In recent years, in order to estimate farm animals breeding value, more and more attention is paid to the method of BLUP (Best Linear Unbiased Prediction). The essence of this method is using of statistical adjustment for the impact of identifiable factors.
The main purpose of this study is a comparative analysis of various algorithms of linear models to obtain reliable estimates of breeding values (EBV) of animals.
As the material for the study were used zootechnical data for 113 cows of Russian Red Steppe Breed, which were keeping in State Enterprise “Plemreproductor “Stepove” (Mykolayiv region) during the 2001-2014 years. As a dependent variable were used data of the milk yield for 305 days of the first lactation.
As randomized factors was used bull-producer’s genotype. As fixed factors – year of birth, month of calving and season of calving. Finally, as the covariance were used weight in different stages of postnatal growth, exterior measurements and the age of the first effective insemination.
It was established, that the model, which includes the effect of bulls’ genotype, as well as year of cows’ birth, unfortunately, gives unsatisfactory results. Although, when considered separately, both these factors seems to has highly likely impact. So, the assessment proved to be very sensitive to the imbalance in the using of different bulls at different times.
Using exterior indicators makes only an insignificant effect in the adjustment of estimates of breeding value. The most significant effect on the estimates of breeding value of bulls had the weight of cows at the age of first lactation and the age of the first effective insemination – in this case the EBV accuracy increases by almost in a half (from 18.3% to 32.6%).
Key words: estimated breeding value (EBV), Linear models (BLUP), dairy cow.
Estimation of the dairy cow’s breeding values using the linear models (BLUP).
Ключевое название (Key title):
Vìsnik agrarnoï nauki Pričornomor'â (Online)
Сокращенное ключевое название (Abbreviated key title):
Vìsn. agrar. nauki Pričornomor'â (Online)
Параллельное название (Parallel title):
Ukrainian Black Sea region agrarian science