Going digital in agriculture: how radio and SMS can scale-up smallholder participation in legume-based sustainable agricultural intensification practices and technologies in Tanzania
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
In 2016, a study was conducted in Tanzania to assess the impact of radio and SMS in scaling-up smallholder participation in legume-based sustainable agricultural intensification (SAI) practices and technologies. The study aimed to answer the following research questions: (i) does participation in the campaign enhance farmers’ knowledge of legume-based sustainable agricultural intensification practices and technologies? (ii) what is the impact of the campaign on the adoption of legume-based sustainable agricultural intensification practices and technologies?; (iii) does exposure to multiple ICT-enabled channels result in larger gains (in terms of knowledge and adoption) than exposure to only one channel? (iv) is it more cost-effective to use radio or SMS alone or use them in combination? The results show that both awareness and adoption are boosted if SMS supports radio campaigns. However, radio alone is the most cost-effective approach. Each dollar spent on the radio campaign results in 2.1 farmers that have adopted at least one new practice, compared with 0.5 farmers for SMS and 0.4 farmers for radio and SMS combined. Other factors were also important in facilitating uptake of legume-based SAI practices, such as gender, age, education and land size, but were not statistically significant when rated against the communication channels used.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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