Management of Insecticide-Resistant Soybean Aphids in the Upper Midwest of the United States
Bibliographic record
Abstract
Abstract Since the first observation of soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), in North America in 2000, it has become the most economically damaging insect of soybean in the Upper Midwest of the United States. For the last 17 yr, soybean aphid management has relied almost entirely on the use of foliar-applied broad-spectrum insecticides. However, in 2015 in Minnesota, failures of foliar-applied pyrethroid insecticides were reported and pyrethroid resistance was confirmed with laboratory bioassays using lambda-cyhalothrin and bifenthrin. In 2016 and 2017, further reports of failures of pyrethroid insecticides and/or laboratory confirmation of resistance occurred in Iowa, North Dakota, South Dakota, and Manitoba. In response to the challenge posed by insecticide-resistant soybean aphids, we recommend several management strategies for minimizing further development of resistance and subsequent pest-induced crop losses: 1) scout and use the economic threshold to determine when to apply insecticides, 2) apply the insecticides properly, 3) assess efficacy 3–5 d after application, and 4) alternate to a different insecticide group if another application is required. In the long term, soybean aphid management must move beyond insecticide-based management to true integrated pest management by incorporating multiple tactics.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".