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.

Purpose – The purpose of the paper is to analyse the main dynamics of the Italian food system, focusing on the relationships between the inclination to innovate and a set of firm characteristics. Design/methodology/approach – The empirical analysis includes two steps. In the first, principal component analysis is carried out in order to identify factors that can explain the features that differentiate Italian food firms. In the second phase the role of such factors on innovation behaviour is quantified by means of a logit model. Findings – The empirical analysis showed that, in the Italian food sector, innovation adoption follows different patterns when product or process innovation is considered. In particular, the probability of introducing product innovation is influenced by the quality of human capital, the geographical context and, to a lesser extent, the age of the firm. Research limitations/implications – The research is restricted in so far as it only considers the Italian food sector. Because the data survey is representative only at the level of the manufacturing industry as a whole and excludes firms with fewer than ten employees, the analysis for the food sector can only be indicative. Practical implications – This paper provides a useful source of knowledge on the innovative behaviour of Italian firms. This highlights the need to provide for diversified intervention strategies to stimulate and enforce innovation in the Italian food sector. Originality/value – The research provides some initial insight into firm perspectives in the role of innovations to enhance firms’ market competitiveness.

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.