Synergism of Nitrogen Rate and Foliar Fungicides in Soft Red Winter Wheat
Bibliographic record
Abstract
Projected global demand for small grain cereals emphasizes the need for sustainable intensification with higher crop yields. Field studies on soft red winter wheat ( Triticum aestivum L.) examined interactions among three fertilizer N rates, eight fungicide application strategies, and various cultivars grown in nine field environments in Ontario, Canada. A synergistic response occurred when a high rate of N was applied with a fungicide strategy that controlled disease. Overall, compared to a typical N rate of 100 kg ha −1 and no fungicide, yields increased by an average of 1.45 Mg ha −1 when N was applied at 170 kg ha −1 with the most intensive fungicide treatment pooled across sites. This yield response varied from 0.80 to 2.11 Mg ha −1 depending on field site and pooled across cultivars. Fungicide strategies that included a T2 (flag leaf timing) or T3 (anthesis timing) produced the highest yields across environments, especially at the high N rate and in most cultivars. The crop response to fungicide depended on the susceptibility of the cultivar to foliar disease and whether the field environment was favorable for disease development. High N rates increased spike numbers and kernels spike −1 , whereas fungicides mainly increased kernel weight and kernels spike −1 . Prior to an economic analysis, results from this study indicates clear potential for increasing wheat performance by intensifying management with increasing N and deploying fungicide application strategies, especially in some cultivars and in high yielding environments that may be favorable for disease development.
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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) | 0.000 | 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 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".