PRECURSOR-INOCULUM INTERACTION FOR THE GROWTH PROMOTION OF COTTON UNDER FIELD CONDITIONS
Bibliographic record
Abstract
The present experiment was conducted during 2019-2021 at Agricultural Biotechnology Research Institute, Ayub Agricultural Research Institute, Faisalabad, Pakistan. The yield of local BT cotton variety FH-142 was evaluated using different combinations of rhizobacteria (free living diazotroph and phosphate solubilizer) with auxin precursor L-tryptophan (L-TRP). The experiment was comprised of seven treatments under Randomized Complete Block Design (RCBD) replicated thrice. The experiment was repeated for consecutive three years to find the effect of microbial inoculants with auxin precursor L-TRP and mean of three years was reported in the manuscript. The microbial inoculants were applied as seed coating. The bacterial inoculation showed promising results as compared to control. However, the consortium of PGPR and P-solubilizer with precursor had the significant effects on cotton yield during all three years of experiment. The consortium increased the yield parameters of cotton i.e., 1810 kg/ha in comparison with un-inoculated control i.e., 1672 kg/ha. Individual applications of PGPR and P-solubilizer also had significant effect on the yield compared to control. Hence, it was concluded that the interaction of bacterial consortium and precursor (precursor inoculum interaction) proved to be the best to improve the cotton yield.
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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) | 0.001 | 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".