Effect of feeding system and grain source on lactation characteristics and milk components in dairy cattle
Bibliographic record
Abstract
The objective of this study was to examine the effect of feeding systems [component and total mixed rations (TMR)] and dietary grain sources (barley, commercial concentrate, corn grain, and high-moisture corn) on lactation characteristics and milk composition. A total of 852,242 test-day records, information on animal characteristics, feed composition, and feeding systems from 104,129 Holstein cows in 4,319 herds covering a period of 5 yr were obtained from Quebec's Dairy Herd Improvement Association (Valacta). We performed descriptive statistics and graphical representations of the data for each type of feeding system and grain source by parity (1 to 3). The milk records were binned in 15-d in milk blocks. Mixed models using a combination of forward and backward stepwise selections were developed to predict milk and milk component yields. The TMR-fed cows had greater yield of milk, fat, protein, and lactose and lower milk urea N (MUN) concentration than component-fed cows at all parities. Cows fed a TMR had higher peak milk yields and greater persistency after peak lactation compared with component-fed cows. In addition, greater yields of milk fat and protein from peak to mid-lactation were found in TMR- versus component-fed cows. In general, greater milk fat and protein yields as well as lower MUN concentration were observed in cows fed corn grain or high-moisture corn compared with barley or commercial concentrate, but parity influenced these relationships. The feeding system by day in milk blocks interaction was significant in models of milk and components yields for all parities, but only for second-lactation cows for MUN concentration. This means that effect of TMR and component feeding differs with stage of lactation. In conclusion, feeding TMR and corn-based diets are associated with greater yield of milk and milk components under commercial conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".