Effects of Pea, Barley, and Alfalfa Silage on Ruminal Nutrient Degradability and Performance of Dairy Cows
Bibliographic record
Abstract
Six Holstein cows in early lactation were used in a double 3 x 3 Latin square design to determine the effects of feeding diets with pea silage, relative to barley silage, or alfalfa silage. Cows were fed rations formulated to contain 50:50 forage:concentrate ratio. Two ruminally fistulated cows were used in a randomized complete block design to determine ruminal nutrient degradability for pea silage relative to barley and alfalfa silages. Pea silage contained lower neutral detergent fiber (NDF), acid detergent fiber, and starch concentrations but higher crude protein than barley silage. Compared with alfalfa silage, pea silage had higher starch and NDF but lower crude protein content. Pea and alfalfa silage had similar effective ruminal degradability of dry matter, which was higher than that of barley silage. The rate of degradation and effective ruminal degradability of NDF was highest for alfalfa silage, intermediate for pea silage and lowest for barley silage. Results of the lactation trial showed that dry matter intake and milk yield were not affected by forage source. Milk composition was similar for cows fed pea or barley silage; however, cows fed pea silage produced milk with a higher fat and a lower protein percentage than those fed the alfalfa silage. Pea silage can replace barley or alfalfa silage as a forage source for dairy cows in early lactation.
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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 | 0.001 | 0.001 |
| 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".