Breeding for sustainability: Development of an index to reduce greenhouse gas in dairy cattle.
Bibliographic record
Abstract
Several genetic selection strategies can be incorporated into dairy cattle breeding programmes to target a reduction in greenhouse gas (GHG) emissions and provide a mitigation strategy with only modest additional cost, or labour expense, to the dairy producer. This can be achieved by targeting genetic progress in a specific trait (i.e. methane) or by building selection indexes that balance economic gain and environmental impact for more conventional traits, or both. Various countries have initiated efforts to incorporate emission-related traits into their national selection indexes. The strategies for reducing emissions vary due to system-specific objectives and limitations, ranging from specific methane breeding values to broader sustainability indexes. While methane breeding values may not be commercially available in most cases, Canada has taken the lead as the first country to release a methane breeding value, developed using mid-IR spectral data from milk samples and GreenFeed phenotyped Holstein cows, and develop a GHG index which includes a direct methane trait. The GHG index proposed for commercialisation is expected to reduce emissions per cow per year by 168 kg CO2e per SD of index, and is composed of Herd Life, Feed Efficiency, Methane Efficiency, and Body Maintenance Requirement traits. The reduction in emissions is largely driven by a genetic gain in Methane Efficiency and Body Maintenance Requirements, with results indicating that omission of a direct methane trait from the index would lead to an unfavourable response in individual cow's own genetic potential to reduce enteric methane output. Other countries are also progressing on this front; Spain has developed a methane estimated breeding value (EBV) and the Netherlands and Denmark are set to publish methane EBV in 2025. Motivation for the use of GHG indexes is strengthening in high-income countries. This motivation could be greatly accelerated if auditable, transparent and scientifically robust ways of recognising emissions changes due to genetic selection were developed. Ideally, these methods would support both national policy setting and supply agreements with milk processors.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".