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Enregistrement W7000021125

An Economic- and Environmental Assessment of Ground Beef in Response to the Introduction of Plant-Based Meat Alternatives in the United States

2021· other· en· W7000021125 sur OpenAlexaboutno aff

Notice bibliographique

RevueeScholarship (California Digital Library) · 2021
Typeother
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSustainabilityBeef cattleEnvironmental impact assessmentFeedlotProduction (economics)Consumption (sociology)LivestockAgriculture
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Cattle production and meat consumption have long been criticized for their environmental impacts and contribution to climate change. In the United States, this has led to increased research into way to mitigate environmental impacts from cattle production as well as alternative foods to replace beef and its associated impacts. The goal of the present dissertation was to review current literature related to the environmental impacts of cattle and meat alternatives and to present research related to mitigating emissions from cattle production as well as research into the comparative impacts of both beef and plant based meat alternatives. Chapter one presents a review of current literature on the sustainability of beef in meat alternatives. This literature review covers three primary areas of environmental impacts that are most prevalent in the literature on environmental impacts of cattle: climate change, land use, and water use. Further discussed are the nuances that accompany evaluation of these impacts from cattle production. Environmental impacts of plant based meat alternatives (MA) are then discussed with a particular focus on Beyond Burger (BB) and Impossible Burger (IB), the two most prevalent MA in the US due to their similar taste and textural profiles compared to ground beef (GB). Chapter two investigates a novel feed additive aimed at promoting environmental stewardship while maintaining or improving the efficiency of cattle production. Selective β modulator (lubabegron; LUB), recently approved by the United States Food and Drug Administration (FDA) to be fed to feedlot cattle during the last 14 to 91 d of the feeding period, was evaluated clinical efficacy for reductions in gaseous emissions/kg of final BW or HCW when different doses of LUB were fed to feedlot cattle over a 91-d duration. A 4 × 2 factorial arrangement design was utilized with the factors of dose (0.0, 1.25, 5.0 or 20.0 g/ton DM basis) and sex (steers or heifers). Three cycles were conducted (112 animals/cycle) with each dose × sex combination being represented by a single cattle pen enclosure (CPE; 14 animals/CPE) resulting in a total of 168 steers and 168 heifers (BW = 453 ± 34.5 kg). Five gases were evaluated based on CPE concentrations relative to ambient air: NH3, CH4, N2O, H2S, and CO2. Lubabegron was shown to reduce emissions of NH3 while simultaneously increasing the pounds of beef produced, demonstrating that feed additives are a promising tool towards reducing environmental impacts of cattle production.Chapters three and four aimed to gain a more complete picture of the sustainability of GB compared to BB or IB through two means: (1) first in chapter three, national and international economic impacts of decreasing GB consumption in the US in response to the introduction of BB and IB were investigated; and (2) in chapter four, the widespread US environmental impacts associated with a decrease in GB consumption and corresponding increase in BB and IB consumption in the US were evaluated. Contrary to previous work comparing these products, the present research utilized a methodology which accounts for the complicated national and international supply chains associated with the production of these products beyond their direct inputs. In chapter three, the Global Trade Analysis Project (GTAP) Model was used to assess the effects of reducing GB consumption in the US on both US and global economies. A 15% reduction in US GB, resulted in rather significant impacts on the US economy. Most notably, national and international consumption behaviors changed which resulted in changes to the structure of world trade. In the US, the reduction in GB consumption resulted in loss of labor (up to 9.9% of labor for the US GB sector and 4.1% for the US cattle sector) as well as substantial reductions in land used for cattle production. Similarly, reductions in labor and land use were observed for the top four countries that the US imports of lean trimmings of GB from (Australia, Canada, Mexico, and New Zealand). In chapter four, GTAP and the multi-regional input-output database, EXIOBASE, were utilized to perform a macro-life cycle assessment (M-LCA) of GB, BB, and IB. Compared to conventional LCA, this method makes it possible to account for the detailed production systems associated with GB, BB, and IB. When replacing 15% of GB consumption with either BB or IB, environmental impacts were variable. When considering impacts to the GB sector alone, replacement with BB resulted in overall reductions to climate change impacts (i.e., greenhouse gas emissions), land use, water use, and energy use. Meanwhile, replacement with IB resulted in reductions of climate change and land use impacts from the GB sector but had the potential to increase overall water and energy use. When considering the effects of changes in the GB sector on a national scale: Climate change impacts (i.e., greenhouse gas emissions) declined minimally (0.08%), with the new GB sector contributing to 0.66% of national emissions. Land use declined by up to 1.6%, nationally. Meanwhile, water use, and energy use had mixed effects at the national level for both BB and IB, each having the potential to increase national resource use, though by relatively small amounts When considering the sustainability of these GB compared to BB or IBs, it remains difficult to draw clear conclusions on which product might be superior, but it is clear that BB and IB will not produce the profound positive impacts suggested by their respective companies.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,031
Score d'incertitude au seuil0,062

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,013
Tête enseignante GPT0,242
Écart entre enseignants0,229 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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