Effects of a mixture of <i>Lentilactobacillus hilgardii, Lentilactobacillus buchneri, Pediococcus pentosaceus</i> and fibrolytic enzymes on silage fermentation, aerobic stability, and performance of growing beef cattle
Bibliographic record
Abstract
Abstract This study evaluated the effects of novel silage inoculants containing lactic acid bacteria (LAB) and fibrolytic enzymes on ensiling, aerobic stability (AS), and the performance of growing beef cattle. Whole-plant corn forage was either uninoculated (CON) or inoculated with a mixture of LAB containing (cfu g−1 fresh forage) 1.5 × 105L. hilgardii (CNCM I-4785), 1.5 × 105L. buchneri (NCIMB 40788) and 1.0 × 105P. pentosaceus (NCIMB 12455) for a total of 4.0 × 105 cfu g−1 fresh forage LAB (IB), or a combination of IB plus fibrolytic enzymes (xylanase + β-glucanase) (IC). All treatments were ensiled in mini-silos, whereas CON and IC were also ensiled in silo bags for the growth performance study. Total bacteria (TB) counts were lower (P = 0.02) for IC than CON after 14 d of ensiling, whereas TB counts of IC and IB were greater (P ≤ 0.01) than CON after 60 d of ensiling in mini-silos. The LAB in IC and IB ensiled in mini-silos were greater than CON on d 60 (P ≤ 0.01) and 90 (P ≤ 0.001) of ensiling and after 3 d (P ≤ 0.01) of aerobic exposure (AE). Silage pH of IC ensiled in silo bags was lower than CON on d 3 (P < 0.01), 7 (P < 0.001), and 14 (P = 0.02) of AE. Yeast counts were lower for IC than CON in terminal silage (P < 0.001), and after 3 (P < 0.001) and 7 d (P < 0.01) of AE. Acetate (AC) concentrations were higher (P ≤ 0.02) for IC than CON throughout AE, whereas lactate (LA) concentrations of IC were greater than CON on d 3 (P < 0.001), 7 (P < 0.01), and 14 (P < 0.001) of AE. Greater AC concentration and lower yeast counts resulted in greater (P < 0.001) stability for IC ensiled in silo bags than CON after 14 d of AE. Growth performance of steers was similar (P > 0.05) as the nutrient composition of silage was similar across diets. Improved AS of IC could potentially have a greater impact on DMI, production efficiency, and growth performance in large-scale commercial feedlot operations where silage at the silo face may be exposed to air for longer periods of time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".