The Nguni cattle breed is a landrace breed adapted to different ecological regions of South Africa. A number of ecotypes are recognised based on phenotype within the breed, but it is not known if they are genetically distinct. In this study molecular characterization was performed on Makhathini (MAK), Pedi (PED), Shangaan (SHA) and Venda (VEN) Nguni cattle ecotypes. Two Nguni cattle populations, not kept as separate ecotypes, from University of Fort Hare (UFH) and Agricultural Research Council Loskop South farm (LOS) were also included. Genotypic data was generated for 189 unrelated Nguni cattle selected based on pedigree records using 22 microsatellite markers. The expected heterozygosity values varied from 69% (UFH) to 72% PED with a mean number of alleles ranging from 6.0 to 6.9. The F ST estimate demonstrated that 4.8% of the total genetic variation was due to the genetic differentiation between the populations and 92.2% accounted for differences within the populations. The genetic distances and structure analysis revealed the closest relationship between MAK, PEDI and SHA ecotypes, followed by SHA and VEN. The UFH population clustered with the MAK ecotype, indicating that they are more genetically similar, while the LOS cattle grouped as a distinct cluster. Results suggest that the genetic differentiation between the PED and SHA ecotypes is low and can be regarded as one ecotype based on 2 limited genetic differences. The results of this study can be applied as a point of reference for further genetic studies towards conservation of Nguni cattle ecotypes.

Aim: A longitudinal study was conducted to assess the epidemiology of common gastrointestinal parasite (GIP) infections affecting goats in South Africa as influenced by agro-ecological zone (AEZ), sampling season, and the age and sex of animals. Materials and Methods: A total of 288 goats (101 male and 187 female) were randomly sampled during winter and summer in areas representing four AEZs (arid: 80; semi-arid: 76; humid: 62; and dry sub-humid: 70) of South Africa. Fecal samples from each animal were collected from the rectum, and the presence of GIP eggs was determined using a modified McMaster technique. A sample was considered positive when a minimum of one GIP egg was detected under the microscope. Fecal cultures were prepared, and infective larvae were collected and identified. The data were analyzed by MiniTab17 (2017) using the FREQ procedure, and the association between the independent factors and the prevalence of various GIPs were evaluated using the Pearson Chi-square test (p<0.05). Results: The overall prevalence of GIP in the present study was 37.1%, with a mean prevalence of 30.0, 26.4, 31.1, 36.6, and 59.6% for Eimeria spp., Trichuris, Strongyloides papillosus, Moniezia spp., and strongyles, respectively. There was a significant (p<0.05) association between the prevalence of strongyles, Trichuris, Moniezia spp., and AEZs, whereas an insignificant (p>0.05) association was observed for the prevalence of Eimeria spp. and S. papillosus. A significant (p<0.05) association between goat age and prevalence of all GIPs was observed, where the prevalence was higher in young goats, followed by adults, and then by suckling goats. The prevalence of various GIPs was similar between male and female goats. The percentage of infection with Eimeria spp., Trichuris, S. papillosus, and strongyle parasitic infections was marginally higher in males than in females, whereas that of the Moniezia spp. was higher in females. A significant (p<0.05) association between the prevalence of Eimeria spp. and sampling season was observed, and there was an insignificant (p>0.05) association between the other GIPs and sampling season. The prevalence of Eimeria spp. infection was higher in winter (34.0%) than in summer (26.0%). Conclusion: AEZs and goat age are the most important risk factors influencing GIP infections in South African communal goats. These epidemiological parameters are important for outlining effective parasite control management systems against these GIPs in goats.

Pedigree information on the registered South African Ayrshire (n = 47 116), Guernsey (n = 18 766), Holstein (n = 892 458) and Jersey (n = 314 403) breeds was analyzed to determine the rate of inbreeding and effective population sizes for the period 1960 to 2003. Inbreeding coefficients were calculated using the Animal Breeder's Tool Kit. The mean inbreeding coefficients for 2003 were 2.02%, 2.04%, 2.30%, and 3.05% for the Ayrshire, Guernsey, Holstein and Jersey, respectively. The corresponding rates of inbreeding per year were 0.05%, 0.05%, 0.06%, and 0.07% indicating that inbreeding is accumulating at a slightly higher rate in Jersey compared to the other three breeds. However, the rates of inbreeding in the current study are still considerably lower than the acceptable rate of less than 0.5% per year. Estimates of effective population sizes were 148, 165, 137, and 108 for the Ayrshire, Guernsey, Holstein and Jersey, respectively. Results indicate that the impact of inbreeding on genetic variability is still minimal. However, the impact of inbreeding on phenotypic performance on traits of economic importance was not investigated in the current study and should therefore receive future consideration. _______________________________________________________________________________________

