Modulating the Plant Microbiome: Effects of Seed Inoculation with Endophytic Bacteria on Microbial Diversity and Growth Enhancement in Pea Plants
Bibliographic record
Abstract
Understanding plant microbe interactions is crucial for achieving sustainable agriculture. This study investigated the effects of inoculating pea plants (Pisum sativum) with two endophytic Bacillus strains, AR11 and AR32, isolated from Artemisia species and characterized by phosphate solubilization, nitrogen fixation, and pathogen antagonism. Utilizing cutting-edge methods such as rarefaction curves, rank abundance modeling, and metagenomic analysis, this research provides a detailed understanding of how these bacterial strains influence plant associated microbiomes. AR11 significantly enhanced microbial diversity, while AR32 showed a moderate effect. Beta diversity analyses revealed distinct shifts in microbial community composition, with AR11-treated samples enriched with beneficial taxa such as Paenibacillus, Flavobacterium, and Methylotenera, known for their roles in nutrient cycling, pathogen suppression, and plant health promotion. This innovative methodological framework surpasses traditional approaches by offering a comprehensive view of ecological and functional microbiome shifts. The study highlights the potential of nonhost bacteria as biostimulants and their role in developing microbiome engineering strategies to enhance plant resilience. These findings contribute to sustainable agriculture by demonstrating how microbial inoculants can be employed to enhance crop productivity and environmental resilience in diverse agricultural systems.
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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 | 0.000 | 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 | 0.001 |
| 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".