Effects of feeding wheat straw and middlings ensiled with whey on digestibility and growth of cattle
Bibliographic record
Abstract
Two studies were conducted with the objective of evaluating the effects of feeding different levels of whey ensiled with wheat straw and wheat middlings (whey silage) compared to control diets on production parameters in growing cattle. Whey silage was included in diets at 55 and 65.5% on a dry matter basis with net energy for maintenance and crude protein calculated to be similar to control diets containing a combination of alfalfa hay, corn silage and wheat middlings at 1.74 Mcal kg-1 and 13.4%, respectively. Dry matter intake averaged 8.41 versus 8.91 kg d-1 (P < 0.05) and 8.68 versus 7.09 kg d-1 (P < 0.05) when whey silage was included at either 55 or 65.5% of the diets compared to the control diets. Average daily gains of cattle fed whey silage incorporated at 55% was 1.00 versus 1.14 kg d-1 (P < 0.05), and reflected the difference in dry matter intake; no differences (P > 0.05) in feed efficiency (8.73:1 versus 8.71:1) were observed. However, the higher dry matter intake (DMI) of cattle fed diets with whey silage incorporated at 65.5% did not (P > 0.05) translate into higher average daily gain (1.23 versus 1.18 kg d-1), resulting in a higher (P < 0.05) feed to gain (7.00:1 versus 6.01:1) for the whey-based silage diets. Incorporation of whey silage in diets at 55% increased (P > 0.05) the dry matter digestibility by 12%, whereas incorporation at 65.5% decreased (P < 0.05) dry matter digestibility by 12% compared to control diets, with no differences (P > 0.05) in percent neutral detergent fiber digestibility (NDFD) at either level. We conclude that whey silage can be included at between 55 and 65.5% of the total dry matter in diets with no adverse effects on production compared to cattle fed diets containing a combination of alfalfa hay, corn silage and wheat middlings. Key words: Agricultural by products, crop residues, digestibility, growing cattle, production, whey silage
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".