Peripartum Performance and Metabolism of Dairy Cows in Response to Prepartum Energy and Protein Intake
Bibliographic record
Abstract
Twenty-six multiparous Holstein cows were used to examine the effects of prepartum energy and protein intake on periparturient metabolism and lactation performance. Two levels of energy, 1.65 Mcal/kg of net energy for lactation (NEL) and 1.30 Mcal/kg of NEL, and two levels of protein, 17.0% CP and 12.5% CP, were tested according to a factorial arrangement in a randomized block design. Dietary treatments were fed ad libitum from 21 d before expected calving date to the day of calving. After calving, all cows were fed the same diet. Increased nutrient density did not affect prepartum feed intake, but postpartum intake was higher for cows fed the high-energy diets. Treatment had no effect on cow body weight and body condition score, however, cows fed the high-energy diets were in greater energy balance throughout the study. Milk and milk component yields were unaffected by treatment. Cows fed the high-energy diets had lower plasma nonesterified fatty acid concentrations than cows fed the low energy diets (354.3 vs. 439.9 mumol/L). Hepatic triglyceride concentrations were lower for cows on the high-energy diets than for those on the low-energy diets. Liver glycogen was unaffected by treatment. Acetyl-CoA carboxylase and fatty acid synthase abundance was significantly lower at calving than pretreatment, and higher for cows on the high-energy diets relative to those on the low-energy diets. The activity of acetyl-CoA carboxylase and lipoprotein lipase was greatly decreased with the onset of lactation. Increased protein intake prepartum resulted in elevated plasma beta-hydroxybutyrate concentrations postpartum. Prepartum plasma urea nitrogen was increased and 3-methylhistidine decreased by the high protein treatments. Overall, increased energy density of prepartum diets had beneficial effects on feed intake and lipid metabolism but did not improve lactation performance. Increasing the protein content of the prepartum diet did not appear to confer any advantages to cow productivity.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".