Status as of: 2010-12-01

Form GE

DESCRIPTION OF NATIONAL GENETIC EVALUATION SYSTEMS

Country (or countries)	Latvia	
Main trait group1
NOTE! Only one trait group per form!	Production	
Breed(s)	Red cattle (RDC), Holstein Black&White and Holstein Red&White (HOL)	
Trait definition(s) and unit(s) of measurement2
Attach an appendix if needed	Milk (kg), fat (kg), protein (kg), fat (%), protein (%) on 24-hour daily basis	
Method of measuring and collecting data	All production data collected using ICAR certified milk recording methods	
Time period for data inclusion	All test day records since 1996	
Age groups (e.g. parities) included	First three lactations	
Other criteria (data edits) for inclusion of records	Age at 1st calving 20 – 40 months
Age at 2nd calving 30 – 56 months
Age at 3rd calving 40 – 75 months
All tests between 5 and 330 DIM
Milk (kg) between 3.0 and 90.0
Fat (%) between 1.5 and 9.0
Protein (%) between 1.0 and 7.0	
Criteria for extension of records (if applicable)	No extension of records required	
Sire categories	All	
Environmental effects3, pre-adjustments 	No pre-adjustments	
Method (model) of genetic evaluation3	ST – ML – RR – TD – BLUP – AM	
Environmental effects3 in the genetic evaluation model	Fixed: herd – test day effect within 1st lactation and combined 2nd and 3rd lactation, fixed lactation curves defined by lactation number * breed * calving age class * calving season * calving interval class with a total of 1350 different curves
Random: permanent environmental effect of a cow within lactation	
Adjustment for heterogeneous variance in evaluation model	Standardization of heterogeneous variances within herd – test date – parity effect is performed prior to solving mixed model equations	
Use of genetic groups and relationships	Genetic groups are defined for unknown parents of animals based on breed, sex, year of birth of the animals	
Blending of foreign/Interbull information in evaluation	–	
Genetic parameters in the evaluation	see PAGE 4 for heritability/genetic variance estimates; for multiple-trait genetic evaluations, provide genetic correlation estimates between traits separately	
System validation	Checks on data quality (raw data, pedigree information, etc.) and results (changes in EBV between evaluations, stability of EBV, etc.)	
Expression of genetic evaluations
If standardised (e.g. RBV), give standardisation formula in the appendix	EBV (milk kg, fat kg, protein kg, fat %, protein %)	
Definition of genetic reference base
Next base change	EBV: all cows born 2005

EBV: august 2015 with the base population including all cows born in 2010	
Calculation of reliability	Using multiple trait effective daughter contribution method	
Criteria for official publication of evaluations	50% reliability with daughters in at least 10 herds	
Number of evaluations / publications per year	3 / March, August, December	
Use in total merit index4	Productivity index = 40% milk, 10% fat kg, 50% protein kg	
Anticipated changes in the near future	–	
Key reference on methodology applied	Liu, Z., Reinhardt, F., and Reents, R. 2000. Estimating parameters of a random regression test day model for first three lactation milk production traits using the covariance function approach. Interbull Bulletin 25: 74 – 80.
Liu, Z., Reents, R., Reinhardt, F., and Kuwan, K. 2000. Approaches to estimating daily yield from single milking testing schemes and use of a.m. – p.m. records in test-day model genetic evaluation in dairy cattle. J. Dairy Sci. 83: 2672 – 2682.
Liu, Z., Reinhardt, F., Bünger, A., Dopp, L., and Reents, R. 2001. Application of a random regression model to genetic evaluation of test day yields and somatic cell scores in dairy cattle. Interbull Bulletin 27: 159 – 166.
Liu, Z., Reinhardt, F., and Reents, R. 2001. The effective daughter contribution concept applied to multiple trait models for approximating reliability of estimated breeding values. Interbull Bulletin 27: 41 – 47.
Reents, R., Dopp, L., Schmutz, M., and Reinhardt, F. 1998. Impact of application of a test day model to dairy production traits on genetic evaluations of cows. Interbull Bulletin 17: 49 – 54.
Reinhardt, F., Liu, Z., Bünger, A., Dopp, L., and Reents, R. 2002. Impact of application of a random regression test day model to production trait genetic evaluation in dairy cattle. Interbull Bulletin 29: 103 – 107.	
Key organisation: name, address, phone, fax, e-mail, web site	State agency ‘Agricultural Data Centre’
Citadeles iela 3
Riga, LV–1010, LATVIA
Phone: +371 67027241
Fax: +371 67027006
e–mail: ldc@ldc.gov.lv
web site: http://www.ldc.gov.lv 	
1) Either: Production (e.g. milk, fat, protein), Conformation, Health (e.g. mastitis resistance, milk somatic cell, resistance to diseases other than mastitis), Longevity, Calving (e.g. stillbirth, calving ease), Female fertility (e.g. non-return rate, interval between reproductive events, number of AI’s, heat strength), Workability (e.g. milking speed, temperament), Beef production, Efficiency (e.g. body weight, energy balance, body conditioning score), or Other traits.
2) Indicate frequencies per category if the trait is categorical and specify transformation of data if practiced.
3) Use abbreviations for most common effects (see document with list of abbreviations at http://www-interbull.slu.se/service_documentation/General/list_of_abbreviations.rtf) and indicate random (R) or fixed (F).
4) Please give economic weights and indicate how they are expressed (preferably in genetic standard deviation units).

