| dc.description.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. | en_US |
