Probability of Cost-Effective Management of Soybean Aphid (Hemiptera: Aphididae) in North America
Bibliographic record
Abstract
Soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), is one of the most damaging pests of soybean, Glycine max (L.) Merrill, in the midwestern United States and Canada. We compared three soybean aphid management techniques in three midwestern states (Iowa, Michigan, and Minnesota) for a 3-yr period (2005-2007). Management techniques included an untreated control, an insecticidal seed treatment, an insecticide fungicide tank-mix applied at flowering (i.e., a prophylactic treatment), and an integrated pest management (IPM) treatment (i.e., an insecticide applied based on a weekly scouting and an economic threshold). In 2005 and 2007, multiple locations experienced aphid population levels that exceeded the economic threshold, resulting in the application of the IPM treatment. Regardless of the timing of the application, all insecticide treatments reduced aphid populations compared with the untreated, and all treatments protected yield as compared with the untreated. Treatment efficacy and cost data were combined to compute the probability of a positive economic return. The IPM treatment had the highest probability of cost effectiveness, compared with the prophylactic tank-mix of fungicide and insecticide. The probability of surpassing the gain threshold was highest in the IPM treatment, regardless of the scouting cost assigned to the treatment (ranging from $0.00 to $19.76/ha). Our study further confirms that a single insecticide application can enhance the profitability of soybean production at risk of a soybean aphid outbreak if used within an IPM based system.
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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 | 0.004 |
| 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 | 0.000 |
| 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".