Estimations of prepartum feed intake and its effects on transition metabolism and subsequent milk production
Bibliographic record
Abstract
The objectives of this study were to identify factors associated with prepartum DMI, evaluate the performance of linear models to estimate prepartum DMI using different classes of predictors, and investigate the consequences of different levels of prepartum DMI on transition metabolism and lactation performance. Individual feed intake of nulliparous (n = 100) and parous cows (n = 173) was measured by automatic feeding bins from d −35 to 98 relative to calving. Rumination and physical activities were monitored by wearable sensors. Blood metabolites were measured on d −21, −10, −3, 0, 3, 7, 10, 14, and 21. Body weight ( BW ) and body condition score ( BCS ) were assessed throughout the study. The average prepartum DMI as percentage of BW ( DMIpBW ) was calculated for each cow and used as dependent variable of linear models. Parity, prepartum BCS and BW, milk production in the previous lactation ( M305 ) and at dry-off ( MYDO ), and length of the dry period were associated with DMIpBW and explained 41% of the variation in all cows, and 49% in parous cows. Estimations of DMIpBW were improved when data on prepartum rumination and blood metabolites were added in the predictive models. In the latter, the adjusted R-Sq increased to values between 47 and 61%, and selected models performed consistently in a 5-fold cross-validation analysis. To evaluate the implications of DMIpBW to transition metabolism and performance, cows were ranked within parity and classified into terciles as low ( LFI ), moderate ( MFI ), or high feed intake ( HFI ). The mean DMI was 1.44, 1.70, and 1.91 ± 0.01% of BW, respectively. No differences in BW were observed in nulliparous cows, but all 3 groups of parous cows differed (LFI = 892, MFI=849, HFI=798 ± 8 kg). The proportion of cows with BCS > 3.5 at enrollment differed among all groups, and averaged 67.4, 55.1, and 36.5 ± 6%, respectively. For parous cows, M305 and MYDO differed among all groups and averaged 9,808, 10,457, and 11,182 ± 233 kg, and 18.1, 23.1, and 26.2 ± 1 kg/d, respectively. After calving, DMI (LFI = 20.9, MFI=21.9, and HFI=22.1 ± 0.2 kg/d) and milk yield (LFI = 36.7, MFI=38.2, and HFI=38.3 ± 0.4 kg/d) was lower in LFI cows compared with the other 2 groups. Postpartum EBAL differed among all groups and averaged −2.79, −1.63, and −0.66 ± 0.3 Mcal/d for LFI, MFI, and HFI, respectively. During the transition period, LFI cows had higher serum concentrations of NEFA, BHB, Cl (prepartum only), and AST (postpartum only), and lower serum concentrations of cholesterol, P, GLDH, GGT (prepartum only), AST (prepartum only), urea (parous only), and SOD activity (parous only). In conclusion, a low level of prepartum DMI was associated with fatter and heavier cows, lower milk production in previous lactation, important adjustments in energy metabolism, and moderate losses in DMI and milk yield in the subsequent lactation. Moreover, the inclusion of prepartum rumination activity and target blood metabolites into predictive models improved the estimations of prepartum DMI.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".