Genetic and environmental relationships between body condition score and milk production traits in Canadian Holsteins
Bibliographic record
Abstract
The objective of this research was to estimate genetic parameters of first-lactation body condition score (BCS), milk yield, fat percentage (Fat%), protein percentage (Prot%), somatic cell score (SCS), milk urea nitrogen (MUN), lactose percentage (Lact%), and fat to protein ratio (F:P) using multiple-trait random regression animal models. Changes in covariances between BCS and milk production traits on a daily basis have not been investigated before and could be useful for determining which BCS estimated breeding values (EBV) might be practical for selection in the future. Field staff from Valacta milk recording agency (Sainte-Anne-de-Bellevue, QC, Canada) collected BCS from Québec herds several times per cow throughout the lactation. Average daily heritabilities and genetic correlations among the various traits were similar to literature values. On an average daily basis, BCS was genetically unfavorably correlated with milk yield (i.e., increased milk yield was associated with lower body condition). The unfavorable genetic correlation between BCS and milk yield became stronger as lactation progressed, but was equivalent to zero for the first month of lactation. Favorable genetic correlations were found between BCS with Prot%, SCS, and Lact% (i.e., greater BCS was associated with greater Prot%, lower SCS, and greater Lact%). These correlations were strongest in early lactation. On an average daily basis, BCS was not genetically correlated with Fat% or MUN, but was negatively correlated with F:P. Furthermore, BCS at 5 and 50 d in milk (DIM) had the most favorable genetic correlations with milk production traits over the lactation (at 5, 50, 150, and 250 DIM). Thus, early lactation BCS EBV shows potential for selection. Regardless, this study showed that the level of association BCS has with milk production traits is not constant over the lactation. Simultaneous selection for both BCS and milk production traits should be considered, mainly due to the unfavorable genetic correlation between BCS with milk yield.
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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 | 0.001 | 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) | 0.002 | 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".