Short communication: Associations between feed push-up frequency, feeding and lying behavior, and milk yield and composition of dairy cows
Bibliographic record
Abstract
Feeding management factors have great potential to influence activity patterns and feeding behavior of dairy cows, which may have implications for performance. The objectives of this study were to assess the effects of feed push-up frequency on the behavioral patterns of dairy cows, and to determine associations between behavior and milk yield and composition. Lactating Holstein dairy cows (n = 28, parity = 1.9 ± 1.1; mean ± SD) were housed in tiestalls, milked twice per day, and offered ad libitum access to water and a total mixed ration (containing, on a dry matter basis: 25% corn silage, 25% grass/alfalfa haylage, 30% high-moisture corn, and 20% protein/mineral supplement), provided twice per day. Cows were divided into 2 groups of 14 (balanced by days in milk, milk production, and parity) and individually exposed to each of 2 treatments in a crossover design with 21-d periods; treatment 1 had infrequent feed push-up (3×/d), whereas treatment 2 had frequent feed push-up (5×/d). During the last 7 d of each period, dry matter intake and milk production were recorded and lying behavior was monitored using electronic data loggers. During the last 2 d of each period, milk samples were collected for analysis of protein and fat content and feed samples of fresh feed and orts were collected for particle size analysis. The particle size separator had 3 screens (19, 8, and 1.18 mm) and a bottom pan, resulting in 4 fractions (long, medium, short, fine). Sorting was calculated as the actual intake of each particle size fraction expressed as a percentage of the predicted intake of that fraction. Feed push-up frequency had no effect on lying time [11.4 ± 0.37 h/d; mean ± standard error (SE)], milk production (40.2 ± 1.28 kg/d) and composition (milk protein: 3.30 ± 0.048%; milk fat: 3.81 ± 0.077%), or feed sorting. Cows sorted against long particles (78.0 ± 2.2%) and for short (102.6 ± 0.6%) and fine (108.4 ± 0.9%) particles. Milk fat content decreased by 0.1 percentage points for every 10% increase in sorting against long particles and was not associated with lying behavior or other cow-level factors. Milk protein content decreased by 0.03 percentage points for every hour decrease in lying time and by 0.04 percentage points for every 10% increase in sorting against long particles. These results suggest that sorting against long ration particles may negatively affect milk composition. Additionally, we did not find that altering feed push-up frequency affected feed sorting or cow standing and lying patterns.
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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) | 0.000 | 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) | 0.003 | 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".