Foodprint 2.0: A computational simulation model that estimates the agricultural resource requirements of diet patterns

• Published in: PloS one Vol. 19; no. 9; p. e0306097
• Main Authors: Conrad, Zach, Wu, Songze, Johnson, LuAnn K, Kun, Julia F, Roy, Eric D, Gephart, Jessica A, Bezares, Nayla, Wiipongwii, Troy, Blackstone, Nicole Tichenor, Love, David C
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 04.09.2024; Public Library of Science (PLoS)
• Subjects: Agricultural commodities; Agricultural resources; Agriculture; Agriculture - methods; Biology and Life Sciences; Complex systems; Computer applications; Computer Simulation; Computer-generated environments; Data transparency; Diet; Dietary supplements; Documentation; Economic aspects; Estimates; Estimation; Fertilizers; Food; Food contamination; Food habits; Food production; Food sources; Food Supply; Health aspects; Humans; Irrigation; Irrigation water; Land use; Mathematical models; Medicine and Health Sciences; Methods; Models, Theoretical; Nutrients; Nutrition; Nutrition research; Nutrition Surveys; Per capita; Pesticides; Requirements; Simulation models; Social Sciences; Space biology; Sustainability; Sustainable agriculture; United States; User requirements; Water use; United States; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0306097

Reducing the environmental pressures stemming from food production is central to meeting global sustainability targets. Shifting diets represents one lever for improving food system sustainability, and identifying sustainable diet opportunities requires computational models to represent complex systems and allow users to evaluate counterfactual scenarios. Despite an increase in the number of food system sustainability models, there remains a lack of transparency of data inputs and mathematical formulas to facilitate replication by researchers and application by diverse stakeholders. Further, many models lack the ability to model multiple geographic scales. The present study introduces Foodprint 2.0, which fills both gaps. Foodprint 2.0 is an updated biophysical simulation model that estimates the agricultural resource requirements of diet patterns and can be adapted to suit a variety of research purposes. The objectives of this study are to: 1) describe the new features of Foodprint 2.0, and 2) demonstrate model performance by estimating the agricultural resource requirements of food demand in the United States (US) using nationally representative dietary data from the National Health and Nutrition Examination Survey from 2009-2018. New features of the model include embedded functions to integrate individual-level dietary data that allow for variance estimation; new data and calculations to account for the resource requirements of food trade and farmed aquatic food; updated user interface; expanded output data for over 200 foods that include the use of fertilizer nutrients, pesticides, and irrigation water; supplementary files that include input data for all parameters on an annual basis from 1999-2018; sample programming code; and step-by-step instructions for users. This study demonstrates that animal-sourced foods consumed in the US accounted for the greatest share of total land use, fertilizer nutrient use, pesticide use, and irrigation water use, followed by grains, fruits, and vegetables. Greater adherence to the Dietary Guidelines for Americans was associated with lower use of land and fertilizer nutrients, and greater use of pesticides and irrigation water. Foodprint 2.0 is a highly modifiable model that can be a useful resource for informing sustainable diet policy discussions.