Purpose This study aims to investigate the territorial patterns of rural innovation in Brazil by integrating technological, institutional and sociodemographic dimensions. It aims to identify distinct innovation ecosystems and provide evidence-based guidance for differentiated policy design. Design/methodology/approach The study uses a hybrid spatial clustering methodology using the Density-Based Spatial Clustering of Applications with Noise algorithm on municipal-level secondary data from Brazilian official databases. Variables include mechanization, irrigation, genetically modified seed adoption, educational attainment, urbanization and participation in public policies such as agroecology, inclusion programs and local markets. The analytical framework is grounded in a multidimensional literature review on rural innovation systems. Findings The analysis identified nine rural innovation clusters with distinct structural and institutional profiles. Technological capacity alone does not guarantee innovation; rather, the interaction between institutional articulation and sociodemographic conditions shapes innovation trajectories. Some clusters exhibit high technological intensity but weak institutional engagement, while others demonstrate strong policy integration despite moderate infrastructure. Research limitations/implications The study relies on cross-sectional secondary data; future research should incorporate longitudinal designs and primary data to capture dynamic innovation processes. Practical implications The results offer policymakers a tool for designing differentiated rural innovation policies aligned with local capacities and governance contexts. Social implications The findings support more equitable innovation policies that address structural disparities across Brazilian rural territories. Originality/value The study contributes a replicable hybrid clustering approach that integrates spatial analysis with innovation system theory. It expands rural innovation research by synthesizing sectoral, governance and demographic dimensions, offering actionable typologies for policy targeting and territorial development.
Satolo et al. (Fri,) studied this question.