Identifying canola ( <i>Brassica napus</i> L.) accessions with superior photosynthetic traits and unique resource partitioning strategies
Bibliographic record
Abstract
Abstract Canola ( Brassica napus L.) yields in Canada are not increasing sufficiently to meet future global demands. Improving photosynthetic efficiency and optimizing photoassimilate allocation represent a promising strategy to enhance yield potential. This study evaluated the photosynthetic and agronomic traits of 168 diverse canola accessions belonging to six pedigree groups: spring canola × spring canola (SP × SP), spring canola × winter canola (WI × SP), spring canola × rutabaga ( B. napus var. napobrassica ) (SP × RU), (winter canola × spring canola) × rutabaga ([WI × SP] × RU), spring canola × B. oleracea (SP × BO), spring canola × B. rapa (SP × BR), and accessions collected from the Plant Gene Resources of Canada, Saskatoon collection. Field experiments conducted over three growing seasons in Central Alberta, Canada, identified moderate to high heritability for four chlorophyll fluorescence parameters and five agronomic traits. Distinct source‐sink allocation strategies emerged among pedigree groups. The SP × SP group optimized resource allocation for maximal seed yield, while winter canola‐derived groups prioritized seed size (1000‐seed weight) while maintaining competitive yields, likely through extended grain‐filling periods. Unique physiological linkages were observed in progenitor‐derived groups: SP × BR accessions exhibited coordinated regulation of non‐photochemical quenching photoprotection, biomass production, and yield, whereas SP × BO demonstrated an association between root biomass and reduced minimal fluorescence ( F o ′), suggesting improved PSII efficiency. These findings highlight the value of physiological trait‐based selection in canola breeding. The identified germplasm and trait relationships provide a foundation for developing improved spring canola cultivars through targeted integration of favorable photosynthetic and allocation characteristics.
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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 | 0.000 | 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".