Evaluating the ability of a lifetime nutrient-partitioning model for simulating the performance of Australian Holstein dairy cows
H. N. Phuong A G , N. C. Friggens C D , O. Martin C D , P. Blavy C D , B. J. Hayes A F , W. J. Wales E and J. E. Pryce A B
+ Author Affiliations
- Author Affiliations
A Department of Economic Development, Jobs, Transport and Resources, Agribio, 5 Ring Road, Bundoora, Vic. 3083, Australia.
B School of Applied Systems Biology, La Trobe University, Bundoora, Vic. 3083, Australia.
C INRA UMR 0791 Modélisation Systémique Appliquée aux Ruminants, 16 Rue Claude Bernard, Paris, France.
D AgroParisTech UMR 0791 Modélisation Systémique Appliquée aux Ruminants, 16 Rue Claude Bernard, Paris, France.
E Department of Economic Development, Jobs, Transport and Resources, Ellinbank, Vic. 3820, Australia.
F Queensland Alliance for Agriculture and Food Innovation, Centre for Animal Science, University of Queensland, Qld 4072, Australia.
G Corresponding author. Email: phuong.ho@ecodev.vic.gov.au
Animal Production Science 57(7) 1563-1568 https://doi.org/10.1071/AN16452
Submitted: 15 July 2016 Accepted: 12 April 2017 Published: 12 May 2017
Abstract
The present study determined the ability of a lifetime nutrient-partitioning model to simulate individual genetic potentials of Australian Holstein cows. The model was initially developed in France and has been shown to be able to accurately simulate performance of individual cows from various breeds. Generally, it assumes that the curves of cow performance differ only in terms of scaling, but the dynamic shape is universal. In other words, simulations of genetic variability in performance between cow genotypes can be performed using scaling parameters to simply scale the performance curves up or down. Validation of the model used performance data from 63 lactations of Australian Holstein cows offered lucerne cubes plus grain-based supplement. Individual cow records were used to derive genetic scaling parameters for each animal by calibrating the model to minimise root mean-square errors between observed and fitted values, cow by cow. The model was able to accurately fit the curves of bodyweight, milk fat concentration, milk protein concentration and milk lactose concentration with a high degree of accuracy (relative prediction errors <5%). Daily milk yield and weekly body condition score were satisfactorily predicted, although slight under-predictions of milk yield were identified during the last stage of lactation (relative prediction errors ≈11.1–15.6%). The prediction of feed intake was promising, with the value of relative prediction error of 18.1%. The results also suggest that the current recommendation of energy required for maintenance of pasture-based cows might be under-estimated. In conclusion, this model can be used to simulate genetic variability in the production potential of Australian cows. Thus, it can be used for simulation of consequences of future genetic-selection strategies on lifetime performance and efficiency of individual cows.
Additional keywords: genetic variability, model validation, production potential.
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