Abstract
Body condition scores (BCS) and/or body weights (BWT) are often used as visual or tactile management tools to evaluate and improve reproductive competency in pasture-managed small ruminant animals. This study was designed to evaluate relationships between BCS, BWT and reproductive traits (number born alive and weaned, litter size, birth and weaning weights) in 16 purebred Kiko, 11 purebred Boer and 7 Kiko x Boer crossbred meat goat dams that were semi-intensively managed and bred to both Boer and Kiko bucks. BWT was recorded using a scale and palpable BCS scale of 1 to 5 (1= emaciated to 5= obese) and was subjectively determined at breeding, parturition and weaning. Pearson correlation coefficient (r) analysis was used to determine the relationships between residual values of reproductive and progeny performance and BWT or BCS. Pre-partum BCS and weaning BCS had a correlation of r=0.09. A moderate correlation was observed between BWT at breeding and the number born alive (r=0.36) suggesting that pre-partum BWT is the key body conformation measurement linked to the reproductive performance of dams both at birth and weaning. Both at breeding and at weaning BCS were negatively correlated with litter size (r= -0.11) and birth weight (r=-0.32) and weakly correlated with the number born alive (r=0.06). Also, negative correlations were obtained between BCS at weaning and kid weaning weight (r=-0.58) and number weaned (r=-0.26). Although BCS had no significant (P≥0.05) effect on kg kids born per dam, birth weight of kids, and kids weaning weights, it is evident that a BCS score of 3 at the mating time could optimize reproductive performance. The results of this project established the important roles that pre-breeding BWT and/or BCS have on reproductive performance (kidding rate) in meat goat herds. We recommend their evaluation as a useful management tool for distinguishing differences in the pre-partum nutritional needs of meat goat herds, especially in the pasture-based production system. Int. J. Agril. Res. Innov. Tech. 12(2): 64-73, December 2022
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