The impact of CRISPR/gene-edited wheat technology adoption on global wheat trade and welfare: evidence from partial equilibrium analysis
Bibliographic record
Abstract
Previous research analysing the impact of biotech has focused on yield and production of corn, soybeans, cotton, and barley with limited studies on wheat. In addition, most of these studies focuses on countries such as the USA, Canada, and Australia with little focus on Argentina and Russia. This study analysed the impact of gene-edited wheat technology adoption on global wheat trade, consumer welfare, and producer welfare for five producing and 23 consuming countries using the partial equilibrium analysis. The results from trade flows show that developing (low-income) countries mainly import gene-edited wheat from Argentina and Russia. In addition, low-income countries such as Argentina, Mexico, Nigeria, Brazil, Egypt, and Venezuela continue to consume gene-edited wheat in all scenarios. The welfare analysis shows that all consuming countries experience a welfare gain except Japan, Korea, Belgium, Netherlands, and Italy. More so, all producing countries experience a gain in producer welfare. The results of the study suggest that the adoption of gene-edited wheat technology promotes both consumer and producer welfare and total welfare from trade. The outcome of the study has important implications for global food security.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".