Effects of the Amount of Concentrate Offered in an Automated Milking System on dry matter intake, Milk Yield, Milk Composition, Ruminal fermentation, and Behaviour of Primiparous Holstein Cows Fed Iso-Caloric Diets
Bibliographic record
Abstract
The objective of this study was to determine if the quantity of concentrate provided in an automated milking system (AMS) affects dry matter intake (DMI), attendance to the AMS, milk and milk component yield, feeding behaviour, cow activity and ruminal fermentation of lactating dairy cows fed iso-caloric diets. Eight ruminally-cannulated primiparous Holstein cows were used in a replicated 4 × 4 Latin square design with 28-d periods. Cows were housed in a free-stall facility with a guided-traffic (feed-first) flow barn-design. Cows were offered 0.5, 2.0, 3.5, or 5.0 kg/d DM of pellet in the AMS, with an equivalent reduction of the same pellet in the partial mixed ration (PMR). Day 21 to 24 of each treatment period were used for DMI, milking performance, behaviour, and ruminal pH determination, while d 25 to 28 were used for ruminal short-chain fatty acid (SCFA) and ammonia concentrations, as well as total tract digestibility. As imposed, consumption of AMS pellet linearly increased (P < 0.01), equating to 0.50, 2.00, 3.49, and 4.93 kg/d. Correspondingly, the standard deviation in AMS pellet intake among days linearly increased from 0.06 to 0.85 kg/d as the quantity of concentrate in the AMS increased from 0.5 to 5.0 kg (P < 0.01). The PMR DMI decreased linearly with increasing AMS concentrate allocation (P < 0.01), but total DMI (PMR + AMS concentrate) was not affected (25.3 kg/d, P = 0.40). As AMS concentrate allocation increased, the selection against particles retained on an 18-mm sieve linearly increased (P = 0.02) and selection against particles retained on the bottom pan decreased (P < 0.01). Milking frequency (3.22 milkings/d, P = 0.82), milk yield (37.5 kg/d, P = 0.59), milk fat yield (1.43 kg/d, P = 0.46), and milk protein yield (1.22 kg/d, P = 0.42) were not affected; however, milk urea nitrogen concentration decreased linearly with increasing AMS concentrate (P = 0.02). Ruminal pH averaged 6.18 and was not affected by AMS concentrate (P = 0.62). Total ruminal SCFA concentration was greatest when 3.5 kg of concentrate was allocated in the AMS and ruminal ammonia decreased linearly with increasing AMS concentrate (P = 0.01). Time spent lying, the number of lying bouts, and average bout duration were not affected by treatment (P ≥ 0.11). These data indicate that increasing the quantity of concentrate in the AMS increases daily variability in AMS concentrate intake while decreasing PMR intake, and increasing AMS pellet provision, under isocaloric dietary settings, is not likely to affect voluntary visits to the AMS, milk and milk component yield, or ruminal fermentation.
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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".