Genetic variability of test-day urea nitrogen and lactose in milk of Jersey, Brown Swiss and Ayrshire cattle breeds in Quebec
Bibliographic record
Abstract
A large dataset of test-day milk records for cows having calved between January 2000 and May 2007, was obtained from the Quebec Dairy Herd Improvement Programme, Valacta, Quebec, Canada. This dataset contained 919,814 test-day milk yield records from 45520 Ayrshire cows on 956 dairy farms, 133,690 records from 7493 Jersey cows on 910 dairy farms, and 118,996 records from 5693 Brown Swiss on 476 dairy farms. Test-day records from the first three parities after editing served in the estimation of genetic parameters using a Restricted Maximum Likelihood method. The genetic parameters of six milk yield traits — milk, fat, lactose and protein yields, somatic cell score, and milk urea nitrogen (MUN) — were estimated using various random regression test-day models in the first three parities separately. Random regression test-day animal models for a single trait with Legendre polynomials of 2nd to 4th order and the Wilmink function with an exponential term of 0.01 to 0.09 with a step size of 0.02 were used for the estimation of daily heritability of the traits. Bivariate random regression test-day animal models with Legendre polynomials as regression coefficients served to estimate genetic and permanent environmental correlations among the milk yield traits. Single-trait random regression models were compared using BIC, AIC, residual sum of squares and mean absolute error. The models using Legendre polynomials for the fixed and random effects were among the best models; the Wilmink function resulted in unrealistic heritability estimates for some of the traits. For Ayrshire cows, mean heritability estimates were 0.39, 0.30 and 0.41 for MUN and 0.39, 0.20 and 0.33 for lactose yield, in the 1st, 2nd and 3rd parities, respectively. For Brown Swiss cows, mean heritability estimates were 0.28, 0.26 and 0.13 for MUN, and 0.30, 0.20 and 0.42 for lactose yields, in the 1st, 2nd and 3rd parities, respectively. Likewise, for Jersey cows, mean heritability estimates were 0.26, 0.20 and 0.14 for MUN, and 0.42, 0.51 and 0.12 for lactose yield, in the 1st, 2nd and 3rd parities, respectively. The largest heritability estimates were obtained for the Ayrshire breed in the first parity and the lowest estimates were obtained for the Jersey and Brown Swiss breeds, particularly in the later parities. Genetic correlations of MUN with other yield traits were negative and near zero in the 1st parity for Ayrshire cows, with the exception of SCS, where the genetic correlation was positive and near zero. In the 2nd parity the genetic correlations of MUN with other yield traits were positive and low. In the 3rd parity the genetic correlations of MUN with other yield traits were positive and low, except the genetic correlations with SCS, which was positive and moderate. The genetic and permanent environmental correlations between MUN and other yield traits were close to zero. The genetic and permanent environmental correlations among milk fat, protein and lactose yields were positive and high in the first three parities for the three breeds. The genetic correlation and permanent environment correlation between SCS and all other traits were negative and moderate to high except in the 1st and 2nd parity for the Jersey breed, where genetic correlations between MUN and SCS were positive and near zero.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".