Predicting breeding values in animals by kalman filter: application to body condition scores in dairy cattle
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
The aim of this study was to investigate usefulness of Kalman Filter (KF) Random Walk methodology (KF-RW) for prediction of breeding values in animals. We used body condition score (BCS) from dairy cattle for illustrating use of KF-RW. BCS was measured by Swiss Holstein Breeding Association during May 2004-March 2005 for 7 times approximately at monthly intervals from dairy cows (n=80) stationed at the Chamau research farm of Eidgenössische Technische Hochschule (ETH), Switzerland. Benefits of KF were demonstrated using random walk models via simulations. Breeding values were predicted over days in milk for BCS by KF-RW. Variance components were predicted by Gibbs sampling. Locally weighted scatter plot smoothing (LOWESS) and KF-RW were compared under different longitudinal experimental designs, and results showed that KF-RW gave more reasonable estimates especially for lower smoother span of LOWESS. Estimates of variance components were found more accurate when the number of observations and number of subjects increased and increasing these quantities decreased standard errors. Fifty subjects with 10 observations each, started to give reasonable estimates. Posterior means for variance components were found (with standard errors) 0.03 (0.006) for animal genetic variance 0.04 (0.007) for permanent environmental variance and 0.21 (0.02) for error variance. Since KF gives online estimation of breeding values and does not need to store or invert matrices, this methodology could be useful in animal breeding industry for obtaining online estimation of breeding values over days in milk.
|Tidsskrift||Kafkas Universitesi Veteriner Fakultesi Dergisi|
|Status||Udgivet - 2012|
- Det tidligere LIFE - Kalman filter, Body condition score, Bayesian methods