Evaluation of ACC-deaminase-producing rhizobacteria to alleviate water-stress impacts in wheat (<i>Triticum aestivum</i> L.) plants
Bibliographic record
Abstract
Application of plant-growth-promoting rhizobacteria (PGPR) is an environmentally sustainable option to reduce the effects of abiotic and biotic stresses on plant growth and productivity. Bacteria isolated from rain-fed agriculture field soils in the Central Himalaya Kumaun region, India, were evaluated for the production of 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase. Those producing ACC deaminase in high amounts were evaluated for their potential to improve wheat (Triticum aestivum L.) plant growth under irrigated and water-stress conditions in two glasshouse experiments. Some of the isolates also showed other plant-growth-promoting (PGP) traits, e.g., N 2 fixation, siderophore production, and phosphate solubilization; however, strains with higher ACC deaminase activity showed the greatest effects. These were Variovorax paradoxus RAA3; Pseudomonas spp. DPC12, DPB13, DPB15, DPB16; Achromobacter spp. PSA7, PSB8; and Ochrobactrum anthropi DPC9. In both simulated irrigated and water-stress conditions, a single inoculation of RAA3 and a consortium of DPC9 + DPB13 + DPB15 + DPB16 significantly improved wheat plant growth and foliar nutrient concentrations and caused significant positive changes in antioxidant properties compared with noninoculated plants especially under water stress. These findings imply that PGPB having ACC deaminase activity together with other PGP traits could potentially be effective inoculants to improve the growth of wheat plants in water-stressed rain-fed environments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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".