Detection of bias in animal model pedigree indices of heifers

The effect of an upgraded Finnish evaluation model on bias in estimated breeding values for protein yield was investigated. Evaluations based on repeatability animal model and on random regression test-day model without and with heterogeneous variance adjustment were compared. Comparisons were based on the average difference between pedigree indices and the future estimated breeding values, based on own or on daughter performance records. This was defined as empirical bias. The pedigree indices were computed from reduced data sets where four years of the most recent data were excluded. Results showed an upward bias in the protein yield pedigree indices for Ayrshire young sires of 2.2 kg, 2.5 kg and 1.8 kg from the repeatability animal model, random regression test-day model and random regression test-day model with heterogeneous variance adjustment, respectively. Pedigree indices for daughters of young sires were upward biased, whereas pedigree indices for daughters of proven sires were slightly underestimated when heterogeneous variance was not accounted. Inclusion of test-day yields from the fourth lactation onwards increased the bias. Moving from repeatability animal model to random regression test-day model did not reduce the bias, whereas adjustment of heterogeneous variance reduced bias.

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bias in genetic evaluation using random regression test-day model

Lidauer¹,

Mäntysaari²

2008

“…The thrust of research has been on estimating the (co)variance components of milk production traits (Mäntysaari and Van Vleck 1989, Juga 1992, Pösö and Mäntysaari 1996 a, b) and developing evaluation procedures for production traits, replacing the sire-model (Syväjärvi et al 1983) with the animal model (AM) (Strandén and Mäntysaari 1992). The latter was taken into routine use in Finland as one of the first countries in the world, in 1990.After implementation of theAM in practice the statistical model has been studied carefully to reduce the bias originating from preferential treatment of bull dams (Uimari andMäntysaari 1995, Lidauer andMäntysaari 1996).…”

Section: Prediction Of Breeding Valuesmentioning

confidence: 99%

“…Ruane and Thompson 1991,Strandén et al 1991,Woolliams 1989 or on maximising the genetic gain while constraining the increase in inbreeding rate (Meuwissen 1997). No country has based its dairy cattle breeding solely on a MOET scheme, but many nucleus breeding programmes have been set up to accomplish AI-breeding schemes, here in Finland too, where we have moved from a decentralised scheme (Mäntysaari et al 1996) to an open centralised nucleus scheme. Nucleus breeding schemes are expected to increase the genetic response in a breeding scheme by better controlled management of animal selection, a shorter generation interval and future prospects of including marker assisted selection (MAS) or other biotechnological methods in the pro- Table 2.…”

Section: Genetic Response In a Large Dairy Cattle Breeding Schemementioning

confidence: 99%

The key elements for genetic response in Finnish dairy cattle breeding

Juga

Voutilainen

1998

This paper reviews some key elements of Finnish animal breeding research contributing to the Finnish dairy cattle breeding programme and discusses the possibilities and problems in collecting data for genetic evaluation, prediction of breeding values both within and across countries, estimation of the economic value of important traits, and selection of bulls and cows. Economic values are calculated for fertility, udder health and production traits when one genetic standard deviation unit (gen. sd.) is changed in each trait independently and the financial returns from selection response in the Finnish dairy cattle breeding programme are estimated. The following components were used to calculate the economic value of mastitis treatments: 1) cost of mastitis including discarded milk and treatment costs, 2) reduction in milk price due to higher somatic cell count, 3) replacement costs and 4) lower production level of the herd due to involuntary culling of cows because of udder problems. A high somatic cell count lowers the price of milk and eventually leads to involuntary culling. For treatments for fertility disorders the following costs were included: 1) treatment costs 2) higher replacement costs and 3) decreased milk production in the herd. Days open included the following costs: 1) extra insemination, 2) reduced annual milk yield and 3) fewer calves born. Animal breeding was found to be a very cost effective investment, yielding returns of FIM 876.9 per cow from one round of selection when the gene flow was followed for over 25 years in the Finnish dairy cattle breeding programme.

“…Estimated breeding values of dairy cows are based on 305 day production. They are estimated using a statistical model (animal model) which in Finland includes the most important environmental effects such as herd-year, interaction between calving year and calving month, and calving age by days open effects within lactation number (Lidauer and Mäntysaari 1996). The largest variation in records is due to herd effects.…”

Section: Introductionmentioning

confidence: 99%

Concentrate feeding and milk yield based on the field data of milk recorded herds

Kaustell¹,

Mäntysaari²,

Huhtanen³

1998

Field data from 16 051 Finnish milk recorded herds including milk yield (MY), feed consumption, feed analyses, and the herd effect for milk yield (HMILK) obtained from the national breeding value estimation program, were analysed to detect the influence of concentrate feeding on milk production. HMILKs are deviations from the average national level with mean of 45 kg and SD of 722 kg. Mean MY was 6917 kg and mean dry matter intake (DMI) 5679 kg per cow per year. The effect of concentrate feeding on HMILK and MY was studied by using quantitative [amount of energy (FUI) and concentrates (CI) in the diet] and qualitative [proportion of grain (Gc) or compound feed (Mc) in concentrates or CP content (CPc) of concentrates] diet characteristics as dependent variables in multivariate regression analysis. The general linear effect of CI was 1.18 kg MY/kg CI. Production response of CI decreased with increasing CI as indicated by significant interactions between CI and CI classes. Gc showed a negative relationship with HMILK, but CPc proved to be a more important factor affecting HMILK. Feeding grain instead of compound feed was connected with too low protein content in concentrates. Mc was positively correlated with CP content of concentrates. However, the use of compound feed appeared to give a slight increase in HMILK even after accounting for the effect of CP. ;