Can the Use of<i>Trichogramma ostriniae</i>(Hymenoptera: Trichogrammatidae) to Control<i>Ostrinia nubilalis</i>(Lepidoptera: Crambidae) Be Economically Sustainable for Processing Sweet Corn?
Bibliographic record
Abstract
European corn borer, Ostrinia nubilalis (Hübner) (Lepidoptera: Crambidae), is the main pest causing damage to sweet corn in North America. Conventional management with multiple use of insecticides is a common practice for processing sweet corn. In Canada, the use of Trichogramma spp. began in the 1990s, but the adoption of this approach for European corn borer management is still limited to the fresh market of sweet corn. Trichogramma ostriniae (Peng & Chen) has great potential as a biological control agent for large areas such as in processing sweet corn. The objective of this study was to evaluate an economically and environmentally sustainable alternative to insecticides for controlling European corn borer populations in processing sweet corn. During the growing season, the mean number of larvae decreased after insecticide (0.07 ± 0.04) and Trichogramma (1.32 ± 0.59) treatments compared with the control (2.42 ± 0.72). At harvest, damages associated with European corn borer were similar after Trichogramma (1.0 ± 0.7%) and insecticide (1.0 ± 0.6%) treatments, but significantly lower than the control (8.7 ± 3.3%). This study showed that the use of T. ostriniae can significantly decrease the pressure exerted by European corn borer and its damage on corn ears. This outcome is particularly interesting considering that it was achieved with lower doses of Trichogramma, a lower number of releases, and on large crop areas, compared with what is actually done to protect fresh market corn from European corn borer. Under these conditions, the use of Trichogramma is an economically and competitive alternative to insecticide applications.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".