Soybean Seed Treatment Evaluation Under Various Levels of Sudden Death Syndrome and Populations of Soybean Cyst Nematode
Bibliographic record
Abstract
Sudden death syndrome (SDS), caused by Fusarium virguliforme, is a major yield-limiting disease of soybean in the United States and Canada. Field trials in 2020 and 2021 across 13 U.S. states and Ontario, Canada, evaluated two SDS-targeted seed treatments and soybean cultivars: one susceptible (S) and one moderately resistant (MR) to SDS. Treatments included a base (prothioconazole + metalaxyl + penflufen, metalaxyl, and imidacloprid), base + fluopyram, and base + pydiflumetofen. Data collection included root rot ratings, SDS foliar symptoms, and yield. Under high SDS pressure (foliar disease index [FDX] ≥ 10), both SDS-targeted seed treatments and MR cultivars significantly reduced foliar symptoms (71 to 80%) compared with the base seed treatment and susceptible cultivars. Although 2020 and 2021 were not epidemic years for SDS, consistent reductions in SDS were observed. In the absence of disease, S cultivars produced greater yield than MR, but under high SDS and low soybean cyst nematode (SCN) pressure (<2,000/100 cm 3 of soil), the MR cultivars produced greater yield than the S cultivars. Only the base + pydiflumetofen treatment maintained greater yield in the absence of SDS. When SDS pressure was high (FDX ≥ 10), both SDS-targeted treatments had 7.0 to 9.0% more yield than the base. Under combined high SDS and SCN pressure (≥2,000/100 cm 3 of soil), only base + fluopyram preserved yield more than the base treatment. These results provide valuable insights for future SDS management through cultivar selection and seed treatments.
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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 | 0.000 | 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".