MétaCan
Menu
Back to cohort
Record W2038181047 · doi:10.3168/jds.2013-6563

Carbon footprint of Canadian dairy products: Calculations and issues

2013· article· en· W2038181047 on OpenAlexaffabout
X.P.C. Vergé, Dominique Maxime, J.A. Dyer, R. L. Desjardins, Yves Arcand, Andrew VanderZaag

Bibliographic record

VenueJournal of Dairy Science · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsPolytechnique MontréalAgriculture and Agri-Food Canada
Fundersnot available
KeywordsCarbon footprintGreenhouse gasLivestockRaw milkAgricultural scienceCarbon dioxide equivalentEnvironmental scienceDairy cattleHerdDairy industryAgricultural economicsAnimal scienceFood scienceGeographyEconomicsBiology

Abstract

fetched live from OpenAlex

The Canadian dairy sector is a major industry with about 1 million cows. This industry emits about 20% of the total greenhouse gas (GHG) emissions from the main livestock sectors (beef, dairy, swine, and poultry). In 2006, the Canadian dairy herd produced about 7.7 Mt of raw milk, resulting in about 4.4 Mt of dairy products (notably 64% fluid milk and 12% cheese). An integrated cradle-to-gate model (field to processing plant) has been developed to estimate the carbon footprint (CF) of 11 Canadian dairy products. The on-farm part of the model is the Unified Livestock Industry and Crop Emissions Estimation System (ULICEES). It considers all GHG emissions associated with livestock production but, for this study, it was run for the dairy sector specifically. Off-farm GHG emissions were estimated using the Canadian Food Carbon Footprint calculator, (cafoo)(2)-milk. It considers GHG emissions from the farm gate to the exit gate of the processing plants. The CF of the raw milk has been found lower in western provinces [0.93 kg of CO2 equivalents (CO2e)/L of milk] than in eastern provinces (1.12 kg of CO2e/L of milk) because of differences in climate conditions and dairy herd management. Most of the CF estimates of dairy products ranged between 1 and 3 kg of CO2e/kg of product. Three products were, however, significantly higher: cheese (5.3 kg of CO2e/kg), butter (7.3 kg of CO2e/kg), and milk powder (10.1 kg of CO2e/kg). The CF results depend on the milk volume needed, the co-product allocation process (based on milk solids content), and the amount of energy used to manufacture each product. The GHG emissions per kilogram of protein ranged from 13 to 40 kg of CO2e. Two products had higher values: cream and sour cream, at 83 and 78 kg of CO2e/kg, respectively. Finally, the highest CF value was for butter, at about 730 kg of CO2e/kg. This extremely high value is due to the fact that the intensity indicator per kilogram of product is high and that butter is almost exclusively fat. Protein content is often used to compare the CF of products; however, this study demonstrates that the use of a common food component is not suitable as a comparison unit in some cases. Functionality has to be considered too, but it might be insufficient for food product labeling because different reporting units (adapted to a specific food product) will be used, and the resulting confusion could lead consumers to lose confidence in such labeling. Therefore, simple units might not be ideal and a more comprehensive approach will likely have to be developed.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.009
GPT teacher head0.223
Teacher spread0.214 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations110
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Dairy ScienceSame topicAgriculture Sustainability and Environmental ImpactFrench-language works237,207