Enhancing In Vitro Degradation of Alfalfa Hay and Corn Silage Using Feed Enzymes
Bibliographic record
Abstract
A series of in vitro fermentation experiments was performed to assess the effects of 4 feed enzyme products (FE) that varied in enzymatic activities on the degradation of alfalfa hay and corn silage. The FE contained a range of endoglucanase, exoglucanase, xylanase, and protease activities, and a range of dose rates (DR) was used. The objective of the study was to identify effective formulations and optimum DR, and to establish if combining FE would further improve fiber degradation. For alfalfa hay, quadratic increases in gas production and degradation of dry matter (DM) and fiber were observed for all FE, with maximum responses at low to medium DR. For corn silage, none of the FE increased gas production or DM degradation, but all FE increased NDF degradation, with optimum DR in the low to medium range. The proteolytic enzyme papain improved fiber degradation of alfalfa hay and corn silage in a manner similar to that observed for polysaccharidase FE. Among the polysaccharidase FE, added activities of endoglucanase and exoglucanase were positively correlated with improvement in neutral detergent fiber (NDF) degradability of corn silage, whereas only added endoglucanase activity tended to be correlated with improvement in NDF degradability of alfalfa hay. Combining effective polysaccharidase FE further improved fiber degradation of both forages, with greater improvements for corn silage. Combining polysaccharidase and proteolytic FE further improved NDF degradation of corn silage, but not alfalfa hay. Combination treatments generally resulted in additive effects with increases in fiber degradation equal to the sum of the improvements for the individual enzyme components. Improved fiber degradation of corn silage was associated with decreased acetate to propionate ratios. Enzyme products that improve in vitro degradation of forages may have the potential to improve lactational performance of dairy 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".