495 Comparison of two internal markers and Near Infrared Spectroscopy (NIRS) for predicting nutrient digestibility in beef cattle offered diets varying in forage quantity and quality
Notice bibliographique
Résumé
Abstract The objective of this study was to evaluate acid detergent lignin (ADL), and amylase-treated ash-corrected undigestible neutral detergent fiber after 240 h of ruminal in vitro incubation (uNDF), and near infrared spectroscopy (NIRS) scanning of feces as methods to estimate the digestibility of diets varying in forage quantity and quality when fed to beef cattle. Five total collection (TC) digestibility studies examined 17 different diets and provided individual fecal samples and the corresponding apparent total tract digestibility of nutrients (n = 229). Feed, orts, and fecal samples were analyzed for dry matter (DM), organic matter (OM), nitrogen (N), amylase-treated ash-corrected neutral detergent fiber (aNDFom), acid detergent fiber (ADF), ADL, and uNDF. Previously developed fecal NIRS digestibility calibrations were expanded with dried and ground samples using a FOSS D3F scanning monochromator (FOSS, Eden Prairie, MN). Marker estimated and NIRS predicted nutrient digestibility coefficients were regressed against those determined by TC and goodness-of-fit statistics were applied. Mean concentrations of ADL and uNDF in diets ranged from 23.4 to 96.4 g/kg DM and 67.7 to 200 g/kg DM, respectively, with mean fecal recoveries of 94.2% (SD ± 15.7%) for ADL and 87.5% (SD ± 11.1%) for uNDF. Regression fit statistics between NIRS and TC were not different from one (P > 0.05), with R2 > 0.83 except for ADF digestibility (R2 = 0.63). In comparison, regression statistics between internal markers and TC were poorer with R2 ranging from 0.21 to 0.78. Concordance correlation coefficients (CCC) between NIRS and TC were greater than 0.90 for DM, OM, N, and aNDFom digestibility, and 0.77 for ADF digestibility. The CCC between ADL and TC ranged between 0.63 and 0.82, and between uNDF and TC from 0.26 to 0.78. Correction bias (Cb) was high for all parameters (Cb > 0.76) except for ADF digestibility (Cb = 0.58) as estimated by uNDF. The mean square error of prediction (MSEP) for NIRS predicted digestibility were lower (< 10.7%) than those estimated by internal markers, and most of the error was attributed to random bias (> 97.9%). Random bias for ADL was also greater (>76.1%); however, for uNDF the error was more evenly partitioned into mean bias (46.4% to 49.4%) and random bias (30.4 to 41.9%). Digestibility predictions from NIRS scanning of feces were more accurate and precise than when estimated using the internal markers ADL and uNDF. In the absence of NIRS, ADL estimations appear to be more precise and accurate than uNDF for forage-based diets, with uNDF possibly of greater value for estimating the digestibility of high concentrate diets.
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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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 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 ».