Maize plants can recover from fall armyworm damage under optimum crop production conditions in humid tropical agro-ecologies
Bibliographic record
Abstract
Farmers in Africa perceive the impact of fall armyworm (FAW) on maize to be significant, but field assessments have shown that yield losses are not significant enough to warrant pesticide interventions. This suggests that relationships between the crop stages, time, and duration of attack can affect the yield. Therefore, assessing the plant's recovery from damage using individual plants based on defoliation levels could guide whether and when pesticides should be applied. To study this, we selected 120 labeled maize plants corresponding to six levels of FAW defoliation, replicated 20 times, based on an initial damage rating. The rating scale ranged from 1 (no defoliation) to 5 (>75% defoliation) during four planting seasons. Plants with a rating scale of 1 were replicated and treated with a chemical insecticide to keep them undefoliated, and that served as a control. Damage severity was recorded weekly on all plants, starting from emergence until maturity, using the same damage rating scale. Results showed that damage severity varied significantly among different defoliation levels during all seasons. Higher levels of defoliation during dry seasons resulted in significant yield loss only for plants with damage levels 4 and 5, with damage severity ranging from 38.7% to 57.5%. These results indicate that FAW control is unnecessary in the rainy season. In contrast, pesticide interventions should be envisaged in seasons of erratic rainfall, with a significant defoliation threshold level of around 50%, occurring at 8 and 5 wk after planting weeks after planting, respectively for the early and late dry season.
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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".