Form GE	Appendix GE

Parameters used in genetic evaluation

Country (or countries):	Latvia	
Main trait group:	Production	
Breed (repeat as necessary):	Red cattle (RDC), Holstein Black&White and Holstein Red&White (HOL)	

Trait	Definition	ITBa	h2b	genetic
varianceb	official proof
standardisation formulac	
Milk yield		x	0.49	314989		
1st lactation			0.53	350378		
2nd lactation			0.35	321393		
3rd lactation			0.34	352384		
Fat yield		x	0.48	534		
1st lactation			0.52	525		
2nd lactation			0.36	541		
3rd lactation			0.36	651		
Protein yield		x	0.48	280		
1st lactation			0.51	295		
2nd lactation			0.38	293		
3rd lactation			0.38	335		

						

a	Indicate, with X, traits that are submitted to Interbull for international genetic evaluations.
b	If repeated records are treated as separate traits, provide heritability estimates and genetic variances separately for each trait, as well as for all traits pooled, i.e. for the trait submitted to Interbull.
c	Expressed as follows:
StandEval=((eval-a)/b)*c+d where a=mean of the base adjustment, b=standard deviation of the base, c=standard deviation of expression (include sign if scale is reversed), and d=base of expression.

Form GE	Appendix PR

Parameters for national genetic evaluations for production traits as provided to Interbull

Country (or countries):	Latvia	
Main trait group:	Production	
Breed(s):	Red cattle (RDC), Holstein Black&White and Holstein Red&White (HOL)	

Trait	h2a	genetic
variancea	official proof
standardisation formulab	
Milk yield:
	0.49	31489		
Fat yield:
	0.48	534		
Protein yield:
	0.48	280		
a	If lactations, or part of lactations, are treated as separate traits, provide heritability estimates and genetic variances separately for each lactation, as well as for all lactations pooled, i.e. for the trait submitted to Interbull.
b	Expressed as follows:
StandEval=((eval-a)/b)*c+d where a=mean of the base adjustment, b=standard deviation of the base, c=standard deviation of expression (include sign if scale is reversed), and d=base of expression.

Table 1: Genetic parameters for milk, fat and protein yield on 305-day basis with heritabilities on diagonal, genetic correlations above diagonal and phenotypic correlations below diagonal
Trait	Lactation	Genetic
variance (kg2)	Lactation	
			1st	2nd	3rd	
Milk yield	1st	350378	.53	.84	.84	
	2nd	321393	.55	.35	.97	
	3rd	352834	.52	.54	.34	
Fat yield	1st	525	.52	.88	.87	
	2nd	541	.54	.36	.97	
	3rd	651	.50	.53	.36	
Protein yield	1st	295	.51	.86	.84	
	2nd	293	.62	.38	.96	
	3rd	335	.57	.64	.38