Evidence map›Paper›PMID 39231169›Full record

ArticlePloS one2024

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

Zach Conrad, Songze Wu, LuAnn K Johnson, Julia F Kun, Eric D Roy, Jessica A Gephart, Nayla Bezares, Troy Wiipongwii, Nicole Tichenor Blackstone, David C Love

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Zach ConradDepartment of Kinesiology, William & Mary, Williamsburg, VA, United States of America.ORCID 0000-0001-5376-8775
Songze WuGlobal Research Institute, William & Mary, Williamsburg, VA, United States of America.
LuAnn K JohnsonIndependent Contractor, Warren, Minnesota, United States of America.
Julia F KunCollege of Arts & Sciences, William & Mary, Williamsburg, VA, United States of America.
Eric D RoyGund Institute for Environment, University of Vermont, Burlington, VT, United States of America.
Jessica A GephartDepartment of Environmental Science, American University, Washington, DC, United States of America.
Nayla BezaresFriedman School of Nutrition Science and Policy, Tufts University, Boston, MA, United States of America.ORCID 0000-0003-4244-9829
Troy WiipongwiiGlobal Research Institute, William & Mary, Williamsburg, VA, United States of America.
Nicole Tichenor BlackstoneFriedman School of Nutrition Science and Policy, Tufts University, Boston, MA, United States of America.
David C LoveDepartment of Environmental Science and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

AgricultureComputer SimulationDietFood SupplyCrops, AgriculturalFertilizersFood IndustryFood PreferencesNatural ResourcesNutrition SurveysPesticidesSustainable DevelopmentUnited StatesWater SupplyFertilizersPesticides

Identifiers

PMID39231169
PMCPMC11373842

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.