Evaluating the ability of a lifetime nutrient-partitioning model for simulating the performance of Australian Holstein dairy cows

Phuong, H. N., Friggens, N. C., Martin, O., Blavy, P., Hayes, B. J., Wales, W. J. and Pryce, J. E. (2017) Evaluating the ability of a lifetime nutrient-partitioning model for simulating the performance of Australian Holstein dairy cows. Animal Production Science, 57 7: 1563-1568. doi:10.1071/AN16452


Author Phuong, H. N.
Friggens, N. C.
Martin, O.
Blavy, P.
Hayes, B. J.
Wales, W. J.
Pryce, J. E.
Title Evaluating the ability of a lifetime nutrient-partitioning model for simulating the performance of Australian Holstein dairy cows
Journal name Animal Production Science   Check publisher's open access policy
ISSN 1836-5787
1836-0939
Publication date 2017-05-01
Sub-type Article (original research)
DOI 10.1071/AN16452
Open Access Status Not yet assessed
Volume 57
Issue 7
Start page 1563
End page 1568
Total pages 6
Place of publication Clayton, VIC, Australia
Publisher C S I R O Publishing
Language eng
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.
Keyword Genetic variability
Model validation
Production potential
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Queensland Alliance for Agriculture and Food Innovation
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Scopus Citation Count Cited 0 times in Scopus Article
Google Scholar Search Google Scholar
Created: Mon, 26 Jun 2017, 01:00:43 EST by Web Cron on behalf of Learning and Research Services (UQ Library)