Responsible Scaling for Agri-Food System Transformation: A Phased R4D Framework

Context: Scaling agricultural innovations is widely promoted as a pathway to transforming agri-food systems under climate change and resource pressures. Yet innovations that succeed in pilots often underperform or generate unintended consequences at scale, reflecting continued reliance on linear ”pilot-to-scale” models and weak integration of responsibility in ex-ante decisions.Objective: This paper develops a practical framework to support responsible scaling for transformative impact by embedding responsibility principles—anticipation, inclusion, reflexivity, and responsiveness—within research-for-development (R4D) decision processes across the innovation lifecycle.Methods: Drawing on the literatures on scaling science, responsible innovation, and agri-food system transformation, we propose a phased R4D framework that structures scaling as a sequence of decision phases: screening, feasibility assessment, field testing, scaling pathway design, and impact assessment. The framework is illustrated with evidence from a climate-smart agriculture program, demonstrating how it supports prioritization, learning, and adaptive management under real-world constraints.Results and Conclusions: The framework shows that responsible scaling is less about accelerating adoption and more about governing trade-offs across innovation pathways. Screening, feasibility, and field testing support selective learning, while pathway design and impact assessment address institutional, financial, and political economy dynamics. Together, these phases reframe scaling as an iterative decision process rather than a linear transition from pilot to expansion.Significance: The framework operationalizes responsible scaling as an ex-ante decision process, equipping researchers and funders with a practical tool to align innovation strategies with inclusive and sustainable transformation. It bridges conceptual debates and R4D practice while helping navigate uncertainty and political economy constraints.