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Record W2606725454 · doi:10.5539/sar.v6n2p152

The Fossil Energy and CO2 Emissions Budget for the Barnyard Operations of Livestock Farms in Canada

2017· article· en· W2606725454 on OpenAlexafffundvenueabout
J.A. Dyer, X.P.C. Vergé, R. L. Desjardins, Devon E. Worth

Bibliographic record

VenueSustainable Agriculture Research · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsNational Association of Friendship CentresAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsFossil fuelEnvironmental scienceElectricityCoalPopulationGreenhouse gasLivestockElectricity generationAgricultural scienceEnvironmental protectionEnvironmental engineeringAgricultural economicsWaste managementEcologyEngineeringBiologyEconomics

Abstract

fetched live from OpenAlex

This paper describes fossil fuel energy use for on-farm transportation, heating of farm buildings, electricity generation, machinery supply and the spreading of manure. These four terms describe the barnyard energy budget. Calculations for this energy budget were driven by population data for beef and dairy cattle, hogs and poultry in Canada. Prior to comparing this energy budget for 2001 and 2011, the year-to-year trends from 1990 to 2014 were analysed. The declines in all livestock populations, except poultry, between 2001 and 2011 reduced the size of the Canadian barnyard energy budget from 25 PJ to 22 PJ. The resulting change in the fossil CO2 emissions between 2001 and 2011 was from 1.62 MtCO2 to 1.36 MtCO2. A sensitivity analysis based on future elimination of coal for generating electricity, introduction of electric pickup trucks (e-pickups) and increased use of electric heat, reduced fossil CO2 emissions during 2011 from dairy farms by 29%, beef farms by 24%, hog farms by 19% and poultry by 13%. The most affected provinces by this test were Alberta and Saskatchewan because of the heavy dependence on coal in electricity generation in these two provinces. This scenario test suggests a Canada-wide potential reduction of 0.30 MtCO2. A second sensitivity test based on a Canada-wide 20% reallocation of protein production from beef to pork revealed a very modest potential to actually reduce barnyard fossil CO2 emissions by 0.09 MtCO2 for Canada.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score0.997

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 studies0.0040.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.283
Teacher spread0.268 · 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

Citations3
Published2017
Admission routes4
Has abstractyes

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