Predicting ruminally undegraded and microbial protein flows from the rumen
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».