Going digital in agriculture: how radio and SMS can scale-up smallholder participation in legume-based sustainable agricultural intensification practices and technologies in Tanzania
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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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".