Crop Residue Removal and Nitrogen Fertilization Affects Seed Production in Meadow Bromegrass
Bibliographic record
Abstract
Seed yield in meadow bromegrass (Bromus riparius Rehm.) declines rapidly after two to three seed crops. This is a critical limitation to economic seed production. Field experiments were conducted at Saskatoon and Outlook, SK, Canada, to determine the influence of residue removal and N fertilization on seed yield. Three N treatments (0, 50, and 100 kg ha−1) were applied in September each year for the first three seed production years, and four residue removal treatments (none, after harvest, October, and after harvest + October) were applied in the second and third seed production years. Residue removal after harvest and N application (100 kg ha−1) increased yield 0 to 572 kg ha−1 in the second seed crop compared with the untreated control. In the third‐year seed crop, residue removal increased seed yield 30 to 90 kg ha−1. Application of N fertilizer increased third‐year seed yield 90 kg ha−1 at Outlook only. Mean seed yield was reduced in the third compared with the second crop year, regardless of treatment. Residue removal after harvest combined with the application of 100 kg N ha−1 increased the cumulative 2‐yr seed yield by 390 to 490 kg ha−1 compared with the untreated control. At the current seed price (Can$2.50 kg−1) and N fertilizer cost (Can$0.66 kg−1) of meadow bromegrass, the additional seed yield from residue removal and 100 kg N ha−1 would provide a net return of Can$975 to Can$1225 ha−1 on an additional investment of −1
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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) | 0.000 | 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 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".