Multi-location evaluation of fluopyram seed treatment and cultivar on root infection by <i>Fusarium virguliforme</i>, foliar symptom development, and yield of soybean
Bibliographic record
Abstract
A study was conducted in five American states and Ontario, Canada, in 2015 and 2016 to determine the effects of fluopyram seed treatment and cultivar on the root rot and foliar phases of sudden death syndrome (SDS) of soybean. Three seed treatments were evaluated: (1) base treatment (control) containing prothioconazole + penflufen + metalaxyl (0.019 mg a.i./seed) + metalaxyl (0.02 mg a.i./seed) + clothianidin + Bacillus firmus I-1582 (0.13 mg a.i./seed), (2) base treatment + fluopyram (0.15 mg a.i./seed), and (3) base treatment + fluopyram (0.075 mg a.i./seed). Three soybean cultivars, categorized as susceptible, moderately resistant and resistant were planted at each location. Both rates of fluopyram reduced root rot and foliar disease index (FDX) and increased yield compared with the base treatment. The two rates of fluopyram did not differ for reducing root rot or FDX, but yield was greater with the higher versus lower rate. Fluopyram reduced root colonization by Fusarium virguliforme as measured with quantitative PCR in one of two study years. Yield was not correlated with root rot at the V2, but was negatively correlated with root rot at the R4/R5 growth stage and with FDX. Root rot at R4/R5 was positively correlated with FDX. A yield benefit to fluopyram was found in a location where root rot but no foliar symptoms were observed. These findings suggest that fluopyram seed treatment can reduce the root rot and foliar phases of SDS, and both phases play an important role in yield and should be managed accordingly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".