Overcoming barriers to trust in agricultural biotechnology projects: a case study of Bt cowpea in Nigeria
Bibliographic record
Abstract
Nigeria, Africa’s most populous country, has been the world’s largest cowpea importer since 2004. The country is currently in the early phases of confined field trials for two genetically modified crops: Bacillus thuringiensis (Bt) cowpea and nutritionally enhanced cassava (“BioCassava Plus”). Using the bio-safety guidelines process as a backdrop, we evaluate the role of trust in the operation of the Cowpea Productivity Improvement Project, which is an international agricultural biotechnology public-private partnership (PPP) aimed at providing pest-resistant cowpea varieties to Nigerian farmers. We reviewed the published literature and collected data through direct observations and semi-structured, face-to-face interviews. Data were analyzed based on emergent themes to create a comprehensive narrative on how trust is understood and built among the partners and with the community. Our findings highlight the importance of respecting mandates and eliminating conflicts of interest; holding community engagement initiatives early on; having on-going internal discussion and planning; and serving a locally-defined need. These four lessons could prove helpful to other agricultural biotechnology initiatives in which partners may face similar trust-related challenges. Overcoming challenges to building trust requires concerted effort throughout all stages of project implementation. Currently, plans are being made to backcross the cowpea strain into a local variety in Nigeria. The development and adoption of the Bt cowpea seed hinges on the adoption of a National Biosafety Law in Nigeria. For countries that have decided to adopt biotech crops, the Nigerian cowpea experiment can be used as a model for other West African nations, and is actually applied as such in Ghana and Burkina Faso, interested in developing a Bt cowpea.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".