Perennial rye as a grain crop in Alberta, Canada: Prospects and challenges
Bibliographic record
Abstract
Abstract Perennial crops may present an opportunity to produce grain in a more environmentally and economically friendly manner. We examined principal agronomic traits of perennial cereal rye (Secale cereale L. × S. montanum Guss ‘ACE‐1’) at two field sites in Alberta, Canada, over two consecutive growing seasons. Treatments included perennial rye, fall rye (S. cereale L. ‘Hazlett’), spring rye (S. cereale L. ‘Gazelle’), and perennial forage (meadow brome [Bromus commutatus Schrad.] and alfalfa [Medicago sativa L.]) with and without N fertilizer addition. Grain yield of the perennial rye in Year 1 averaged 64 and 51% of the fall and spring rye yields at the Breton and Edmonton sites, respectively. Grain yield of the perennial rye in Year 2 at the Edmonton site averaged 42% of the fall and spring rye. Perennial rye at the Breton site in Year 2 was subject to competition with weeds, resulting in minimal grain productivity. Perennial rye at the Edmonton site yielded significantly more aboveground biomass (without grain) than the other rye crops over both years. Likewise, perennial rye at the Breton site produced 1.5 times more aboveground biomass than the perennial forage in Year 1. The experiment was terminated after virtually nonexistent regrowth at both sites in the spring after two growing seasons. Overall, perennial rye may be an option as a dual‐purpose forage‐grain crop, however, perennial rye cropping beyond 2 yr faces issues of winter survival and weed competition; hence, multi‐year perennial rye cropping is not yet a feasible option for cold temperate conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".