Changes to the microbiome of alfalfa during the growing season and after ensiling with Lentilactobacillus buchneri and Lentilactobacillus hilgardii inoculant
Bibliographic record
Abstract
AIMS: This study evaluated changes in epiphytic microbial population of alfalfa (Medicago sativa) during the growing season. First cut forage was harvested to study the effects of an inoculant combining two obligate heterofermentative lactic acid bacteria strains on the bacterial and fungal communities and the fermentation of alfalfa silage. METHODS AND RESULTS: The epiphytic microbiome of alfalfa was evaluated 10-times during the growing season. Alfalfa wilted to 395.0 g/kg was treated with water (Control) or with a combination of L. buchneri NCIMB 40788 and L. hilgardii CNCM-I-4785 (LBLH). Mini-silos were opened after 1, 4, 8, 16, 32, and 64 days of ensiling. The relative abundance (RA) of the epiphytic bacterial and fungal families varied during the growing season. After 1 day, Weissella was the most abundant genus and present at similar RA in the two treatments (average 80.4%). Compared with Control, LBLH had a higher RA of Lactobacillus at day 1, 16, 32, and 64, and a lower RA of Weissella from day 8 to 64. Control contained more bacteria belonging to the Enterobacteriales than LBLH up to day 16. Inoculated silage had more acetate than Control at day 32 and 64. The fungal population were similar between treatments. The enhanced development and dominance of Lactobacillus in inoculated silage led to greater accumulation of acetate and propionate, which reduced the numbers of culturable yeasts but did not markedly affect the fungal community structure. CONCLUSIONS: The bacterial community composition of alfalfa stands in the filed changed over time and was affected by cutting. For the ensiling trial, inoculation modified the composition of the bacterial community of alfalfa, increasing the RA of Lactobacillus while reducing the RA of Weissella and of Enterobacteriaceae. SIGNIFICANCE AND IMPACT OF STUDY: Inoculation increased the RA of Lactobacillus, hampering the dominance of Weissella in the early stages of ensiling, improving antifungal compounds production and reducing the numbers of culturable yeasts.
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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 | 0.000 |
| 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".