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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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".