Moving to System Approaches: Building System Dynamic Models of Local Food System Dynamics

Introduction: Local food systems are composed of multiple interacting and dynamic components, and strategically investing in them can have positive impacts on community health and economic development. Substantial research, policy, and programming efforts are targeting different factors within food systems. Yet, many of these efforts have not yielded clear answers about the most effective strategies to improve outcomes related to food and nutrition security, as well as local economic growth. System modeling techniques are increasingly being employed by researchers in a variety of health-based fields as our general understanding of the complexities of population health issues increases. Utilizing a participatory system dynamics approach, the Modeling the Future of Food in your Neighborhood study aims to explore the dynamics of food systems within the low-resource neighborhoods of Cleveland, Ohio. A central idea of the study is that a healthy, equitable, and sustainable food system can be used as a tool to lift people out of poverty.

Methods: Data for this paper were drawn from the Core Modeling Team meetings, comprised of community residents, workers and volunteers in food-based nonprofits, food system researchers, and food retailers (N=20). Qualitative data collected using a participatory system dynamic modeling approach was used to develop a Causal Loop Diagram of the key attributes and tipping points related to healthy, sustainable, and equitable food systems in Cleveland.

Results: Economic opportunity, nutrition equity, and food security were identified as main outcomes of a healthy, sustainable, and equitable food system. Within these outcomes, 28 additional critical variables were identified, such as employability for living wages, use of emergency food assistance, fruit and vegetable incentives, and functional health. These variables were used to create a causal loop diagram of the local food system, which consisted of nine reinforcing loops and five balancing loops. The variables were further operationalized into measures of the preferred state and ranked in terms of their importance when it comes to measuring a successful food system. These measures were supported with data sources from the team’s various organizations and institutions.

Conclusion: This study utilized a participatory system dynamic modeling approach to explore the underlying forces influencing the food system within Cleveland’s low resource neighborhoods. Findings resulted in the development of a causal loop diagram consisting of 28 dynamically interdependent variables influencing Cleveland’s food system that may be utilized to guide future food system related research, interventions, policies, and programs.