Getting the message out: Information and communication technologies and agricultural extension
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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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".