Integrated Management Strategies for <i>Phytophthora sojae</i> Combining Host Resistance and Seed Treatments
Bibliographic record
Abstract
Phytophthora sojae has re-emerged as a serious soybean pathogen in the past decade. This may be due in part to changes in resistance levels in current cultivars, adoption of P. sojae populations to deployed Rps genes, and highly favorable environments in the past decade. This multilocation study evaluated the effect of seed treatments on the incidence and severity of Phytophthora root and stem rot on soybeans with different combinations of Rps genes and levels of partial resistance. The efficacy of the seed treatments was highly variable across locations. Seed treatments (metalaxyl and mefenoxam) provided protection and increased yields across cultivars in locations where rain or irrigation occurred shortly after planting (Ohio, South Dakota, and Ontario). However, there were no significant differences in stand or yield consistently across cultivars in Iowa, Nebraska, Wisconsin, or Ohio, where heavy precipitation did not occur until later growth stages. The environment, levels of inoculum, and pathogen complex may have played a role in the different responses to the seed treatments and to the different combinations of Rps genes and levels of partial resistance to P. sojae in the cultivars. Fields that are poorly drained and have P. sojae populations with complex pathotypes may benefit the most from seed treatments. Individual fields where producers may see the greatest benefit to utilizing these integrated management strategies will need to be identified.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".