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The process of adopting innovation, especially with regard to precision farming (PF), is inherently complex and social, and influenced by producers, change agents, social norms and organizational pressure. An empirical analysis was conducted among Italian farmers to measure the drivers and clarify “bottlenecks” in the adoption of agricultural innovation. The purpose of this study was to analyze the socio-structural and complexity factors that affect the probability to adopt innovations and the determinants that drive an individual’s decisions. Preliminary results found high levels of adoption among younger farmers, those that had a high level of education, those with high intensity of information, with large farm sizes, and high labor intensity. A logit model was used to understand the role played by labor intensity and perceived in the adoption process. In light of the Common Agricultural Policy Reform post 2020, the findings suggest relevant policy implications, such as the need to increase awareness of PF tools and foster dissemination of information aimed at reducing the degree of perceived complexity.

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This paper deals with Geographical Indications (GI) as a critical strategy aimed to boost agri-food local supply chain and rural development. The main concern of this work, therefore, is to verify how the identification of these products and establishment of GI systems can propel sustainable development in local areas in Africa. Two assumptions are at the basis of our paper: viable GI systems can be established in Africa, and the establishment of a GI system can stimulate rural development. In order to clarify the process of GI setting up and valorization, we will test GI virtuous circle and the rural web, with the purpose of exploring the multiple dimensions (endogeneity, social capital, sustainability, novelty, institutional arrangements, governance of markets) interfering in the process at both the agri-food supply chain and rural context levels of analysis. The analysis is applied to the shea butter production in Ghana and will employ primary data. Questionnaires and interviews were administered in the study area (Yendi Municipality of Ghana) to gather both qualitative and quantitative data. Our analysis confirms the potentialities of the shea butter as GI, by underlying its engine for the development of local rural communities.

In this work, we explore the link between the perception of complexity and the possibility of adopting precision agricultural tools (PATs). Many studies have analysed the role of perception, mostly considering it a determinant of adoption on the same level as other contextual factors. In contrast, this study contributes by assuming that farmers' perceived complexity is the main factor influencing their propensity to innovate and should be analysed on a different level. Starting from this assumption, a new theoretical model is proposed with the aim of studying the “factors–perception of complexity–adoption” (FACOPA) process. To test the validity of our hypothesis, a survey is conducted based on a purposive sample of 285 farmers. First, a linear regression model permits us to identify determinants of the perception of complexity. Then, a multinomial logistic model is used to determine which aspects of perceived complexity may affect the choice to adopt precision farming tools made by three different types of agricultural entrepreneurs: adopters, non-adopters, and planners. First, the linear regression results show that socio-structural variables have a logical relationship with perceived complexity, with age, farm size, the intensity of information and the intensity of work being significant. Then, the multinomial logistic model highlights that non-adopters perceive almost all aspects of complexity as barriers to adoption. Planners show a lower perception of complexity than non-adopters, with complexity being determined by financial and network aspects. The results provide interesting suggestions for policy-makers. Indeed, the FACOPA model offers insights into an intervention framework in which policy measures can be diversified to disseminate PATs based on farmer categories. Non-adopters require a broader set of policy instruments, while planners should be encouraged to become adopters through financial support and the activation of innovation networks.