Technological advances in trait development: from conventional breeding and untargeted mutagenesis to precision genome editing
Bibliographic record
Abstract
Plant biotechnology has revolutionized modern agriculture by enabling precise and efficient crop improvement strategies. This review explores the evolution of selective breeding, mutation breeding, and precision breeding, highlighting their applications in Canada's agricultural sector. Conventional selective breeding has been instrumental in developing high-yielding and disease-resistant cultivars, while mutation breeding, through physical and chemical mutagenesis, has introduced valuable genetic diversity. The advent of transgenic breeding allowed for the direct insertion of foreign genes, leading to the development of crops with herbicide tolerance, pest resistance, and improved nutritional content. However, concerns over regulatory restrictions and public acceptance have driven the rapid adoption of genome editing tools, which enable precise modifications without introducing foreign DNA. Canada has played a key role in applying these biotechnological innovations, successfully developing genetically modified canola, CRISPR-edited wheat, stress-resistant soybean, and barley and oat cultivars improved for stress resistance and yield. While each breeding approach presents distinct advantages and limitations, integrating conventional and molecular techniques is essential for maximizing genetic potential, ensuring agriculture, and effectively food security challenges.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".