Canola Meal versus Soybean Meal as Protein Supplements in the Diets of Lactating Dairy Cows Affects the Greenhouse Gas Intensity of Milk
Bibliographic record
Abstract
Soybean meal (SBM) and canola meal (CM) are protein supplements used in lactating dairy cow diets and, recently, an enteric methane-mitigating effect (i.e., lower Ym value) was reported for CM. Before recommending CM as a greenhouse gas (GHG) mitigation strategy, it is necessary to examine the net impact on total GHG emissions from milk production. The objective was to determine whether using CM rather than SBM in lactating dairy cow diets decreases GHG per kilogram of fat and protein corrected milk (FPCM), and whether the decrease depends upon where the meals are produced. Cradle to farm-gate life cycle assessments were conducted for a simulated dairy farm in eastern (Quebec) and western (Alberta) Canada. Scenarios examined the source of protein meal, location where meals were produced, and the methane-mitigating effect of CM. The Holos model was used to estimate GHG emissions from animals, manure, crop production, imported feeds, and energy use. GHG intensities (CO2e/kg FPCM) were 0.85–1.02 in the east and 1.07–1.11 in the west for the various scenarios, with enteric methane comprising 34 to 40% of total emissions. CM produced in western Canada with a low up-stream emission factor and low Ym value reduced CO2e/kg FPCM by 3% (western farm) to 6.6% (eastern farm) compared with SBM. We conclude that using CM rather than SBM in the diet of lactating dairy cows can be a GHG mitigation strategy depending upon where it is produced and whether it decreases enteric methane emissions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".