Utilization of Natural Plant Volatiles for Pest Control in Maize
Bibliographic record
Abstract
Maize is a critical staple crop globally, but pest infestations present a significant challenge to its cultivation, often leading to reduced yields. Conventional pest control methods, particularly synthetic pesticides, have raised environmental and health concerns, prompting interest in alternative approaches. This study explores the utilization of natural plant volatiles for pest control in maize, focusing on essential oils, terpenoids, alkaloids, and volatile organic compounds (VOCs) that have pest-repelling properties. The mechanisms through which plant volatiles affect insect pests-such as disrupting olfaction and behavior, inducing repellency, and interacting synergistically with other pest control agents-are examined. Field trials were conducted to evaluate the efficacy of plant volatiles against key maize pests, with a comparative analysis against synthetic pesticides. This study also explores the benefits and challenges of using natural volatiles in integrated pest management (IPM), particularly for smallholder farmers. Results demonstrate that plant volatiles are environmentally sustainable, reduce chemical inputs, and offer a promising tool for future pest control strategies. However, large-scale implementation remains a challenge, requiring further research on formulation, delivery methods, and potential genetic modifications to enhance volatile production in maize varieties.
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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) | 0.000 | 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".