Feeding amount affects the sorting behavior of lactating dairy cows
Bibliographic record
Abstract
The objective of this study was to determine whether feed sorting behavior in dairy cattle is influenced by the amount of feed provided. Six lactating Holstein cows, individually fed a total mixed ration once daily, were exposed to two treatments in a crossover design with 7-d periods. The treatments were: (1) lower feed amount (LFA; target 10% orts), and (2) higher feed amount (HFA; target 20% orts). Dry matter intake (DMI) was monitored daily for each animal. On the final 4 d of each treatment period, fresh feed and orts were sampled for particle size analysis. The particle size separator had three screens (19, 8, 1.18 mm) and a bottom pan, resulting in four 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. Actual orts percentage averaged 11.5% for the LFA and 18.0% for the HFA treatments. When on the HFA cows sorted for the medium particles to a greater extent than on the LFA (103.0 vs. 101.1%). Further, when on the HFA treatment cows sorted against short particles to a greater extent than on the LFA (95.2 vs. 98.6%). Despite greater sorting on the HFA treatment, the concentrations of neutral detergent fiber (NDF; 29.6%) and starch (27.1%) in the feed consumed were similar between treatments. Given this, and that DMI was greater on the HFA treatment compared with the LFA treatment (29.7 vs. 26.5 kg d-1), greater intakes of NDF (8.7 vs. 7.8 kg d-1) and starch (8.0 vs. 7.2 kg d-1) were also observed on the HFA treatment. The results suggest that, despite causing greater feed sorting, increasing the feeding amount for lactating dairy cows promoted higher DMI and did not prevent the consumption of a ration balanced to meet their nutritional requirements.Key words: Feeding amount, sorting behavior, dairy cow
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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) | 0.000 | 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".