Meta-Analysis of Soybean Yield Response to Foliar Fungicides Evaluated from 2005 to 2018 in the United States and Canada
Bibliographic record
Abstract
Random-effect meta-analyses were performed on data from 240 field trials conducted between 2005 and 2018 across nine U.S. states and Ontario, Canada, to quantify the yield response of soybean after application of foliar fungicides at beginning pod (R3) stage. Meta-analysis showed that the overall mean yield response when fungicide was used compared with not applying a fungicide was 2.7% (110 kg/ha). Moderator variables were also investigated and included fungicide group, growing season, planting date, and base yield, which all significantly influenced the yield response. There was also evidence that precipitation from the time of planting to the R3 growth stage influenced yield when fungicide was used (P = 0.059). Fungicides containing a premix of active ingredients from multiple groups (either two or three ingredients) increased the yield by 3.0% over not applying a fungicide. The highest and lowest yield responses were observed in 2005 and 2007, respectively. Better yield response to fungicides (a 3.0% increase) occurred when soybean crops were planted not later than 21 May and when total precipitation between planting and the R3 application date was above historic averages. Temperatures during the season did not influence the yield response. Yield response to fungicide was higher (a 4.7% increase) in average yield category (no spray control yield 2,878 to 3,758 kg/ha) and then gradually decreased with increasing base yield. Partial economic analyses indicated that use of foliar fungicides is less likely to be profitable when foliar diseases are absent or at low levels.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".