Whole-tract digestibility and nitrogen-use efficiency of partial mixed rations with and without canola meal
Bibliographic record
Abstract
Increasing the crude protein (CP) concentration of a ration fed to grazing dairy cows by adding canola meal can increase milk production. The present study investigated the effect of extra CP intake on nitrogen-use efficiency and the fate of the additional dietary nitrogen (N). Sixteen spring-calved rumen fistulated cows were housed in metabolism stalls for a 9-day period and offered one of the following four treatment diets: (1) 8 kg DM/cow.day of fresh perennial ryegrass (PRG) supplemented with 12 kg DM/cow.day of a partial mixed ration (PMR) comprising oaten hay, crushed maize and wheat grain (PMR 8); (2) 12 kg DM/cow.day of fresh-cut PRG and 12 kg DM/cow.day of PMR (PMR 12); (3) the same as for PMR 8 cows, except some wheat in the PMR was replaced with canola meal (PMR+C 8); and (4) the same as the PMR 12 cows, except some wheat in the PMR was replaced with canola meal (PMR+C 12). The PMR and the PMR+C diets were iso-energetic, but the canola meal provided extra CP. Crude protein intake was 14.4%, 14.8%, 16.8% and 17.4% DM for PMR 8, PMR 12, PMR+C 8 and PMR+C 12 respectively. The addition of canola meal increased DM intake (P < 0.05) from 20.4 to 21.6 kg/day and increased N intake (P < 0.001) from 478 to 590 g/day. Nitrogen digestibility increased (P < 0.05) from 67% to 71%, nitrogen-use efficiency decreased (P < 0.05) from 37% to 32% and urinary-N output increased (P < 0.01) from 118 to 160 g/day, indicating that the additional CP fed resulted in additional N surplus. Energy-corrected milk yield for the experimental period was 34 ± 3.1 kg/cow.day (mean ± s.d.); however, due to the low number of cows, the ability to rigorously assess the effects on milk production was limited.
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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) | 0.001 | 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".