Trade implications of gene-edited wheat under different regulatory scenarios
Bibliographic record
Abstract
Wheat is a key global commodity and an essential component in the global food basket. However, in an era of climate change, dry weather conditions, and plant diseases, meeting the increasing demand for food is still a challenge. New breeding techniques (NBTs) or gene editing are rapidly emerging as alternative and sustainable methods of improving methods wheat traits. Although these innovative breeding methods contribute to higher yields and develop crops with valuable traits, the regulatory status and the adoption of these new breeding technologies are still being debated. Focusing on the regulatory approach of gene-editing in Canada and its main trade partners, this study analyzes the impact of different regulatory approaches to NBT on the potential trade of gene-edited wheat. The research simulates five regulatory case scenarios that test the effect of exporting Canadian gene-edited wheat. The first scenario states that Canada does not commercialize editing, while its competitors Australia and the US do. The second scenario presents the opposite case, only Canada commercializes gene-editing while the US and Australia do not. In the third scenario, the three exporters commercialize gene-edited wheat. In scenarios 1 to 3, countries with stringent regulations such as Italy (EU) and Algeria ban the import of gene-edited wheat. The fourth scenario assumes that only Italy establishes a trade ban for Canadian gene-edited wheat, and only Canada commercializes gene-edited wheat. The fifth scenario assumes that the three major wheat exporters: Canada, the US, and Australia export gene-edited wheat, and previously stringent countries (Italy (EU) and Algeria) become open to importing gene-edited wheat. Using data from the World Trade Organization (WTO) in 2019, a Global Simulation Model (GSIM) was devised to calculate the change in net welfare for each scenario. Results show that Canada registered a negative change in the net welfare when adopting gene editing versus a positive change in welfare when not using gene editing. The results suggest that heterogeneity in policy frameworks regarding gene-editing products disrupts trade continuity and shuts down the export markets in some cases, mainly in countries/ jurisdictions such as the European Union and Algeria, where gene-editing products are regulated as GMOs and their imports are banned. Additionally, the genetic gain from using gene-editing technology does not offset the market loss by countries that export gene-edited wheat.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".