1 ESTIMATES OF GENETIC PARAMETERS FOR GROWTH OF KENYA BORAN CATTLE USING RANDOM REGRESSION MODELS C.B. Wasike 1,2 , D. Indetie 3 , J. M. K. Ojango 1 and A.K. Kahi 1 1 Animal Breeding and Genetics Group, Department of Animal Sciences, Egerton University, P. O. Box 536, Njoro 20107, Kenya 2 Department of Animal Science, Kilifi Institute of Agriculture, P.O. Box 195, 80108, Kilifi 3 Kenya Agricultural Research Institute-Lanet, P.O. Box 3840, Nakuru 20100, Kenya Abstract Data consisting of 18 884 weight records collected from 1 273 Boran cattle from birth to 24months of age were used to estimate genetic parameters for growth of Boran cattle using random regression (RR) models under a situation of small herd size and inconsistent recording. The RR model fitted quadratic Legendre polynomials of age at recording for additive genetic and permanent environmental effects. Direct heritability and permanent environmental variance as a proportion of phenotypic variance fluctuated during the early ages but stabilised at intermediate to later ages; the estimates ranged from 0.11 to 0.33 and 0.18 to 0.83, respectively. Genetic correlation estimates were positive ranging from 0.10 to unity. The estimates declined with increase in lag between the age points. Phenotypic correlation pattern was erratic between early ages, negatively low (-0.02) between the extreme data points and moderate to highly positive (>0.50) between intermediate and later points with prominent spikes along the diagonal. Conditions of small herd sizes and inconsistent recording notwithstanding, RR models have potential to model growth of Boran cattle. Introduction Growth in Boran cattle has been described using univariate and multivariate models, in which each individual weight measured at a particular age was considered a different trait assuming constant variances between ages (Demeke et al., 2003; Wasike et al., 2006). Growth follows a prescribed trajectory and performance is measured by repeatedly recording weight as the animal grows, this constitutes longitudinal data. To minimise the loss of information and reduction in the accuracy of parameter estimates associated with univariate and multivariate models, random regression (RR) models based on covariance functions methodology are used in evaluation of longitudinal data. The advantages of RR models over univariate and multivariate models are well articulated in literature. They include ability to allow for easy interpolation between ages at which recording took place, accurate prediction of selection response and more efficient use of the data (Kirkpatrick and Heckman, 1989; Albuquerque and Meyer, 2001). Inconsistent recording of small selected groups from the larger herds and overlapping age categories when weights are recorded characterise growth performance recording in commercial beef production systems in Kenya. Random regression models may seem a better alternative to univariate and multivariate models under such circumstances. However, the models have not been used in evaluation of growth data in beef ranches in the Arid and semi-arid land (ASAL) of Kenya. The objective of this paper was to estimate genetic parameters for growth in Boran cattle RR models under situations of small data sizes and inconsistent recording. Material and methods Data source Data on growth performance of the Kenya Boran cattle were made available by the national beef research station of KARI-Lanet located in Nakuru district, Kenya. A detailed description of the farm and management of the animals is presented in Wasike et al. (2006). Data consisted of weights recorded once a month from birth to 24 months of age on the Kenya Boran cattle born between 1989 and 2003. Weights were clustered in age- classes of 0-2days (birth weight), 3-35days, 36-70days, 71-105days…701-735days to cover the first 24month growth trajectory from birth due to high variability in ages at weighing. There were 22 age classes in total. All the data were checked for consistency of pedigree information and correct dates of birth and weighing. Only animals that had at least 3 weight records within their first 24-month life were retained for analyses. A final data set for analysis after editing comprised 18,884 records from 1,273 animals. The distribution of weight records among age points and average weights at various age intervals with their standard errors are presented in Figure 1