Maximizing spring wheat productivity in the eastern Canadian Prairies II. Grain nitrogen, grain protein, and nitrogen use
Bibliographic record
Abstract
Abstract The marketability of spring wheat ( Triticum aestivum L.) across the Canadian prairies is largely dependent on grain protein content. New high‐yielding cultivars require a large investment in fertilizer N to achieve milling quality standards. When high rates of N fertilizer are applied, N use efficiencies tend to decrease, lowering returns on investment. The objectives of this study were to identify patterns of N use for spring wheat cultivars and how they are influenced by agronomic management practices. Field trials were conducted in 2018 and 2019 in Manitoba, Canada, to evaluate N uptake timing, N remobilization from vegetative tissue and the resulting grain N yield and protein content. Three spring wheat cultivars were evaluated using five N fertilizer treatments with and without an application of a plant growth regulator (PGR). When high N rates were applied, average N use efficiency, for grain N production, was 60%. On average 21–36% of N uptake occurred after anthesis and this portion was highly dependent on late‐season precipitation. Targeting increases in early season N accumulation and grain‐fill remobilization, to produce optimal grain N levels, may be used to managing risk associated with unknown precipitation during the growing season. Cultivars tested produced similar grain N levels through fundamentally different N use patterns, indicating that there may be opportunity for breeding programs to target N use patterns that best fit environmental conditions of the Canadian prairies to maximize grain N production.
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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 | 0.001 | 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 | 0.002 | 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".