Animal Production Science Animal Production Science Society
Food, fibre and pharmaceuticals from animals
RESEARCH ARTICLE

Bayesian analysis of energy balance data from growing cattle using parametric and non-parametric modelling

L. E. Moraes A , E. Kebreab A , A. B. Strathe B , J. France C , J. Dijkstra D , D. P. Casper E and J. G. Fadel A F
+ Author Affiliations
- Author Affiliations

A Department of Animal Science, University of California, Davis, CA 95616, USA.

B Department of Veterinary Clinical and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, 1870 Frederiksberg C, Denmark.

C Centre for Nutrition Modelling, Department of Animal and Poultry Science, University of Guelph N1G 2W1, ON, Canada.

D Animal Nutrition Group, Wageningen University, Wageningen, The Netherlands.

E Dairy Science Department, South Dakota State University, Brookings, SD 57007, USA.

F Corresponding author. Email: jgfadel@ucdavis.edu

Animal Production Science 54(12) 2068-2081 https://doi.org/10.1071/AN14535
Submitted: 1 May 2014  Accepted: 24 July 2014   Published: 20 October 2014

Abstract

Linear and non-linear models have been extensively utilised for the estimation of net and metabolisable energy requirements and for the estimation of the efficiencies of utilising dietary energy for maintenance and tissue gain. In growing animals, biological principles imply that energy retention rate is non-linearly related to the energy intake level because successive increments in energy intake above maintenance result in diminishing returns for tissue energy accretion. Heat production in growing cattle has been traditionally described by logarithmic regression and exponential models. The objective of the present study was to develop Bayesian models of energy retention and heat production in growing cattle using parametric and non-parametric techniques. Parametric models were used to represent models traditionally employed to describe energy use in growing steers and heifers whereas the non-parametric approach was introduced to describe energy utilisation while accounting for non-linearities without specifying a particular functional form. The Bayesian framework was used to incorporate prior knowledge of bioenergetics on tissue retention and heat production and to estimate net and metabolisable energy requirements (NEM and MEM, respectively), and the partial efficiencies of utilising dietary metabolisable energy for maintenance (km) and tissue energy gain (kg). The database used for the study consisted of 719 records of indirect calorimetry on steers and non-pregnant, non-lactating heifers. The NEM was substantially larger in energy retention models (ranged from 0.40 to 0.50 MJ/kg BW0.75.day) than were NEM estimates from heat-production models (ranged from 0.29 to 0.49 MJ/kg BW0.75.day). Similarly, km was also larger in energy retention models than in heat production models. These differences are explained by the nature of y-intercepts (NEM) in these two models. Energy retention models estimate fasting catabolism as the y-intercept, while heat production models estimate fasting heat production. Conversely, MEM was virtually identical in all models and approximately equal to 0.53 MJ/kg BW0.75.day in this database.

Additional keywords: energy retention, heat production, maintenance.


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