Role of Microbial Volatile Organic Compounds in Promoting Plant Growth and Disease Resistance in Horticultural Production
Bibliographic record
Abstract
Microbial volatile organic compounds (MVOCs) are a diverse group of volatile organic compounds that microorganisms may produce and release into the environment. These compounds have both positive and negative effects on plants, as they have been shown to be effective at mitigating stresses and functioning as immune stimulants. Furthermore, MVOCs modulate plant growth and systemic plant resistance, while also serving as attractants or repellents for insects and other stressors that pose threats to plants. Considering the economic value of strawberries as one of the most popular and consumed fruits worldwide, harnessing the benefits of MVOCs becomes particularly significant. MVOCs offer cost-effective and efficient solutions for disease control and pest management in horticultural production, as they can be utilized at low concentrations. This paper provides a comprehensive review of the current knowledge on microorganisms that contribute to the production of beneficial volatile organic compounds for enhancing disease resistance in fruit products, with a specific emphasis on broad horticultural production. The review also identifies research gaps and highlights the functions of MVOCs in horticulture, along with the different types of MVOCs that impact plant disease resistance in strawberry production. By offering a novel perspective on the application and utilization of volatile organic compounds in sustainable horticulture, this review presents an innovative approach to maximizing the efficiency of horticultural production through the use of natural products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".