Optimizing inputs for winter durum wheat in Ontario
Bibliographic record
Abstract
Tamburic-Ilincic, L., Brinkman, J. M. P., Sparry, E. and Hooker, D. C. 2015. Optimizing inputs for winter durum wheat in Ontario. Can. J. Plant Sci. 95: 361–368. Best management practices need to be determined for a new wheat class in Ontario: winter durum. The objectives of this study were to determine optimal nitrogen rates (75, 100, and 125 kg N ha−1), seeding rates (400, 440, and 480 seeds m−2), and fungicide applications on the grain yield, grain protein, and leaf disease control of ‘OAC Amber’ winter wheat durum at five field locations in Ontario. Seeding rates between 400 and 480 seeds m−2 did not impact performance. Overall, fungicide applications at flag leaf and flowering increased grain yield by an average of 0.52 Mg ha−1, increased seed weight and test weight, reduced powdery mildew [Blumeria graminis (DC.) Speer f. sp. tritici emend. É.J. Marchal] and septoria leaf blotch [Mycosphaerella graminicola (Fuckel) J. Schröt.] in the canopy, but decreased grain protein from 128 to 126 g kg−1. Grain yields did not increase with N rates higher than 100 kg N ha−1, and the response to N rate did not depend on the application of fungicides. Grain protein concentrations increased with N rates up to 125 kg N ha−1, which was the highest N rate investigated in this study. An economic analysis is needed to determine the impact of agronomic management strategies specific to winter durum wheat in Ontario.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".