PSXII-21 The Impact of Genomic Selection on Canadian Holstein Cattle Population Structure
Bibliographic record
Abstract
Abstract The Canadian dairy industry implemented genomic selection in 2009 to improve breeding in Holstein cattle and has been applied to several other dairy cattle breeds since then. Genomic selection is an effective breeding tool that can increase the accuracy of prediction and help to select young animals using genotypic information. As well as shortening generation intervals, genomic selection has also increased the genetic gain per generation and the profitability of the industry. However, genomic selection tends to increase inbreeding, reduce the effective population size and cause a loss in genetic variation. These consequences could lead to future challenges like lower selection response. To evaluate the sustainability of current breeding practices, investigation is warranted to observe the effects of genomic selection. Therefore, the objective of this study is to compare the population structure of Canadian Holstein cattle before and after the implementation of genomic selection in 2009. Using pedigree information to identify the number of sires used for breeding at three different time points (2000, 2010 and 2020), the number of daughters per sire, and length of time sires were used were investigated. Results show that the average number of offspring per top sire (top 10%) has decreased by 50%, however the average relatedness of available sires has increased considerably. This information will add to our knowledge of the effects genomic selection has had on the Canadian dairy industry and will inform future recommendations for sustainable breeding strategies that facilitate continual genetic progress.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".