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Abstract This analysis quantifies the network dynamics, geographic concentration, and disparities in perishable food supply networks for temperature-controlled food shipments in the United States. The United States forms the core of global food systems and produces more high-quality data for network analysis than most other countries. We use the 2017 US Census Commodity Flow Survey and other publicly available data to derive empirical results from the Food Flow Model for perishable meats and perishable prepared foods. We identify the top ten counties for perishable food distribution and find that the Los Angeles and Chicago regions support the greatest volumes of perishable food movements. States that largely exist outside national perishable food networks are Arizona, Michigan, Montana, North Dakota, Texas, and West Virginia. Our analysis of US data highlights the importance of certain counties, states, and regions in perishable food networks and suggests areas where interventions could improve systems’ functions by increasing access to markets for farmers and access to food for underserved communities, especially those in rural regions. 

Abstract Irrigation is increasingly important to agricultural production and supply chains in the United States. In this study, we seek to understand how irrigation (blue) water footprints of production are spatially distributed and how they differ in drought versus non‐drought years. Similarly, we aim to understand the impact of drought on the irrigation virtually embedded in domestic supply chains and exports. To this end, we quantify the blue water footprints of agricultural products per unit mass produced (Virtual Water Content (VWC)) by surface, groundwater, and groundwater depletion sources, and then trace how this water is embedded in domestic agricultural commodity transfers and exports (Virtual Water Transfers (VWT)) for counties in a drought (2012) and non‐drought (2017) year. Overall, we find that total VWC values are larger in drought than non‐drought conditions across commodity groups, driven by surface water withdrawals. Conversely, VWT is larger in non‐drought than drought, driven by larger commodity mass fluxes during non‐drought. Our results highlight the importance of sustainably managing water resources so that they are available to mitigate the impact of future droughts on agricultural production and supply chains. 

Abstract We provide a dataset of irrigation water withdrawals by crop, county, year, and water source within the United States. We employ a framework we previously developed to establish a companion dataset to our original estimates. The main difference is that we now use the U.S. Geological Survey (USGS) variable ‘irrigation — total’ to partition PCR-GLOBWB 2 hydrology model estimates, instead of ‘irrigation — crop’ as used in previous estimates. Our findings for Surface Water Withdrawals (SWW), total Groundwater Withdrawals (GWW), and nonrenewable Groundwater Depletion (GWD) are similar to those of prior estimates but now have better spatial coverage, since several states are missing from the USGS ‘irrigation — crop’ variable that was originally used. Irrigation water use increases in this study, since more states are included and ‘irrigation — total’ includes more categories of irrigation than ‘irrigation — crop’. Notably, irrigation in the Mississippi Embayment Aquifer is now captured for rice and soy. We provide nearly 2.5 million data points with this paper (3,142 counties; 13 years; 3 water sources; and 20 crops). 

Abstract Global grain trade plays a key role in food security. Many nations rely on imported grain to meet their dietary requirements. Grain imports may be at risk due to weather shocks, economic crises, or international conflicts. Countries aim to balance import risk with the expected return of their grain supplies. This research brings these dual objectives together in an innovative modern portfolio theory framework. Modern portfolio theory provides a set of concepts to formulate the trade-off between risk and expected return in national grain imports. Using Markowitz’s mean-variance optimization model, we identify opportunities to reduce risk in existing national grain import accounts, without increasing costs under realistic supply mass constraints of trade partners. Several major grain importers may be able to reduce risk in their grain imports without increasing cost, such as wheat imports in Egypt, maize imports in Vietnam, and rice imports in Saudi Arabia. However, some countries would indeed have to pay more to achieve more stable grain supplies, such as wheat imports in Turkey. This study provides a framework to quantify the different costs, benefits, and levels of risk in grain trade that can inform future research and decision-making. 

Abstract Civil infrastructure underpins urban receipts of food, energy, and water (FEW) produced in distant watersheds. In this study, we map flows of FEW goods from watersheds of the contiguous United States to major population centers and highlight the critical infrastructure that supports FEW flows. To do this, we draw upon detailed records of agriculture, electricity, and public water supply production and couple them with commodity flow and infrastructure information. We also compare the flows of virtual water embedded in food and energy commodity flows with physical water flows in inter‐basin water transfer projects around the country. We found that the virtual blue water transfers through crops and electricity to major US cities was 53 billion and 8 billion m³in 2017, respectively, while physical interbasin water transfers for crops, electricity, and public supply water averaged 20.8 billion m³. Highways are the primary infrastructure used to import virtual water associated with food and fuel into cities, although waterways and railways are most utilized for long‐distance transport. All of the 204 watersheds in the contiguous US support the food, energy, and/or water supplies of major US cities, with dependencies stretching far beyond each city's borders. Still, most cities source the majority of their FEW and embedded water resources from nearby watersheds. Infrastructure such as water supply dams and inland ports serve as important buffers for both local and supply‐chain sourced water stress. These findings can inform efforts to reduce water resources and infrastructure risks in domestic supply chains. 

