Determining the Optimal Timing and Economic Return of Corn Fungicide Applications Using a Network Meta-Analysis
Bibliographic record
Abstract
A network meta-analysis was conducted to assess the efficacy of fungicides in reducing disease and protecting yield in corn. Uniform protocols were designed to test the efficacy of 12 widely available corn fungicides applied at one of the following timings: in-furrow with the seed at planting, applied 5.1 cm to the side and 5.1 cm below the seed at planting, 10 to 12 leaves with a visible collar, tasseling to silking (VT/R1), or milk stage. A total of 152 trials were conducted across 18 states in the United States and Ontario, Canada, from 2019 to 2022. Studies were analyzed using network meta-analyses to determine the fungicide efficacy and expected yield benefit of individual products compared with a nontreated control (NTC). All fungicides significantly reduced disease severity compared with the NTC ( P < 0.001), and all fungicides resulted in greater yields compared with the NTC, except for Xyway LFR. Final disease severity influenced yield effect size, with fungicide application resulting in a greater yield effect size when final disease severity exceeded 5%. Fungicide application timing also influenced yield effect size, with fungicides applied at VT/R1 resulting in significantly lower disease (–7.6%) compared with the NTC. The yield effect size was typically greater in studies with the fungicide applied at VT/R1 compared with applications occurring at planting. Economic analyses concluded that expected net benefits were positive for all fungicides tested except for Delaro Complete and Xyway LFR. Most fungicides resulted in greater breakeven probabilities with increasing disease severity. The results emphasize that fungicide applications occurring at VT/R1 and when disease severity exceeds 5% are more likely to result in a positive economic gain. [Formula: see text] Copyright © 2026 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .
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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 | 0.030 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.040 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".