Review: How the efficiency of utilization of essential amino acids can be applied in dairy cow nutrition
Bibliographic record
Abstract
How the efficiency of utilization of essential amino acids (EffUEAA) can be applied in dairy cow nutrition is presented in this review. The concept of EffUEAA proposed by the National Academies of Sciences, Engineering and Medicine (NASEM, 2021) is first detailed. It represents the proportion of the metabolisable essential amino acids (mEAA) supply used to support protein secretions and accretions (scurf, metabolic fecal, milk and growth). For these processes, the efficiency of each individual EAA is variable, and considered to vary similarly for all the protein secretions and accretions. The anabolic process of gestation is ascribed to a constant efficiency (33%), whereas the efficiency of endogenous urinary loss (EndoUri) is set at 100%. Therefore, the NASEM model EffUEAA was calculated as the sum of EAA in the true protein of secretions and accretions divided by the available EAA (mEAA − EndoUri − gestation net true protein/0.33). In this paper, the reliability of this mathematical calculation was tested through an example where the experimental efficiency of His was calculated assuming that liver removal represents catabolism. The NASEM model and experimental efficiencies were in the same range and varied in similar manner. Assuming that the NASEM model EffUEAA reflects EAA metabolism in the dairy cow, its different applications were examined. In NASEM, target efficiencies were determined for each EAA: 75, 71, 73, 72, 73, 60, 64, 86 and 74% for His, Ile, Leu, Lys, Met, Phe, Thr, Trp, and Val, respectively. From these, recommendations for mEAA supply can be calculated as: [(secretions + accretions)/(target EffUEAA × 0.01) + EndoUri + gestation/0.33], assuming energy supply is adequate. In addition to NASEM propositions, equations to predict EffUEAA with precision and accuracy are detailed, using the ratio of (mEAA-EndoUri) to digestible energy intake, in a quadratic model that includes days in milk. Moreover, milk true protein yield predictions from predicted EffUEAA or efficiency of utilization of metabolisable protein are better than those from the multivariate equation of NASEM (2021) and superior to those predicted with a fixed efficiency. Finally, either the NASEM model or the predicted EffUEAA can be used to assess the responsiveness of a ration to supplementation with a single EAA. If the EffUEAA of the EAA to supplement is higher than the target EffUEAA, while the EffUEAA of the other EAA are lower than the target value, this suggests a potential improvement in milk true protein yield to supplementation with this EAA.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".