Chemical cues in grain storage: A review on semiochemical types, pest behavior, and control strategies
Bibliographic record
Abstract
The excessive reliance on synthetic chemical pesticides has led to adverse environmental impacts, prompting the exploration of sustainable alternatives like semiochemicals (pheromones and allelochemicals) for pest management. These chemical compounds offer innovative approaches to controlling stored product insects, which pose significant threats to global food security by infesting and degrading grains and stored food products. This review delves into different kinds of semiochemicals, their action mechanism, and their applications in insect control methods, including mass trapping, mating disruption, attract-and-kill, and push-and-pull strategies. Recent advancements at the molecular level, particularly in understanding pheromone receptors, have enhanced insights into insect recognition and response to chemical signals. Different kinds of semiochemicals and their roles in managing stored grain pests belonging to Coleoptera , Lepidoptera , and Psocoptera are discussed. Additionally, the effectiveness of various pheromone-based traps, such as sticky, funnel and cone, pitfall, and light-activated traps, in grain storage monitoring are discussed, along with insights from field and laboratory case studies. Integration of pheromone traps with biological, chemical, and mechanical control methods is examined to highlight the potential for holistic pest management strategies. Despite their effectiveness, challenges such as environmental variability and species-specific responses persist. Future directions emphasize innovations in pheromone synthesis, trap design, and interdisciplinary approaches to enhance scalability and applicability across diverse storage ecosystems. This review underscores the critical role of semiochemical-mediated techniques in reducing stored product losses, offering a sustainable and effective alternative to chemical pesticides. • Covers semiochemicals, including pheromones and allelochemicals. in pest ecology and behavior. • Outlines pest control strategies like attracticides, mass trapping, and push-pull. • Reviews trap types and effectiveness in stored product pest monitoring. • Discusses combining pheromones with other methods and related challenges. • Summarizes global trends and future directions in semiochemical pest control.
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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 | 0.001 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".