Increasing grain yield while maintaining baking quality in Canada Western Red Spring wheat
Bibliographic record
Abstract
Grain protein concentration (GPC) is considered one of the most important quality factors, and it has remained a major culling criterion in the Canadian wheat cultivar development and registration process. However, grain protein composition also plays a critical role in determining the end-use quality of cereal-based products. The objective of this study was to determine whether high-yielding, lower protein Canada Western Red Spring (CWRS) analog wheat lines can exhibit acceptable baking properties (comparable with CWRS cultivars) under contrasting soil nitrogen levels. Five CWRS-analog lines together with four CWRS and one Canada Prairie Spring Red wheat cultivar, representing wide ranges of quality and grain yield potential, were assessed for agronomic and quality traits in multi-environment trials at three locations and five nitrogen fertilizer rates. CWRS analog lines produced significantly higher grain yield and, on average, 0.9% less GPC than the CWRS cultivars. Despite the lower GPC, CWRS-analog lines such as W07786 exhibited suitable and stable baking performance across all nitrogen levels. Based on the genotype × trait biplot analysis, CWRS-analog baking properties were mainly associated with sodium dodecyl sulfate sedimentation and flour water absorption. Our findings revealed that it is possible to develop wheat cultivars with up to 15% higher grain yield than modern CWRS cultivars and comparable end-use characteristics by reducing current GPC requirements (by up to 1%) while simultaneously selecting for improved baking attributes. This would facilitate an increase in CWRS grain yield genetic gains while maintaining favorable end-use quality and improving the crop competitiveness in western Canada.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".