Review: Impact of protein and energy supply on the fate of amino acids from absorption to milk protein in dairy cows
Bibliographic record
Abstract
Making dairy farming more cost-effective and reducing nitrogen environmental pollution could be reached through a reduced input of dietary protein, provided productivity is not compromised. This could be achieved through balancing dairy rations for essential amino acids (EAA) rather than their aggregate, the metabolizable protein (MP). This review revisits the estimations of the major true protein secretions in dairy cows, milk protein yield (MPY), metabolic fecal protein (MFP), endogenous urinary loss and scurf and associated AA composition. The combined efficiency with which MP (EffMP) or EAA (EffAA) is used to support protein secretions is calculated as the sum of true protein secretions (MPY + MFP + scurf) divided by the net supply (adjusted to remove the endogenous urinary excretion: MPadj and AAadj). Using the proposed protein and AA secretions, EffMP and EffAA were predicted through meta-analyses (807 treatment means) and validated using an independent database (129 treatment means). The effects of MPadj or AAadj, plus digestible energy intake (DEI), days in milk (DIM) and parity (primiparous v. multiparous), were significant in all models. Models using (MPadj, MPadj × MPadj, DEI and DEI × DEI) or (MPadj/DEI and MPadj/DEI × MPadj/DEI) had similar corrected Akaike's information criterion, but the model using MPadj/DEI performed better in the validation database. A model that also included this ratio was, therefore, used to fitting equations to predict EffAA. These equations predicted well EffAA in the validation database except for Arg which had a strong slope bias. Predictions of MPY from predicted EffMP based on MPadj/DEI, MPadj/DEI × MPadj/DEI, DIM and parity yielded a better fit than direct predictions of MPY based on MPadj, MPadj × MPadj, DEI, DIM and parity. Predictions of MPY based on each EffAA yielded fairly similar results among AA. It is proposed to ponder the mean of MPY predictions obtained from each EffAA by the lowest prediction to retain the potential limitation from AA with the shortest supply. Overall, the revisited estimations of endogenous urinary excretion and MFP, revised AA composition of protein secretions and inclusion of a variable combined EffAA (based on AAadj/DEI, AAadj/DEI × Aadj/DEI, DIM and parity) offer the potential to improve predictions of MPY, identify which AA are potentially in short supply and, therefore, improve the AA balance of dairy rations.
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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 | 0.000 |
| 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".