Factors influencing the occurrence of fall armyworm parasitoids in Zambia
Bibliographic record
Abstract
Abstract Invasive alien species have environmental, economic and social impacts, disproportionally threatening livelihood and food security of smallholder farmers in low- and medium-income countries. Fall armyworm (FAW) ( Spodoptera frugiperda ), an invasive insect pest from the Americas, causes considerable losses on maize to smallholder farmers in Africa since 2016. The increased use of pesticides to control FAW in Africa raises concerns for health and environmental risks resulting in a growing interest in research on biological control options for smallholder farmers. In order to evaluate the occurrence of local natural enemies attacking FAW, we collected on a weekly basis FAW eggs and larvae during a maize crop cycle in the rainy season of 2018–2019 at four locations in the Lusaka and Central provinces in Zambia. A total of 4373 larvae and 162 egg masses were collected. For each location and date of collection, crop stage, the number of plants checked and amount of damage were recorded to analyse which factors best explain the occurrence of the natural enemy species on maize. Overall parasitism rates from local natural enemies at each location varied between 8.45% and 33.11%. We identified 12 different egg-larval, larval and larval-pupal parasitoid species. Location, maize growth stage, pest density and larval stage significantly affected parasitoid species occurrence. Our findings indicate that there is potential for increasing local populations of natural enemies of FAW through conservation biological control programmes and develop safe and practical control methods for smallholder farmers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".