Understanding and modeling food flow networks across spatial scales

Lin, Xiaowen

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https://hdl.handle.net/2142/104794 Copy

Description

Title

Understanding and modeling food flow networks across spatial scales

Author(s)

Lin, Xiaowen

Issue Date

2019-04-10

Director of Research (if dissertation) or Advisor (if thesis)

Konar, Megan

Doctoral Committee Chair(s)

Konar, Megan

Committee Member(s)

• Sivapalan, Murugesu
• Kumar, Praveen
• Guan, Kaiyu
• Ruddell, Benjamin

Department of Study

Civil Engineering

Discipline

Civil Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• food flow network
• modeling

Abstract

We live in an increasingly global society, in which food commodity transfers enable production and consumption activities to be separated in space via complex supply chains. Here, we refer to the movement of food commodities from one location to another as ‘food flows’, reserving the term ‘food trade’ for the international exchange of food commodities between countries. Food flows underpin the complex food supply chains that are prevalent in our increasingly globalized world. Recently, much effort has been devoted to evaluating the resources (e.g. water, carbon, nutrients) embodied in food trade. Now, research is needed to understand the scientific principles of the food commodity flows that underpin these virtual resource transfers. What are the network properties of food flows within a country? How do food flows vary with spatial scale? How can we model food flows in locations without empirical information? This dissertation seeks to address these three overarching questions. First, this dissertation presents a novel application of network analysis to empirical information of domestic food flows within the USA, a country with global importance as a major agricultural producer and trade power. We find normal node degree distributions and Weibull node strength and betweenness centrality distributions. An unassortative network structure with high clustering coefficients exists. These network properties indicate that the USA food flow network is highly social and well-mixed. However, a power law relationship between node betweenness centrality and node degree indicates potential network vulnerability to the disturbance of key nodes. We perform an equality analysis which serves as a benchmark for global food trade, where the Gini coefficient = 0.579, Lorenz asymmetry coefficient = 0.966, and Hoover index = 0.442. These findings shed insight into trade network scaling and proxy free trade and equitable network architectures. Second, this dissertation presents an empirical analysis of food commodity flow networks across the full spectrum of spatial scales: global, national, and village. We discover properties of both scale invariance and scale dependence in food flow networks. The statistical distribution of node connectivity and mass flux are consistent across scales. Node connectivity follows a generalized exponential distribution, while node mass flux follows a Gamma distribution across scales. Similarly, the relationship between node connectivity and mass flux follows a power law across scales. However, the parameters of the distributions change with spatial scale. Mean node connectivity and mass flux increase with increasing scale. A core group of nodes exists at all scales, but node centrality increases as the spatial scale decreases, indicating that some households are more critical to village food exchanges than countries are to global trade. Remarkably, the structural network properties of food flows are consistent across spatial scales, indicating that a universal mechanism may underpin food exchange systems. Finally, we use our understanding of food flow networks across spatial scales to model food flows at resolutions for which empirical information is not available. Detailed spatial information on food flows is rare, but it is increasingly important to understand spatially resolved food flows to assess their embodied resources and vulnerability to supply chain disturbances. To this end, we develop the Food Flow Model, a data-driven methodology to estimate spatially explicit food flows for subnational locations without data. The Food Flow Model integrates machine learning, network properties, production and consumption statistics, mass balance constraints, and linear programming. We use the Food Flow Model to infer food flows between counties within the United States. Specifically, we downscale empirical information on food flows between 132 Freight Analysis Framework (FAF) locations (17,292 potential links) to the 3,142 counties and county-equivalents of the United States (9,869,022 potential links). Future work can build on these efforts to improve our understanding of vulnerabilities within a national food supply chain, determine critical infrastructures, and enable spatially detailed footprint assessments.

Graduation Semester

2019-05

Type of Resource

text

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http://hdl.handle.net/2142/104794

Copyright and License Information

Copyright 2019 Xiaowen Lin

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