Molecular Assessment of Pathotype Diversity of <i>Phytophthora sojae</i> in Canada Highlights Declining Sources of Resistance in Soybean
Bibliographic record
Abstract
The large-scale deployment of resistance to Phytophthora sojae (Rps) genes in soybean has led to the rapid evolution of the virulence profile (pathotype) of P. sojae populations. Determining the pathotypes of P. sojae isolates is important in selecting soybean germplasm carrying the proper Rps, but this process is fastidious and requires specific expertise. In this work, we used a molecular assay to assess the pathotypes of P. sojae isolates obtained throughout the provinces of Québec, Ontario, and Manitoba. In preliminary assays, the molecular tool showed equivalent prediction of the pathotypes as a phenotyping assay and proved to be much faster to apply while eliminating intermediate values. Upon analysis of nearly 300 isolates, 24 different pathotypes were detected in Québec and Ontario, compared with only eight in Manitoba, where soybean culture is more recent. Pathotypes 1a, 1c, and 1d was predominant in Québec, while 1a, 1b, 1c, 1d, and 1k pathotypes were the most common in Manitoba. Overall, the results showed that 98 and 86% of the isolates carried pathotype 1a or 1c, respectively, suggesting that Rps1a and Rps1c were no longer effective in Canada. Based on the history of soybean varieties used in surveyed fields, it was found that 84% of them contained Rps genes that were no longer resistant against the pathotypes of the isolates found in the fields. While highlighting an easier and more precise option to assess pathotypes, this study presents the first pan-Canadian survey of P. sojae and stresses the importance of carefully managing the declining sources of resistance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".