10th U6+ Consortium of African Universities International Conference

Agrifood incubation hubs are increasingly promoted as pathways for youth employment, enterprise development, and food systems transformation. However, limited attention has been given to what application-stage evidence reveals before incubation begins, particularly who applies, how applicants articulate their ventures, and whether selection tools can distinguish livelihood-oriented enterprises from innovation-ready ventures. This study draws on application data from an agrifood incubation hub to examine entrepreneurial readiness, business model articulation, and evidence-based selection challenges. Using a mixed-methods case study approach, the paper analyses applicant profiles, venture stage, sector focus, funding history, application completeness, review outcomes, and Business Model Canvas responses. The findings reveal strong demand for agrifood incubation, but also expose a significant readiness gap. Many applicants presented conventional or livelihood-oriented businesses with limited evidence of innovation, market differentiation, scalability, or clearly defined value propositions. Incomplete Business Model Canvas submissions further indicate the need for structured pre-incubation support before entrepreneurs enter formal incubation. The study also identifies application data quality as a critical constraint. Missing, inconsistent, or incomplete fields limited fair comparison among ventures and weakened evidence-based selection. The findings suggest that incubation application systems should serve not only as administrative screening tools, but also as diagnostic instruments for identifying entrepreneurial capacity gaps, training needs, and ecosystem constraints. The paper contributes to debates on agrifood entrepreneurship, incubation design, and youth enterprise development by showing that the transition from livelihood enterprise to innovation venture is not automatic. It requires deliberate support in business model development, market validation, innovation framing, and data-informed selection workflows.