Comparasion of statistical models for longitudinal data in animal breeding

Code

J4-6191 (C) - included in ARIS records

Head

PhD Milena Kovač

Period

7/1/2004 - 6/30/2007

Range in 2007

0.29 FTE

Science

Biotechnical sciences (10)
Other (1)

Reseacher status

Researcher (10)
Junior expert or technical associate (1)

Education

Doctor's degree (7)
Master's degree (2)
Other (2)

Sex

Woman (6)
Man (5)

Status

Employed at RO and RRD (7)
No data on employment in RO (3)
Retired (1)

No. of publications

100–999 (10)
1,000–9,999 (1)

Projects / Programmes source: ARIS

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

Keywords

longitudinal data, test-day models, random regression, growth, fertility, milk traits, animal breeding

Evaluation (rules)

source: COBISS

Evaluation of bibliographic research performance indicators according to ARIS methodology

Citations Citations for bibliographic records in COBIB.SI that are linked to records in citation databases

source: WoS

source: Scopus

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	459
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	724
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	172
9.	19045	PhD Špela Malovrh	Animal production	Researcher	2004 - 2007	895
10.	14933	PhD Dušan Terčič	Animal production	Researcher	2004 - 2007	213
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	66,847

Abstract

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.