Abstract The United States and China are key nations in global agricultural and food trade. They share a complex bilateral agri-food trade network in which disruptions could have a global ripple effect. Yet, we do not understand the spatially resolved connections in the bilateral US–China agri-food trade. In this study, we estimate the bilateral agri-food trade between Chinese provinces and U.S. states and counties. First, we estimate bilateral imports and exports of agri-food commodities for provinces and states. Second, we model link-level connections between provinces and states/counties. To do this, we develop a novel algorithm that integrates a variety of national and international databases for the year 2017, including trade data from the US Census Bureau, the US Freight Analysis Framework database, and Multi-Regional Input-Output tables for China. We then adapt the food flow model for inter-county agri-food movements within the US to estimate bilateral trade through port counties. We estimate 2,954 and 162,922 link-level connections at the state-province and county-province resolution, respectively, and identify core nodes in the bilateral agri-food trade network. Our results provide a spatially detailed mapping of the US–China bilateral agri-food trade, which may enable future research and inform decision-makers. 

Abstract The production of food, electricity, and treated water is often tracked and managed along political or infrastructure boundaries. Yet, water resources, a critical input in the production of these goods, are delineated along natural landscape features (i.e., watersheds). The boundary mismatch between water resources and the associated production of economic goods conceals hydrologic dependencies and vulnerabilities in the provisioning of Food‐Energy‐Water (FEW) resources. In this study, we pair economic, infrastructure, and hydrologic data to evaluate the production of food, electricity, and treated water within watersheds of the conterminous United States. The US FEW sectors produced 950 million tonnes of crops, 3,973 million MWh of electricity, and supplied water to 263 million people in 2017. FEW production consumed 128 km³of blue water (18%) and 583 km³of green water (82%). Watersheds in central and southern California, the Midwest, and the Southwest have the largest FEW blue water consumption and the greatest exposure to water stress. Nearly three‐fifths of FEW production occurs in regularly water‐stressed watersheds. FEW production in watersheds in the Great Plains and Midwest relies heavily on groundwater to buffer against intra‐ and inter‐annual streamflow variability, while surface reservoir storage buffers against water shortages in all watersheds. We show where FEW production may be susceptible to curtailments due to ongoing groundwater depletion or known infrastructure deficiencies. This study adds to our understanding of how a nation's water resources and associated infrastructure support economic activity, as well as areas where economic activity is exposed to hydrological and infrastructure risks. 

Abstract Agriculture is the largest user of water in the United States. Yet, we do not understand the spatially resolved sources of irrigation water use (IWU) by crop. The goal of this study is to estimate crop‐specific IWU from surface water withdrawals (SWW), total groundwater withdrawals (GWW), and nonrenewable groundwater depletion (GWD). To do this, we employ the PCR‐GLOBWB 2 global hydrology model to partition irrigation information from the U.S. Geological Survey Water Use Database to specific crops across the Continental United States (CONUS). We incorporate high‐resolution input data on agricultural production and climate within the CONUS to obtain crop‐specific irrigation estimates for SWW, GWW, and GWD for 20 crops and crop groups from 2008 to 2020 at county spatial resolution. Over the study period, SWW decreased by 20%, while both GWW and GWD increased by 3%. On average, animal feed (alfalfa/hay) uses the most irrigation water across all water sources: 33 from SWW, 13 from GWW, and 10 km³/yr from GWD. Produce used less SWW (43%), but more GWW (57%), and GWD (27%) over the study time‐period. The largest changes in IWU for each water source between the years 2008 and 2020 are: rice (SWW decreased by 71%), sugar beets (GWW increased by 232%), and rapeseed (GWD increased by 405%). These results present the first national‐scale assessment of irrigation by crop, water source, and year. In total, we contribute nearly 2.5 million data points to the literature (3,142 counties; 13 years; 3 water sources; and 20 crops). 

Abstract Food supply chains are essential for distributing goods from production to consumption points. These complex supply chains are important for food security and availability. Recent research has developed novel methods to estimate food flows with high spatial resolution, but we do not currently understand how fine-grained food supply chains vary in time. In this study, we use an improved version of the Food Flow Model to estimate food flows (kg) between all county pairs across all food commodity groups for the years 2007, 2012, and 2017 (which requires estimating 206.3 million links). We then determine the core counties to the US food flow networks through time with a multi-criteria decision analysis technique. Our estimates of county-to-county food flows in time are freely available with this paper and could be useful for future research, policy, and decision-making. 

Abstract Global assessments of climate extremes typically do not account for the unique characteristics of individual crops. A consistent definition of the exposure of specific crops to extreme weather would enable agriculturally-relevant hazard quantification. To this end, we develop a database of both the temperature and moisture extremes facing individual crops by explicitly accounting for crop characteristics. To do this, we collate crop-specific temperature and moisture parameters from the agronomy literature, which are then combined with time-varying crop locations and high-resolution climate information to quantify crop-specific exposure to extreme weather. Specifically, we estimate crop-specific temperature and moisture shocks during the growing season for a 0.25^∘spatial grid and daily time scale from 1961 to 2014 globally. We call this the Agriculturally-Relevant Exposure to Shocks (ARES) model and make all ARES output available with this paper. Our crop-specific approach leads to a smaller average value of the exposure rate and spatial extent than does a crop-agnostic approach. Of the 17 crops included in this study, 13 had an increase in exposure to extreme heat, while 9 were more exposed to extreme cold over the past half century. All crops in this study show a statistically significant increase in exposure to both extreme wetness and dryness. Cassava, sunflowers, soybeans, and oats had the greatest increase in hot, cold, dry, and wet exposure, respectively. We compare ARES model results with the EM-DAT disaster database. Our results highlight the importance of crop-specific characteristics in defining weather shocks in agriculture.