Metataxonomic insights into the microbial ecology of farm-scale hay, grass or legume, and corn silage produced with and without inoculants
Bibliographic record
Abstract
The microbiota of silage is a key determinant of its quality. Although commercial inoculants are often used to improve silage quality, studies to analyze their impact on the microbiota of preserved forage at farm-scale facilities are scarce. We assessed the diversity of viable bacterial communities of hay (unfermented dry forage) and grass or legume (GL) and corn (C) silage to deepen our knowledge of how inoculant addition drives microbial occurrence patterns on dairy farms. Forage samples were collected from 24 dairy farms over two sampling periods. Samples were analyzed by high-throughput sequencing and quantitative PCR after being treated with propidium monoazide to account for viable cells. We found consistent significant differences between hay and silage community structures across sampling periods. Silage was generally dominated by lactic acid bacteria (LAB), while Pantoea and Sphingomonas were the main co-dominant genera in hay. The GL silage dominated by Pediococcus, Weissella, and Bacillus was phylogenetically different from C silage enriched in Acetobacter. The use of inoculants including Lentilactobacillus buchneri either alone or in combination with Lactiplantibacillus plantarum, Lacticaseibacillus casei, Pediococcus pentosaceus, or Enterococcus faecium did not systematically prevent the occurrence of undesirable bacteria, especially when corn-based, probably because of factors that can mitigate the effect of inoculation on the microbiota. The core Lactobacillales constituted the dominant LAB in silage with up to 96% relative abundance, indicating either the ubiquity of inoculants or the high competitiveness of epiphytes. Silage chemical profiles varied inconsistently with sampling periods and the use of inoculants. Multivariate multi-table analyses allowed the identification of bacterial clusters mainly driven by moisture and magnesium content in hay, while pH, lactic, and fatty acids were the main drivers for silage. Bacterial network analyses showed considerable variations in the topological roles with the use of inoculants. These results may help evaluate the effectiveness of forage management practices implemented on dairy farms and, therefore, are useful for fine-tuning the search for new additives. Such knowledge can be used by forage makers to adjust processing routines to improve the hygienic quality, nutritional potential, and aerobic stability of preserved forage.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".