Actinobacteria from Extreme Niches in Morocco and Their Plant Growth-Promoting Potentials
Bibliographic record
Abstract
The objectives of this study were to assess actinobacterial diversity in five Moroccan extreme habitats and to evaluate their plant growth-promoting (PGP) activities. The soil samples were collected from different locations, including soils contaminated with heavy metals, from a high altitude site, from the desert, and from a marine environment. In total, 23 actinobacteria were isolated, 8 from Merzouga sand soil; 5 from Cannabis sativa rhizospheric soil; 5 from Toubkal mountain; 4 from a Draa sfar mining site; and 1 from marine soil. Based on their genotypic classification using 16S rRNA gene sequences, 19 of all belonged to the genus Streptomyces (82%) while the rest are the members of the genera Nocardioides (4.5%), Saccharomonospora (4.5%), Actinomadura (4.5%), and Prauserella (4.5%). Isolates Streptomyces sp. TNC-1 and Streptomyces sp. MNC-1 showed the highest level of phosphorus solubilization activity with 12.39 and 8.56 mg/mL, respectively. All 23 isolates were able to solubilize potassium, and 91% of them could grow under nitrogen-free conditions. The ability of the isolated actinobacteria to form indole-3-acetic acid (IAA) ranged from 6.70 to 75.54 μg/mL with Streptomyces sp. MNC-1 being the best IAA producer. In addition, all of the actinobacteria could produce siderophores, with Saccharomonospora sp. LNS-1 synthesizing the greatest amount (138.92 μg/mL). Principal coordinate analysis revealed that Streptomyces spp. MNC-1, MNT-1, MNB-2, and KNC-5; Saccharomonospora sp. LNS-1; and Nocardioides sp. KNC-3 each showed a variety of high-level plant growth-promoting activities. The extreme environments in Morocco are rich with bioactive actinobacteria that possess a variety of plant growth-promoting potentials that can further benefit green and sustainable agriculture.
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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 | 0.001 | 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".