Effect of dietary dry matter concentration on the sorting behavior of lactating dairy cows fed a total mixed ration
Bibliographic record
Abstract
The objective of this study was to determine whether addition of water to a high-moisture total mixed ration reduces feed sorting by dairy cattle. Twelve lactating Holstein cows, individually fed once per day, were tested on 2 diets in a crossover design with 21-d periods. Diets had the same dietary composition and differed only in dry matter content, which was reduced by the addition of water. Treatment diets were 1) dry (57.6% DM) and 2) wet (47.9% DM). Dry matter intake (DMI) and milk production were monitored for each animal for the last 7 d of both treatment periods. For the final 3 d of each period, milk samples were taken for composition analysis and fresh feed and orts were sampled for particle size analysis. The particle size separator had 3 screens (19, 8, and 1.18 mm) and a bottom pan, resulting in 4 fractions (long, medium, short, fine). Sorting was calculated as the actual intake of each particle size fraction expressed as a percentage of the predicted intake of that fraction. Contrary to the hypothesis, cows sorted the wet diet more extensively than the dry diet. Sorting of the dry diet was limited to a tendency to refuse short particles, whereas the wet diet was sorted against long particles and for short and fine particles. Water addition reduced DMI, neutral detergent fiber intake, and starch intake of cows on the wet diet. Increased sorting on the wet diet resulted in a tendency for decreased concentration of dietary neutral detergent fiber consumed and also resulted in increased starch concentration of the diet consumed. Milk production and components were unaffected by treatment. Our results suggest that water addition to high-moisture total mixed rations, containing primarily haylage and silage forage sources, may not be an effective method to reduce sorting. Furthermore, water addition may negatively affect DMI and encourage sorting, resulting in the consumption of a ration with different nutrient composition than intended.
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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 | 0.001 |
| 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 | 0.001 | 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".