PSVIII-14 The significance of colostrum quality on feed efficiency in Canadian Holstein calves
Bibliographic record
Abstract
Abstract For a more sustainable dairy industry it is crucial to select for feed-efficient cattle. Although feed efficiency research has primarily focused on lactating animals, genetic selection for feed efficiency in calves has continued to gain research interest. Previously estimated genetic parameters for pre-weaned calf feed efficiency suggest residual metabolizable energy intake (RMEI) as a potential trait for selection. However, the trait definition of RMEI needs further evaluation. In previous studies only the intake of colostrum, fed in the first hours of the lif of a calf, was included to define RMEI. The inclusion of colostrum quality ([grams of immunoglobulin (g IgG)] might better represent the transfer of passive immunity to calves and enhance the indicator trait for feed efficiency. Therefore, the objective of this study was to evaluate the impact of colostrum characteristics on estimating RMEI as a measure of calf feed efficiency in Canadian Holstein calves. Records from 330 pre-weaned Canadian Holstein calves between 0 and 65 d of age, born between 2016 and 2021 were provided by The Ontario Dairy Research Centre (Elora, ON, Canada). Data included calf health (incidence of scours, 1 = event, 2 = no event), feed intake (kg), body weight (BW; kg), and colostrum quality (g IgG) ranging between 17.41 and 151.69 g IgG. RMEI was estimated by four linear regression models using metabolizable energy intake (MEI) for two time periods (RMEI1 = first month of age calves, RMEI2 = second month of age calves) according to previous studies. For both time periods, colostrum quality was then included in the linear regression model, in addition to scours incidence, year and season, trial, and regression on average daily gain and metabolic BW to estimate RMEI corrected for colostrum quality (i.e., RMEIc1 and RMEIc2). Colostrum quality was fitted as a fixed effect with four classes (≤72 g IgG, 73-76 g IgG, 77-96 g IgG, ≥97 g IgG), divided according to the quartiles of its distribution. The inclusion of colostrum quality had a significant effect on MEI in the first month of age (P ± 0.001) and a trend for significance in the second month of age (P = 0.095). RMEIc model had an adjusted R2 value of 0.86 with the inclusion of colostrum quality in the first time-period. As a next step, genetic parameters need to be estimated to determine any possible gain using RMEIc over RMEI. Improving the definition of RMEI will further the potential to select for feed efficiency in younger animals to achieve sustainability goals.
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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 | 0.001 |
| 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) | 0.001 | 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".