Field and Greenhouse Assessment of Seed Treatment Fungicides for Management of Sudden Death Syndrome and Yield Response of Soybean
Bibliographic record
Abstract
Seed treatments for the management of sudden death syndrome (SDS) caused by Fusarium virguliforme are available in the United States and Canada; however, side-by-side comparisons of these seed treatments are lacking. Sixteen field experiments were established in Illinois, Indiana, Iowa, Michigan, and Wisconsin, United States, and Ontario, Canada, in 2019 and 2020 to evaluate seed treatment combinations. Treatments included a nontreated check (NTC), fungicide and insecticide base seed treatments (base), fluopyram, base + fluopyram, base + saponin extracts from Chenopodium quinoa, base + fluopyram + heat-killed Burkholderia rinojenses, base + pydiflumetofen, base + thiabendazole + heat-killed B. rinojenses, and base + thiabendazole + C. quinoa extracts + heat-killed B. rinojenses. Treatments were tested on SDS moderately resistant and susceptible soybean cultivars at each location. Overall, NTC and base had the most root rot, most foliar disease index (FDX), and lowest yield. Base + fluopyram and base + pydiflumetofen were most effective for managing SDS. Moderately resistant cultivars reduced FDX in both years but visual root rot was greater on the moderately resistant than the susceptible cultivars in 2020. Yield response to cultivar was also inconsistent between the 2 years. In 2020, the susceptible cultivar provided significantly more yield than the moderately resistant cultivar. Treatment effect for root rot and FDX was similar in field and greenhouse evaluations. These results reinforce the need to include root rot evaluations in addition to foliar disease evaluations in the breeding process for resistance to F. virguliforme and highlights the importance of an integrated SDS management plan because not a single management tactic alone provides adequate control of the disease.
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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".