Agricultural commodity flow networks are a critical component of modern food systems. They also serve as conduits for pest, pathogen and contaminant dispersal. Understanding these food flows and their role in invasive species spread is essential for food security, and preserving biodiversity, health and economic stability. This project seeks to develop (i) novel network representations and analytics to understand domestic agricultural commodity flows in the United States (ii) pest spread and impact models that account for natural and human-mediated pathways of spread. We apply our models to the study of Tuta absoluta, a devastating pest of the tomato crop.The project will employ state-of-the-art statistical and machine-learning techniques for data integration and network construction. We will develop methods for structural and dynamical analysis of these networks in a novel context of directed and time-varying networks. Agent-based epidemiological models from the infectious disease literature will be adapted for the pest spread model with implementation of various types of interventions. Partial equilibrium models will be used for economic impact analysis.The project will contribute novel network-based approaches for data integration, data analytics and computational modeling. In the context of invasive species, the developed tools will provide policy makers with guidance and support to identify vulnerabilities in the food system, inform monitoring efforts and assess various intervention strategies. These analyses will be particularly valuable and timely to address the imminent threat of T. absoluta invasion. The project will nurture graduate, undergraduate and K-12 programs through interdisciplinary research and team science.
National Institutes of Food and Agriculture