Biological Control of Alfalfa Snout Beetle with a multi-species application of locally adapted persistent entomopathogenic nematodes: The first success
Bibliographic record
Abstract
Alfalfa snout beetle (ASB), Otiorhynchus ligustici (L.), is a very severe pest of alfalfa, grown in areas of northern New York and southeastern Ontario, Canada, bordering Lake Ontario and the St. Lawrence River. ASB often kills out entire fields in a single year from larval root feeding when populations are high. This parthenogenetic insect was first reported in North America in 1896 in Oswego Co., NY, where it was introduced by the dumping of sailing ballast between 1848 and 1896. ASB was identified as an alfalfa pest when alfalfa was introduced into the area in the 1920's. With ASB adults and larvae spending all of their 2-year life cycle in close association with soil, a biological control strategy focused on the use of locally adapted persistent entomopathogenic nematodes (EPNs) appeared to be a logical research direction. Research was initiated in 1989 and these studies were conducted primarily on a single farm (the John Peck Farm, located in Great Bend, Jefferson Co., New York) that historically contained extremely high populations of ASB. In 2002, the Peck Farm experienced an unexpected and sudden collapse of the ASB infestation. Subsequent studies on the Peck Farm revealed that the combination of two locally adapted nematodes had dispersed throughout the entire Peck Farm. We believe that the coexistence and soil residence partitioning of H. bacteriophora 'Oswego' with the native S. carpocapsae 'NY001' together exposed a wider range of life stages of ASB to nematode attack and subsequently applied enough biological control pressure on snout beetle to reduce the population to sub-economic levels. This research suggests that using locally adapted EPNs in a multi-species application provides a more effective approach to the biological control of ASB and potentially that of other soil pests.
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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) | 0.000 | 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 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".