Across flock genetic parameter estimation for yearling body weight and fleece

Abstract

Accurate genetic parameter estimates are needed upon which to perform multiple-trait across flock breed analyses. Genetic parameters for yearling body weight (BW), clean fleece weight (CFW) and mean fibre diameter (MFD) were estimated using records of 107 389 individuals (the progeny of 1 530 sires and 45 178 dams) collected between 1992 and 2004 in the South African Dohne Merino population. Fixed effects included in the model were flock-year-season-sex-management group (1 594 classes), type of birth (singles, multiples), age of dam (2 -7+ years) and age at measurement, fitted as a linear covariate (385 ± 12 days). Six different single-trait animal models were fitted, where different combinations of the following random effects were fitted: direct additive, the sire-flock interaction, the sire-flock-year-season interaction (SFYS), the dam genetic effect, the direct-maternal correlation and the dam permanent environmental effect. These analyses were followed by a three-trait analysis structured according to the log likelihood ratios obtained for the single-trait analysis. This analysis allowed the calculation of relevant correlations among traits together with their respective standard errors. Direct heritability estimates from the three-trait analysis were 0.17 for BW, 0.19 for CFW and 0.45 for MFD. Maternal heritability estimates were 0.01 for BW and 0.006 for CFW, with corresponding dam permanent environmental ratios of respectively 0.03 and 0.02. The genetic correlation between animal effects amounted to 0.48 for BW. Derived proportions of the total phenotypic variance due to SFYS were 0.02, 0.02 and 0.02, respectively. Genetic correlations of BW with CFW and MFD were 0.11 and 0.13, respectively, and of CFW with MFD 0.16. It was concluded that the inclusion of some form of a genotype by environmental interaction as part of the national evaluation is essential, although it controlled only a modest portion of the overall phenotypic variation. ________________________________________________________________________________

Wool production in Dohne Merino, Dormer, Merino and South African Mutton Merino lambs

“…The lack of differences in fleece weight between the Dohne Merino and SAMM breeds may result because of compensatory differences in bodyweight. This hypothesis is supported by the observation of a positive correlation (r = 0.32) between yearling weight and clean fleece weight in Dohne Merinos (Van Wyk et al, 2008). As yearlings, ram lambs presented greater wool growth than ewe lambs.…”

“…Such coarse wools with low comfort factors are not suitable for fabrics that have direct contact with the skin, and are more suited to the manufacturing of carpets and blankets. The national average fibre diameter for Dohne Merino stud ewes was reported to be 19.4 µm (Van Wyk et al, 2008). Snyman et al (1998) reported that fibre diameter of Merino ewes varied (19.8 -23.0 µm) among South African research flocks.…”

The adaption of the South Africa sheep industry to new trends in animal breeding and genetics: A review

“…In the Dohne Merino, weaning weights were commonly recorded and used for preliminary index selection, but seldom submitted to the registration authority. Performance recording in this breed concentrated on yearling liveweight and fibre traits, as is evident from publications by Swanepoel (2006) and Van Wyk et al (2008). However, birth registrations and weaning weights are used to generate reproduction records in the NSSIS.…”

“…The integration of data in the 1990s from various breeders in a common database for the important breeds resulted in the first across-flock genetic analysis for South African Merinos in 1998 . Data from the scheme have since been used in across-flock analyses of several breeds, namely Dohne Merino (Van Wyk et al, 2008;Olivier & Cloete, 2011), Merino , Dormer, Ile de France and Merino Landsheep (Zishiri et al, 2014) and the Dorper (Olivier & Cloete, 2006;Zishiri et al, 2013a). Research continues on these breeds, and the national analyses are improved as superior techniques and methods become available.…”

“…The total amount invested in research in South Africa was approximately R 21 billion (Anon, 2009). Research and development in agriculture contributed between 5.5% and 6.9% to the total research budget from 2007-2008(Anon, 2009Mjwara et al, 2013). This amount took seventh place, behind engineering, natural sciences, medical and health sciences, information and communication sciences, social sciences and humanities, and applied sciences and technology (Figure 3).…”