Digging deeper to understand dynamics and interactions of pea root rot pathogens with soil physico-chemical properties in soil zones of Saskatchewan, Canada
Bibliographic record
Abstract
Abstract The soilborne pathogens, Aphanomyces euteiches, Fusarium avenaceum and F. solani , pose a threat to sustainable pea production in Canada. The objectives of this study were to correlate pea root rot severity and pathogen levels with soil physico-chemical factors, assess pathogen levels in different soil depths, their spatial distribution pattern within a field, and determine whether their DNA quantification in soil can accurately predict root rot potential. Soil at three depths from 21 pea fields located in four soil zones in Saskatchewan, Canada were sampled after crop harvest in 2015 and 2016, and tested for disease-causing potential using a greenhouse pea bioassay. Quantification of pathogens was performed using droplet digital PCR on soil DNA. DNA quantification of soils revealed that A. euteiches was the most common with 72.7% incidence in both years, followed by F. solani at 58.4–68.7% and F. avenaceum at 44.4 − 57.6% incidence. Disease severity and levels of all three pathogens was highest in the top layer of soil. The spatial pattern for the distribution of A. euteiches was mostly (57.1%) uniform. The distributions of F. solani and F. avenaceum were predominantly random or clumped. A weak linear relationship between disease severity and pathogen quantities in soil was observed. There was no consistent correlation with soil type or soil physico-chemical properties. Weak interactions between disease severity and soil pathogen levels suggest that the quantification of inoculum potential from soil in the absence of a susceptible host crop underestimates the true disease-causing potential of a soil.
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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".