Maximizing spring wheat productivity in the eastern Canadian Prairies: I. Yield, yield components, and lodging risk
Bibliographic record
Abstract
Abstract Spring wheat ( Triticum aestivum L.) yields across the Canadian prairies are rising, however, a yield gap remains between average yield and yield potential, which can be explored further under Prairie conditions. The high rates of nitrogen (N) fertilizer required for current high‐yielding cultivars may increase lodging risk adding to this yield gap. The objectives of this study were to determine the influence of intensive management through N fertilization and plant growth regulator (PGR) application, on grain yield, yield components, and their relationships, in addition to lodging risk. Field trials were conducted at two locations in 2018 and 2019 in Manitoba, Canada, to evaluate three high‐yielding spring wheat cultivars, three N management strategies, and the application of a PGR. Kernels per spike had the strongest overall linear relationship with grain yield ( r 2 = .47), while the response of spikes per plant was highly related to yield response with alterations in N and PGR management practices ( r 2 = .98). Splitting N fertilizer applications showed potential to reduce lodging risk of spring wheat, while maintaining yield and increasing protein. The PGR applications reduced lodging and increased grain yield by 91 kg ha −1 mainly through increases in kernels per spike. To achieve consistent yield increases, management practices should maximize early season N availability to target increases in spikes per plant and kernels per spike, thus a PGR application may be required to balance increased lodging risk.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".