Effect of fibrolytic enzymes and an inoculant on <i>in vitro</i> degradability and gas production of low‐dry matter alfalfa silage
Bibliographic record
Abstract
Abstract BACKGROUND: The objective of this study was to investigate the effect of polysaccharide‐degrading enzymes (a cellulase and a xylanase) alone or in a combination with a bacterial inoculant on fermentation parameters and in vitro degradability and gas production of low‐dry matter (DM) alfalfa silage. First cut alfalfa ( Medicago sativa L.), harvested at about 5% bloom stage [260 g kg −1 dry matter (DM)] was ensiled in laboratory‐scale silos without preservatives or preserved with formic acid, a cellulase (Cell), a xylanase, a cellulose/xylanase enzyme combination (Cell/Xyl), a lactic acid bacteria‐based inoculant (Inoc), and a mix of Inoc and Cell (Inoc/Cell). Triplicate silos were opened on days 1, 3, 7, 15 and 60. RESULTS: Silage pH and ammonia N and total free amino acids concentrations were the lowest ( P < 0.05) for the formic acid silage. Inoc and Inoc/Cell produced the highest ( P < 0.05) lactate concentration in the 60‐day silage. In vitro degradability of silage DM was not affected ( P = 0.998) by treatment, but amylase‐treated neutral detergent fiber degradability was increased ( P < 0.05) by formic acid. Compared with the control (51.3 mL 100 mg −1 silage DM), all treatments except Cell/Xyl increased ( P < 0.001) the 24 h cumulative gas production. CONCLUSIONS: Overall, enzyme and lactic acid bacteria‐based preparations had minor effects on silage fermentation in this experiment. The increased cumulative gas production indicates some preservation or liberation of fermentable organic matter with most treatments tested. It is not clear, however, to what extent this effect would impact silage ruminal degradability in vivo . Copyright © 2008 Society of Chemical Industry
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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 | 0.000 | 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 | 0.001 |
| 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".