Estimation of genetic parameters for milk mid-infrared-predicted methane production in Holstein dairy cattle
Bibliographic record
Abstract
Livestock methane emissions contribute to 18% of global greenhouse gas emissions. Integrating methane production into breeding goals has the potential to mitigate methane emissions and contribute to more sustainable livestock systems. To achieve this through genetic selection, large datasets of phenotypic records are required, making the use of direct measurement of methane emissions limited on a broad scale. Consequently, alternative approaches, such as using predicted methane emissions derived from more easily measured traits, have been proposed. The objective of this study was to estimate the genetic parameters of weekly average milk mid-infrared–predicted methane production (PCH 4 ) and its genetic correlation with milk yield (MY), fat yield (FY), and protein yield (PY) in Canadian Holstein dairy cows. We analyzed a total of 37,202 PCH 4 records and corresponding test-day records for MY, FY, and PY collected between 119 and 179 DIM from 18,505 first-lactation Canadian Holstein cows distributed over 100 herds. Several statistical models were evaluated to estimate the genetic parameters of PCH 4 . These included a single-trait model and 3 separate 2-trait random regression models that employed fifth-degree Legendre polynomials on weeks of lactation. The estimated heritability of PCH 4 ranged from 0.37 (SE = 0.06) to 0.45 (SE = 0.02), indicating a moderate genetic influence on predicted methane production. The genetic correlations between PCH 4 and test-day MY, FY, and PY ranged from −0.23 (SE = 0.06) to −0.13 (SE = 0.06), 0.3 (SE = 0.06) to 0.48 (SE = 0.08), −0.17 (SE = 0.07) to −0.09 (SE = 0.06), respectively, suggesting low to moderate associations between PCH 4 and the production traits. These results suggest that PCH 4 is moderately heritable and can be used for genetic selection to mitigate methane emissions in dairy cows. However, the positive unfavorable genetic correlation with FY should be considered when selecting animals for lower PCH 4 to avoid potential negative impacts on fat yield. These findings contribute to the ongoing efforts to improve the environmental sustainability of the livestock industry while maintaining productivity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".