Purpose <p>Digital farming technologies encompass a diverse range of tools, such as in-field sensors for data collection, variable-rate input application techniques, decision-support software, and advanced drones or robots to automate agricultural processes. This paper proposes an integrative empirical framework to identify the factors associated with soybean farmers’ decisions on the adoption and intensity of use of digital technologies.</p> Methods <p>A cross-sectional primary database comprising 150 Brazilian soybean farmers was used to assess the adoption of eight digital technologies: autopilot, georeferenced soil sampling, yield monitor, yield map, variable-rate seeding, variable-rate fertilizer application, farm management software, and drones. The study evaluates the role of ten explanatory variables, including personal and behavioral farmers’ characteristics, technology expectations, management practices, farm characteristics, and institutional factors. Logistic regression models were employed to analyze the determinants of adoption for each technology, while a Poisson count model assessed the factors affecting the intensity of digital technology use.</p> Results <p>Soybean area, flat slope, access to credit, use of private consultancy services, farmer innovativeness, experience in soybean cultivation, and formal education were positively associated with both the adoption and intensity of digital technology use. Conversely, high perceived costs function as a barrier to adoption and intensity in use, alongside specific constraints affecting individual technologies.</p> Conclusion <p>These findings provide valuable guidance for extension professionals, farmers, technology developers, and policymakers in identifying and addressing barriers to the adoption and intensity in use of digital technologies.</p>