Effects of partially replacing barley silage or barley grain with dried distillers grains with solubles on rumen fermentation and milk production of lactating dairy cows
Bibliographic record
Abstract
Dried distillers grains with solubles (DDGS) has been commonly used as a dietary protein source for lactating dairy cows. However, there is a paucity of data evaluating the use of DDGS as a partial replacement of forage or grain. The objective of this study was to determine the effects of partially replacing barley silage or barley grain with corn/wheat-based DDGS on dry matter intake (DMI), chewing activity, rumen fermentation, and milk production. Six ruminally cannulated lactating Holstein cows were used in a replicated 3 x 3 Latin square design with 21-d periods. Cows were fed the control diet (CON: 45% barley silage, 5% alfalfa hay, and 50% concentrate mix), a low forage (LF) diet or a low grain (LG) diet, in which barley silage or barley grain was replaced by DDGS at 20% of dietary dry matter, respectively. All diets were formulated to contain 18% crude protein and fed as total mixed rations. Compared with CON, cows fed the LF diet had greater DMI (26.0 vs. 22.4 kg/d), yields of milk (36.4 vs. 33.0 kg/d), milk protein (1.18 vs. 1.05 kg/d), and milk lactose (1.63 vs. 1.46 kg/d), but milk fat yield was not affected. The LF diet decreased chewing time compared with the CON diet (29.7 vs. 39.1 min/kg of DMI), but did not affect rumen pH and duration of rumen pH below 5.8. Compared with CON, feeding the LG diet tended to increase minimum and maximum rumen pH, but did not affect DMI, milk yield, and milk composition in this study. These results indicate that a partial replacement of barley silage with DDGS can improve the productivity of lactating dairy cows without negatively affecting rumen fermentation and milk fat production. Barley grain can also be partially replaced by DDGS in diets for lactating dairy cows without causing negative effects on productivity.
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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) | 0.001 | 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".