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Record 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 on OpenAlexafffundabout
Nicole Bamber, Melanie D. Jones, Louise M. Nelson, Kirsten Hannam, Craig Nichol, Nathan Pelletier

Bibliographic record

VenueJournal of Cleaner Production · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsUniversity of British Columbia, Okanagan CampusAgriculture and Agri-Food CanadaOkanagan University CollegeUniversity of British Columbia
FundersAgriculture and Agri-Food Canada
KeywordsEnvironmental scienceLife-cycle assessmentMulchBark (sound)OrchardGreenhouse gasAgroforestryAgronomyForestryProduction (economics)EcologyBiologyGeography

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.276
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2020
Admission routes3
Has abstractyes

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