Farmers’ Perception of Fall Armyworm (Spodoptera frugiperda) as an Invasive Pest and Its Management
Bibliographic record
Abstract
This study was carried out with the aim of understanding how farmers perceive and deal with fall armyworm (FAW) infestations in maize crops. Data based on a questionnaire were collected across nine districts of the Punjab province in Pakistan. Findings revealed that about 38% of farmers had medium-sized landholdings and had been cultivating maize crop for the last 11-20 years. Hybrid maize seed was used by 55% of the farmers, and 60% of the maize grown was used as fodder, as well as a cash crop. Surprisingly, only 39% of farmers were able to correctly identify FAW; however, the majority (72%) recognized the larvae as the most damaging stage. Although most of the farmers (71%) grew maize crops during both seasons, only 34% of the farmers recognized autumn as the peak infestation period of FAW. However, despite limited awareness, a high percentage (86%) of farmers managed FAW effectively using various chemical treatments. This study also highlighted the influence of some factors on farmers' perception of FAW such as: age, farming experience, and maize cultivation practices. Overall, the findings emphasize the need for increased awareness of the basic biology and targeted management strategies for FAW to safeguard maize crops in the region.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".