부산대학교 도서관

Context. Mitigating financial risk in the feedlot environment is an ongoing occurrence, and good production is a key risk mitigator. However, production protocols are based on historic averages because of the inability to predict growth potential of incoming calves. Production profiling of individual incoming feeder calves could address these limitations. Aims. The aim of this study was to establish criteria for optimal sorting of incoming feeder calves into various cattle groups in a feedlot that maximises feedlot profit. Methods. South African feeder calves (n = 436) were classified into four production-profile (PP) categories according to a predetermined set of phenotypic traits: PP 3 (n = 72) representing feeder calves with the poorest feedlot growth potential, PP 2- (n = 191) with below-average potential, PP 2+ (n = 139) with above-average potential and PP 1 (n = 34) with above-average feedlot growth potential. After combining the data of PP 2- and PP 2+ into PP 2, mixed modelling of economically important feedlot growth traits (average daily gain (ADG), carcass ADG, and carcass exit weight) was performed to evaluate the effect of PP classification (PP 1 and PP 3), while adjusting for potential confounding effects such as starting weight (entry weight) and gender. Key results. Carcass weights for calves with a PP classification of 3 and 1 were 15.54 kg less (P < 0.000), and 11.34 kg more (P = 0.007) respectively, than those with a PP classification of 2 (261.27 kg, 95% CI 257.94--264.57), after adjusting for entry weight, calf gender and the random effect of the feeding pen. Similar to carcass weight, calves with a PP 3 classification were outperformed by other classifications in all the measured traits (P < 0.05). Conclusions. This is the first report demonstrating the ability of subjective production-profile classification to predict growth performance of individual feeder calves. Implications. The opportunity of the PP classification systemlies in value-based procurement of incoming feeder calves based on their growth potential at the start of the feeding period, and then to use technology to improve and finalise the current subjective PP classification system. [ABSTRACT FROM AUTHOR]