Prediction of nitrogen use in dairy cattle: a multivariate Bayesian approach
K. F. Reed A E , L. E. Moraes A , J. G. Fadel A , D. P. Casper B , J. Dijkstra C , J. France D and E. Kebreab A
+ Author Affiliations
- Author Affiliations
A Department of Animal Science, University of California, Davis, CA 95616, USA.
B Department of Dairy Science, South Dakota State University, Brookings 57007, SD, USA.
C Animal Nutrition Group, Wageningen University, PO Box 338, 6700 AH Wageningen, The Netherlands.
D Centre for Nutrition Modelling, Department of Animal Nutrition and Poultry Science, University of Guelph, Guelph, Ontario N1G 2W1, Canada.
E Corresponding author. Email: kfreed@ucdavis.edu
Animal Production Science 54(12) 1918-1926 https://doi.org/10.1071/AN14534
Submitted: 1 May 2014 Accepted: 16 June 2014 Published: 2 September 2014
Abstract
Quantification of dairy cattle nitrogen (N) excretion and secretion is necessary to improve the efficiency with which feed N is converted to milk N (ENU). Faecal and urinary N excretion and milk N secretion are correlated with each other and thus are more accurately described by a multivariate model that can accommodate the covariance between the three observations than by three separate univariate models. Further, by simultaneously predicting the three routes of excretion and taking advantage of the mass balance relationships between them, covariate effects on N partitioning from feed to faeces and absorbed N and from absorbed N to milk and urine N and animal ENU can be estimated. A database containing 1094 lactating dairy cow observations collated from indirect calorimetry experiments was used for model development. Dietary metabolisable energy content (ME, MJ/kg DM) increased ENU at a decreasing rate, increased the efficiency with which feed N was converted to absorbed N and decreased the efficiency with which absorbed N was converted to milk N. However, the parameter estimate of the effect of ME on post-absorption efficiency was not different from zero when the model was fitted to a data subset in which net energy and metabolisable protein were at or above requirement. This suggests the effect of ME on post-absorption N use is dependent on the energy status of the animal.
Additional keywords: protein nutrition, modelling.
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