Agriculture is undergoing a new technological revolution, promoted by the fourth industrial revolution, and, at the same time, it is experiencing a technological transformation due to the adoption of Precision Agriculture. However, this adoption is occurring at a slower pace than expected, as confirmed by the abundant literature on the factors affecting the adoption. Despite this literature, the factors related to farmer behaviour and agricultural operations management are little explored. Furthermore, little research has been carried out on Agriculture 4.0, paying little attention to people and social impacts of new technologies. However, some researchers suggest the farmer behaviour and the agricultural operations management as potential areas of investigation. Therefore, this work proposes to explore the factors related to farmer behaviour and the agricultural operations management to explain the Precision Agriculture adoption in the context of Agriculture 4.0, and to apply the behavioural approach and the Theory of Planned Behavior. Considering the incipient theoretical basis on Agriculture 4.0 and the exploratory nature of the research questions, in this work a multimethod methodology is adopted, combining an inductive and a deductive approach. As a result of a systematic literature review, expert interviews and case studies, the factors explaining the adoption and a model of irrigation operations management are proposed. This model provides a conceptual framework to study the identified factors, the relationships between them, the theoretical propositions generated as a result of the inductive approach, and, consequently, the adoption. In contrast to previous literature, this model distinguishes between irrigation planning and irrigation scheduling. A System Dynamics simulation model is built to study the relationships between the factors. Two simulation scenarios are developed, considering the irrigation system of center pivot, to study the causal relationships between the factors involved in irrigation planning and the adoption of satellite sensing technology and the use of weather station technology. Simulation results suggest policies to drive the adoption and highlight the role of inputs, water and energy, in operations management. A sensitivity analysis, varying the unit cost of water, suggests a change in the farmer mental model regarding the irrigation planning. Furthermore, the results of the case studies make it possible to explore the relationships between the adoption, educational level and farmer training. The factors identified contribute to broadening the understanding of the determinants of Precision Agriculture adoption in the 4.0 era, adding farmer behaviour and operations management to the literature and explaining the still untapped potential for irrigation operations. As a result of empirical research, a definition of Agriculture 4.0 is proposed, highlighting the key role of IoT sensing technologies in the evolution from Precision Agriculture to Agriculture 4.0. Finally, a future research agenda is formulated, highlighting the proposal for an integrated management model of agricultural operations based on the three agronomic pillars, culture, climate and soil