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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 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 food system is an important contributor to carbon dioxide (CO 2 ) emissions. The refrigerated food supply chain is an energy-intensive, nutritious and high-value part of the food system, making it particularly important to consider. In this study, we develop a novel model of cold chain food flows between counties in the United States. Specifically, we estimate truck transport via roadways of meat and prepared foodstuffs for the year 2017. We use the roadway travel distance in our model framework rather than the haversine distance between two locations to improve the estimate for long-haul freight with a temperature-controlled system. This enables us to more accurately calculate the truck fuel consumption and CO 2 emissions related to cold chain food transport. We find that the cold chain transport of meat emitted 8.4 × 10 6 t CO 2 yr −1 and that of prepared foodstuffs emitted 14.5 × 10 6 t CO 2 yr −1 , which is in line with other studies. Meat has a longer average refrigerated transport distance, resulting in higher transport CO 2 emissions per kg than processed foodstuffs. We also find that CO 2 emissions from cold chain food transport are not projected to significantly increase under the temperatures projected to occur with climate change in 2045. These county-level cold chain food flows could be used to inform infrastructure investment, supply chain decision-making and environmental footprint studies. 

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.