Rearing strategy and optimizing first-calving targets in dairy heifers: a review
Bibliographic record
Abstract
Much research has been carried out and published on dairy replacement management, in order to rear heifers as efficiently as possible, from both a technical and economical point of view. In most cases, the aim is to rear the heifers at the lowest cost possible without any deleterious effects on future performances. However, the importance of dairy heifer husbandry is not sufficiently well recognized and probably mishandled by most farmers. The present review aims to give an actual overview of rearing procedures in dairy heifers and possible ways to achieve optimal goals. For many years, it has been well known that rapid rearing lowers the age of sexual maturity and consequently may be an efficient way to reduce the non-producing period prior to conception. But this may impair mammary development and consequently future milk production, at least during first lactation. In addition, a growth rate that is too low may not only be costly but also result in animals that are too fat at first calving, creating problems such as calving difficulties, dystocia, etc. Genetic considerations must also be factored, i.e. frame, size, body weight, etc. have changed during the last 20 years and there are differences between breeds. As a result, some time-honoured recommendations may not be appropriate. The present paper reviews factors and management practices that may affect rearing and subsequent performance of dairy heifers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".