Effects of pasture allowance on milk production of dairy cows offered increasing amounts of partial mixed rations in spring
Bibliographic record
Abstract
Milk production was measured in Holstein–Friesian cows offered low, medium or high allowances of ryegrass pasture (15, 25 or 40 kg DM/cow.day to ground level) and receiving different amounts of supplement (6, 10, 12 or 14 kg DM/cow.day) as a partial mixed ration (PMR). The 27-day experiment was conducted in spring when cows were 45 ± 17.3 days in milk (mean ± s.d.). Two groups of six cows received each of the 12 combinations of pasture allowance and PMR amount. The PMR comprised wheat grain (38%, DM basis), maize grain (18%), lucerne hay (22%) and canola meal (22%). When 6 kg DM PMR/cow.day was offered, cows grazing the low allowance produced less milk and energy-corrected milk than did cows grazing the medium and high allowances. There was no effect of pasture allowance on production of milk or energy-corrected milk at any other amount of PMR offered. When cows were offered 14 kg DM PMR/cow.day, milk fat concentrations were lower for cows grazing the high pasture allowance than for cows grazing the medium allowance. There were no differences in milk fat concentrations between cows grazing the different pasture allowances at any other amount of PMR offered. When cows were offered 6 kg DM PMR/cow.day, cows grazing the low pasture allowance yielded less milk fat than did cows grazing the medium pasture allowance. Cows grazing the high allowance had greater concentrations and yields of milk protein than did cows grazing the low allowance at all amounts of PMR offered, while cows grazing the medium allowance yielded more milk protein at some amounts of PMR. Pasture utilisation decreased with increases in both pasture allowance and amount of PMR. These results will enable farmers to better optimise feeding systems that combine both pasture and PMR.
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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 | 0.001 |
| 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".