The impact of ICT-enabled extension campaign on farmers’ knowledge and management of fall armyworm in Uganda
Bibliographic record
Abstract
This study evaluates the unique and combined effects of three complementary ICT-based extension methods - interactive radio, mobile SMS messages and village-based video screenings - on farmers' knowledge and management of fall armyworm (FAW), an invasive pest of maize that is threatening food security in sub-Saharan Africa and Asia. Building on a survey of maize farmers in western Uganda and using various selection-on-observables estimators, we find consistent evidence that participation in the ICT-based extension campaigns significantly increases farmers' knowledge about FAW and stimulates the adoption of agricultural technologies and practices for the management of the pest. We also show that exposure to multiple campaign channels yields significantly higher outcomes than exposure to a single channel, with some evidence of additive effects. These results are robust to alternative estimators and also to hidden bias. Results further suggest that among the three ICT channels, radio has greater reach, video exerts a stronger impact on the outcome measures, and greater gains are achieved when video is complemented by radio. Our findings imply that complementary ICT-based extension campaigns (particularly those that allow both verbal and visual communication) hold great potential to improve farmers' knowledge and trigger behavioural changes in the identification, monitoring and sustainable management of a new invasive pest, such as FAW.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".