Bibliographic record
Abstract
Silage quality, as with all forages, is governed by the maturity of the crop at harvest. However, fermentation in the silo further influences nutritive value of silage by reducing voluntary intake and utilization of digestible nutrients. Silage research up to the present time has focussed on closing the gap between feeding value of the original crop and that of the resulting silage. This review focuses on the advances made towards closing that gap, and explores the possibility that in the future ensiling can become a tool for actually increasing the feeding value of forages. Following a section defining silage quality, the relationships between silage fermentation quality and voluntary intake and between silage fermentation and protein and energy utilization will be examined, with emphasis placed on measures to minimize the negative effects of fermentation on animal production. Recent literature is reviewed, which suggests that many factors previously thought to reduce silage intake, such as pH, lactic acid and dry matter (DM), have, in fact, only a casual relationship with intake. Concentrations of fermentation acids do not seem closely related to silage intake; however, they are critical in determining the balance of volatile fatty acids (VFA) produced in the rumen. This in turn, affects the non-glucogenic ratio and can influence milk and body composition in productive livestock. While rumen ammonia is often implicated in reducing silage intake, protein solubility may be more the causal agent than ammonia per se. Protein solubility is also a major factor in reducing the efficiency of silage protein utilization. Methods to reduce protein solubility in silages are discussed. Methods shown to improve silage feeding value include effective wilting and rapid acidification, either by direct acidification or the use of inoculants. Their widespread adoption has undoubtedly contributed to improvements in animal production from silages in recent years. Key words: Silage, feeding value, voluntary food intake, fermentation, ruminant
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".