Association of housing and management practices with milk yield, milk composition, and fatty acid profile, predicted using Fourier transform mid-infrared spectroscopy, in farms with automated milking systems
Bibliographic record
Abstract
Milk fatty acid (FA) profile can be divided into (1) de novo (C4-C14) that are synthesized in the mammary gland; (2) preformed (≥C18) that are absorbed from blood and originate from mobilized adipose tissues or dietary fat; and (3) mixed (C16), which have both origins. Our objectives were to describe the FA profile, as predicted using Fourier transform mid-infrared spectroscopy, of bulk tank milk from automated milking system (AMS) farms and to assess the association of management and housing factors with the bulk tank milk composition and FA profile of those AMS farms. The data used were collected from 124 commercial Canadian Holstein dairy farms with AMS, located in the provinces of Ontario (n = 68) and Quebec (n = 56). The farms were visited once from April to September 2019, and information were collected on barn design and herd management practices. Information regarding individual cow milk yield (kg/d), days in milk, parity, and the number of milking cows were automatically collected by the AMS units on each farm. These data were extracted for the entire period that the bulk tank milk samples were monitored, from April 2019 to April 2020 in Quebec and from August 2019 to May 2020 in Ontario. Across herds, milk yield averaged (mean ± standard error) 35.9 ± 0.4 kg/d, with 3.97 ± 0.01% fat and 3.09 ± 0.01% protein, whereas FA profile averaged 26.2 ± 0.1, 33.1 ± 0.1, and 40.7 ± 0.2 g/100 g of FA for de novo, mixed, and preformed, respectively. The FA yield averaged 0.34 ± 0.01, 0.44 ± 0.01, and 0.54 ± 0.01 kg/d for de novo, mixed, and preformed, respectively. Multivariable regression models were used to associate herd-level housing factors and management practices with milk production, composition, and FA profile. Milk yield was positively associated with using a robot feed pusher (+2.1 kg/d) and the use of deep bedding (+2.6 kg/d). The use of a robot feed pusher, deep bedding, and greater stall raking frequency were positively associated with greater yield (kg/d) of de novo, mixed, preformed, and de novo + mixed FA. Use of deep bedding was negatively associated with concentration of fat, de novo FA, mixed FA, and de novo + mixed FA, expressed in grams per 100 g (%) of milk. A wider lying alley width (≥305 cm) was associated with a greater concentration (g/100 g of milk) of de novo and de novo + mixed FA. Greater frequency of partial mixed ration delivery (>2×/d vs. 1 and 2×/d) was positively associated with a greater proportion (g/100 g of FA) of de novo, mixed, and de novo + mixed FA and negatively associated with the proportion of preformed FA. Overall, these associations indicated that bulk tank FA profile can be used as a tool to monitor and adjust management and housing in AMS farms.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".