Short communication: Associations of feeding behavior and milk production in dairy cows
Bibliographic record
Abstract
Identification of the associations of cow feeding behavior with productivity is important for supporting recommendations of strategies that optimize milk yield and composition. The objective of this study was to identify associations between measures of feeding behavior and milk production using data collated from studies of the feeding behavior of lactating dairy cows. A database containing behavior and production data for 132 dairy cow-week observations (mean of 7 d of consecutive data per cow) was assembled from 5 studies. Cows averaged (mean ± standard deviation) 1.8 ± 0.9 lactations, 108.4 ± 42.7 d in milk, and 654.6 ± 71.4 kg of body weight during each observation week. Production data included dry matter intake (27.0 ± 3.1 kg/d), milk yield (43.0 ± 7.0 kg/d), milk fat content (3.60 ± 0.49%), and milk protein content (3.05 ± 0.25%). Behavioral data included feeding time (230.4 ± 35.5 min/d), feeding rate (0.13 ± 0.03 kg/min), meal frequency (9.0 ± 2.0 meals/d), meal size (3.2 ± 0.9 kg/meal), daily mealtime (279.6 ± 51.7 min/d), and rumination time (516.0 ± 90.7 min/d). Data were analyzed in multivariable mixed-effect regression models to identify which behavioral variables, when accounting for other cow-level factors (days in milk, parity, and body weight) and dietary characteristics (forage level, nutrient content, and particle distribution), were associated with measures of production. Dry matter intake was associated with feeding time (+0.02 kg/min) and tended to be associated with rumination time (+0.003 kg/min) and meal frequency (+0.2 kg/meal). Similarly, milk yield was associated with feeding time (+0.03 kg/min) and rumination time (+0.02 kg/min), and tended to be associated with meal frequency (+0.3 kg/meal). Milk fat yield was associated with meal frequency (+0.02 kg/meal). Overall, our results suggest that milk yield and component production may be improved in situations where cows are able to increase their time spent feeding, in more frequent meals, and time spent ruminating.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".