Community-based natural resource management (CBNRM) in Botswana's socio-ecological systems seeks to balance conservation and community livelihoods. Despite successes, challenges persist related to equitable resource distribution and sustainable use. We employed hierarchical cluster analysis (HCA) with k-means clustering for stratification, integrating qualitative and quantitative data using geospatial technologies. Uncertainty in our models was addressed via bootstrapping. Hierarchical clustering revealed distinct resource management zones (RMRZs), contributing to more precise target area delineation and equitable distribution of resources among communities. Our stratification techniques have identified key RMRZs, facilitating improved resource allocation strategies in Botswana's socio-ecological systems. Policy makers should consider these stratified zones for targeted interventions to ensure sustainable CBNRM outcomes. Community-based natural resource management, hierarchical cluster analysis, data integration, socio-ecological system, Botswana The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.