Reconciling Reduced Red Meat Consumption in Canada with Regenerative Grazing: Implications for GHG Emissions, Protein Supply and Land Use
Bibliographic record
Abstract
This paper reconciled the soil ecology benefits of forages and ruminants with reducing GHG emissions from beef. A scenario analysis compared four integrated systems for beef, pork and broilers. Slaughter cattle diets were either business as usual (BAU) or grass fed (GF), and the combined red meat consumption could not exceed the recommendation for human health. The four consumption scenarios included (PS1) equal beef and pork with BAU beef, (PS2) equal beef and pork with GF beef, (PS3) more pork and less beef with GF beef and (PS4) more pork and less beef with BAU beef. Broiler consumption was increased to force all four scenarios to provide the same amount of protein. All four scenarios required similar feed grain areas (2.5 to 3 Mha). At 2.3, 4.4, 2.2 and 1.2 Mha for PS1, PS2, PS3 and PS4, respectively, the areas in harvestable forage showed much greater variation. From high to low GHG emissions, the ranking of scenarios was PS2, PS1, PS3 and PS4. Although allowing for avoidance of soil organic matter loss changed this ranking, the carbon footprint of beef production was still double or more that of pork in all four PS. To reconcile the agro-ecological benefits of beef with reducing GHG emissions, PS3 may be better than PS4. Along with eating more pork and broilers, and less red meat overall, consumers can choose GF beef as an occasional alternative.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".