Preliminary investigation of corn intercropping for potential grazing of beef cattle: biomass yield and chemical composition in late fall and early winter
Bibliographic record
Abstract
Standing corn ( Zea mays) offers benefits as a strategy to extend the grazing season into fall and winter months for beef cattle in western Canada, including wind shelter, large biomass yield, and high energy concentration. However, low CP concentration limits its potential use, particularly for growing animals; thus, underseeding with high CP intercrop species may improve its net feed value. A preliminary trial with crimson clover ( Trifolium incarnatum), forage radish ( Raphanus sativus), Italian ryegrass ( Lolium multiflorum), hairy vetch ( Vicia villosa), and a mixture of all 4 species intercropped with corn at 2 levels of nitrogen application (45 and 112 kg N ha −1 ) was conducted at 2 sites with contrasting soil texture in Manitoba, Canada. Biomass yield and chemical composition of corn and intercrop were determined in late fall (October) and early winter (December). Although yield was significantly limited at both sites due to drought conditions during the growing season, the presence of intercrop did not affect corn biomass yield when compared to the no intercrop control and average intercrop CP concentration was numerically 3-fold higher than corn. However, further investigation of agronomic management is necessary to increase intercrop yield to maximize its use for extended grazing.
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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 | 0.000 | 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".