Getting the message out: Information and communication technologies and agricultural extension
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract There has been much optimism about the potential of information and communication technologies (ICTs) to provide agricultural extension services to remote households. Yet, little is known about how different communication methods fare, and, moreover, whether different segments of the population adopt information communicated via different means equally. We conduct a randomized controlled trial comparing the effectiveness of three ICTs—radio, voice response messages, and a smartphone app—with traditional extension training in communicating fertilizer management practices across four districts in rural Nepal. We find that farmers in the smartphone app and the extension training programs are on average 8.4 and 13 percentage points more likely to adopt top dressing fertilizer practices compared to control farmers, statistically significant at the 1% and 5% levels, respectively. Farmers in the smartphone app treatment achieve the highest agronomic literacy test scores, 7.8 percentage points higher than the control, statistically significant at the 1% level. In contrast, farmers receiving radio or voice response messages were not more likely to adopt the same fertilizer recommendations nor show improved specific or general agronomic knowledge relative to control farmers. Our results suggest that smartphone apps are more cost effective at inducing farmer knowledge and technology adoption than extension trainings. Heterogeneous treatment effects, however, reveal that a targeted ICT approach may be more effective in disseminating extension advice.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it