Is radio an effective method for delivering actionable information for responding to emerging pest threats? A case study of fall armyworm campaign in Zambia
Bibliographic record
Abstract
Abstract Background The Fall Army Worm (FAW) radio campaign was implemented between November 2018 and April 2019 in key maize growing areas and locations with reported high severity of fall armyworm as identified by national stakeholders. We evaluated the effectiveness of radio mass extension campaign in achieving scale, and effect on farmers’ knowledge and uptake of management practices for fall armyworm (FAW). We also assessed the factors determining farmers’ participation in radio campaign, to inform future and similar campaigns. Methods Data were gathered through a household survey targeting locations where the campaign was implemented; and 250 male and 215 female farmers were surveyed. The study was conducted in four of the seven provinces where the campaign took place—Eastern, Luapula, Copperbelt and Southern provinces. Selection of the sample provinces was based on reported rainfall distribution during the season and severity of FAW infestation, radio coverage areas and maize growing intensities. Results The radio campaign reached an estimated 1.4 million farmers. Survey results show that both male and female radio listeners were significantly more aware of fall armyworm, and more likely to adopt management practices than non-radio listeners, in particular preventive measures such as frequent monitoring, intercropping and crop rotation. This means that participation in the radio-based extension campaign significantly increased farmers’ knowledge and stimulated uptake of management practices for FAW. However, the survey showed that only 49% of the respondents listened to at least one FAW radio episode. Predictors of farmer participation in radio campaign were; gender, education level, and maize farm size. Conclusions The results imply that deliberate promotion of such programs would enhance listenership and improve interactivity while at the same time integrating other extension approaches. The integration also provides opportunities for equally reaching women as men, given the observed digital divide.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".