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Journal of Animal Science Abstract - Animal Genetics

Towards an improved estimation of the biological components of residual feed intake in growing cattle1


This article in JAS

  1. Vol. 92 No. 2, p. 467-476
    Received: July 12, 2013
    Accepted: Nov 20, 2013
    Published: November 24, 2014

    2 Corresponding author(s):

  1. D. Savietto*†‡§,
  2. D. P. Berry 2 and
  3. N. C. Friggens‡§
  1. Institute for Animal Science and Technology, Universitat Politècnica de València, Camino de Vera s/n 46022 Valencia, Spain
    Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Co. Cork, Ireland
    INRA, UMR0791 Modélisation Systémique Appliqué aux Ruminants, 16 rue Claude Bernard 75231 Paris, France; and
    AgroParisTech, UMR0791 Modélisation Systémique Appliqué aux Ruminants, 16 rue Claude Bernard 75231 Paris, France


Residual feed intake (RFI) is the difference between observed and predicted feed intake. It is calculated as the residuals from a multiple regression model of DMI on the various energy expenditures (e.g., maintenance, growth, activity). Residual feed intake is often cited to be indicative of feed efficiency differences among animals. However, explaining a large proportion of the (phenotypic and genetic) interanimal variation in RFI remains difficult. Here we first describe a biological framework for RFI dwelling on similarities between RFI and energy balance. Alternative phenotypic and genetic statistical models are subsequently applied to a dataset of 1,963 growing bulls of 2 British and 3 Continental breeds. The novel aspect of this study was the use of a mixed model framework to quantify the heritable interanimal variation in the partial regression coefficients on the energy expenditure traits within the RFI equation. Heritable genetic variation in individual animal regression coefficients for metabolic live weight existed. No significant genetic variation in animal-level regression coefficients for growth or body fat level, however, existed in the study population. The presence of genetic variation in the partial regression coefficient of maintenance suggests the existence of interanimal variation in maintenance efficiency. However, it could also simply reflect interanimal genetic variation in correlated energy expenditure traits not included in the statistical model. Estimated breeding values for the random regression coefficient could be useful phenotypes in themselves for studies wishing to elucidate the underlying mechanisms governing differences among animals in RFI.

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