Multimodal and Multicereal Grain Transportation Patterns in the United States Across Cost, Emission, and Adaptability Optimization Scenarios

Cereal grains are staple foods in the daily diets of millions of Americans, given their affordability and availability. However, transportation of these commodities between domestic supply and demand locations has a significant environmental impact, as it often involves long distances, large quantities, and diesel trucks. Fortunately, their dry and nonperishable nature allows decision-makers to redesign their nationwide bulk transit to lower carbon dioxide emissions without compromising the ability to reroute in case of disruptions. Here, we adopt a data-driven multiobjective optimization model to compare empirical multimodal and multicereal grain transit patterns in the United States with cost, emission, and adaptability optimization scenarios. We uncover that actual patterns are highly adaptable, yet by strategically allocating cereal grains across highways, railways, and waterways, transportation scenarios with reduced carbon emissions are possible. Our findings can inform efforts to balance efficiency, resilience, and sustainability in agri-food transportation.