Invited review: Effects of milk ration on solid feed intake, weaning, and performance in dairy heifers
Bibliographic record
Abstract
A feeding regimen that allows a smooth transition from milk to solid feed is vital for successful heifer-rearing programs. In the past, research efforts have focused on the development of feeding methods that allow early weaning, perhaps because the risk of disease is highest during the milk feeding stage. To encourage early intake of calf starter, conventional feeding programs have limited the supply of milk (often to 10% of BW at birth). However, dairy calves provided free access to milk will typically consume more than twice this amount. We critically review the available literature examining the relationship between milk feeding method, solid feed consumption, and rumen development in young dairy calves and identify areas where new work is required. We conclude that milk-fed dairy calves can safely ingest milk at approximately 20% of body weight (BW)/d, and greater milk consumption supports greater BW gain, improved feed efficiency, reduced incidence of disease, and greater opportunity to express natural behaviors, which in combination suggest improved welfare. Method of weaning greatly influences feed consumption, rumen development, and growth check in calves provided higher amounts of milk. Gradual weaning encourages starter intake during the preweaning period, and both weaning age and duration of weaning influence this consumption. Increased solid feed consumption during the weaning process contributes to rumen development, permitting higher starter intake and BW gain after weaning. Growth factors in milk may also enhance the growth and maturation of the gastrointestinal tract, but more research is required to understand the role of these factors. Greater nutrient supply through increased amount of milk appears to improve immune function and long-term performance of heifer calves; for example, reducing the age at first breeding and increasing first-lactation milk yield, but more research is needed to confirm these effects.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".