Carbon footprint of Canadian dairy products: Calculations and issues
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".