Foundations of a learning system for food system transformation under uncertainty

Abstract Despite a broad consensus on the necessity of fundamental change, endeavors to transform food systems appear to have reached an impasse. Greater engagement with the uncertainty of food systems could open up new ways of triggering transformation directed towards achieving more sustainable and inclusive outcomes. As a way of reorienting current food system change efforts to better embrace uncertainty, we propose a framework for a transformative learning system that serves two aims. First, the framework highlights the importance of locally led action, experimentation, and learning, providing a way of focusing on the core capacities and skills needed to act in the face of uncertainty. Second, it outlines the different types of learning functions that need to operate at different scales of food systems to trigger disruptive, coordinated, and more democratic change processes. The operationalization of this framework necessitates shifts in roles and ways of working across the landscape of food system interventions. The discussion will address the who and how of this potential change, as well as its subsequent impact on the operational modalities of individuals, the process of change itself, and the structures and institutions involved in the process. We argue that embracing uncertainty and the focus on learning has the potential to facilitate a more agile and locally relevant change process. This would allow actors to learn from decentrally pursued food systems reforms, leading to the emergence of diverse pathways that complement on-going efforts and potentially accelerate transformation efforts.