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Enregistrement W3105418619 · doi:10.1016/j.jclepro.2020.125022

Life cycle assessment of mulch use on Okanagan apple orchards: Part 2 - Consequential

2020· article· en· W3105418619 sur OpenAlexafffundabout
Nicole Bamber, Melanie D. Jones, Louise M. Nelson, Kirsten Hannam, Craig Nichol, Nathan Pelletier

Notice bibliographique

RevueJournal of Cleaner Production · 2020
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueEnvironmental Impact and Sustainability
Établissements canadiensUniversity of British Columbia, Okanagan CampusAgriculture and Agri-Food CanadaOkanagan University CollegeUniversity of British Columbia
Organismes subventionnairesAgriculture and Agri-Food Canada
Mots-clésEnvironmental scienceLife-cycle assessmentMulchBark (sound)OrchardGreenhouse gasAgroforestryAgronomyForestryProduction (economics)EcologyBiologyGeography

Résumé

récupéré en direct d'OpenAlex

Wood and bark chip mulch has been shown to reduce net orchard greenhouse gas (GHG) emissions on an Okanagan Valley (British Columbia, Canada) apple orchard. However, this benefit was shown to be outweighed by the (attributional) life cycle impacts associated with mulch production. The current study expanded the scope of prior investigations to perform a consequential life cycle assessment of the impacts of increasing wood chip/bark mulch use in the production of apples on Okanagan orchards. This assessment included the impacts of the orchard system as well as other current alternative uses of wood chip/bark mulch which included bioenergy production and paper manufacturing. Many environmental impact categories were examined including human toxicity, freshwater aquatic ecotoxicity, depletion of abiotic resources (elements, ultimate reserves), photochemical oxidation, ozone layer depletion, terrestrial ecotoxicity, acidification potential, climate change, eutrophication, land use – land competition, and energy use (including non-renewable: fossil, nuclear, primary forest; and renewable: biomass, geothermal, solar, water and wind). One scenario was modelled to represent the case in which no mulch was used on orchards (only used for alternative products). A second model was created to represent the marginal impacts of adding the amount of mulch to an apple orchard necessary to produce 1 kg of apples (0.575 kg bark and 0.144 kg wood chips). These amounts of bark and wood chips were assumed to be taken away from their current alternative uses (co-generation for bark and paper production for wood chips), thereby decreasing the amount of electricity and heat produced by bark by 0.653 kWh electricity and 0.653 MJ heat, and the amount of paper produced by wood chips by 0.144 kg paper. In turn, these amounts of electricity, heat and paper were assumed to be produced by their marginal production technologies – hydro-electric generation for electricity, natural gas for heat, and recycled paper for paper production. Finally, the scenarios were modelled assuming the marginal market for co-generation from bark was in Washington, USA rather than the Okanagan, as a sensitivity analysis. The results did not show a clear environmental benefit to either using or not using mulch on orchards. In the scenario in which bark mulch was assumed to be used either on apple orchards or for co-generation in British Columbia, impacts in 14 categories (including climate change, eutrophication, acidification, all toxicities, land use and some renewable energy use) were lower when mulch was used on the orchard, and results for 5 categories (including some non-renewable and renewable resources/energy use) were higher. When bark mulch was assumed to be used either on orchards or for co-generation in Washington, terrestrial ecotoxicity, land use, biomass and solar energy use were lower when mulch was used on the orchard, and all others (15 categories) were higher. There was a large amount of uncertainty in the model, coming from data variability, data quality and impact assessment uncertainty. Overall, the orchard system played a significant role in the impact assessment results, and was the main contributor to the overall uncertainty. Based on these results, mulch use on orchards cannot be recommended to reduce environmental impacts, but the marginal impacts of using mulch warrant further investigation.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,401
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,028
Tête enseignante GPT0,276
Écart entre enseignants0,249 · 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 tête enseignante, pas un consensus.

Devis d'étudeObservationnel
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

Citations5
Publié2020
Routes d'admission3
Résumé présentoui

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