Impact of Foliar Spray of Salicylic Acid on Morpho-Physiological and Biochemical Responses of Pea (Pisum sativum L.) under Drought Stress
Bibliographic record
Abstract
Drought stress reduces growth, yield, and photosynthetic efficiency in pea plants, limiting nutrient uptake and requiring mitigation strategies. Salicylic acid plays a key role in plant development, and pea (Pisum sativum L.) ranks fourth in global production. A pot experiment was conducted to evaluate the effect of salicylic acid on two pea cultivars viz: Super Classic and Madarna, under water stress conditions at the Botanical Garden Research Area, University of Agriculture Faisalabad PARS, in 2021. Salicylic acid was applied as a foliar spray at concentrations of 0 (control), 50 ppm, 100 ppm, and 150 ppm. Drought stress was imposed at two levels: 100% field capacity (FC) and 75% FC. The experiment was conducted in completely randomized design (CRD) with a factorial arrangement and each treatment was replicated three times. The analysis revealed that drought intervals significantly affected various growth indicators, including fresh and dry weights of both shoots and roots, their lengths, physiological pigments such as chlorophyll and carotenoids, ionic contents of Na + , K + , and Ca2 + , and the overall yield. Notably, the application of 150 ppm salicylic acid effectively mitigated the effects of drought stress in pea plants. In conclusion, salicylic acid demonstrated beneficial effects against drought in pea plants by enhancing growth and yield while protecting photosynthetic pigments. Additionally, the Super Classic variety exhibited greater growth compared to Madarna when treated with salicylic acid at the 150 ppm concentration under drought stress.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".