Projects / Programmes source: ARIS

Comparasion of statistical models for longitudinal data in animal breeding

Research activity

Code Science Field Subfield
4.02.00  Biotechnical sciences  Animal production   

Code Science Field
B006  Biomedical sciences  Agronomics 
B400  Biomedical sciences  Zootechny, animal husbandry, breeding 
longitudinal data, test-day models, random regression, growth, fertility, milk traits, animal breeding
Evaluation (rules)
source: COBISS
Researchers (11)
no. Code Name and surname Research area Role Period No. of publicationsNo. of publications
1.  05096  MSc Marko Čepon  Animal production  Researcher  2004 - 2007  458 
2.  24798  Darja Čop  Animal production  Researcher  2005 - 2007  117 
3.  05098  PhD Peter Dovč  Biotechnology  Researcher  2004 - 2007  935 
4.  02977  PhD Franc Habe  Animal production  Researcher  2004  264 
5.  08405  PhD Marija Klopčič  Animal production  Researcher  2004 - 2007  721 
6.  20025  PhD Andreja Komprej  Animal production  Researcher  2004 - 2007  145 
7.  09755  PhD Milena Kovač  Animal production  Head  2004 - 2007  1,215 
8.  20790  MSc Jurij Krsnik  Animal production  Researcher  2004 - 2007  171 
9.  19045  PhD Špela Malovrh  Animal production  Researcher  2004 - 2007  895 
10.  14933  PhD Dušan Terčič  Animal production  Researcher  2004 - 2007  212 
11.  17514  Irena Ule    Technical associate  2004 - 2007  423 
Organisations (1)
no. Code Research organisation City Registration number No. of publicationsNo. of publications
1.  0481  University of Ljubljana, Biotechnical Faculty  Ljubljana  1626914  66,306 
Production functions will be studied for growth traits in pigs and cattle, fertility traits in pigs and small ruminants, and milk traits in cattle and small ruminants. Longitudinal data for mentioned production traits will be modelled by single and multiple trait models as well as random regression models. Furthermore, multiple trait analyses will be applied to more than one trait measured subsequently as for milk traits. In cattle, growth traits described by random regression models will be joined with a set of carcass traits previously selected among traits obtained by carcass dissection. Fertility will be described mainly by litter size changing over parities. Production functions for observed traits are different. Measurements for growth in cattle are taken at different ages, while pigs are weighed six times at well determined ages. Lactation curves in small ruminants have missing observations before weaning of youngs, genetic evaluation is suppose to be ready before mating season and before lactation finishes. In general, test-days are not missing in cattle. Besides changes of overall production level, changes in (co)variance components will be observed over time. The models with random regressions are expected to describe changes of production over time better than other models. At the same time, it allows extraction of genetic values at different production stage. Genetic values may express overall level and rate of change like persistency. Test of animals may be simplified, data records may come from different sources, no precorrections for common age or weight are needed. The importance of regression coefficient will be determined by eigenvalues analyses and tested by appropriate statistical tests. Fixed part of the model will be suggested on the basis of Least Square Methods in SAS, while evaluation of dispersion parameters will be done by VCE 5. PEST will be used for appropriate coding and in part, for genetic evaluation.
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