Multi-trait and multi-source selection indices for milk production and reproductive traits in a herd of Holstein cattle in Egypt - Eslam Faid-Allah (Jurnal Ilmu Ternak dan Veteriner, 2015)
The main aim of this study was explore possibility to improve milk production and reproductive traits of Holstein cattle via selection index method which include general, reduced, sub and Multi-source of information indices (Own-Performance, Full-Sibs and Half-Sibs). Data was obtained from a commercial farm (Safi Masr for Developing the Animal Resources), located in the Nile Delta, Dakahlia, Egypt. Data included 4791 records of 1797 cows, 794 dams and 67 sires that represented the period from 2002 to 2012. Estimates of genetic and phenotypic parameters for studied traits were computed and used to construct 18 selection indices to improve milk production and reproductive traits. Full index incorporating milk yield at 305d (305-dMY), lactation period (LP), days open (DO) and age at first calving (AFC) had the highest correlation with aggregate breeding value (Rih = 0.518; RE=100%). The correlation fell to 0.455 when 305-dMY was omitted from the index. The general index has the maximum expected genetic gain in 305-dMY (132.6 kg) per generation were accompanied by decrease of LP (-4.679 day), DO (-3.449 day) and AFC (-1.41 month) when all four traits were included in the index (I 1). The expected genetic gain for 305-dMY decreased to 26.84 kg/generation when 305-dMY was excluded in index 5 (I5). In addition, Using multi-source of information will enhance correlation with aggregate breeding value (Rih= 0.740; RE=142.91%) and raised the expected genetic gain per generation for 305-dMY (209 kg) and decreasing the expected genetic gain for LP (-6.37 day), DO (-4.244 day) and AFC (1.843 month) when all four traits were included in the index (I16). It could be suggested using the higher indexes of Rih (I1 (RE=100)) to improve milk production and reproductive traits in Holstein cattle under own-performance strategy and using (I16 (RE=142.91)) under multi-source strategy to get high accuracy and higher expected genetic changes per generation compare to general index.