FORAGE YIELD AND QUALITY OF WARM SEASON ANNUAL CEREAL CROPS IN THE NORTHERN ALBERTA OF CANADA
Bibliographic record
Abstract
In Alberta Canada, many livestock producers look at alternative ways to reduce their winter feed costs through a series of options to extend the summer grazing season into the fall and winter months. Cool season cereals, such as oat and barley play significant role in providing winter feed in the form of greenfeed hay, silage and swath grazing. The objective of the present study was to evaluate warm season cereals (9 com hybrids, 2 millet varieties and a forage sorghum hybrid, CFSH 30) in a temperate environment compared to temperate cool season cereals (2 oat varieties - controls) for forage dry matter (DM) yield and nutritive value. The corn hybrids mostly had higher forage DM yield than other crop types (millets, sorghum and oat). The forage DM yield significantly (P<0.05) varied from 3878 kg ha' for German millet to 11529 kg ha for 39M26 corn hybrid.Both millets and CFSH 30 had significantly higher crude protein (CP) than corn hybrids as well as the two cool season crop controls (oat). The forage CP was lowest (8.15% ) for 39F44 corn hybrid and highest (14.8%) for German millet. The forage Ca, P, K and Mg respectively varied from 0.21-0.40%, 0.11 -0.29%, 0.96-2.82% and 0.09-0.47%. The forage energy (total digestible nutrients, TDN) was mostly >60.0% for all crops tested. The findings from the present study are discussed as relating to selecting warm season crops for use in the beef cattle production systems in parts of the Peace Country region of Alberta, Canada, with focus on nutritive value in relation to the nutrient requirements of a mature beef cattle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".