Metabolic responses, performance, mammary gland development and gene expression in liver and muscle of Holstein × Gyr crossbred heifers grazing intensively-managed brachiaria decumbens supplemented with varied crude protein and 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
The objectives of the first and second study were to evaluate the effect of crude protein (CP) supplementation on the metabolic characteristics, performance, muscle, and mammary gland development and expression of genes involved in the urea cycle, and muscle tissue development of Holstein × Gyr crossbreed heifers grazing Brachiaria decumbens throughout the year. Thirty-eight heifers were randomly assigned to four treatments: three protein supplements (SUP) fed at 5g/kg of body weight, plus a control group. The supplement CP levels were 12, 24, and 36%. The experimental period was divided into four seasons: rainy, dry, rainy-dry transition, and dry-rainy transition. The data were analyzed using PROC GLIMMIXED of the SAS with repeated measures. SUP animals had a greater intake of dry matter, metabolizable energy, and metabolizable protein. Furthermore, SUP animals had a greater average daily gain, rib eye area and fat thickness than non-supplemented animals. Among SUP animals, we observed a quadratic response to ADG, with the highest level in S24. No supplementation effects were detected on mammary gland development. In muscle, we observed greater expression of AMPK in non-supplemented animals than SUP animals. No differences were observed for mTOR. We observed greater urea excretion and retention coefficient in SUP animals than non-supplemented animals. In this sense, we also observed greater gene expression of CPS, ASL, and ARG in SUP animals than non-supplemented, and among SUP animals, the supplement CP linearly affected CPS expression. We observed a positive linear effect of urea excretion, nitrogen intake, nitrogen retention, and retention coefficient among SUP animals. In conclusion, SUP animals had greater intake, performance than non- supplemented animals, with S24% demonstrating the best results. The third study aimed to describe the FA profile, as predicted using Fourier transform mid-infrared (FTIR) 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. Information regarding individual cow milk yield (kg/d), days in milk (DIM), parity, and the number of milking cows were automatically collected by the AMS units on each farm. 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 FA, mixed FA, preformed FA, and de novo + mixed FA. Greater frequency of PMR delivery (>2x/d vs. 1 and 2 x/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 indicate that bulk tank FA profile can be used to monitor and adjust management and housing in AMS farms. Keywords: Nitrogen metabolism. Pasture. Robotic milking. Season.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 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".