Incorporating natural enemy units into a dynamic action threshold for the soybean aphid, <i>Aphis glycines</i> (Homoptera: Aphididae)
Bibliographic record
Abstract
BACKGROUND: Recommended action thresholds for soybean aphid, Aphis glycines, do not adjust for natural enemy impact, although natural enemies contribute important biological control services. Because individual natural enemy species have varied impacts on pest population dynamics, incorporating the impact of a diverse predator guild into an action threshold can be cumbersome. RESULTS: Field surveys identified an aphidophagous natural enemy complex dominated by Orius insidiosus, Coccinella septempunctata, Harmonia axyridis and Aphelinus certus. Functional responses of O. insidiosus were determined in the laboratory, while predation rates of all other natural enemies were obtained from the literature. Natural enemy impacts were normalized using natural enemy units (NEUs), where 1 NEU = 100 aphids consumed or parasitized. A dynamic action threshold (DAT) was developed by combining NEUs with an A. glycines population growth model. With the DAT, an insecticide application was only triggered if natural enemy numbers were insufficient to suppress pest populations. In field experiments, DAT provided equivalent yields to the conventional action threshold and reduced the average number of pesticide applications. CONCLUSION: The DAT approach has the potential to reduce pesticide use, will help preserve natural enemy populations and can be applied to other pest systems with diverse natural enemy guilds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".