Performance of lactating dairy cows fed macerated forage conserved as silage and hay
Bibliographic record
Abstract
The effect of forage maceration at harvest on silage characteristics and its effect on lactation performance of Holstein cows were determined. Either a roller conditioner or a prototype forage macerator manufactured by PAMI were used to cut a 25-ha field of alfalfa (Medicago sativa) forage. The harvested forage was wilted and preserved as silage or hay. Maceration of alfalfa forage resulted in a lower crude protein concentration of fresh forage. Silage volatile fatty acid and ethanol concentration and hay and silage nutrient profiles were not affected by harvest methods. Thirty-four Holstein cows (602.9 ± 3.4 kg) in early lactation were used in a 14-wk lactation study. The cows were fed two dietary treatments in the form of a total mixed ration (TMR); one contained roller conditioner-harvested alfalfa silage and hay and the other contained macerator-harvested alfalfa silage and hay. Feed, weighbacks and milk were sampled daily. Daily dry matter intake (21.6 ± 0.5 kg) was not affected by harvest method. Daily milk yield (38.7 ± 0.3 kg) and milk composition were not affected by dietary treatment during the 14-wk lactation trial; however, cows fed the macerated forage as part of a TMR had a 0.23 kg greater daily body weight gain (P < 0.05). Dietary energy input and energy output (total energy in milk, maintenance and body weight change) were not affected by dietary treatment; however, energy contained in body weight change was greater (P < 0.05) in cows fed a TMR containing the macerated forage. Key words: Alfalfa, maceration, milk yield, body weight, forage energy
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".