Foliar application of mepiquat chloride and nitrogen improves yield and fiber quality traits of cotton (Gossypium hirsutum L.)
Post-publication record
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Bibliographic record
Abstract
Cotton (Gossypium hirsutum L.) is one of the most important cash crops primarily grown for fiber. It is a perennial crop with indeterminate growth pattern. Nitrogen (N) is extremely important for vegetative growth as balanced N-nutrition improves photosynthesis, resulting in better vegetative growth. Excessive N-supply results in more vegetative growth, which increases the incidence of insect pest and diseases' infestation, pollute surface and ground water, delays maturity and produces low crop yield with poor quality. The use of plant growth regulators (PGRs) is an emerging option to control excessive vegetative growth. The PGRs help in improving plant architecture, boll retention, boll opening, yield and quality by altering growth and physiological processes such as photosynthesis, assimilate partitioning and nutrients dynamic inside the plant body. Mepiquat chloride (1,1-dimethylpiperidinum chloride) is globally used PGR for canopy development and control of excessive vegetative growth in cotton. This study investigated the effect of mepiquat chloride (MC) and N application on yield and yield components of transgenic cotton variety 'BT-FSH-326'. Two N rates (0, 198 kg ha-1) and five MC rates (0, 30,60, 90 and 120 g ha-1) were included in the study. Results revealed that MC and N application improved boll weight, number of bolls per plant, and seed cotton and lint yields. The highest seed cotton and lint yields (3595 kg ha-1 and 1701 kg ha-1, respectively) were observed under foliar application of 198 kg ha-1 N and 120 g ha-1 MC. Fiber length, fiber strength, micronaire and uniformity were significantly improved with foliar application of MC and N. In conclusion, foliar application of MC and N could be helpful in improving yield and fiber quality of cotton.
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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 | 0.000 | 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 | 0.000 |
| 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".