Forage Yield and Quality for Monocrops and Mixtures of Small Grain Cereals
Bibliographic record
Abstract
Cereals are an important substrate for silage production in the short growing season of the northern Prairies. Our objectives were to determine the effects of seeding rate, species, and harvest date on the forage yield and quality of cereals. Three field studies were conducted to evaluate the productivity of barley (Hordeum vulgare L.), oat (Avena sativa L.), triticale (× Triticosecale rimpaui Wittm.), and rye (Secale cereale L.) grown as monocrops or in various mixtures. Seeding rates ranged from 250 to 750 seeds m−2 Harvest times were based on the maturity of the principal cereal in each mixture. Few effects of seeding rate on yield or quality were found, but when effects were found, higher seeding rates were associated with higher yields, lower moisture content, and higher fiber content. All treatments produced high quality forage as measured by neutral detergent fiber (NDF), from 515 g kg−1 for early‐harvested tests to 656 g kg−1 for late‐harvested tests, and acid detergent fiber (ADF) contents, from 310 g kg−1 for early‐harvested tests to 387 g kg−1 for late‐harvested tests. Protein was low, ranging from 61.5 to 101.0 g kg−1 Biomass yields ranged from 10.1 to 16.5 Mg ha−1 in the barley cultivar tests, 7.0 to 18.5 Mg ha−1 in the spring cereal tests, and 10.8 to 12.2 Mg ha−1 in the winter cereal tests. Although, some exceptions occurred, forage yield and quality of cereal mixtures were generally intermediate to monocrop production, especially for moisture and fiber content, suggesting that planting species mixtures could extend the harvest period and result in higher‐quality silage.
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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) | 0.001 | 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".