Silage review: Recent advances and future uses of silage additives
Bibliographic record
Abstract
Additives have been available for enhancing silage preservation for decades. This review covers research studies published since 2000 that have investigated the efficacy of silage additives. The review has been divided into 6 categories of additives: homofermentative lactic acid bacteria (LAB), obligate heterofermentative LAB, combination inoculants containing obligate heterofermentative LAB plus homofermentative LAB, other inoculants, chemicals, and enzymes. The homofermentative LAB rapidly decrease pH and increase lactic acid relative to other fermentation products, although a meta-analysis indicated no reduction in pH in corn, sorghum, and sugarcane silages relative to untreated silages. These additives resulted in higher milk production according to the meta-analysis by mechanisms that are still unclear. Lactobacillus buchneri is the dominant species used in obligate heterofermentative LAB silage additives. It slowly converts lactic acid to acetic acid and 1,2-propanediol during silo storage, improving aerobic stability while having no effect on animal productivity. Current research is focused on finding other species in the Lb. buchneri group capable of producing more rapid improvements in aerobic stability. Combination inoculants aim to provide the aerobic stability benefits of Lb. buchneri with the silage fermentation efficiency and animal productivity benefits of homofermentative LAB. Research indicates that these products are improving aerobic stability, but feeding studies are not yet sufficient to make conclusions about effects on animal performance. Novel non-LAB species have been studied as potential silage inoculants. Streptococcus bovis is a potential starter species within a homofermentative LAB inoculant. Propionibacterium and Bacillus species offer improved aerobic stability in some cases. Some yeast research has focused on inhibiting molds and other detrimental silage microorganisms, whereas other yeast research suggests that it may be possible to apply a direct-fed microbial strain at ensiling, have it survive ensiling, and multiply during feed out. Chemical additives traditionally have fallen in 2 groups. Formic acid causes direct acidification, suppressing clostridia and other undesired bacteria and improving protein preservation during ensiling. On the other hand, sorbic, benzoic, propionic, and acetic acids improve silage aerobic stability at feed out through direct inhibition of yeasts and molds. Current research has focused on various combinations of these chemicals to improve both aerobic stability and animal productivity. Enzyme additives have been added to forage primarily to breakdown plant cell walls at ensiling to improve silage fermentation by providing sugars for the LAB and to enhance the nutritive value of silage by increasing the digestibility of cell walls. Cellulase or hemicellulase mixtures have been more successful at the former than the latter. A new approach focused on Lb. buchneri producing ferulic acid esterase has also had mixed success in improving the efficiency of silage digestion. Another new enzyme approach is the application of proteases to corn silage to improve starch digestibility, but more research is needed to determine the feasibility. Future silage additives are expected to directly inhibit clostridia and other detrimental microorganisms, mitigate high mycotoxin levels on harvested forages during ensiling, enhance aerobic stability, improve cell wall digestibility, increase the efficiency of utilization of silage nitrogen by cattle, and increase the availability of starch to cattle.
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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) | 0.005 | 0.001 |
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".