Benefits and Profitability of Fluopyram-Amended Seed Treatments for Suppressing Sudden Death Syndrome and Protecting Soybean Yield: A Meta-Analysis
Bibliographic record
Abstract
A meta-analytic approach was used to summarize data on the effects of fluopyram-amended seed treatment on sudden death syndrome (SDS) and yield of soybean (Glycine max L.) in over 200 field trials conducted in 12 U.S. states and Ontario, Canada from 2013 to 2015. In those trials, two treatments-the commercial base (CB), and CB plus fluopyram (CBF)-were tested, and all disease and yield data were combined to conduct a random-effects and mixed-effects meta-analysis (test of moderators) to estimate percent control and yield response relative to CB. Overall, a 35% reduction in foliar disease and 295 kg/ha (7.6%) increase in yield were estimated for CBF relative to CB. Sowing date and geographic region affected both estimates. The variation in yield response was explained partially by disease severity (19%), geographic region (8%), and sowing date (10%) but not by the resistance level of the cultivar. The probability of not offsetting the cost of fluopyram was estimated on a range of grain prices and treatment cost combinations. There was a high probability (>80%) of yield gains when disease level was high in any cost-price combinations tested but very low when the foliar symptoms of the disease were absent.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.020 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".