Optimizing genetic selection for Sow Productivity, Growth and Carcas Traits in South African large white Pigs

Abstract

The objective of this study was to develop an approach for efficient genetic selection in pig breeding programs. This approach will optimize genetic selection for sow productivity, growth and carcass traits by integrating genetic and economic parameters. Non-genetic effects on sow productivity, growth and carcass traits were identified so that they could be adjusted during the genetic analyses of these traits. Genetic analyses for sow productivity traits were done on 29719 records for 7983 sows from 29 herds, using a repeated records model. Twenty thousand and seventy nine animals from 29 herds, which were performance-tested between 1990 and 2008, were included in the analyses for growth traits. Analyses for carcass traits were done on 5406 animals from 20 herds, which were carcass-evaluated between 1993 and 2007. Growth and carcass traits were analyzed using a maternal effects model. The sow productivity traits analyzed were: number born alive (NBA), litter birth weight (LBWT), 21-day litter size (D21LS), 21-day litter weight (D21LWT) and inter-farrowing period (IFP). For growth and carcass traits, the traits analyzed were ultrasonic backfat thickness (BFAT), test period gain (TPG), lifetime gain (LTG), feed conversion ratio (FCR), age at slaughter (AGES), lean percentage (LEAN), drip-free lean percentage (DLEAN), drip loss percentage (DRJP), dressing percentage (DRESS), carcass length (CRL TH), eye muscle area (AREA) and carcass fat (CFAT). These traits were analyzed using Residual Maximum Likelihood (REML) procedures in ASREML. Estimates of heritability for sow productivity traits ranged from 0.01±0.010 for IFP to 0.10±0.011 for LBWT, while those for growth and carcass traits ranged from 0.13±0.062 for DRIP to 0.63±0.104 for AREA. Permanent environmental effects were significant for NBA, LBWT, D21LS and D21LWT. Significant maternal genetic effects were observed in TPG, LTG, AGES, LEAN, DLEAN, DRIP and DRESS. In sow productivity traits, genetic correlations ranged from -0.01±0.164 between IFP and D21LS to 0.68±0.038 between D21LS and D21LWT. Genetic correlations among growth and carcass traits ranged from -0.02±0.262 between DRESS and TPG to -0.99±0.012 between LTG and AGES. Low genetic variations were observed in all sow productivity traits, whereas substantial genetic variation exists in growth and carcass traits. Genetic correlations among sow productivity traits suggest that improvement of litter size may be done by selecting for litter weight or vice versa. Selecting for growth rate may improve feed conversion ratio and reduce age at slaughter, which may be improved without significantly compromising on carcass traits. Improving carcass leanness may be associated with improved carcass yield. The genetic trends for various traits show that there has been no consistent improvement in these traits, suggesting that farmers might have not used breeding values all the time during selection. Genetic parameters can be utilized efficiently when economic parameters are incorporated into breeding programs. Thus, economic values can play a pivotal role in improving the efficiency of pig breeding programs. Simulation models based on a 100-sow herd were used in the derivation of economic values for sow productivity, growth and carcass traits. Traits included in the study were NBA, D21 LS, D21LWT, average daily (lifetime) gain (ADG), FCR, AGES, DRESS, LEAN and BFAT. Economic values for NBA, D21LS, D21LWT, ADG, FCR, AGES and DRESS were derived using the partial budgeting approach. Partial differentiation of the profit function was used to derive economic values for LEAN and BFAT. An economic value was defined as change in profit per unit genetic change in a trait. The respective economic values (ZAR) for sow productivity traits were 61.26, 38.02 and 210.15. For growth and carcass traits, the economic values (ZAR) were 33.34, -21.81, -68.18, 5.78, 4.69 and -1.48, respectively. These economic values indicate the direction and emphasis of selection and were sensi6ve to changes in feed prices and marketing prices of carcasses and maiden gilts. The economic values were used with genetic parameters to develop possible breeding objectives and indices. Genetic responses to selection and economic return for each index and its corresponding breeding objective were computed. The best index for sow productivity consisted of NBA and D21LWT. For growth and carcass traits, the best index consisted of AGES, DRESS and BF AT, where AGES and BF AT were included as indicator traits for ADG and LEAN, respectively. Genetic progress and economic return were sensitive to changes in economic values. In order to benefit from changes in economic values, the economic values should be updated so that appropriate weights are placed on the index traits. Producers and consumers may benefit from index selection as it results in optimum improvement in all breeding objective traits and assist in relating selection programs to profit.