Associations of bulk tank milk free fatty acid concentration with farm type and time of year
Bibliographic record
Abstract
Triacylglycerol (TAG) hydrolysis yields free fatty acids (FFA) and ≥1.20 mmol FFA/100 g of milk fat in bulk tank milk is associated with off-flavor, rancidity, reduced foam stability, and inhibited cheese coagulation. The objective of this study was to compare milk FFA concentrations among conventional (CON), organic (ORG), and certified grass-fed (CGF) dairy farm types in Ontario (ON), Canada, and describe monthly and yearly FFA patterns. Bulk tank FFA data measured at every milk pick-up from August 2018 to December 2022 were collected from all dairy farms in ON and averaged by month. A mixed model of monthly average FFA with herd as a random effect was used to investigate associations with month, year, and farm type. There were 171,843 observations from 3,771 farms over 53 mo (2 mo were excluded due to FFA calibration concerns). Ninety-seven percent (n = 166,355) of observations were from CON farms (n = 3,659), and the other 3% (n = 5,488) were from ORG (n = 72) and CGF (n = 40) herds. Conventional farms had the lowest overall average FFA (0.83 mmol/100 g of fat) with 7% (n = 11,645) of monthly averages ≥1.20 mmol/100 g of milk fat. Grass-fed herds had the highest overall average FFA (1.10 mmol/100 g of fat), and 23% (n = 842) of months had elevated FFA averages. Seventy-five percent (n = 30) of CGF farms had at least 1 elevated monthly average FFA over the 53 mo. In the mixed model, monthly average FFA levels were lower in May ( β = −0.02 to −0.21) and higher in July ( β = 0.01 to 0.12) than in other months. Conventional herds had lower monthly average FFA than CGF herds ( β = −0.27, 95% CI [−0.18, −0.35]) or ORG herds ( β = −0.08, 95% CI [−0.01, −0.14]). This research suggests that bulk tank milk FFA concentration varies among farm types, months, and years. The mechanisms underlying these associations warrant further investigation.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".