Global adoption of porcine reproductive and respiratory syndrome–resistant pigs will have significant economic and market impacts
Bibliographic record
Abstract
Objective: To evaluate the global economic impacts of adopting gene-edited pigs resistant to porcine reproductive and respiratory syndrome (PRRS) on pork markets and producer profitability. Methods: A model linking hog supply to consumer pork demand in 6 global regions, Canada, China, Japan, Mexico, the US, and the rest of the world, was constructed and parametrized using pork production and trade statistics, published supply and demand elasticities, PRRS prevalence rates, and productivity metrics by PRRS health status. The model projects changes in pork prices, production, trade, and producer profits. Results: In the baseline scenario (70% adoption over 12 years), assuming no change in pork demand or additional cost of swine genetics, the marginal cost of production declines and pork prices fall while pork production increases in adopting countries by 11% in China to 7% in the US and Canada, and pork production falls in nonadopting countries. In the 15th year after initial adoption, profits for pork producers increase, ranging from $33/head in China to $15/head in Canada relative to preadoption baseline. Producers in the rest of the world, who are assumed not to adopt, are less profitable. Conclusions: The adoption of PRRS-resistant pigs is likely to significantly increase productivity, which translates into market impacts that are substantial and likely positive for the adopting producers, assuming there is no significant demand reduction or exorbitant increase in the cost of swine genetics. Clinical Relevance: Pork producers who adopt PRRS-resistant pigs experience higher productivity and lower veterinary costs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".