Consumers’ Willingness-To-Pay for RNAi versus Bt Rice: Are all biotechnologies the same?
Bibliographic record
Abstract
Consumers’ valuation of food products derived from Genetically Modified Organisms (GMOs) have played a pivotal and often constraining role in the development of biotechnology advances in agriculture. As a result, agricultural companies have started exploring new biotechnologies that do not require the genetic modification of crops. One of these emerging biotechnologies is a non-GMO RNA interference (RNAi) liquid application that could be used to control specific insect pests. When ingested by a targeted sub-species of an insect during production, RNAi blocks the expression of a vital gene, which in turn kills it. RNAi is non-toxic to humans and kills only targeted sub-species of insects, which differs from most conventional pesticides. For example, RNAi could selectively eliminate a specific sub-species of caterpillar pest, while not harming a monarch butterfly caterpillar. In contrast, conventional pesticides often kill insects indiscriminately and vary in human toxicity levels. Since agricultural producers and researchers have faced opposition to GMOs, this may be an alternative to controlling commonly encountered insects; however, consumers’ valuation of traditional GM compared to RNAi derived foods has not been evaluated in the scientific literature. Thus, we conducted a Willingness-To-Pay (WTP) survey in the USA, Canada, Australia, France, and Belgium to analyze whether consumers need a premium or discount for: (1) a hypothetical GMO rice using the Bacillus thuringiensis (Bt) gene for insect control; and (2) a hypothetical non-GMO rice using RNAi for insect control. Since there is currently no commercially-available GMO rice, measuring consumers’ valuation of rice produced by alternative biotechnologies provides vital information for crop breeders and policy makers. The results suggest that consumers require a discount for RNAi and Bt rice compared to a conventionally produced rice, but the discount required for the non-GMO RNAi rice was 30-40 percent less than that needed to purchase GMO Bt rice (p < 0.01).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".