Biological Control and Habitat Management for the Control of Onion Thrips, Thrips tabaci Lindeman (Thysanoptera: Thripidae), in Onion Production in Quebec, Canada
Bibliographic record
Abstract
Onion thrips (Thrips tabaci) can pose a significant threat to onion crops, causing leaf damage, reduced bulb size and quality, and yield loss during severe infestations. Conventional insecticide use has been the primary method for managing this pest species, but the efficacy of this approach is inconsistent. Furthermore, emerging pest resistance is a growing concern in some regions. This two-year field study aimed to assess the effectiveness of several pest management strategies in controlling onion thrips populations and limiting their impact on onion yields. The strategies tested consisted of habitat manipulations (including flower strips and straw mulch), biological control agents (Stratiolaelaps scimitus, Neoseiulus cucumeris, Amblyseius swirskii, and Beauveria bassiana), as well as physical barrier control methods (exclusion nets, kaolin, and mineral oil). Habitat manipulation techniques, particularly the use of flower strips, reduced thrips populations by up to 50% and increased onion yields by 25%. In contrast, exclusion nets had a detrimental effect on onion yields, and the other alternative control methods produced results comparable to those obtained for untreated controls. When used alone, biological control agents were not effective at maintaining thrips populations below economically damaging levels. This study offers valuable insights into effective and sustainable pest management practices for the onion industry.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".