Using superior plant growth-promoting microorganisms through bioprospecting to create inoculants for peanut (Arachis hypogaea L.) farming
Bibliographic record
Abstract
Experiments with pot-grown plants are among the most common in plant research. In this study, we isolated plant growth-promoting bacteria (PGPB) from the rhizosphere soils and root nodules of peanut plants grown under greenhouse conditions, and evaluated their PGP properties to select elite strains as inoculants. The isolates were characterized for the following PGP activities: phosphate (P) solubilization, auxin and siderophore production, fluorescence emission, and nodule formation capacity. Isolates were also identified through 16S rRNA gene sequencing. A total of 90 isolates were obtained and characterized, belonging to five genera: Burkholderia, Bacillus, Brevundimonas, Dyella and Leifsonia. Among these, B. cepacia and B. territorii were the most abundant species, exhibiting the highest levels of auxin and siderophore production, as well as superior P solubilization. Furthermore, nodule isolates demonstrated more intense PGP activities than free-living isolates from rhizosphere soils. This is the first report documenting the nodulation capacity of the genus Dyella, and further studies targeting the nifH gene will be necessary to confirm its nitrogen-fixing ability. The in vitro screening provided sufficient evidence for further in vivo peanut growth-promoting tests of both Burkholderia and Dyella isolates. Such agricultural applications could enhance peanut yields while reducing environmental pollution. Key words: Peanut, biofertilizers, promoting rhizobacteria, characterization, soil and root nodule.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".