Adding liquid feed to a total mixed ration reduces feed sorting behavior and improves productivity of lactating dairy cows
Bibliographic record
Abstract
This study was designed to determine the effect of adding a molasses-based liquid feed (LF) supplement to a total mixed ration (TMR) on the feed sorting behavior and production of dairy cows. Twelve lactating Holstein cows (88.2±19.5 DIM) were exposed, in a crossover design with 21-d periods, to each of 2 treatment diets: 1) control TMR and 2) control TMR with 4.1% dietary dry matter LF added. Dry matter intake (DMI), sorting, and milk yield were recorded for the last 7 d of each treatment period. Milk samples were collected for composition analysis for the last 3 d of each treatment period; these data were used to calculate 4% fat-corrected milk and energy-corrected milk yield. Sorting was determined by subjecting fresh feed and orts samples to particle separation and expressing the actual intake of each particle fraction as a percentage of the predicted intake of that fraction. Addition of LF did not noticeably change the nutrient composition of the ration, with the exception of an expected increase in dietary sugar concentration (from 4.0 to 5.4%). Liquid feed supplementation affected the particle size distribution of the ration, resulting in a lesser amount of short and a greater amount of fine particles. Cows sorted against the longest ration particles on both treatment diets; the extent of this sorting was greater on the control diet (55.0 vs. 68.8%). Dry matter intake was 1.4 kg/d higher when cows were fed the LF diet as compared with the control diet, resulting in higher acid-detergent fiber, neutral-detergent fiber, and sugar intakes. As a result of the increased DMI, cows tended to produce 1.9 kg/d more milk and produced 3.1 and 3.2 kg/d more 4% fat-corrected milk and energy-corrected milk, respectively, on the LF diet. As a result, cows tended to produce more milk fat (0.13 kg/d) and produced more milk protein (0.09 kg/d) on the LF diet. No difference between treatments was observed in the efficiency of milk production. Overall, adding a molasses-based LF to TMR can be used to decrease feed sorting, enhance DMI, and improve milk yield.
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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) | 0.001 | 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".