IntroductionThis study examines stakeholder perspectives on the strengths, limitations, and potential uses of a climate-driven agricultural land-suitability model developed to assess future crop suitability across British Columbia. The study contributes to understanding how such models can be used to support long-term agricultural planning and climate-change adaptation.MethodsThe model integrates climate, land-classification, and property-value data. Two online focus groups involving 19 participants from farming, government, academia, community organizations, consulting, and private-sector settings were conducted in October 2025. Participants explored the model outputs using the Agrilyze spatial-data platform and discussed the advantages and shortcomings of using them to inform agricultural practice, policy, and planning. Transcripts were checked and analyzed in NVivo using an iterative combination of deductive and inductive thematic coding.ResultsThe analysis produced three main findings. First, projected climatic suitability is meaningful for practice only when interpreted alongside crop requirements, water availability, infrastructure, market access, and production economics. Second, the capacity to respond to emerging opportunities or declining suitability is uneven and shaped by land values, institutional support, local knowledge, and access to appropriate technologies. Third, the model is most useful for regional exploration, communication, and long-term planning rather than site-specific prescription. Participants also identified risks of misuse, particularly where projected declines could be mobilized to justify agricultural land conversion.DiscussionThis study advances understanding of end-user perspectives on the usefulness of spatially explicit models for climate-change adaptation, planning, and policy in the agricultural sector. The findings reveal that the usefulness of such models depends not only on predictive performance but also on interpretability, governance, and the systems through which model information is translated into decisions.