The effect of pyraclostrobin on soybean plant health, yield, and profitability in Ontario
Bibliographic record
Abstract
Mahoney, K. J., Vyn, R. J. and Gillard, C. L. 2015. The effect of pyraclostrobin on soybean plant health, yield, and profitability in Ontario. Can. J. Plant Sci. 95: 285–292. Prophylactic fungicides have been advocated to manage foliar diseases, optimize plant heath, and increase yields. Studies were conducted in 2009, 2010, and 2011 using 20 soybean cultivars to determine if pyraclostrobin, a strobilurin, induced plant health effects to increase yield and profitability under conditions with low levels of foliar disease. Pyraclostrobin applied at the R3 stage significantly reduced leaf defoliation caused by brown spot (Septoria glycines Hemmi) compared with the untreated control with 27 and 45% defoliation, respectively, across all cultivars. Pyraclostrobin delayed maturity, but the response varied among cultivars. For example, cultivars with low levels of leaf defoliation responded with an increase in the number of days to maturity, whereas cultivars with high levels of defoliation generally did not. Pyraclostrobin increased yield by 4.1% compared with the untreated control across all cultivars with 4.49 and 4.31 t ha−1 harvested, respectively. Increased revenue from increased yield was offset by increased fungicide costs, resulting in a negligible effect on profitability; however, effects of pyraclostrobin application on profit margins in individual environments ranged from -$50.02 ha−1 in 2009 to $53.73 ha−1 in 2011. Overall, these results suggest that if environmental conditions are conducive for foliar disease, a pyraclostrobin application could be warranted.
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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 | 0.001 | 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) | 0.001 | 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".