Smart Farming Technology Adoption and Perceived Impacts: Evidence from Italian Farms.

While a growing body of literature highlights the potential of smart farming technologies (SFTs) to improve farm yield, efficiency, and sustainability, existing evidence remains largely focused on perceived drivers, barriers, and intentions to adopt, rather than observed outcomes among actual adopters. Moreover, little attention has been given to whether these technologies deliver economic and resource-related impacts under real farming conditions. This study addresses these gaps by providing a national-level assessment of smart farming adoption and perceived impacts in Italy, focusing on identifying determinants of adoption, quantifying perceived economic and resourcesaving impacts, and examining heterogeneity across technologies and regions. Using survey data from 345 farms and applying descriptive analysis alongside Probit and Heckman selection models, we examine the adoption of seven categories of SFTs and their impacts on revenue, costs, water, labour, fertiliser, and pesticide use. Results indicate that adoption is primarily determined by farmers' digital knowledge, cooperative membership, gender, and arable cropping area, while formal education and age play more limited roles. Among SFTs, robotics and autonomous vehicles, decision support systems (DSS), and data collection technologies consistently showed the highest mean impacts, whereas management-oriented tools such as farm management information systems (FMIS) and cloud platforms show more modest gains. Although adoption is higher in the northern and central regions, farms in the South and the Islands report significantly greater revenue growth, cost reductions, and input savings. Overall, the findings suggest that SFTs primarily function as a mechanism for narrowing performance gaps by delivering the largest marginal benefits where constraints are most severe.