Impacts of planting date on winter wheat (Triticum aestivum L.) growth and yield formation under standard and intensive management strategies
Bibliographic record
Abstract
The effects of planting date and agronomic management interactions on grain yield formation and physiology must be understood to optimize Ontario winter wheat yields and profitability. The effects of winter wheat planting date were compared under an intensive or standard management strategy at Ridgetown and Exeter, Ontario in 2021 and 2022. Relationships between crop growth rates and yield formation were established by tracking biomass accumulation, canopy PAR interception and grain filling throughout the season across various planting dates and management strategies. Early winter wheat planting improved grain yields over late planting by 0.71-3.44 Mg ha-1. Yield increases were attributed to an increase in grain spike number in early planted wheat. Intensive management did not consistently increase grain yields over standard management. A secondary objective was to test the effectiveness of harvest aid desiccation at advancing harvest maturity in soybean, to enable more timely winter wheat planting. In Ridgetown and Exeter from 2020-2022, three desiccants were applied to full-maturity and late-maturity soybean cultivars at three application timings to evaluate the optimal application timing of each desiccant to advance soybean harvest maturity without compromising soybean yield or seed quality. Soybean desiccation advanced harvest maturity by 1-11 d when diquat was applied at R6.5 depending on the site-year. Desiccant application at R6.5 decreased soybean yields at 2 out of 6 site-years. The advance in soybean harvest maturity will allow higher-yielding, late-maturity soybean to be grown while still maintaining timely winter wheat planting.
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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 | 0.000 | 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) | 0.001 | 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".