179 Breeding for enhancing feed efficiency in dairy cattle
Bibliographic record
Abstract
Abstract Genetically selecting for improved feed efficiency has been recognized by the dairy cattle industry as an important economic and environmental goal. Improved feed efficiency has the potential to significantly reduce costs, improving dairy farmers’ profitability and, at the same time, minimize environmental impact, for example by reducing nutrient loss in manure and methane emissions. Feed efficiency is recognized as a complex trait that may be define in different ways, but it generally describes units of product output per unit of feed required. An overview of genetic selection for improved feed efficiency and international initiatives to implement genomic selection for feed efficiency in dairy cattle is presented. In general, studies have indicated that feed efficiency, defined and assessed in alternative ways, is moderately heritable and genetic selection could be successfully implemented. Various initiatives around the world have worked collaboratively to carried out research and create reference datasets for joint genomic evaluations. An example is the large international Efficient Dairy Genome Project (EDGP) led by Canada. The EDGP database was developed in 2017 to allow data sharing among the international collaborators. Currently, the database contains genotypes and records on feed intake of 5,289 cows and on methane emissions of 1,337 cows from eight research herds in six countries (Australia, Canada, Denmark, Switzerland, United Kingdom and United States). Genetic parameters (heritability and genetic correlations) were estimated for dry matter intake, metabolic body weight and energy corrected milk at two time-periods: a) 5–60 DIM and b) 60–150 DIM. These parameters provide a basis for development of breeding value estimation procedures and subsequent selection index for feed efficiency, which will incorporate genomic information.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".