Predicting ruminally undegraded and microbial protein flows from the rumen
Bibliographic record
Abstract
The objectives of the present work were (1) to identify the cause of the linear bias in predictions of rumen-undegradable protein (RUP) content of feeds, and devise methods to remove the bias from prediction equations, and (2) to further explore the impact of rumen-degradable protein (RDP) on microbial N (MiN) outflow from the rumen. The kinetic model used by NRC (2001), which is based on protein fractionation and rates of degradation (Kd) and passage (Kp), displays considerable slope bias (-0.30 kg/kg), indicating parameter or structural problems. Regressing Kp by feed class and a static adjustment factor for the in situ-derived Kd on observed RUP flows completely resolved the slope bias problem, and the model performed significantly better than models using unadjusted Kd and marker-based Kp. The Kd adjustment was 3.82%/h, which represents approximately a 50% increase in rates of degradation over the in situ values, indicating that in situ analyses severely underestimate true rates of protein degradation. The Kp for concentrate-derived protein was 5.83%/h, which was slightly less than the marker-predicted rate of 6.69%/h. However, the derived forage protein rate was 0.49%/h, which was considerably less than the marker-based rate of 5.07%/h. Compartmental analysis of data from a single study corroborated the regression analysis, indicating that a 25% reduction in the overall passage rate and an 87% increase in the rate of degradation were required to align ruminal N pool sizes and the extent of protein degradation with the observed data. Therefore, one must conclude that both the in situ-derived degradation rates and the marker-based particle passage rates are biased relative to protein passage and cannot be used directly to predict RUP outflow from the rumen. The effects of RDP supply on microbial nitrogen (MiN) flow were apparent when intakes of individual nutrients were offered but not when DM intake and individual nutrient concentrations were offered, due to collinearity problems. Microbial N flow from the rumen was found to be linearly related to ruminally degraded starch, ruminally degraded neutral detergent fiber (NDF), RDP, and forage NDF intakes; and quadratically related to residual OM intake. More complicated models containing 2- and 3-way interactions among nutrients were also supported by the data. Independent MiN responses to RDP, ruminally degraded starch, and ruminally degraded NDF aligned with the expected responses to each of those nutrients. Nonlinear representations of MiN were found to be inferior to the linear models. Despite using unbiased predictions of RUP and MiN as drivers of AA flows, predictions of Arg, His, Ile, and Lys flow exhibited linear slope bias relative to the observed data, indicating that representations of the AA composition of the proteins may be biased or the observed data are biased. This is an improvement over the NRC (2001) predictions, where bias adjustments were required for all of the essential AA. Despite the bias for 4 AA flows, the revised prediction system was a substantial improvement over the prior work.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".