Characterisation of productive performance, body reserve dynamics and energy metabolic parameters of Holstein Friesian and Normande dairy cows managed in a low-input pastoral dairy system
Bibliographic record
Abstract
Context Dairy cows with autumn-calving season in low-input pastoral systems are subjected to challenging conditions as their early lactation falls in autumn and winter when shortages in pasture availability and harsh weather conditions often occur. Aims We aimed to assess the associations between breed (Holstein Friesian (HF) vs Normande (NR)) and stage of lactation on productive and metabolic responses over lactation in a low-input pastoral dairy system. Methods Twenty-seven HF (n = 14) and NR (n = 13) cows were studied. Milk yield was measured fortnightly, and milk composition, milk solid yield, liveweight (LW), body condition score (BCS) and blood plasma energy metabolites were assessed monthly. Key results The greater milk for HF versus NR cows (e.g. 17.4 vs 12.1 ± 1.0 kg/day of milk; P < 0.001) were associated with a lower LW (510 vs 540 ± 10 kg; P = 0.021) and BCS (1.84 vs 2.37 ± 0.10; P = 0.001) at nadir for HF than NR cows. Blood plasma concentrations of non-esterified fatty acids (NEFA) were high for longer in HF versus NR cows (18 vs 8 WRC). Plasma glucose was greater (P = 0.002) and insulin was lower (P = 0.021) for HF versus NR cows at 3 WRC. During mid-lactation, glucose (22 WRC, P = 0.021) and insulin (26 to 34 WRC, P = 0.041) were lower for HF vs. NR cows. Conclusions Because of their lower milk yields, NR cows had a shorter period of body reserves mobilisation, which was associated with an earlier decrease of plasma concentrations of NEFA and greater insulin concentrations at 3 WRC, and then again during mid-lactation when LW and BCS were also greater for NR cows. Implications In constraining pastoral environments, NR cows would redirect fewer nutrients for milk synthesis, leading to lower depletion of body reserves, which could be associated with a better ability to cope with winter challenging conditions. Although HF cows had a greater depletion of body reserves, there was no increase in the incidence of health problems for these cows.
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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) | 0.001 | 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".