Supplementing fresh pasture with maize, lotus, sulla and pasture silages for dairy cows in summer
Bibliographic record
Abstract
Abstract A trial was conducted to compare benefits obtained from feeding four types of silage. There were two silages that contained condensed tannins (CT)—lotus ( Lotus corniculatus ) and sulla ( Hedysarum coronarium )—maize silage or traditional ryegrass pasture silage, all fed at 5 kg dry matter (DM) cow −1 day −1 with restricted pasture (RP). Cows on the RP (control) treatment and those fed the silage treatments were offered an allowance of 25 kg pasture DM cow −1 day −1 , while the full pasture (FP) cows were offered 50 kg pasture DM cow −1 day −1 . Silage supplementation increased both DM intake and milk yield compared with cows given RP only. Cows on the lotus silage supplement and the FP treatment had significantly higher milk production than the other silage supplemented cows ( P < 0.001). For cows given lotus silage, the high milk yield was probably due to a combination of the higher nutritive value of the silage and possibly to the protein‐sparing effects of the lotus condensed tannins because the total DM intake of cows fed the lotus silage was the same as that of cows given the pasture and maize silages ( P > 0.25). The high milk yield of the FP treatment was mainly a result of the cows having a higher intake of pasture than cows on all the other treatments. This study demonstrated the potential benefit of silage supplementation, particularly with lotus silage, for increased milksolids yield in summer when low pasture growth rates and quality may otherwise limit production. Copyright © 2006 Society of Chemical Industry
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".