Effect of water addition to a total mixed ration on feed temperature, feed intake, sorting behavior, and milk production of dairy cows
Bibliographic record
Abstract
The objective of this study was to determine the effects of water addition to a high-moisture total mixed ration (TMR) on feed temperature, feed intake, feed sorting behavior, and milk production of dairy cows. Twelve lactating Holstein cows (155.8+/-60.1 DIM), individually fed once daily at 1000 h, were exposed to 3 diets in a Latin square design with 28-d treatment periods. Diets had the same ingredient composition [30.9% corn silage, 30.3% alfalfa haylage, 21.2% high-moisture corn, and 17.6% protein supplement; dry matter (DM) basis] and differed only in DM concentration, which was reduced by the addition of water. Treatment diets averaged 56.3, 50.8, and 44.1% DM. The study was conducted between May and August when environmental temperature was 18.2+/-3.6 degrees C and ambient temperature in the barn was 24.4+/-3.3 degrees C. Dry matter intake (DMI) was monitored for each animal for the last 14 d of each treatment period. For the final 7 d of each period, milk production was monitored, feed temperature and ambient temperature and humidity were recorded (daily at 1000, 1300, and 1600 h), and fresh feed and orts were sampled for determination of sorting. For the final 4 d of each period, milk samples were taken for composition analysis. Samples taken for determining sorting were separated using a Penn State Particle Separator that had 3 screens (19, 8, and 1.18 mm) and a bottom pan, resulting in 4 fractions (long, medium, short, and fine). Sorting was calculated as the actual intake of each particle size fraction expressed as a percentage of the predicted intake of that fraction. Greater amounts of water added to the TMR resulted in greater increases in feed temperature in the hours after feed delivery, greater sorting against long particles, and decreased DMI, reducing the overall intake of starch and neutral detergent fiber. Milk production and composition were not affected by the addition of water to the TMR. Efficiency of production of milk was, however, increased with greater amounts of water added to the TMR. The increases in feed temperature in the hours after feed delivery were enhanced by higher ambient temperatures; this may be indicative of feed spoilage and thus may have contributed to the reduced DMI observed. Overall, these results suggest that the addition of water to high-moisture TMR (less than 60% DM) containing primarily haylage and silage forage sources will not always discourage cows from sorting, but rather may increase this behavior and limit the nutrient consumption of cows, particularly when ambient temperature is high.
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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 | 0.001 | 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 | 0.001 |
| 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".