Potential of increasing yield of spring Brassica napus canola by using Brassica rapa gene pool with emphasis on yellow sarson
Bibliographic record
Abstract
Context Broadening the genetic base of canola (Brassica napus) is needed to develop improved hybrid cultivars. Wide genetic diversity is present in its parental species B. rapa and B. oleracea. In the case of B. rapa, the yellow sarson type from Asia is genetically distinct from all other types. Aims The objective of this research was to investigate the prospect of using yellow sarson to improve the performance of hybrid canola cultivars. Methods Inbred B. napus canola lines derived from an B. napus × B. rapa interspecific cross, and their F1 hybrids with the B. napus parent, as a tester, were compared on agronomic and seed quality traits; the inbreds were also evaluated for genetic diversity by using molecular markers. Key results Seed yield of the hybrids was significantly greater than the inbreds and the B. napus parent and exhibited more than 15% mid-parent heterosis (MPH). Genetic diversity did not show significant correlation with seed yield in the inbred population; however, it showed a positive correlation with MPH. Inbred yield as well as MPH showed a positive correlation with hybrid yield. For other traits, the performance of the inbreds showed a significant positive correlation with the performance of the hybrids; the average MPH for these traits was low or close to zero. Conclusions The yellow sarson gene pool showed great potential for use in the breeding of hybrid canola. Implications The knowledge gained and germplasm developed from this research can be used by breeders and researchers to develop improved canola cultivars.
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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 | 0.000 |
| 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".