Foliage applications of jasmonic acid modulate the antioxidant defense under water deficit growth in sugar beet
Bibliographic record
Abstract
Aims of study: Water deficit (WD) is becoming an alarming problem in many regions of the world. Jasmonic acid (JA) is considered an important intracellular plant growth regulator. The aim of the current research was to investigate the important role of JA in mitigating the negative effects of WD on plant growth.Area of study: Sugar beet production systems of two locations in Chaharmahal-Bakhtiari province, Iran.Material and methods: A field trial was conducted to assess the foliar applications of JA (0, 5 µM and 10 µM) and WD (50%, 75%, 100% plant water requirements) effects on physiological yield components of sugar beet (Beta vulgaris L.) plants.Main results: WD significantly (p<0.05) increased catalase, ascorbate and peroxidase activities, and malondialdehyde, hydrogen peroxide and white sugar content (WSC); however, it caused a reduction in white sugar yield and root yield (RY). JA foliage applications further enhanced the enzymes activity in WD treated plants resulting in higher WSC, potassium concentrations, white sugar and final RY. Interestingly the effects of JA applications were more pronounced under severe WD (50%) compared to mild (75%) or well-watered plants (100%). JA (10 µM) foliage applications increased the RY and white sugar production by 21% and 24% under severe WD.Research highlights: JA can ameliorate the adverse effects of WD and increase the WD tolerance of sugar beet crop by upregulating the antioxidant enzyme activities to withstand adverse environmental conditions.
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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 | 0.001 |
| 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".