Meta-Analysis of Genetic and Phenotypic Parameters for Performance Traits in Indian Crossbred Cattle

Genetic and phenotypic parameters of traits related to growth, production and reproduction are crucial in the formulation of successful breeding programmes. Given the diversity in the cattle population of India, meta-analysis offers a comprehensive insight into the overall performance of the crossbred population for the efficient implementation of animal improvement programmes. The present study aimed to undertake meta-analysis and estimate the genetic and phenotypic parameters of performance traits in Indian crossbred cattle. A total of 130 articles were included in this study after data editing and quality control. The heterogeneity index reached 91.2% for phenotypic parameters, 99.4% for heritability estimates, and 99.4% for phenotypic correlations. Pooled least squares mean ranged from 20.54 to 25.84 kg for body weight at birth, 1542.05 to 2691.44 kg for first lactation milk yield, 996.05 to 1259.38 days for age at first calving in crossbred cattle. Pooled heritability estimates ranged from 0.13 to 0.69 for growth traits, 0.09 to 0.76 for production traits, and 0.05 to 0.25 for reproduction traits. The pooled repeatability estimates were moderate and ranged from 0.22 to 0.42. Pooled genetic correlations were positive and ranged from 0.09 to 0.97. This is the first attempt to estimate the genetic and phenotypic parameters of performance traits in Indian crossbred cattle using a meta-analytical approach. Meta-analysis revealed that the heritability estimate of production and reproduction performance was low to moderate in all the crossbred populations. This suggested the influence of environment and other non-additive genetic effects, wherein improvement is possible through the integration of effective selection and optimum management strategies. Pooled estimates of genetic and phenotypic parameters from this investigation may be used for the effective implementation of breeding programmes in the herds/populations, where these parameters are not estimated/available for any reason.