ACC Deaminase Producing PGPR Bacillus amyloliquefaciens and Agrobacterium fabrum along with Biochar Improve Wheat Productivity under Drought Stress
Bibliographic record
Abstract
Drought stress retards wheat plant’s vegetative growth and physiological processes and results in low productivity. A stressed plant synthesizes ethylene which inhibits root elongation; however, the enzyme 1-Aminocyclopropane-1-Carboxylate (ACC) deaminase catabolizes ethylene produced under water stress. Therefore, the ACC deaminase producing plant growth promoting rhizobacteria (PGPR) can be used to enhance crop productivity under drought stress. Biochar (BC) is an organically active and potentially nutrient-rich amendment that, when applied to the soil, can increase pore volume, cation exchange capacity and nutrient retention and bioavailability. We conducted a field experiment to study the effect of drought tolerant, ACC deaminase producing PGPR (with and without timber waste BC) on plant growth and yield parameters under drought stress. Two PGPR strains, Agrobacterium fabrum or Bacillus amyloliquefaciens were applied individually and in combination with 30 Mg ha−1 BC under three levels of irrigation, i.e., recommended four irrigations (4I), three irrigations (3I) and two irrigations (2I). Combined application of B. amyloliquefaciens and 30 Mg ha−1 BC under 3I, significantly increased growth and yield traits of wheat: grain yield (36%), straw yield (50%), biological yield (40%). The same soil application under 2I resulted in greater increases in several of the growth and yield traits: grain yield (77%), straw yield (75%), above- and below-ground biomasses (77%), as compared to control; however, no significant increases in chlorophyll a, b or total, and photosynthetic rate and stomatal conductance in response to individual inoculation of a PGPR strain (without BC) were observed. Therefore, we suggest that the combined soil application of B. amyloliquefaciens and BC more effectively mitigates drought stress and improves wheat productivity as compared to any of the individual soil applications tested in this study.
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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) | 0.000 | 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".