Meta-analysis of apparent ruminal synthesis and postruminal flow of B vitamins in dairy cows
Bibliographic record
Abstract
As milk production has significantly increased over the past decade(s), existing estimates of the B-vitamin needs of the modern dairy cow are currently being reconsidered, as suboptimal B-vitamin supply may affect metabolic efficiency. At the same time, however, "true" (i.e., biologically active forms, excluding nonfunctional analogs) B-vitamin supply also cannot be adequately estimated by dietary intake, as the rumen microbiota has been shown to play a significant role in synthesis and utilization of B vitamins. Given their complex impact on the metabolism of dairy cows, incorporating these key nutrients into the next generation of mathematical models could help to better predict animal production and performance. Therefore, the purpose of this study was to generate hypotheses of regulation in the absence of supplemental B vitamins by creating empirical models, through a meta-analysis, to describe true B-vitamin supply to the cow (postruminal flow, PRF) and apparent ruminal synthesis (ARS). The database used for this meta-analysis consisted of 340 individual cow observations from 15 studies with 16 experiments, where diet and postruminal digesta samples were (post hoc) analyzed for content of B vitamins (B 1 , B 2 , B 3 , B 6 , B 9 , B 12 ). Equations of univariate and multivariate linear form were considered. Models describing ARS considered dry matter intake (DMI, kg/d), B-vitamin dietary concentration [mg/kg of dry matter (DM)] and rumen-level variables such as rumen digestible neutral detergent fiber (NDF) and starch (g/kg of DM), total volatile fatty acids (VFA, m M ), acetate, propionate, butyrate, and valerate molar proportions (% of VFA), mean pH, and fractional rates of degradation of NDF and starch (%/h). Models describing PRF considered dietary-level driving variables such as DMI, B-vitamin dietary concentration (mg/kg of DM), starch and crude protein (g/kg of DM) and forage NDF (g/kg of DM). Equations developed were required to contain all significant slope parameters and contained no significant collinearity between driving variables. Concordance correlation coefficient was used to evaluate the models on the developmental data set due to data scarcity. Overall, modeling ARS yielded better-performing models compared with modeling PRF, and DMI was included in all prediction equations as a scalar variable. The B-vitamin dietary concentration had a negative effect on the ARS of B 1 , B 2 , B 3 , and B 6 but increased the PRF of B 2 and B 9 . The rumen digestible NDF concentration had a negative effect on the ARS of B 2 , B 3 , and B 6 , whereas rumen digestible starch concentration had a negative effect on the ARS of B 1 and a positive effect on the ARS of B 9 . In the best prediction models, the dietary starch increased PRF of B 1 , B 2 , and B 9 but decreased PRF of B 12 . The equations developed may be used to better understand the effect of diet and ruminal environment on the true supply of B vitamins to the dairy cow and stimulate the development of better-defined requirements in the future.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.035 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".