Review: Breeding spring canola (<i>Brassica napus</i>L.) by the use of exotic germplasm
Bibliographic record
Abstract
Rahman, H. 2013. Review: Breeding spring canola ( Brassica napus L.) by the use of exotic germplasm. Can. J. Plant Sci. 93: 363–373. The need of broadening genetic diversity in spring canola (Brassica napus) breeding programs seems to be the general consensus among canola breeders and researchers. Diversity analysis by the use of molecular markers has identified several B. napus gene pools as well as allied Brassica species that are genetically distinct from spring canola B. napus; and these gene pools can be used for the improvement of this crop. Use of genetically diverse and un-adapted B. napus germplasm in the breeding of spring canola can be challenging, as introduction of several unwanted traits/alleles from exotic germplasm into spring canola occurs, and this would require repeated cycles of breeding for improvement. Similarly, use of allied species can be even more challenging due to the difficulties associated with interspecific hybrid production, sterility of hybrids, linkage drag, and the introduction of unwanted alleles. However, this can be compensated in the long-term perspective for the improvement of this crop. Some research efforts have been made in recent years to broaden allelic diversity in spring canola for the improvement of seed yield and other traits in open-pollinated and hybrid cultivars with promising results. Seed yield is a complex trait which is controlled by several gene loci with multiple alleles at these loci as well as interactions between loci and different alleles. This makes the identification of right allelic combinations an extremely challenging task. However, canola breeders have been able to make steady improvements in this crop in past decades based on the amount of allelic diversity present in existing breeding material. Introduction of favourable new alleles in breeding programs would allow breeders to create superior allelic combinations, enhancing the diversity in current breeding materials to further improve the crop. With the availability of the Brassica genome sequence, knowledge of sequence variation in specific genes and cost-effective high-throughput genotyping, it is expected that molecular plant breeding will play an important role in the breeding of canola cultivars. Discovery of favourable allele combinations in a short span of time is likely to be facilitated through the application of modern breeding tools.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".