Chemical Seed Priming: Molecules and Mechanisms for Enhancing Plant Germination, Growth, and Stress Tolerance
Bibliographic record
Abstract
Food security is one of the world's top challenges, specifically considering global issues like climate change. Seed priming is one strategy to improve crop production, typically via increased germination, yields, and/or stress tolerance. Hydropriming, or soaking seeds in water only, is the simplest form of seed priming. However, the addition of certain seed priming agents has resulted in a variety of modified strategies, including osmopriming, halopriming, hormonal priming, PGR priming, nutripriming, and others. Most current research has focused on hormonal and nutripriming. This review will focus on the specific compounds that have been used most often over the past 3 years and the physiological effects that they have had on crops. Over half of recent research has focused on four compounds: (1) salicylic acid, (2) zinc, (3) gibberellic acid, and (4) potassium nitrate. One of the most interesting characteristics of all chemical seed priming agents is that they are exposed only to seeds yet confer benefits throughout plant development. In some cases, such benefits have been passed to subsequent generations, suggesting an epigenetic effect, which is supported by observed changes in DNA methylation and histone modification. This review will summarize the current state of knowledge on molecular changes and physiological mechanisms associated with chemical seed priming agents and discuss avenues for future research.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".