Farmer Risk Preferences and Willingness to Pay for African Rice Landrace Seed: An Experimental Choice Analysis
Bibliographic record
Abstract
Most literature that investigates the impact of farmer risk preferences on agricultural technology adoption has focused on how risk preferences impact the adoption of technologies that enhance productivity but that may be riskier than local technologies. In this work, we elicit the risk preferences of smallholder rice farmers and investigate how these preferences impact how they value having access to seed of farmers’ varieties of African rice (Oryza glaberrima) maintained in the gene bank of the Rice Biodiversity Center for Africa. These varieties may have lower yields, but they may be more consistent and less risky given their adaptation to the local climatic conditions. We use a Becker–DeGroot–Marschak (BDM) mechanism to elicit farmers’ willingness to pay for small amounts of landrace seed (35 g) in Côte d’Ivoire. We find that farmers generally value having access to African rice landraces (with a mean willingness to pay of ~US$0.50), and that this willingness to pay is influenced positively by loss-aversion preferences (but not risk aversion), along with several other factors. This finding is in contrast with past evidence suggesting that loss aversion is connected to slower adoption of novel technologies, and suggests that the impact of risk preferences on technology adoption may depend on the potential ability of technologies to guard against economic losses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".