Nutrient Intake and Feeding Behavior of Growing Dairy Heifers: Effects of Dietary Dilution
Bibliographic record
Abstract
The objective of this study was to determine how the addition of straw to a total mixed ration offered to growing dairy heifers affects their nutrient intake and feeding behavior. Six prepubescent Holstein heifers (226.2 +/- 6.3 d old and weighing 250.1 +/- 17.7 kg), fed once per day for 1.0 kg/d of growth, were subjected to each of 3 treatment diets using a replicated 3 x 3 Latin square design. The treatment diets were 1) control (17.0% corn silage, 52.1% grass silage, 30.9% concentrate), 2) control diet with 10% straw, and 3) control diet with 20% straw. Dry matter intake and feeding behavior were monitored for 7 d for each animal on each treatment. Fresh feed and orts were sampled on the last 3 d of each treatment period for each heifer and were then subjected to particle size analysis. The particle size separator contained 3 screens (19, 8, and 1.18 mm) and a bottom pan, resulting in 4 fractions (long, medium, short, and fine). Sorting activity for each fraction was calculated as actual intake expressed as a percentage of the predicted intake. Heifers sorted against long particles and for short particles on all 3 diets. On the 10 or 20% straw diets the heifers sorted for medium particles. Heifers also sorted for fine particles on the 20% straw diet. There was a linear increase in sorting for medium, short, and fine particles with increased straw in the diet. Dry matter intake linearly decreased with increased straw in the diet. Feeding time and meal duration increased linearly with the addition of straw to the diet, whereas feeding rate, meal size, and meal frequency decreased with the addition of straw. Requirements for maintenance and growth of 1.0 kg/d were sufficiently met when the animals consumed the control and 10% straw diet. On the 20% straw diet the animals consumed sufficient nutrients to achieve a 0.9 kg/d growth rate. These results indicate that the addition of straw to the diet of prepubescent heifers strongly influences their sorting behavior. Despite this sorting, the results suggest that a low-quality feedstuff may be included in the diet to target nutrient intake and reduce feed costs without negatively affecting feeding behavior or growth potential.
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 | 0.001 |
| 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 | 0.001 |
| 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".