Wheat variety R&D investment and adoption in Western Canada
Bibliographic record
Abstract
Wheat is a key agricultural product for Canada, the majority of which is grown in the Prairie Provinces and exported. While several new wheat varieties are registered each year, most of the wheat acreage in Western Canada is produced by a select number of new varieties. This study investigates the gap between recommended, registered, and commercially available varieties in Western Canada and estimates the R&D cost of non-adopted varieties. Results show a robust slate of commercially available new wheat varieties overall, as only 18% of proposed varieties did not receive registration and 7% of registered varieties were not adopted over the past 25 years. This translates to $3.25–$5.2 million in annual R&D funding invested in varieties that do not provide direct economic benefit to producers upon commercialization between 2000 and 2010 and $7.5–$12 million between 2011 and 2021. Non-adopted varieties may have value as parents in future crosses. In addition, Prairie-wide results reveal that varieties with higher yield and yield-enhancing traits such as disease resistance and lodging resistance are more widely grown. The current findings provide the best indicative data about the performance of wheat breeding programs and shed light on how Canada is performing with respect to research priorities and investments in varietal innovations. Limited and inconsistent varietal data across the Prairies and lack of transparent R&D costs on newly developed wheat varieties in Western Canada are among the challenges encountered in this research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".