0742 Mitigation of variability in feeding patterns between competitively fed dairy cows through increased feed delivery frequency
Bibliographic record
Abstract
The objective of this study was to determine if increased frequency of feed delivery can mitigate the effects of feed bunk competition. We hypothesized that at a greater frequency of feed delivery, 1) there will be improved access to feed (i.e., greater feeding time and consumption of more meals per day) and 2) there will be greater consistency in feeding and meal patterns between cows. Sixteen lactating Holstein dairy cows, with an average DIM of 72 ± 35 d and production of 42 ± 6 kg/d at the start of the trial, were categorized by parity as either young (≤second lactation) or mature (≥third lactation) and paired to maximize difference in parity. Pairs were housed 4 at a time and competitively fed at a ratio of 2 cows:1 feed bin. They were exposed, at a pair level, in a crossover design to each of 2 different treatments: 1) lower feed delivery frequency (2x/d) or 2) higher feed delivery frequency (6x/d). Treatments were applied for 10 d, with DMI and feeding behavior (feeding time, feeding rate, and meal patterns) for each cow recorded using an automated feed intake system on d 6 to 10 of each period. Data were summarized by pair and treatment period and analyzed using a general linear mixed model. Dry matter intake (27.1 kg/d), feeding time (180.2 min/d), and feeding rate (0.17 kg DM/min) were unaffected by increased feed delivery frequency (P ≥ 0.22). There was a tendency for rumination time to increase with higher frequency of feed delivery (low = 520.5 min/d and high = 547 min/d; SE = 11.32, P = 0.06). No differences in meal patterns were found between feed delivery frequency treatments (P ≥ 0.20). However, comparing the young and mature individuals within each treatment pair revealed differences in both feeding and meal patterns. Feeding rate (young = 0.16 kg DM/min and mature = 0.19 kg DM/min; SE = 0.032, P = 0.02) and DMI (young = 25.6 kg DM/min and mature = 28.6 kg DM/min; SE = 1.36, P = 0.04) were lower for the young cows on both treatments. Meal frequency was greater in young cows (young = 9 meals/d and mature = 7 meals/d; SE = 0.7, P = 0.03) and meal size was greater in mature cows (young = 3.2 kg DM/meal and mature = 4.2 kg DM/meal; SE = 0.35, P < 0.001) across treatments. These results suggest that for cows fed at a high level of competition, increasing feed delivery frequency from 2x/d to 6x/d did not improve access to feed. However, under these conditions, the relative parity of competitively fed cows had a greater impact on feeding behavior and meal patterns than the frequency of feed delivery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 teacher head, 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".