The role of community engagement in the adoption of new agricultural biotechnologies by farmers: the case of the Africa harvest tissue-culture banana in Kenya
Bibliographic record
Abstract
BACKGROUND: The tissue culture banana (TCB) is a biotechnological agricultural innovation that has been adopted widely in commercial banana production. In 2003, Africa Harvest Biotech Foundation International (AH) initiated a TCB program that was explicitly developed for smallholder farmers in Kenya to help them adopt the TCB as a scalable agricultural business opportunity. At the heart of the challenge of encouraging more widespread adoption of the TCB is the question: what is the best way to introduce the TCB technology, and all its attendant practices and opportunities, to smallholder farmers. In essence, a challenge of community or stakeholder engagement (CE). RESULTS: In this paper, we report the results of a case study of the CE strategies employed by AH to introduce TCB agricultural practices to small-hold farmers in Kenya, and their impact on the uptake of the TCB, and on the nature of the relationship between AH and the relevant community of farmers and other stakeholders. We identified six specific features of CE in the AH TCB project that were critical to its effectiveness: (1) adopting an empirical, "evidence-based" approach; (2) building on existing social networks; (3) facilitating farmer-to-farmer engagement; (4) focusing engagement on farmer groups; (5) strengthening relationships of trust through collaborative experiential learning; and (6) helping farmers to "learn the marketing game". We discuss the implications of AH's "values-based" approach to engagement, and how these guiding values functioned as "design constraints" for the key features of their CE strategy. And we highlight the importance of attention to the human dimensions of complex partnerships as a key determinant of successful CE. CONCLUSION: Our findings suggest new ways of conceptualizing the relationship between CE and the design and delivery of new technologies for global health and global development.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.036 | 0.016 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.003 |
| 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".