The majority of people in the world live in urban areas, and their high population densities, heavy reliance on external sources of food, energy, and water, and disproportionately large waste production result in severe and cumulative negative environmental effects. The proposed work will create a system-of-systems analytical framework, integrating social and biophysical models for an urban FEWS via an innovative co-simulation approach to describe current and predict future conditions, with an emphasis on local (urban and urban-adjacent) food production. This framework will enable simultaneous analyses of climate dynamics, changes in land cover, built forms, energy use, and environmental outcomes associated with a set of five potential drivers of system change related to policy, crop management, technology, social interaction, and market forces affecting local food production. The goal is to use data-driven co-simulation to enable coupling of disparate (spatial/temporal scale) FEW system simulation models to quantify the environmental footprint (energy use and water quality outcomes) for current systems, and to determine if environmental effects are decreased and local food supply increased as influenced by drivers that influence food production in urban and urban-adjacent areas. The Des Moines-West Des Moines Metropolitan Statistical Area (MSA) serves as a test bed to represent cities embedded in a rain-fed agricultural area. The transdisciplinary team will use accessible simulation models and expert knowledge to guide modeling for individual and combined systems in the urban FEWS nexus.
An innovative process/framework will enable the effective integration of social and biophysical models for urban and urban-adjacent FEW systems. The use of empirical data to create an urban-agriculture ABM that describes current/predicts future decision-making by producers and consumers is a novel contribution that will further understanding of urban agriculture and use of local food production to increase city resiliency and sustainability. The unique approach to linked parameterization of a comprehensive set of single-system models allows for characterization of current and future conditions in individual systems as well as the urban FEW system-of-systems under predicted climate change. Use of adaptive sampling strategies and creation of a functional mock-up interface framework will enable efficient co-simulations exploring the influence of individual drivers of system changes and allow analyses of critical system feedbacks, thresholds, and resiliency. Another novel contribution of this project is the evaluation of the five drivers for modeling that influence human decisions leading to FEW systems changes. These analyses will provide critical new knowledge about how negative environmental impacts of urban FEW systems can be decreased and local food supplies can be increased. The open source coupling standard created in this project will be made available to ensure the broader research community can use it to further analyze other FEW systems of systems.
The proposed research was developed in collaboration with local stakeholder participants, who have expressed strong interest in improving local food systems as part of a larger set of sustainability strategies. This research will create scalable and transferable models that will support efforts to simultaneously improve local food production, reduce energy use, and protect surface water quality in urban and urban-adjacent landscapes and that will be shared widely through a project website and direct dissemination via workshops and professional presentations. It will support ongoing efforts in many urban areas to promote local food production, secure health and livelihoods, and preserve environmental resources. Knowledge gained from this project will also be directly integrated into undergraduate and graduate education in architecture, psychology, urban ecology, urban planning, sustainable agriculture, data science, and engineering courses, contributing to the development of the next generation workforce.
This work is supported by the National Science Foundation, Award # 1855902. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.