An experimental approach to farmer valuation of African rice genetic resources
Bibliographic record
Abstract
Abstract Genebanks serve as both providers of valuable traits for breeding programs and repositories of diverse crop genetic material representing society's agricultural heritage. In this study, we use a Becker‐DeGroot‐Marschak mechanism to elicit the willingness‐to‐pay of rice farmers in Côte d'Ivoire for small amounts of African rice (Oryza glaberrima) landraces held by the genebank of the Rice Biodiversity Center for Africa, and for seed of newly developed ARICA rice varieties bred using genebank materials. Using a field experiment, we additionally investigate how randomized exposure to and experimentation with small amounts of African rice landrace seed or seed of advanced rice varieties developed by AfricaRice affect how smallholder rice farmers value these novel genetic resources. Surprisingly, we find that farmers generally value having access to African rice landraces at approximately the same level as for advanced rice varieties (and far above market rates for improved seed), and that those farmers who grew landrace seed in the offseason were willing to pay more than those who did not. Our results demonstrate the additional value provided by the conservation of African rice landrace varieties (apart from their use in breeding) and highlight the importance of experimentation in the adoption process.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".