The Role of Plant Growth Regulators in Modulating Root Architecture and Tolerance to High-Nitrate Stress in Tomato
Bibliographic record
Abstract
Plant growth regulators are known to exert strong influences on plant performance under abiotic stress, including exposure to high nitrate, as occurs commonly in intensive vegetable production. However, direct comparative evaluations of growth regulators under otherwise identical conditions in major crop species are scarce. In this study, tomato (Solanum lycopersicum L.) was used as a model crop, and the roles of four common exogenously applied plant growth regulators (MT, melatonin; SA, salicylic acid; HA, humic acid; SNP, sodium nitroprusside) in regulating crop growth were studied under high-nitrate stress. We provide a particular focus on root system architecture and root physiological responses. Our data show that all four growth regulators improve tomato tolerance under high nitrate, but that this occurs to differing extents and via differing mechanisms. Optimal concentrations of MT, SA, HA, and SNP were 50 μmol L–1, 25 μmol L–1, 25 mg L–1, and 50 μmol L–1, respectively. MT and SNP produced the strongest effects. MT enhanced root growth while SNP enhanced above-ground growth. Growth of coarse and thin lateral roots was significantly improved. Furthermore, an enhancement of root vitality and metabolism, improved integrity of root cell membranes, and an increase in antioxidant enzyme activities were found, but regulatory mechanisms were different for each growth regulator. Our results show that in particular the application of MT and SNP can improve growth of tomato in intensive vegetable production under high-nitrate stress and that root growth stimulation is of special importance in procuring these beneficial effects.
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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) | 0.000 | 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 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".