The right banker plant for the right application: Comparison of three candidates for aphid biocontrol, barley (<i>Hordeum vulgare</i><scp>L</scp>.), corn (<i>Zea mays</i><scp>L</scp>.), and finger millet (<i>Eleusine coracana</i> (<scp>L</scp>.) <scp>Gaertn</scp>)
Bibliographic record
Abstract
BACKGROUND: In temperate regions, aphid biological control in greenhouses is mostly achieved by the regular release of biocontrol agents. Due to the rapid growth rate of the aphid population, biocontrol agents must be released frequently in order to be present before pest outbreaks and to act rapidly to prevent exceeding the economic threshold. Banker plants reduce these numerous releases by providing natural enemies with a high-quality environment to develop and reproduce. Optimally, banker plants should be easy to produce, resistant to environmental conditions, provide a large amount of suitable banker prey in order to produce a high number of biocontrol agents, and resist the herbivory pressure of the banker prey. The present study aimed to compare the value of three banker plant candidates of the Poaceae family under laboratory and greenhouse conditions: barley (Hordeum vulgare L.), finger millet (Eleusine coracana (L.) Gaertn), and corn (Zea mays L.). RESULTS: Our results show that the three plants were suitable for different contexts. Finger millet yielded the biggest fresh plant biomass, supported the highest load of banker prey, and resisted aphid feeding longer than the other plant species. Corn was the cheapest to produce, and barley was the fastest to grow. CONCLUSIONS: Overall, finger millet could be more fitted for long crop cycles, pests with rapid population growth rates, and voracious or fast-reproducing biocontrol agents. Meanwhile, barley and corn may be better suited for rapid crop cycles, pests with slow population growth rates, and biocontrol agents that are not too voracious or have low reproductive rates. © 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".