1241 Accuracy and precision of diets for high-producing dairy cows and their impacts on production and milk composition
Bibliographic record
Abstract
The goal of this study was evaluate associated feeding management and nutritional accuracy with milk production and composition on commercial herds. Twenty high-producing dairy farms from Campos Gerais county, Paraná State, Southern Brazil, were visited for 3 consecutive days in the 2015 fall season. Feeding management and TMR preparation related variables, and the physical and chemical characteristics of the offered diets and orts were collected. Production performance and milk composition from the high-production group of cows were obtained from regular milk testing, performed on average 1 ± 5d before or after the data collection period. Pearson correlations were estimated among the management and diet variables with production, milk composition, and feed sorting estimates. Using the Penn State Particle Separator, the offered diets had on average 14.9, 41.8, 32.8, and 10.5% (DM) of long, medium, short, and fine particles, respectively. Long particles showed a daily refusal rate of 9.0%, whereas short and medium particles were preferentially consumed at 1.1 and 1.7%, respectively. A high proportion of long particles in the forage (78.2% of haylage and hay) was associated with reduction in milk fat % (%MF) (r = −0.50; P < 0.05), and an increased proportion of cows with fat:protein ratio lower than 1 (FPR < 1) (r = 0.50; P < 0.05). Errors associated with loading an excess of concentrate ingredients in the TMR wagon were negatively associated with %MF (r = −0.52; P = 0.05) and milk production (r = −0.47; P < 0.05). By comparing the formulated diet with the one delivered to the cows, we noted, on a DM basis, a decrease in CP (−3.1%), fat (−7.0%), and ash contents (−10.5%), and an increase in NDF (+10.3%). The accuracy observed between formulated and delivered diets was not associated with the performance of the cows. However, daily variation of the DM content of the diet was associated with a greater proportion of cows with FPR < 1, and reduced FPR (r = 0.40; P = 0.09 and r = −0.43; P = 0.07, respectively). Low homogeneity (across 3 d) of the % of long particles in the diet was associated with greater selection against these particles (r = −0.64; P < 0.05), which showed a curvilinear association with %MF. These results demonstrated that the addition of more concentrate ingredients than expected, as well as the inconsistent intake of different particle sizes throughout the day, had a negative impact on milk production and composition of the studied herds.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".