Intellectual Property Rights and Canadian Wheat Breeding for the 21st Century
Bibliographic record
Abstract
Abstract Although wheat is the largest field crop in Canada, the intellectual property rights for the self‐pollinated, nongenetically modified crop have been too weak to allow significant royalty flow to plant breeders. The sector heavily relies on resource‐constrained public breeding programs for new variety development. This long‐standing situation could change with the 2015 Agricultural Growth Act, which strengthens Canadian plant breeders’ rights to be consistent with the UPOV 91 convention. We explore the potential implications for Canada by examining the experience with UPOV 91 implementation in the United Kingdom, France, and Australia. Using the royalty structures in these countries as potential pathways for Canada, an ex ante benefit–cost framework is used to illustrate the choice of pathway is important. Over a 40‐year period, the Australian, French, and U.K. implementation pathways generate 4.8, 4.0, and 1.5 $CDN billion in net benefit (respectively) over the status quo. Over shorter time horizons of 20 and 30 years, France is quicker to establish uniform endpoint royalties that provide the highest net benefits. Proactive industry engagement to develop a more robust royalty collection system is required to realize these potential benefits.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".