Nitrogen efficiency in cows fed red clover- or alfalfa-silage-based diets differing in rumen-degradable protein supply
Bibliographic record
Abstract
Nitrogen pollution is an important issue in modern agriculture. For the dairy industry, increasing the efficiency of milk protein production may reduce the environmental impact of commercial farms. Optimal N utilization can be achieved by combining forage and concentrate varying in their degradability in the rumen. The aim of this study was to gain insight into the fate of dietary N as affected by forage type and rumen-degradable protein supply in lactating dairy cows, with an emphasis on milk N efficiency. Four rumen-fistulated Holstein cows were used in a 4 × 4 Latin square design balanced for residual effects. Four treatments consisting of a 2-by-2 factorial arrangement of alfalfa silage (AL) or red clover (RC)-based diets designed to satisfy 100 (rumen-degradable protein (RDP)-100) or 85% (RDP-85) of rumen degradable protein demand, by varying the amounts of ground and steam-flaked corn grain, as well as non-treated and heat-treated soybean meals. Dietary CP concentrations were greater in diets based on AL as compared with RC (P < 0.01). Kinetics of in sacco N disappearance revealed that the rapidly disappearing fraction was lower with RC than with AL diets, and this difference tended to be more pronounced for RDP-85 than RDP-100 (interaction, P = 0.06). On the contrary, slowly disappearing N fraction and fractional rate of disappearance were greater for RC as compared with AL diets (P < 0.01). Reducing RDP from 100 to 85% of requirements increased the slowly disappearing N fraction, but decreased the rapidly disappearing fraction and the fractional rate of disappearance of N (P < 0.01). Intakes of DM and N were lesser with RC compared with AL, and these differences were more pronounced with RDP-85 than with RDP-100 (interaction, P ≤ 0.04). As a proportion of N intake, fecal excretion tended to be greater (P = 0.09), whereas urinary excretion was lower (P = 0.04) with RC than with AL diets. Milk yield (tendency; P = 0.07) and total N secreted in milk were reduced (P < 0.01) with RC compared with AL diets. Feeding RC increased milk N efficiency (g of milk N/ kg of N intake) as compared with AL, and this difference tended to be of greater magnitude for RDP-85 than RDP-100 (interaction, P = 0.06). In conclusion, combining forage and concentrate of different CP degradability can improve milk N efficiency in dairy cows.
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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 | 0.000 |
| 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 | 0.000 |
| 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 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".