Influence of <i>Fusarium virguliforme</i> Temporal Colonization of Corn, Tillage, and Residue Management on Soybean Sudden Death Syndrome and Soybean Yield
Bibliographic record
Abstract
The asymptomatic host range of Fusarium virguliforme includes corn, a common crop rotated with soybean that we hypothesize may alter F. virguliforme population dynamics and disease management. A field-based approach explored the temporal dynamics of F. virguliforme colonization of corn and soybean roots under different tillage and residue managements. Experiments were conducted in Iowa, Indiana, Michigan, and Wisconsin, United States and Ontario, Canada from 2016 to 2018. Corn and soybean roots were sampled at consecutive timepoints between 1 and 16 weeks after planting. DNA was extracted from all roots and analyzed by real-time quantitative PCR for F. virguliforme quantification. Trials were rotated between corn and soybean, containing a two-by-two factorial of tillage (no-tilled or tilled) and corn residue (with or without) in several experimental designs. In 2016, low amounts (approximately 100 fg per 10 mg of root tissue) of F. virguliforme were detected in the inoculated Iowa, Indiana, and Michigan locations and noninoculated Wisconsin corn fields. However, in 2017, greater levels of F. virguliforme DNA were detected in Iowa, Indiana, and Michigan across sampling timepoints. Tillage practices showed inconsistent effects on F. virguliforme root colonization and sudden death syndrome (SDS) foliar symptoms among trials and locations. However, residue management did not alter root colonization of corn or soybean by F. virguliforme. Plots with corn residue had greater SDS foliar disease index in Iowa in 2016. However, this trend was not observed across the site-years, indicating that corn residue may occasionally increase SDS foliar symptoms depending on the disease level and soil and weather factors.
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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 | 0.001 | 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".