Best Management Practices to Delay the Evolution of Bt Resistance in Lepidopteran Pests Without High Susceptibility to Bt Toxins in North America
Bibliographic record
Abstract
Canadian and United States (US) insect resistance management (IRM) programs for lepidopteran pests in Bacillus thuriengiensis (Bt)-expressing crops are optimally designed for Ostrinia nubilalis Hübner in corn (Zea mays L.) and Chloridea virescens Fabricius in cotton (Gossypium hirsutum L.). Both Bt corn and cotton express a high dose for these pests; however, there are many other target pests for which Bt crops do not express high doses (commonly referred to as nonhigh dose pests). Two important lepidopteran nonhigh dose (low susceptibility) pests are Helicoverpa zea Boddie (Lepidoptera: Noctuidae) and Striacosta albicosta Smith (Lepidoptera: Noctuidae). We highlight both pests as cautionary examples of exposure to nonhigh dose levels of Bt toxins when the IRM plan was not followed. Moreover, IRM practices to delay Bt resistance that are designed for these two ecologically challenging and important pests should apply to species that are more susceptible to Bt toxins. The purpose of this article is to propose five best management practices to delay the evolution of Bt resistance in lepidopteran pests with low susceptibility to Bt toxins in Canada and the US: 1) better understand resistance potential before commercialization, 2) strengthen IRM based on regional pest pressure by restricting Bt usage where it is of little benefit, 3) require and incentivize planting of structured corn refuge everywhere for single toxin cultivars and in the southern US for pyramids, 4) integrate field and laboratory resistance monitoring programs, and 5) effectively use unexpected injury thresholds.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".