Association between milk pump type and free fatty acid concentrations on dairy farms with automated milking systems
Bibliographic record
Abstract
High levels of free fatty acids (FFA) in milk (≥1.20 mmol FFA/100 g of fat) indicate excessive milk fat breakdown and compromise milk quality. Automated milking systems (AMS) have become more common in the dairy industry, but questions about their effect on milk quality, including FFA, remain. On average, AMS-milked herds have greater FFA levels in bulk tank milk than parlor-milked herds. The difference in milk pump type between some AMS and parlor systems may be a contributing factor. The objective of this study was to investigate whether a positive displacement milk pump (PDMP) would be associated with greater FFA when compared with a centrifugal milk pump (CMP) on AMS farms. We hypothesized that a PDMP would be associated with greater FFA levels because of the potential impact of high flow rates on milk fat globules. We conducted an observational pilot study using farm and milk quality data collected from Ontario, Canada, AMS herd visits. Monthly average milk composition data surrounding the farm visit date were obtained from the Dairy Farmers of Ontario and included FFA concentration, milk fat (% weighted volume), milk protein (% weighted volume), and milk shipment volume (L). A linear regression analysis was conducted with monthly average FFA as the outcome and pump type as the explanatory variable of interest, with other factors identified in previous research to be associated with increased FFA accounted for in the model. One hundred twenty-one AMS herds were visited between 2019 and 2021, with an average (± SD) monthly FFA of 0.86 ± 0.18 mmol/100 g of fat. Seventy-four farms (61%) had a PDMP, and the average FFA level was 0.88 mmol FFA/100 g of fat, which was above the provincial industry average and greater than AMS farms with a CMP. The results suggest that FFA may be slightly greater on AMS farms with PDMP (β = 0.04 mmol FFA/100 g milk fat, 95% CI -0.02 to 0.10), but the difference was not statistically significant and is small compared with other previously identified FFA factors. However, this could be due to a small sample size and few study farms with high FFA levels.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".