Effects of Corn Silage Particle Length and Forage:Concentrate Ratio on Milk Fatty Acid Composition in Dairy Cows Fed Supplemental Flaxseed
Bibliographic record
Abstract
This study aimed to evaluate the effects of length of chop of corn silage and forage:concentrate ratio (F:C) on performance and milk fatty acid profiles in dairy cows supplemented with flaxseed. Our hypothesis was that decreasing forage particle length and F:C ratio would increase unsaturated fatty acid flow to the small intestine and subsequent transfer of these unsaturated fatty acids into milk. Eight Holstein cows (648.1 +/- 71.5 kg body weight; 109.6 +/-43.6 days in milk) were used in a replicated 4 x4 Latin square design with 21-d periods and a 2 x2 factorial arrangement of dietary treatments. Dietary factors were: 1) F:C ratios (dry matter basis) of 55:45 and 45:55; and 2) corn silage particle lengths of 9.52 and 19.05 mm. All experimental cows received 1 kg of flaxseed to substitute for 1 kg of a rolled barley grain-based concentrate daily. Diets were fed twice daily as a total mixed ration. Corn silage particle length and F:C ratio had no effect on dry matter intake, milk yield, and milk composition; however, feeding short cut corn silage depressed milk protein yield. Significant particle size xF:C ratio interactions were observed for milk fat proportions of C(16:0), C(18:1) cis-9, and C(18:2) cis-9, trans-11 (a conjugated linoleic acid isomer). At short corn silage particle size, decreasing F:C ratio depressed milk fat proportion of C(16:0). Conversely, feeding short corn silage at high F:C ratio increased the proportion of C(18:1) cis-9 and C(18:2) cis-9, trans-11 in milk fat. The milk fat proportion of C(18:2) trans-10, cis-12, a conjugated linoleic acid isomer that is associated with milk fat depression, was not affected by dietary treatment. Our results show that corn silage particle length and F:C ratio influence milk fatty acid profiles in dairy cows fed supplemental flaxseed as a source of polyunsaturated fatty acids.
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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".