Short communication: Varying dietary dry matter concentration through water addition: Effect on nutrient intake and sorting of dairy cows in late lactation
Bibliographic record
Abstract
The objective of this study was to determine if feed sorting can be reduced and if nutrient consumption can be limited in late-lactation cows through water addition to a nutrient-dense total mixed ration (TMR) with a dry matter (DM) content greater than 60%. Twelve lactating Holstein cows (214.8±28.5 d in milk) were exposed to 2 diets in a crossover design with 28-d periods. Diets had the same ingredient composition and differed only in DM percentage, which was reduced by the addition of water. Treatment diets were (1) dry TMR (61.7% DM) and (2) wet TMR (51.9% DM). Dry matter intake and milk production (4% fat-corrected milk; FCM) were recorded for the last 14 d of each treatment period. For the final 4 d of each period, fresh feed and orts were sampled for particle size analysis and subsequent calculation of sorting activity (expressed as a percentage of predicted intake). Adding water to the diet tended to decrease the amount of DM in the fine particle fraction, increase starch concentration in the longer ration particles, and reduce starch concentration in the shortest ration particles. All cows sorted against long ration particles; the extent of this sorting did not differ between the dry and wet treatments (72.9 vs. 77.6%). There tended to be more sorting for fine ration particles on the dry diet compared with the wet (106.3 vs. 104.0%). Water addition had no effect on production, with similar DMI (27.9 vs. 26.5 kg/d), 4% FCM (28.7 vs. 27.6 kg/d), and efficiency of production (0.98 vs. 1.00 kg of 4% FCM/kg of DMI) between the dry and wet treatments. Adding water to a TMR with greater than 60% DM containing primarily haylage and corn silage forage sources may change ration particle DM distribution and particle starch content, possibly contributing to less sorting for the smallest ration particles. This research does not provide evidence that water addition to such a TMR can effectively limit DMI in late-lactation cows and, thus, improve efficiency of milk production.
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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 | 0.000 | 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".