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Milk metabolites can characterise individual differences in animal resilience to a nutritional challenge in lactating dairy goats
Institution:1. INRA UMR 791, Modélisation Systémique Appliquée aux Ruminants (MoSAR), Paris, France;2. GABI, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France;3. Department of Animal Science, Aarhus University, Tjele, Denmark;4. GenPhySE, Université de Toulouse, INRA, INPT, ENVT, Castanet Tolosan, France
Abstract:The aim of this study is built in two phases: to quantify the ability of novel milk metabolites to measure between-animal variability in response and recovery profiles to a short-term nutritional challenge, then to derive a resilience index from the relationship between these individual variations. At two different stages of lactation, sixteen lactating dairy goats were exposed to a 2-d underfeeding challenge. The first challenge was in late lactation, and the second was carried out on the same goats early in the following lactation. During the entire experiment period, samples were taken at each milking for milk metabolite measures. For each metabolite, the response profile of each goat was characterised using a piecewise model for describing the dynamic pattern of response and recovery profiles after the challenge relative to the start of the nutritional challenge. Cluster Analysis identified three types of response/recovery profiles per metabolite. Using cluster membership, multiple correspondence analyses (MCAs) were performed to further characterise response profile types across animals and metabolites. This MCA analysis identified three groups of animals. Further, discriminant path analysis was able to separate these groups of multivariate response/recovery profile type based on threshold levels of three milk metabolites: β-hydroxybutyrate, free glucose and uric acid. Further analyses were done to explore the possibility of developing an index of resilience from milk metabolite measures. Different types of performance response to short-term nutritional challenge can be distinguished using multivariate analyses of a panel of milk metabolites.
Keywords:Adaptability  Clustering method  Perturbations  Resilience index  Stress biomarkers
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