Entomopathogenic nematodes as an effective and sustainable alternative to control the fall armyworm in Africa
Bibliographic record
Abstract
The recent invasion of the fall armyworm (FAW), a voracious pest, into Africa and Asia has resulted in unprecedented increases in insecticide applications, especially in maize cultivation. The health and environmental hazards posed by these chemicals have prompted a call for alternative control practices. Entomopathogenic nematodes are highly lethal to the FAWs, but their application aboveground has been challenging. In this study, we report on season-long field trials with an innocuous biodegradable gel made from carboxymethyl cellulose containing local nematodes that we specifically developed to target the FAW. In several Rwandan maize fields with distinct climatic conditions and natural infestation rates, we compared armyworm presence and damage in control plots and plots that were treated with either our nematode gel formulation, a commercial liquid nematode formulation, or the commonly used contact insecticide cypermethrin. The treatments were applied to the whorl of each plant, which was repeated three to four times, at 2-week intervals, starting when the plants were still seedlings. Although all three treatments reduced leaf damage, only the gel formulation decreased caterpillar infestation by about 50% and yielded an additional ton of maize per hectare compared with untreated plots. Importantly, we believe that the use of nematodes can be cost-effective, since we used nematode doses across the whole season that were at least 3-fold lower than their normal application against belowground pests. The overall results imply that precisely formulated and easy-to-apply nematodes can be a highly effective, affordable, and sustainable alternative to insecticides for FAW 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 | 0.000 |
| 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".