Preliminary results on effects of planting dates and maize growth stages on fall armyworm density and parasitoid occurrence in Zambia
Bibliographic record
Abstract
Abstract Fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), has become one of the main invasive species on earth since it was first found outside its native range in Africa in 2016. Integrated pest management (IPM) is a comprehensive tool that can help farmers managing pests while reducing the need of synthetic pesticides. Within an IPM strategy, proper time of planting is a critical management decision for farmers as planting too early or too late can lead to complete loss of the crop. Commonly, planting early to avoid peak infestation of FAW is recommended to farmers, however, no empirical data in Africa is available to sustain the advice. We studied the effects of planting dates of maize as well of maize growth stages on FAW density and on its local parasitoids in a field study. Three plots were setup (early, intermediate and late planting) and data was collected weekly in each plot. Plots were 20 m × 20 m to avoid small-plot effects, but the relatively large size of the plots was resource intensive and prevented replication. As such, this paper presents preliminary results due to the lack of true replicates across locations and years. Generalized Linear Models were used to model FAW density and parasitoids abundance and diversity. Our results showed an increase of egg masses over time from early to late planting. Additionally, parasitism probabilities were lower in the early planting treatment than for the intermediate and late plantings and decreased with increased maize maturity. Results on biodiversity of parasitoids show a less even trend for early and late whorl stages which are dominated by one or two species while maize reproductive stages show a more even distribution of species. Our preliminary research is the first to provide empirical evidence that planting early helps to avoid the peak activities of FAW moths. These findings provide important information for the sustainable management of FAW in Zambia with the aim to reduce chemical inputs and increase farmers’ incomes and livelihood.
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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 | 0.000 | 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".