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.

The adoption of innovations may boost the transition to sustainable agricultural models. Among these innovations, precision farming offers a fundamental contribution to sustainable soil management and the improvement in product quality. The work is set against this background and aims to analyse the rate of introducing precision farming tools and the variables that prevent/facilitate this adoption. Although adoption rates in Italy remain relatively low, it is vital to underline the obstacles that limit the broader use of precision agricultural technologies inside farms. To this end, the literature has highlighted various elements of complexity (farm characteristics, socio-economic and psychological), which can hinder or generate perceived complexity and significantly reduce the potential for technology adoption. In this context emerges the increasing importance of public and private activities related to knowledge transfer. The paper focuses on agricultural knowledge and innovation systems, which are also relevant in light of the recent proposal for the new regulation on rural development. The awareness–knowledge–adoption–product (AKAP) sequence was used to reveal the gap between the potential and actual adoption of innovation on Italian farms to comprehend the adoption process and identify relevant barriers and the role knowledge systems played. Empirical findings show that AKIS has a critical mediating function in promoting innovation uptake. Strengthening knowledge systems, acting on the different phases of the AKAP sequence, could allow a greater understanding of precision agriculture techniques and bottlenecks to adoption.

Research dealing with the adoption of various precision agriculture technologies has shown that guidance and recording tools are more widespread than reactive ones (such as variable rate technology), with much lower utilization rates in European case studies. This study aims to analyze the propensity to innovate variable rate technologies among young Italian farmers. A cluster analysis was carried out revealing four groups. The first two groups represent non-adopters who think technological innovation is very complex from a technical point of view, as well as not very accessible as capital-intensive technology. The third and fourth groups represent adopters. The third reports an early level of adoption, still considering the cost of access a major barrier to technology implementation. The fourth, on the other hand, shows a more intensive level and considers the lack of institutional support a major limitation. The cluster with the most intensive adoption is characterized by the youngest age group, the farms with the largest size, and a prevalence of female entrepreneurs. The need for management training in day-to-day business operations upon adoption is detected for all groups. This paper identified relevant drivers and barriers in characterizing the adopting farm of variable rate technologies. Results may offer insights to the policy maker to better calibrate support interventions.

This paper deals with the analysis of decision-making processes at the family-farm level with reference to the transition towards sustainable agriculture. Despite literature that has underlined the relevance of education in strategic decision making, less attention has been devoted to the (family) collective decision-making process by taking into account the maximum level of education of the family members regardless of the position and whether they are the manager of the family farm or not. Therefore, this paper tries to fill a gap in literature by emphasizing the family farm’s collective decision-making process. In order to empirically measure this relevance, an econometric model was carried out that allowed us to evidence clear differences in the transition paths among various typologies of family farms on the basis of the level of education. Our results confirm the impact of education at the collective family level on transition towards more sustainable agricultural practices. This is particularly true in remote rural areas, where the transition is realized with higher intensity with respect to other territorial contexts. This brings about policy implications on enskilling farmers and upgrading their level of human capital.

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.

Italy is among the most important countries in Europe for milk production. The new European policies encourage a transition towards sustainability and are leading European dairy farms to follow new trajectories to increase their economic efficiency, reduce their environmental impact, and ensure social sustainability. Few studies have attempted to classify dairy farms by analyzing the relationships between the structural profiles of farms and the social, environmental, and economic dimensions of sustainability. This work intends to pursue this aim through an exploratory analysis in the Italian production context. The cluster analysis technique made it possible to identify three types of dairy farms, which were characterized on the basis of indicators that represented the three dimensions of sustainability (environmental, social, and economic sustainability) and the emerging structural relationships based on the structural characteristics of the dairy farms. The classification made it possible to describe the state of the art of the Italian dairy sector in terms of sustainability and to understand how different types of farms can respond to the new European trajectories.