Effect of Salicylic Acid Pretreatment on Yield, Its Components and Remobilization of Stored Material of Wheat under Drought Stress
Bibliographic record
Abstract
Due to higher needs of food in growing populations leads to accelerate the efforts of food production now days. Yield which is obtained from cereal farm is not at the amount of what we expected from their genetic potential. So it is possible to use different agro-techniques to increase total yield and help the crops to reach their genetic potential. In order to investigate the effect of salicylic acid on total yield and yield component of wheat under stress condition an experiment was conducted base on split factorial design with three replications. Treatments were drought stress at three levels (control, drought stress in mid florescence and drought stress in grain filling stage). Second treatment was application of salicylic acid as a priming agent, foliar application at beginning of tillering and foliar application of salicylic acid at beginning of flowering, and the third treatment was different dosage of salicylic acid (0, 0.7, 1.2 and 2.7 mmol). Results of experiment showed that drought stress significantly decreased grain yield, efficiency of material distribution while the highest grain yield was obtained at non-stressed condition with application of 0.7 mmol Salicylic acid. The highest redistribution of stored material, redistribution efficiency and partitioning was at time of salicylic application in vegetative stage, whereas the highest proportion of the metabolism in the grain yield observed in control condition (without stress). Grain yield exhibited high and positive correlation with number of spikes in m2, number of grain in spike, biological yield and harvest index.
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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".