Effects of forage provision to dairy calves on growth performance and rumen fermentation: A meta-analysis and meta-regression
Bibliographic record
Abstract
A meta-analysis of the potential effect of forage provision on growth performance and rumen fermentation of dairy calves was conducted using published data from the literature (1998-2016). Meta-regression was used to evaluate the effects of different forage levels, forage sources, forage offering methods, physical forms of starter, and grain sources on the heterogeneity of the results. We considered 27 studies that reported the effects of forage provision to dairy calves. Estimated effect sizes of forage were calculated on starter feed intake, average daily gain (ADG), feed efficiency (FE), body weight (BW), and rumen fermentation parameters. Intake of starter feed, ADG, BW, ruminal pH, and rumen molar proportion of acetate increased when supplementing forage but FE decreased. Heterogeneity (the amount of variation among studies) was significant for intake of starter feed, ADG, FE, final BW, and rumen fermentation parameters. Improving overall starter feed intake was greater in calves offered alfalfa hay compared with those offered other types of forages. During the milk feeding and overall periods, improving ADG was greater for calves fed a high level of forage (>10% in dry matter) compared with those fed a low level of forage (≤10% in dry matter) diets. The advantages reported in weight gain at a high level of forage could be due to increased gut fill. Improving overall ADG was lower for calves offered forages with textured starter feed compared with ground starter feed. The meta-regression analysis revealed that changes associated with forage provision affect FE differently for various forage sources and forage offering methods during the milk-feeding period. Forage sources also modulated the effect of feeding forage on ruminal pH during the milk-feeding period. In conclusion, forage has the potential to affect starter feed intake and performance of dairy calves, but its effects depend on source, level, and method of forage feeding and physical form of starter feed independently of grain sources included in the starter feed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".