Effect of particle size of alfalfa hay and reconstitution with water on intake, digestion and milk production in Holstein dairy cows
Bibliographic record
Abstract
Twenty-four lactating Holstein dairy cows (12 first lactation and 12 multiparous; day in milk = 11 ± 5 days) were allotted to a randomised complete block design in a 2 × 3 factorial with four replicates per treatment to evaluate the effects of two methods of alfalfa feeding (dry and reconstituted to achieve a theoretical dry matter (DM) content of 350 g/kg) and three geometric mean (GM) particle sizes of alfalfa (9.13, 4.51 and 1.20 mm) on performance of dairy cows for a period of 28 days. Diets were offered for ad libitum intake as total mixed rations (TMR). The GM particle size, its standard deviation, and the values of physical effectiveness factor of alfalfa and TMR decreased as alfalfa particle size decreased. Reduction of particle size and reconstitution of alfalfa increased the bulk density and the functional specific gravity of alfalfa and rations. Reduction of particle size decreased insoluble dry matter, water-holding capacity, and hydration rate of alfalfa. As particle size decreased, the amount of physically effective NDF in the ration (g/kg) decreased but the daily intake of physically effective NDF (kg/day) increased. Reduction of particle size and reconstitution increased dry matter intake (DMI) and ruminal passage rate, but reduced NDF and ash digestibilities, ruminal pH, N-NH3, milk fat, total chewing activity, rumination and eating time, total and ruminal mean retention time, and time delay of marker. Increased functional specific gravity, from reduced forage particle size and the reconstitution of alfalfa, was the most important factor influencing DMI, milk composition, and chewing activity.
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 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 | 0.000 |
| 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) | 0.000 | 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".