Performance and feeding behavior of dairy cows fed high-concentrate diets containing steam-flaked or ground corn varying in particle size
Bibliographic record
Abstract
Steam-flaked corn (SFC) and ground corn (GC) of different particle sizes were evaluated for their effects on dry matter intake (DMI), milk yield and components, chewing activity, ration sorting, ruminal fermentation, and digestibility in lactating dairy cows. Eight multiparous Holstein cows in mid-lactation (46.6 ± 3.5 kg/d milk production and 101 ± 10 d in milk) were used in a double 4 × 4 Latin square design with 21-d periods. Cows were fed diets (dry matter basis) containing 36.2% forage (alfalfa hay and corn silage), 37.4% corn grain, and 26.4% other ingredients. The corn grain was ground (coarse: 1.08 mm; medium: 0.84 mm; and fine: 0.73 mm) or steam-flaked (SFC; density = 0.40 kg/L). The dry matter proportion retained on an 8-mm sieve was greater for the SFC diet than for the GC diets. There were no treatment effects on DMI, milk yield, fat-corrected milk, energy-corrected milk, fat or lactose yield, protein or lactose content, or milk urea nitrogen concentration. However, digestibility of dry matter and organic matter were greater for fine GC and SFC than the other diets. In addition, cows fed SFC had lower total-tract starch digestibility than cows fed GC diets. Cows fed SFC tended to have lower propionate proportion (22.8 vs. 27.1 mM) and total volatile fatty acid concentration (88.6 vs. 99.8 mM) in ruminal fluid than those fed GC diets. Acetate and butyrate concentration, acetate to propionate ratio, and ruminal concentration of ammonia-nitrogen were not affected by treatments. Ruminal pH (6.46 vs. 6.01) as well as milk fat content (2.75 vs. 2.59%) and efficiencies (fat-corrected milk/DMI and energy-corrected milk/DMI) were greater for SFC than GC, regardless of its particle size. Milk fat content tended to increase linearly with increasing particle size of GC. Eating activity (min/d) tended to be less for SFC compared with GC, but rumination activity (min/d) and total chewing activity (min/d) were not affected by processing or particle size. The results of study indicate that, compared with GC, steam flaking of corn with 400 g/L density increased milk fat content and efficiency of high-producing dairy cows without any negative effect on milk yield. For GC, milk fat content tended to linearly increase and starch digestibility decreased linearly with increasing particle size.
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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) | 0.001 | 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".