Enhancing Abiotic Stress Resilience in Horticultural Crops Through Seed Priming: A Comprehensive Review
Bibliographic record
Abstract
Abiotic stresses such as drought, salinity, heat, and cold significantly limit productivity in horticultural crops.Seed priming has emerged as an effective pre-sowing strategy to enhance stress resilience by activating physiological and biochemical pathways that prepare seeds for adverse environments.This review synthesizes current knowledge on priming-induced cross-tolerance mechanisms in horticultural species, focusing on antioxidant activation, membrane stability, hormonal balance and osmotic regulation.Practical applications and case studies across a range of vegetables demonstrate improved germination, seedling vigor and stress adaptation through diverse priming agents.The review also examines the agronomic benefits and limitations of priming, highlighting the influence of genotypic variability and environmental interactions.Finally, it outlines future research directions, emphasizing the need for multifactorial studies and the integration of priming with microbiome-based approaches, gene editing, and cultivar selection.Overall, seed priming offers a scalable and sustainable tool to enhance crop performance under multi-stress conditions, with broad implications for climate-resilient horticulture.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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) | 0.002 | 0.001 |
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".