Predicted essential fatty acid intakes for a group of dairy cows also apply at individual animal level.


Journal

Animal : an international journal of animal bioscience
ISSN: 1751-732X
Titre abrégé: Animal
Pays: England
ID NLM: 101303270

Informations de publication

Date de publication:
Nov 2023
Historique:
received: 20 04 2023
revised: 21 09 2023
accepted: 28 09 2023
medline: 22 11 2023
pubmed: 29 10 2023
entrez: 28 10 2023
Statut: ppublish

Résumé

The ruminant requirements for essential fatty acids (EFAs), particularly linoleic acid (LA) and alpha-linolenic acid (ALA), have not been fully determined, although evidence suggests that an adequate supply of polyunsaturated fatty acids (FAs) could improve immunity and reproduction in transition cows. In previous studies, we predicted EFA intake for a group of cows based on animal characteristics and milk EFA secretions. However, to support precision livestock feeding, we need to match the nutrient requirements and intakes of each cow as closely as possible. Our group-level predictions may not be accurate enough to estimate the EFA intake of an individual cow, due to inter-individual variations in EFA digestion and metabolism related to differences in feed intake, intake patterns, and the composition and functioning of the rumen microbiota. To address this issue, here we set out to establish specific equations that predict EFA intake for an individual cow based on the difference (i.e. the residuals) between observed EFA intake and the predicted EFA intake based on our group-level equations. We studied a database of individual dairy cows (26 experiments; 503 datapoints from three research teams) and we predicted the residuals from (1) dietary and animal-related factors (i.e. full predictions) and (2) animal-related factors only (i.e. field predictions), which are considered more field-amenable. The variance of predicted LA and log ALA intake was explained to 68% by observed LA intake and 66% by observed log ALA intake, respectively. The residuals of LA intake were predicted by dietary ALA content, total FA intake, BW, milk yield and fat content in full predictions, and by BW, feeding level, milk yield and fat content, and sum of milk C4:0 to C14:0 FA in field predictions. The log residuals of ALA intake were predicted by dietary NDF and total FA contents, NDF intake, BW, milk protein, LA and ALA contents, and fat yield in full predictions, and by BW, DM intake, milk LA and ALA contents, and fat yield in field predictions. The field predictions showed a moderate loss of accuracy compared to full predictions based on RMSE of prediction (from 38 to 54 g/d for LA and from 0.090 to 0.12 log (g/d) for ALA). This work is the first to predict the EFA intake of an individual cow based on previously established group-level predictions of EFA intake adjusted for dietary and animal-related factors.

Identifiants

pubmed: 37897870
pii: S1751-7311(23)00322-1
doi: 10.1016/j.animal.2023.101005
pii:
doi:

Substances chimiques

Fatty Acids, Essential 0
Fatty Acids, Unsaturated 0
Linoleic Acid 9KJL21T0QJ
Fatty Acids 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

101005

Informations de copyright

Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.

Auteurs

P Denis (P)

Université Clermont Auvergne, INRAE, VetAgro Sup, UMR Herbivores, 63122 Saint-Genès-Champanelle, France.

P Schmidely (P)

Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 75005 Paris, France.

P Nozière (P)

Université Clermont Auvergne, INRAE, VetAgro Sup, UMR Herbivores, 63122 Saint-Genès-Champanelle, France.

R Gervais (R)

Département des Sciences Animales, Université Laval, 2425 rue de l'Agriculture, Québec G1V 0A6, Canada.

V Fievez (V)

Faculty of Bioscience Engineering, Laboratory for Animal Nutrition and Animal Product Quality, Ghent University, Ghent, Belgium.

C Gerard (C)

ADM, 56250 Saint-Nolff, France.

A Ferlay (A)

Université Clermont Auvergne, INRAE, VetAgro Sup, UMR Herbivores, 63122 Saint-Genès-Champanelle, France. Electronic address: anne.ferlay@inrae.fr.

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Classifications MeSH