Spring Cereals for Forage and Grain Production in a Cool Maritime Climate
Bibliographic record
Abstract
Abstract Newfoundland's climate is marginal for agricultural production. The availability of locally grown cereal grain and high‐quality forage are major limitations to successful animal agriculture in this region. Here, our overall objective was to compare several spring cereal species for both annual forage and grain production in Newfoundland's cool Maritime climate. Several varieties of barley (Hordeum vulgare L.), wheat (Triticum aesitivum L.), oats (Avena sativa L.) and pea (Pisum sativum L.)–cereal mixtures for forage yield and quality, as well as grain yield and maturity, were compared in field trials on the east and west coasts in both 1999 and 2000. Barley headed earliest, yielded greatest forage dry matter, had lowest forage protein and acid detergent fibre (ADF) percentages, and had neutral detergent fibre (NDF) mean values greater than those of pea–cereal mixtures, but less than those of oats and wheat. Forage harvested from pea–cereal mixtures was similar to that of barley for yield, ADF and NDF, while P and protein percentage were much greater. Barley matured 10–15 days earlier than both wheat and oats. In general terms, all three spring cereals exhibited similar grain yield potential. Oats tillered less, but compensated by producing more kernels spike−1. Days to maturity for cereal grains in western Newfoundland were roughly similar to those reported for the Maritime provinces of Canada. Yield and maturity results for both forage and grain production suggest that eastern Newfoundland is a unique agro‐ecoregion in North America, and agronomic recommendations specific to other regions may not be applicable in this region.
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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 | 0.001 | 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".