Six decades of soybean breeding in Ontario, Canada: a tradition of innovation
Bibliographic record
Abstract
Soybean has been widely grown by Canadian farmers for more than 80 years, especially in southern Ontario. In recent decades, the Canadian growing region has expanded east and north. An average of 1% soybean yield improvement is achieved annually, thanks to efforts by public and private soybean breeding programs. However, to meet future food demands, an average 2.4% annual increase in soybean yield is required. Soybean breeders are mostly dealing with complex traits that are under control by several intrinsic and extrinsic factors, so sufficient information about past and current breeding efforts is required to modify future breeding programs accordingly. Here, we review public soybean breeding efforts over the past 25 years in southern Ontario, one of the most productive regions for Canadian soybean growers. Furthermore, we explain how recent advances could facilitate soybean breeding programs by reducing the time and cost and increasing selection accuracy in a large breeding population. Finally, we summarize future directions in three important sections, that is, multi-omics, environmental, and data-driven approaches, and provide a vision for future soybean breeding programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".