Article

Application of an Autoregressive Process to Estimate Genetic Parameters and Breeding Values for Daily Milk Yield in a Tropical Herd of Lucerna Cattle and in United States Holstein Herds

Department of Animal Science, Cornell University, Ithaca 14853, NY, USA.
Journal of Dairy Science (Impact Factor: 2.57). 11/1998; 81(10):2738-51. DOI: 10.3168/jds.S0022-0302(98)75831-X
Source: PubMed

ABSTRACT

The objectives of this study were to estimate from test day records the genetic and environmental (co)variance components, correlations, and breeding values to increase genetic gain in milk yield of Lucerna and US Holstein cattle. The effects of repeated observations (within cow) were explained by first-order autoregressive processes within and across lactations using an animal model. Estimates of variance components and correlation coefficients between test days were obtained using derivative-free REML methodology. The autoregressive structure significantly reduced the model error component by disentangling the short-term environmental effects. The additional information and the more heterogeneous environmental variances between lactations in the multiple-lactation test day model than in the first lactation model provided substantially larger estimates of additive genetic variance (0.62 kg2 for Lucerna; 14.73 kg2 for Holstein), heritability (0.13 for Lucerna; 0.42 for Holstein), and individual genetic merit. Rank correlations of breeding values from multiple lactations and from first lactations ranged from 0.18 to 0.37 for females and from 0.73 to 0.89 for males, respectively. Consequently, more selection errors and less genetic gain would be expected from selection decisions based on an analysis of first lactation only, and greater accuracy would be achieved from multiple lactations. Results indicated that substantial genetic gain was possible for milk yield in the Lucerna herd (34 kg/yr). Estimates of genetic variance for Holsteins were larger than previously reported, which portends more rapid genetic progress in US herds also; under our assumptions, increases would be from 173 to 197 kg/yr.

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    • "This method of genetic evaluation is almost grasped worldwide instead of classical 305 day based genetic evaluation. Test day model has some advantages in respect to 305 day based method, amongst them are: computation and considering environmental factors that could affect the performance of cows throughout the lactation (Ptak and Schaeffer, 1993), increasing accuracy of genetic evaluation due to increasing the volume of data per animal (Carvalheira et al., 1998), lack of need to extend uncompleted milking period and reducing the cost of recording (Bilal and Khan, 2009). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Genetic parameters of milk, protein and fat yield traits in Iranian Holstein dairy cattle were predicted using records gathered in Iran Breeding Centre during 2005 to 2010 across 79 production farms. Information included 276962 test day (TD) records belonged to 30729 primiparous dairy cows. Estimation with the assumption of residual variance homogeneity for whole lactation period was accomplished using restricted maximum likelihood (REML) method in the framework of multiple-trait random regression animal model. Estimated heritability for 305 day milk fat and protein yield showed almost similar results (0.32 to 0.33). For milk yield, predicted heritability as function of days in milk (DIM) resulted in higher value for the middle of DIM than both onset and end of milk production periods. Within traits, estimated correlations for consecutive TD records were higher and with increasing time interval among TD records. It became downwardly lower. Correlations between traits were calculated from covariance components for curve parameters estimated in multi-trait analysis. The range of genetic and permanent environment correlations across traits for 305 day production resulted in 0.76 to 0.93 and 0.87 to 0.97, respectively. The highest and lowest aforementioned parameters were observed among milk protein and milk fat yield, respectively. To cite this article: Ghaderi-Zefrehei M, Kh Kheirabadi and E Rowghani, 2014. Application of multiple-trait random regression animal model to predict genetic production traits in Iranian Holstein dairy cattle.
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