Cost Effective Greenhouse Gas Mitigation in the Ontario Dairy Sector
Bibliographic record
Abstract
This study determines the feeding practices that can reduce greenhouse gas (GHG) emissions from milk production in Ontario at least cost, and estimates the associated impact on farm gross margin. A linear programming model is developed to account for emissions both from the farm as well as from the production of feed and other inputs upstream. The results indicate that changing rations can reduce GHG emissions from milk production by up to 35% from original levels, with a corresponding decline in farm gross margin of 23.4%. The cause of the declines in GHG emissions is because corn silage is replaced by high quality perennial forage (alfalfa hay). This allows for an increase in carbon storage in land, due to the enhanced carbon storage capacity of perennial forages as opposed to annual crops (i.e. corn), and due to lower capital and chemical inputs. The implications are that there is large potential for reducing GHG emissions from milk production in Ontario due to the potential for perennial forages to capture and store carbon in soil. This necessitates that perennial forages replace corn silage as the primary source of roughage in dairy rations. Current rations have corn silage as about 20% of dry matter intake.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".