Effect of Seed Treatment and Foliar Crop Protection Products on Sudden Death Syndrome and Yield of Soybean
Bibliographic record
Abstract
Sudden death syndrome (SDS), caused by Fusarium virguliforme, is an important soilborne disease of soybean. Risk of SDS increases when cool and wet conditions occur soon after planting. Recently, multiple seed treatment and foliar products have been registered and advertised for management of SDS but not all have been tested side by side in the same field experiment at multiple field locations. In 2015 and 2016, seed treatment fungicides fluopyram and thiabendazole; seed treatment biochemical pesticides citric acid and saponins extract of Chenopodium quinoa; foliar fungicides fluoxastrobin + flutriafol; and an herbicide, lactofen, were evaluated in Illinois, Indiana, Iowa, Michigan, South Dakota, Wisconsin, and Ontario for SDS management. Treatments were tested on SDS-resistant and -susceptible cultivars at each location. Overall, fluopyram provided the highest level of control of root rot and foliar symptoms of SDS among all the treatments. Foliar application of lactofen reduced foliar symptoms in some cases but produced the lowest yield. In 2015, fluopyram reduced the foliar disease index (FDX) by over 50% in both resistant and susceptible cultivars and provided 8.9% yield benefit in susceptible cultivars and 3.5% yield benefit in resistant cultivars compared with the base seed treatment (control). In 2016, fluopyram reduced FDX in both cultivars by over 40% compared with the base seed treatment. For yield in 2016, treatment effect was not significant in the susceptible cultivar while, in the resistant cultivar, fluopyram provided 3.5% greater yield than the base seed treatment. In this study, planting resistant cultivars and using fluopyram seed treatment were the most effective tools for SDS management. However, plant resistance provided an overall better yield-advantage than using fluopyram seed treatment alone. Effective seed treatments can be an economically viable consideration to complement resistant cultivars for managing SDS.
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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".