Milking-to-Milking Variability for Milk Yield, Fat and Protein Percentage, and Somatic Cell Count
Bibliographic record
Abstract
The main objectives were to analyze milking-to-milking variability in milk yield, fat and protein percentages, and somatic cell count (SCC). Additional objectives were to investigate the factors that affect variation in milk fat percentage and to study the seasonal variations in milk, fat, and protein yields and SCC. A total of 16 farms (14 milked 2x and 2 milked 3x) across Canada participated in a 5-d milk-sampling study, with 27,328 milk samples collected and analyzed for fat and protein yields and SCC. Descriptive statistics for both 2x and 3x herds for milk yield and fat and protein percentages followed a typical pattern throughout lactation, and the somatic cell linear scores were higher in early lactation for first-lactation cows (4.7 vs. 3.8) but were higher at the end of lactation for cows in second lactation or greater (5.1 vs. 4.9). The 2x herds had higher milk yields in the morning (approximately 17 vs. approximately 14 kg), whereas the 3x herds had the lowest milk yields in the morning, and yields peaked at the evening milking (approximately 9 vs. approximately 11.2 kg). A herd management questionnaire was distributed to participating producers to investigate the relationship between management variables and variations in milk fat percentage over the 5-d sampling period. Data from the questionnaire determined that milking period had a significant effect on milk fat in 2x herds, with fat percentage 1.11% lower in the morning compared with the evening milking period. Seasonal differences in milk, fat, and protein yields were investigated in 910 cows on 3 farms, with 5,517 fat and 5,534 protein samples. The seasonal differences in fat yield [summer = 1.02 +/- 1.05 kg/d (SEM); winter = 1.19 +/- 1.05 kg/d] and protein yield (summer = 0.85 +/- 1.05 kg/d; winter = 0.96 +/- 1.05 kg/d) were significant only for first lactation. Understanding the variability in milk yield, fat and protein percentages, and SCC is important when making management decisions and in milk-recording programs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".