Management of Soybean Cyst Nematode and Sudden Death Syndrome with Nematode-Protectant Seed Treatments Across Multiple Environments in Soybean
Bibliographic record
Abstract
As soybean (Glycine max) production continues to expand in the United States and Canada, so do pathogens and pests that directly threaten soybean yield potential and economic returns for farmers. One such pathogen is the soybean cyst nematode (SCN; Heterodera glycines). SCN has traditionally been managed using SCN-resistant cultivars and rotation with nonhost crops, but the interaction of SCN with sudden death syndrome (SDS; caused by Fusarium virguliforme) in the field makes management more difficult. Nematode-protectant seed treatments have become options for SCN and SDS management. The objectives of this study were to evaluate nematode-protectant seed treatments for their effects on (i) early and full season SCN reproduction, (ii) foliar symptoms and root-rot caused by SDS, and (iii) soybean yield across environments accounting for the above factors. Using a standard protocol, field trials were implemented in 13 states and one Canadian province from 2019 to 2021 constituting 51 site-years. Six nematode-protectant seed treatment products were compared with a fungicide + insecticide base treatment and a nontreated check. Initial (at soybean planting) and final (at soybean harvest) SCN egg populations were enumerated, and SCN females were extracted from roots and counted at 30 to 35 days postplanting. Foliar disease index (FDX) and root rot caused by the SDS pathogen were evaluated, and yield data were collected for each plot. No seed treatment offered significant nematode control versus the nontreated check for in-season and full-season nematode response, no matter the initial SCN population or FDX level. Of all treatments, ILEVO (fluopyram) and Saltro (pydiflumetofen) provided more consistent increases in yield over the nontreated check in a broader range of SCN environments, even when FDX level was high.
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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".