Achieving scale of farmer reach with improved common bean technologies: the role of village-based advisors
Bibliographic record
Abstract
Purpose: We assessed the effectiveness of Village-based Advisors (VBAs) as a novel approach for scaling up improved common bean technologies in southern highlands of Tanzania.Design/methodology/approach: Data were gathered through focus group discussions (FGDs) and interviews with 11 VBAs and 102 farmers (37% female). The effectiveness of VBAs was assessed based on farmer reach, farmer knowledge, and application of new technologies.Findings: VBAs played important roles in reaching a wide audience of farmers, with common bean technologies. There was evidence of uptake of promoted common bean practices by farmers, enhanced by judicious incentives such as higher yields, increased land productivity, and labor-saving. VBAs shared information mainly through farm visits and community meetings. Extension materials facilitated VBA engagement of farmers even in informal settings, enhancing information flow beyond village boundaries. The current success of the VBA approach stems from the fact that VBAs are motivated by the rewards they receive – both cash and non-cash.Practical implications: VBAs are relevant in scaling up improved common bean technologies in rural Tanzania and similar settings, because of farmer understanding and trust. For sustainability, there is need to develop a more systematic incentive structure for VBAs through business development, and knowledge enhancement to keep pace with innovations to address emerging production challenges.Theoretical implications: Access to extension service providers who are knowledgeable of farmers’ context enhances learning and uptake of innovations.Originality/value: Results fill information gap on the effectiveness of VBAs as knowledge and input disseminators, achieving scale of farmer reach with agricultural innovations.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".