An evaluation of the accuracy and precision of methane prediction equations for beef cattle fed high-forage and high-grain diets
Notice bibliographique
Résumé
The study determined the performance of equations to predict enteric methane (CH4) from beef cattle fed forage- and grain-based diets. Many equations are available to predict CH4 from beef cattle and the predictions vary substantially among equations. The aims were to (1) construct a database of CH4 emissions for beef cattle from published literature, and (2) identify the most precise and accurate extant CH4 prediction models for beef cattle fed diets varying in forage content. The database was comprised of treatment means of CH4 production from in vivo beef studies published from 2000 to 2015. Criteria to include data in the database were as follows: animal description, intakes, diet composition and CH4 production. In all, 54 published equations that predict CH4 production from diet composition were evaluated. Precision and accuracy of the equations were evaluated using the concordance correlation coefficient (r c ), root mean square prediction error (RMSPE), model efficiency and analysis of errors. Equations were ranked using a combined index of the various statistical assessments based on principal component analysis. The final database contained 53 studies and 207 treatment means that were divided into two data sets: diets containing ⩾400 g/kg dry matter (DM) forage (n=116) and diets containing ⩽200 g/kg DM forage (n=42). Diets containing between ⩽400 and ⩾200 g/kg DM forage were not included in the analysis because of their limited numbers (n=6). Outliers, treatment means where feed was fed restrictively and diets with CH4 mitigation additives were omitted (n=43). Using the high-forage dataset the best-fit equations were the International Panel on Climate Change Tier 2 method, 3 equations for steers that considered gross energy intake (GEI) and body weight and an equation that considered dry matter intake and starch:neutral detergent fiber with r c ranging from 0.60 to 0.73 and RMSPE from 35.6 to 45.9 g/day. For the high-grain diets, the 5 best-fit equations considered intakes of metabolisable energy, cellulose, hemicellulose and fat, or for steers GEI and body weight, with r c ranging from 0.35 to 0.52 and RMSPE from 47.4 to 62.9 g/day. Ranking of extant CH4 prediction equations for their accuracy and precision differed with forage content of the diet. When used for cattle fed high-grain diets, extant CH4 prediction models were generally imprecise and lacked accuracy.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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 tête enseignante, 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 ».