agriculture adoption and accounting for the lack of empirical evidence at the farmer level; second, it has empirically examined the role of social networks in different dimensions, which is crucial to understanding the role of neighborhood versus industrial organization forces in the diffusion of organic agriculture, which ultimately, can help policymakers to effectively induce behavioral changes by prioritizing programs that target either individual households or neighborhood networks and communities.
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