Optimal Agronomics Increase Grain Yield and Grain Yield Stability of Ultra-Early Wheat Seeding Systems
Bibliographic record
Abstract
Ultra-early seeding of spring wheat (Triticum aestivum L.) on the northern Great Plains can increase grain yield and grain yield stability compared to current spring wheat planting systems. Field trials were conducted in western Canada from 2015 to 2018 to evaluate the impact of optimal agronomic management on grain yield, quality, and stability in ultra-early wheat seeding systems. Four planting times initiated by soil temperature triggers were evaluated. The earliest planting was triggered when soils reached 0–2.5 °C at a 5 cm depth, with the subsequent three plantings completed at 2.5 °C intervals up to soil temperatures of 10 °C. Two spring wheat lines were seeded at each planting date at two seeding depths (2.5 and 5 cm), and two seeding rates (200 and 400 seeds m−2). The greatest grain yield and stability occurred from combinations of the earliest seeding dates, high seeding rate, and shallow seeding depth; wheat line did not influence grain yield. Grain protein content was greater at later seeding dates; however, the greater grain yield at earlier seeding dates resulted in more protein production per unit area. Despite extreme ambient air temperatures below 0 °C after planting, plant survival was not reduced at the earliest seeding dates. Planting wheat as soon as feasible after soil temperatures reach 0 °C, and prior to soils reaching 7.5–10 °C, at an optimal seeding rate and shallow seeding depth increased grain yield and stability compared to current seeding practices. Adopting ultra-early wheat seeding systems on the northern Great Plains will lead to additional grain yield benefits as climate change continues to increase annual average growing season temperatures.
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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".