Effects of Feeding Forage Soybean Silage on Milk Production, Nutrient Digestion, and Ruminal Fermentation of Lactating Dairy Cows
Bibliographic record
Abstract
The objective of this study was to determine the feeding value of forage soybean silage (SS) for dairy cows relative to a fourth-cut alfalfa silage (AS). Forage soybean was harvested at full pod stage. Two isonitrogenous diets were formulated with a 48:52 forage:concentrate ratio. Soybean silage and AS constituted 72% of the forage in each diet, with corn silage constituting the remaining 28%. Twenty Holsteins cows in early lactation were used in a switchback design. Four lactating Holsteins cows fitted with ruminal cannulas were used to determine the effects of dietary treatments on ruminal fermentation parameters and in vivo total tract nutrient utilization. Relative to AS, SS contained 15, 28, and 25% more neutral detergent fiber, acid detergent fiber, and crude protein, respectively. Dry matter intake (23.5 vs. 25.1 kg/d) and milk yield (35.5 vs. 37.2 kg/d) were lower for cows fed SS than for those fed AS. However, energy-corrected milk and milk efficiency were similar for both dietary treatments. Milk protein, lactose, and total solids concentrations were not influenced by dietary treatments (average 3.0, 4.7, and 12.6%, respectively). However, cows fed SS produced milk with greater milk fat (3.8 vs. 3.6%) and milk urea nitrogen concentrations (15.6 vs. 14.3 mg/dL) compared with cows fed AS. Ruminal pH was lower, whereas ruminal NH3-N concentration was greater in cows fed SS than in cows fed AS. Total tract digestibilities of dry matter, crude protein, and neutral detergent fiber were not influenced by silage type. We concluded that forage SS, when compared with AS, had a negative impact on feed intake and milk yield, whereas energy-corrected milk, milk efficiency, and total tract nutrient digestion were similar.
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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) | 0.000 | 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 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".