Evaluation of New Fall Rye Cultivar ‘Bono’ in Single and Double Cropping Systems
Bibliographic record
Abstract
A new fall rye (FR, Secale cereale L.) cv. Bono was investigated as a novel cropping option in Saskatchewan, Canada. In this study, the performance of Bono was compared to Hazlet FR, and both cultivars were compared to winter triticale (WT, Triticosecale Wittm.) cv. Pika in single cropping (SC) or in double cropping (DC) systems with spring barley (Hordeum vulgare L.) was evaluated in the Dark Brown soil zone, 2019–2021. Five replicated (n = 4) treatments were: (i) BonoFR; (ii) HazletFR; (iii) PikaWT; (iv) Barley–BonoFR; and (v) Barley–HazletFR. The first crop of barley was harvested at soft dough stage, followed by the second crop of FR seeded in the same year and harvested between flag leaf to heading emergence the following summer for greenfeed hay. Bono did not differ (p > 0.05) in DMY (1.2 Mg ha−1) or nutritive value from Hazlet, however, both FRs differed (p = 0.01) from WT by higher nitrogen use efficiency (NUE, 41.0 vs. 33.7) and NDF (541.8 vs. 479.3 g kg–1), but lower CP (155.3 vs. 187.1 g kg–1). Double cropping barley with fall ryes increased total DMY, nutrients yield per ha, and minerals uptake by up to 83% and NUE by 35.3%. In conclusion, Bono fall rye could be an equal quality alternative to Hazlet, although the current higher seed price may delay its adoption.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".