Relationships Between Milk Urea Concentrations and Nutritional Management, Production, and Economic Variables in Ontario Dairy Herds
Bibliographic record
Abstract
The objectives of this study were to describe the relationships between milk urea concentrations and nutritional management, production, and economic variables in commercial dairy herds. Dairy Herd Improvement (DHI) test-day milk urea data, production data, and information on ration nutrient composition and feeding management programs were collected over a 13-mo period from 53 commercial Ontario dairy herds. Economic variables included gross milk revenue, feed costs, and income over feed costs. Herd mean milk urea concentrations had a positive relationship with dietary levels of crude protein (CP), rumen degradable protein (RDP), and rumen undegradable protein (RUP) and a negative relationship with dietary levels of nonfiber carbohydrates (NFC), forage:concentrate (F:C) ratio, NFC:CP ratio, and NFC:RDP ratio. These findings are consistent with experimental studies that used chemical methods of milk urea analysis. Herd mean milk urea concentration was not associated with feeding management (e.g., total mixed rations, component feeding, feeding frequency, or synchrony of forage and concentrate feeding). Herd mean milk urea was not associated with either mean milk yield or linear score. Herd mean milk urea had a positive relationship with feed costs per cow per day but was not associated with gross milk revenue per cow per day. Herds with a high mean milk urea concentration tended to have lower income over feed costs per cow per day. High herd mean milk urea concentrations were associated with higher feed costs per kilogram of milk fat but lower gross milk revenue and lower income over feed costs per kilogram of milk fat. The results of this study demonstrate that DHI milk urea measurements produced by an infrared test method offer a useful tool for monitoring the efficiency of nitrogen utilization in commercial dairy herds. The results also suggest that diets may be balanced to achieve greater efficiency of nitrogen utilization, lower milk urea concentrations, and lower feed costs, while still achieving high milk production. This may lead to improved income over feed costs.
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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 | 0.001 | 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 | 0.001 |
| 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".