Carbon footprint of Canadian dairy products: Calculations and issues
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».