The Effect of Fibrolytic Enzymes Sprayed onto Forages and Fed in a Total Mixed Ratio to Lactating Dairy Cows
Bibliographic record
Abstract
We investigated the effect of spraying different combinations of fibrolytic enzymes onto forages on their nutritive value for lactating cows. Holstein cows were fed a TMR consisting of 30% corn silage, 15% alfalfa hay, and 55% concentrate (dry matter basis). During a 12-wk treatment period, the forages were treated with no enzymes (control), cellulase D and sultanas B, or cellulase D and xylanase C. Enzymes were diluted in water and sprayed onto the forages while mixing. Both combinations of enzymes supplied similar amounts of fibrolytic activity based on classical enzyme assays conducted at 50 degrees C. Cows fed forages treated with cellulase D and xylanase B tended to produce more 3.5% FCM (+2.5 kg/d) than did cows fed the untreated forages. Dry matter intake, milk production, milk fat, and milk protein were unaffected by treatment. In vitro production of gas from forages treated with enzymes was greater than from untreated forage, but 96-h volatile fatty acid production was not different among treatments. With an alternative enzyme assay based on the depolymerization of dyed substrate at 40 degrees C, activity of xylanase C was greatest at a pH of 6.5 but was substantially reduced as the pH of the assay was decreased. In contrast, xylanase B showed highest activity at pH 5 and enzyme activity was twice that of xylanase B at pH 5.5 and 6. Overall, the results of this study provide more evidence that fibrolytic enzymes can be used to improve milk production in lactating cows.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".