Compatibility of the fungus <scp><i>Beauveria bassiana</i></scp> and <scp><i>Trichoplusia ni</i> SNPV</scp> against the cabbage looper <scp><i>Trichoplusia ni</i></scp>: crop plant matters
Bibliographic record
Abstract
BACKGROUND: Microbial insecticides are an important weapon in insect pest management, but their use is still relatively limited. One approach for increasing their efficacy and use could be to combine different pathogens to increase pest mortality. However, little is known about whether increasing pathogen diversity will improve pest management. Here, we investigated the compatibility of two pathogens for the management of the cabbage looper, Trichoplusia ni, T. ni nucleopolyhedrovirus (TniSNPV) and the entomopathogenic fungus Beauveria bassiana, on two crops, tomato and broccoli. The pathogens were applied to individual plants using ultra low volume sprays, alone or in combination, either synchronously or asynchronously. Healthy third-instar T. ni larvae were introduced to the plants before application and collected by destructive sampling 24 h after the last pathogen application. RESULTS: Combined applications did not result in an increase in larval mortality compared to TniSNPV alone, although mortality was generally high. B. bassiana was considerably less effective on broccoli compared to tomato. In both the combined treatments, virus-induced mortality was approximately 50% lower when applied together with the fungus, while fungus-induced mortality was not affected by the virus, even when the virus was introduced 24 h before the fungus. CONCLUSION: While our results suggest that applying this combination of entomopathogens would not be beneficial for pest management, this study illustrates the need to consider the target crop as an important driver of the efficacy of both single and mixed pathogen applications in the field. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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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 | 0.000 |
| 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".