Relationships between dietary factors and nitrogen partitioning to milk and urine in temperate grazing dairy cattle
Bibliographic record
Abstract
ABSTRACT Urinary nitrogen (UN) excretion and milk production traits of 35 groups of 15 grazing cows each were measured over two years. Urine volume and N concentration were measured with urine sensors and daily UN excretion was calculated for four consecutive days. Milk yield, composition and cow liveweight (LW) were used to estimate daily dry matter intakes (DMI) based on back‐calculated animal energy requirements and feed metabolisable energy (ME). Different N fractions in the diet were estimated using laboratory data and protein digestion equations. Mean estimates of N intake and UN excretion were 460 and 227 g N/d, respectively. Urinary‐N represented 52% of the N consumed, which aligns with indoor N balance studies. Urinary N excretion was weakly correlated ( r = 0.29) with dietary N intake, but moderately correlated ( r = 0.63–0.67) with diet N concentration, diet N:ME ratio, and diet effective rumen degradable protein (ERDP). The ERDP balance had moderate to strong correlations with N utilisation efficiency ( r = −0.89) and the UN expressed relative to N intake ( r = 0.59) and N in milk ( r = 0.78). These relationships illustrate the potential of urine sensors and energy‐based estimations of intake to assess the influence of dietary management strategies to mitigate UN excretion from grazing animals.
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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 | 0.001 |
| 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) | 0.000 | 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".