Genetic Improvement in Grain Yield and other Traits of Wheat Grown in Western Canada
Bibliographic record
Abstract
Breeding efforts have been important in addressing the challenges of wheat production in western Canada. We studied the effect of breeding on grain yield and other important traits of 100 wheat cultivars released in Canada from 1885 to 2012. The cultivars were grown in seven environments during 2011 to 2013. Grain yield was positively correlated with days to maturity and kernel weight but negatively correlated with plant height, lodging, and grain protein content. Results indicate that grain yield increased at a rate of 0.28% year–1 in 62 cultivars of Canada western red spring (CWRS) class, 1.2% year–1 in 9 cultivars of Canada prairie spring (CPS) class, but not in 14 studied cultivars of Canada western amber durum (CWAD) class due to breeding efforts. Grain protein content exhibited an increasing trend in cultivars of CWRS (0.05% year–1) and CPS (0.79% year–1) classes, and a decreasing trend in those of CWAD (0.23% year–1). Days to maturity decreased in CWRS (0.02% year‐1) and CWAD (0.09% year‐1) classes but remained unchanged in CPS class. Plant height exhibited a gradual decline in cultivars of CWRS (0.16% year–1) and CWAD class (0.44% year–1), but an increase in those of CPS class (0.50% year–1). Test weights showed an increasing trend in CWRS (0.04% year–1) and CPS (0.17% year–1) classes but not in CWAD class. Grain weight also increased over time in CWRS (0.06% year–1) and in CPS (0.41% year–1), but not in CWAD class. These results suggest that breeding efforts have improved yield, quality and other attributes in wheat cultivars of different Canadian wheat classes over the last 100 years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".