Forage potential of corn intercrops for beef cattle diets in northwestern Alberta
Bibliographic record
Abstract
Abstract Intercropping systems involving cereals with legumes provide several advantages such as elevated forage yield and improved forage nutritive value. This study was designed to assess viability of corn (Zea mays L.) intercrops to improve the forage crude protein (CP) of corn forage for beef cattle production. A corn monocrop (C‐M) was compared with seven corn intercrops (five annual legumes, a non‐legume crop (radish (Raphanus sativus L.), C‐RA) and an annual crop mixture (ACM)). The corn forage dry matter (DM) yield was significantly improved (P < .05) for C‐M than all intercrops. Of the seven intercrops, only corn‐radish intercrop (C‐RA) produced significantly lower total forage DM yield (corn + companion) than C‐M. Of the seven corn intercrops, only corn‐hairy vetch (Vicia villosa Roth) (C‐HV) and corn–annual crop mixture (C‐ACM) had significantly (P < .05) improved forage CP and digestible CP than C‐M. Both C‐HV and C‐ACM exceeded the CP recommendations for mature beef cattle and also had adequate CP for young (growing and finishing calves) beef cattle, thereby eliminating the need for protein supplementation during the feeding of either C‐HV or C‐ACM beef cattle. Forage minerals were not significantly affected (P > .05) by corn intercrops. Forage total digestible nutrients (TDN) was significantly (P < .05) influenced by intercrops and varied from 65.9‐71.2%. Results indicate that selected corn intercrops can improve nutritive value of forage for beef cattle production.
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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 | 0.001 | 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".