0480 The effects of partial replacement of barley starch with lactose on production and ruminal fermentation characteristics in dairy cows
Bibliographic record
Abstract
Previous studies have reported improved DMI and milk production when dietary starch was replaced with sugars in corn-based diets, but there is limited work with barley-based diets. Because corn and barley starch differ in their rates and extents of ruminal degradation, it is important to determine if replacement of barley starch with sugars is beneficial as has been reported for corn. The objective of this study was to determine the effects of partial replacement of barley starch with lactose (as dried whey permeate; DWP) on DMI, milk yield and composition, and ruminal fermentation characteristics. Eight lactating Holstein cows (97 ± 10 d-in-milk; 733 ± 63 kg BW) were used in a replicated 4 × 4 Latin square design experiment with four dietary treatments. Experimental periods consisted of 18 d of adaptation and 10 d of measurements. Four cows in one Latin square were ruminally cannulated. Cows were fed a barley-based diet (3.6% total sugar [TS]; control), or diets that contained 6.6, 9.6 or 12.6% TS on a DM basis. Dietary TS content was increased by the replacement of barley grain with DWP, which contained 83% lactose. Diets were isonitrogenous (17.2% CP) and starch contents of the control, 6.6, 9.6, and 12.6% TS diets were 24.3, 22.2, 21.2 and 19.1%, respectively. The inclusion of DWP did not affect DMI (mean = 26.6 kg/d) and milk yield (34.3, 35.0, 35.6, and 34.6 kg/d for the control, 6.6, 9.6, and 12.6% TS diets, respectively); however, milk lactose content tended to increase quadratically (P = 0.07) as TS content increased. There was a linear decrease (P = 0.03) in ruminal NH3–N concentrations as TS content increased. Ruminal pH tended to decrease linearly as TS content increased (P = 0.06; 6.32, 6.31, 6.34, and 6.22 for the control, 6.6, 9.6, and 12.6% TS diets, respectively). Total ruminal VFA concentrations were not affected (P > 0.05) by diet; however, there was a linear increase (P = 0.04) in butyrate concentration as TS content increased. Plasma urea-nitrogen concentrations were not affected by diet, but milk urea-nitrogen concentrations tended to change in a cubic manner as TS content increased (P = 0.08; 14.1, 15.1, 14.0, and 13.7 mg/dL for control, 6.6, 9.6, and 12.6% TS diets, respectively). These results suggest that partial replacement of barley starch with lactose improves ruminal N efficiency by decreasing ruminal NH3–N concentration, but production performance was unaffected
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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 | 0.001 | 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".