Characterization of Control Methods for Fall Armyworm (Spodoptera frugiperda J. E. Smith) in the Maize (Zea mays L.) Cropping Systems in Central Benin
Bibliographic record
Abstract
In Benin, the Fall armyworm (Spodoptera frugiperda J.E. Smith) causes severe damage to maize crop and threatens the food security of thousands of small farmers. The objectif of this study was to inventory local knowledge on the management of the Fall armyworm (FAW) by maize farmers in central Benin. A semi-structured questionnaire was used to collect information from 1885 maize farmers in six communes in central Benin. Data were analyzed using descriptive statistics, multivariate analysis and logistic regressions. Results showed that farmers consider FAW attacks as a major constraint to maize production. The common control method used by maize farmers is chemical control (90% of respondents) with synthetic products. Chemical families such as Pyrethroids, Avermectins, Neonicotinoids, Organophosphates are used. The farmers (4%) use organic products such as aqueous extracts of Azadirachta indica, Jatropha curcas and Carica papaya to control FAW. Certain farmers do not used any control method for FAW. Socioeconomic characteristics such as area planted, age, experience in maize production, farmer’s organization membership, level of education, gender, and income level of the farmer significantly determine (p < 0.05) the type of control method used against FAW. These factors should be taken into account by extension programs. Extension services can use farmers in these socio-economic categories as innovators to spread new and more effective control methods against Fall armyworm.
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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 | 0.000 |
| 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".