A protocol for spring milling wheat production in the Maritimes
Bibliographic record
Abstract
Maritime cereal producers have had difficulty in producing spring milling wheat (Triticum aestivum L.) crops of 13.5% grain protein with consistency. A 3-yr study was conducted from 1999 to 2001 to develop a nitrogen management protocol to produce spring milling wheat with 13.5% grain protein. Experiments were conducted at three locations: Harrington, Prince Edward Island; Hartland, New Brunswick; and Truro, Nova Scotia. Ten split nitrogen treatments were applied to four spring milling wheat cultivars, AC Walton, Glenlea, Grandin and SS Maestro. Generally, any split nitrogen application of 75 to 100 kg ha-1 or greater produced grain protein of 13.5%. However, 10 cultivars out of 36 cultivar × site combinations failed to reach the desired 13.5% grain protein at any nitrogen treatment. Moisture stress after flowering, excessive precipitation during early plant development and protein dilution due to high grain yield were some of the possible reasons why 13.5% grain protein was not obtained. Milling quality was enhanced with increasing amounts of applied nitrogen because as grain protein increased, water absorption, development time, dough stability and time to dough breakdown increased while mixing tolerance index and 20-minute drop decreased. There was little or no improvement in milling quality with nitrogen applications greater than 75 to 100 kg ha-1. Maritime grain producers can produce high-quality, high-yielding spring wheat with relatively low levels of nitrogen input; however, environmental factors may override management in determining the ultimate product quality. Key words: Spring wheat, Triticum aestivum, protein, milling quality, nitrogen
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.016 |
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".