Environmentally friendly management of barley yellow dwarf virus infection: Challenges, strategies, and prospects
Bibliographic record
Abstract
Barley yellow dwarf virus (BYDV) infection in cereal crops significantly reduces grain yield and quality, which may further exacerbate the damage inflicted by aphid vectors. Developing effective and environmentally sustainable management strategies to mitigate the damage from BYDV and its vectors is essential for ensuring food security and sustainable agriculture globally. One promising approach is the widespread cultivation of BYDV-resistant cultivars. However, direct introgression of resistance genes into elite crop varieties has rarely resulted in durable resistance. Recent progress in identifying and characterizing viral proteins, receptors, and vector effectors has provided critical insights into breeding strategies aimed at enhancing resistance to viral infections. These approaches typically involve disrupting virus acquisition or strengthening phytohormone-mediated defenses via genetic improvement, thereby reducing virus transmission. Due to limited attention given to BYDV control in previous research, there remains a strong need to identify additional candidate genes that influence interactions among BYDV, aphids, and host plants. This review provides a comprehensive overview of recent progress and existing challenges in cultivar improvement programs aimed at enhancing resistance to both BYDV and its vectors. We also discuss alternative approaches, such as applying phytohormones and herbivore-induced plant volatiles, to strengthen crop resistance mechanisms—specifically antixenosis and antibiosis—against aphid vectors.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".