Gibberellic and salicylic acids improve seedling emergence, early growth and some physiological characteristics of <i>Phaseolus vulgaris</i> L. under soil compaction
Bibliographic record
Abstract
Heavy soil compaction and crusting adversely affect the percentage and speed of seedling emergence, particularly in dicotyledonous epigeal plants. This study aimed to evaluate ways to cope with this phenomenon in kidney bean through hormonal priming. A factorial experiment was conducted based on a completely randomised design with three replications. The factors were seed priming (control, hydropriming, gibberellic acid 50 mg kg−1 (GA 50), GA 100, salicylic acid 50 mg kg−1 (SA 50), SA 100 and GA 50 + SA 50) and soil compaction (bulk density 1 340, 1 410, 1 470, 1 540 and 1 610 kg m–3). Results showed that soil compaction decreased root length and increased root branches. The longest root was observed following priming with GA 50 + SA 50 and no soil compaction (intact soil). Seed priming significantly affected seedling emergence and early growth indices. Priming improved root length and the number of root branches, while soil compaction suppressed root length and increased the number of root branches. Seed priming also significantly affected chlorophyll a, carotenoid, catalase and superoxide dismutase levels. Superoxide dismutase and catalase activities were increased by soil compaction, especially at 15% and 20% soil bulk density (ρ) (ρ = 1 540 and 1 610 kg m−3). In general, mild soil compaction (5% or ρ = 1 410 kg m−3) accelerated seedling emergence and improved growth. However, increased soil compaction by 15% or more, decreased root and shoot growth. Hydropriming decreased seedling emergence under soil compaction, and was unsuitable for such conditions. Combined hormonal priming (GA 50 + SA 50) was the best treatment for seed resilience to soil compaction.
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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".