Managing Stink Bugs on Soybean Fields: Insights on Chemical Management
Bibliographic record
Abstract
Stink bugs are a major concern for pest management in soybean crops. With agricultural frontiers expanding in Brazil and cultivation techniques being heavily intensified, stink bug populations have become increasingly dispersed and hard to control, causing severe economic losses to soybean growers across the country. Chemical insecticides known as neonicotinoids, organophosphates and pyrethroids currently represent the main control strategy for this pest, being often mixed together in order to enhance control efficacy and prevent resistance development. Each of these chemical groups is characterized by a different mode of action inside the insect’s body, which determines if the insecticide will provide a fast knockdown effect or a long residual control effect. The aim of this work was to evaluate the knockdown and residual control effects delivered by these groups of insecticides under field conditions and during two cropping seasons, both in isolated and combined use, determining the most efficient strategy for chemical management of stink bugs on soybean crops. The pyrethroid lambda-cyhalothrin (250 g L-1) had the best knockdown effect, while the neonicotinoid imidacloprid (700 g kg-1) provided the longest residual control. The highest control efficacy was obtained with the combination of lambda-cyhalothrin + thiamethoxam (106 + 141 g L-1), which resulted in 84.8% of stink bug control.
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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".