Influence of Carbohydrate Source and Buffer on Rumen Fermentation Characteristics, Milk Yield, and Milk Composition in Late-Lactation Holstein Cows
Bibliographic record
Abstract
The effects of concentrate-to-forage ratio and buffer on rumen fermentation and production parameters were examined in four rumen-cannulated cows (240 ± 18 d in milk) fed a total mixed ration ad libitum in a 4 × 4 Latin square design.The treatments were a 50:50 concentrate to forage ratio with [1.2% of dry matter, (DM)] and without (0% of DM) buffer and a 75:25 concentrate to forage ratio with (1.2% of DM) and without (0% of DM) buffer.Rumen pH declined in response to increased concentrate but was not influenced by buffer.In the absence of the buffer, rumen acetate declined and propionate was elevated at the higher level of concentrate inclusion.The milk fat concentration was lower for cows fed the high concentrate diet without buffer; however, the addition of buffer to the diet prevented the milk fat depression.Milk fat depression was associated with elevated trans-C 18:1 fatty acids in milk, which provides additional support for an inhibitory effect of these fatty acids on mammary fat synthesis.We concluded that the potential of nutrition as a tool to alter milk composition is greater in later lactation as these animals are better able to cope with the negative effects of high grain diets, and the treatment response is greater than in early lactation.(Key words: lactating cows performance, forage inclusion, buffer, rumen digestion) Abbreviation key: (A+B)/P = acetate + butyrate to propionate, A:P = acetate to propionate, FA = fatty acids, HCD = high-concentrate diet, HCNB = concentrate to forage 75:25 without buffer, HCWB = concentrate to forage 75:25 with buffer, MCD = medium-concentrate diet, MCNB = concentrate to forage 50:50 without buffer, MCWB = concentrate to forage 50:50 with buffer.
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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 | 0.001 |
| 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".