Evidence map›Paper›PMID 39215094›Full record

ArticleNature food2024

Greenhouse gas emissions in US beef production can be reduced by up to 30% with the adoption of selected mitigation measures.

Rylie E O Pelton, Clare E Kazanski, Shamitha Keerthi, Kelly A Racette, Sasha Gennet, Nathaniel Springer, Eugene Yacobson, Michael Wironen, Deepak Ray, Kris Johnson and 1 more

Abstract read
In one paragraph

Article in Nature food, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–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

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

11 authors.

Rylie E O PeltonInstitute on the Environment, University of Minnesota, St Paul, MN, USA. olso4235@umn.edu.ORCID http://orcid.org/0000-0002-6015-2263
Clare E KazanskiThe Nature Conservancy, Minneapolis, MN, USA.ORCID http://orcid.org/0000-0001-7432-5666
Shamitha KeerthiThe Nature Conservancy, Arlington, VA, USA.
Kelly A RacetteThe Nature Conservancy, Arlington, VA, USA.
Sasha GennetThe Nature Conservancy, Minneapolis, MN, USA.ORCID http://orcid.org/0000-0002-6338-5337
Nathaniel SpringerInstitute on the Environment, University of Minnesota, St Paul, MN, USA.
Eugene YacobsonThe Nature Conservancy, Arlington, VA, USA.
Michael WironenThe Nature Conservancy, Arlington, VA, USA.
Deepak RayInstitute on the Environment, University of Minnesota, St Paul, MN, USA.ORCID http://orcid.org/0000-0002-2856-9608
Kris JohnsonThe Nature Conservancy, Minneapolis, MN, USA.ORCID http://orcid.org/0000-0002-3533-2043
Jennifer SchmittInstitute on the Environment, University of Minnesota, St Paul, MN, USA.ORCID http://orcid.org/0000-0002-5427-5208

Funding

Foundation for Food and Agriculture Research (FFAR) DSnew-0000000007
6 · The paper itself

Abstract

Greenhouse gas (GHG) emissions from beef production in the United States are unevenly distributed across the supply chain and production regions, complicating where and how to reduce emissions most effectively. Using spatially explicit life cycle assessment methods, we quantify the baseline GHG emissions and mitigation opportunities of 42 practices spanning the supply chain from crop and livestock production to processing. We find that the potential to reduce GHGs across the beef sector ranges up to 30% (20 million tonnes CO

Indexed as

Greenhouse GasesAnimal HusbandryAnimalsCarbon DioxideCattleGreenhouse EffectRed MeatUnited StatesCarbon DioxideGreenhouse Gases

Identifiers

PMID39215094
PMCPMC11420059

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.