Repeated records (n = 58,295) of Bonsmara cows were used to evaluate mature cow weight (CW), using random regression animal models (RRM). Cows (n = 11,847), age two to 11 years (AY), were weighed at weaning of their calves. Data were analysed with the Restricted Maximum Likelihood (REML) procedure, using orthogonal (Legendre) polynomials on age in months (AM). The model included fixed regression on AM (range from 30 to 138 mo) and the effect of herd-measurement date concatenation. Random parts of the model were RRM coefficients for additive and permanent environmental effects, while residual effects were modelled to account for heterogeneity of variance by AY. Estimates of variances increased with cow age, though tended to be flat at older ages. Heritability estimates ranged from 0.39 to 0.47, and were comparable with estimate of 0.41 obtained using a simple repeatability model (SRM). Estimates of genetic correlations were greater than 0.82 among measures of weight at all ages. The resulting covariance functions were used to estimate breeding values of each animal along the age trajectory. Genetic trends for CW over the years showed only a slightly increasing pattern, suggesting that CW did not change much, and was similar whether SRM or RRM was used. Results suggest that selection for CW could be effective and that RRM could be useful for the National Genetic Evaluation of CW in Bonsmara cattle. However, given the complexity of the RRM, for practical purposes a SRM might be an acceptable approximation for prediction of breeding values.

The objectives of the study were to estimate genetic parameters for tick resistance and to evaluate the effect of the level of tick infestation on the estimates of genetic parameters for South African Bonsmara cattle. Field data of repeated tick count records (n = 11 280) on 1 176 animals were collected between 1993 and 2005 by 10 breeders participating in the National Beef Recording and Improvement Scheme. The distribution of tick count records were normalized using a Box-Cox transformation. Data were divided into seven sub-data sets based on the mean tick count per contemporary group, to facilitate the investigation of the effect of level of tick infestation on the derived genetic parameters. A repeatability animal model including the fixed effects of contemporary group and age of animal at tick counting and random effects of the direct additive genetic, permanent environmental and residual effects was used to estimate genetic parameters using REML procedures. The additive genetic variance for tick count ranged from 0.01 to 0.08. The animal permanent environmental variance ranged from 0.00 to 0.03. Phenotypic variance decreased with increasing mean tick count level while additive genetic variance increased with mean tick count level. The heritability also increased with mean tick count level until a mean tick count level of ≥30. The highest heritability estimate obtained in the current study was 0.17 for data with mean tick count level ≥25. These results suggest that sufficient genetic variation for tick count exists in the Bonsmara cattle. Genetic selection for tick resistance is feasible even though genetic progress may be slow.

Nephawe, K. A.; Cundiff, L. V.; Dikeman, M. E.; Crouse, J. D.; and Van Vleck, L. Dale, "Genetic relationships between sex-specific traits in beef cattle: Mature weight, weight adjusted for body condition score, height and body condition score of cows, and carcass traits of their steer relatives" (2004 ABSTRACT: Data from the first four cycles of the Germplasm Evaluation Program at the U.S. Meat Animal Research Center (USMARC) were used to investigate genetic relationships between mature weight (MW, n = 37,710), mature weight adjusted for body condition score (AMW, n = 37,676), mature height (HT, n = 37,123), and BCS (n = 37,676) from 4-to 8-yr old cows (n = 1,800) and carcass traits (n = 4,027) measured on their crossbred paternal half-sib steers. Covariance components among traits were estimated using REML. Carcass traits were adjusted for age at slaughter. Estimates of heritability for hot carcass weight (HCWT); percentage of retail product; percentage of fat; percentage of bone; longissimus muscle area; fat thickness adjusted visually; estimated kidney, pelvic, and heart fat percentage; marbling score; Warner-Bratzler shear force; and taste panel tenderness measured on steers were moderate to high (0.26 to 0.65), suggesting that

Conservation of locally adapted indigenous livestock breeds has become an important objective in sustainable animal breeding, as these breeds represent a unique genetic resource. Therefore, the Agricultural Research Council of South Africa initiated a conservation programme for four South African indigenous chicken breeds. The evaluation and monitoring of the genetic constitution of these conservation flocks is important for proper management of the conservation programme. Using molecular genetic analyses, the effective population sizes and relatedness of these conservation flocks were compared to village (field) chicken populations from which they were derived. Genetic diversity within and between these populations are further discussed within the context of population size. The conservation flocks for the respective breeds had relatively small effective population sizes (point estimate range 38.6–78.6) in comparison to the field populations (point estimate range 118.9–580.0). Furthermore, evidence supports a transient heterozygous excess, generally associated with the occurrence of a recent population bottleneck. Genetic diversity, as measured by the number of alleles, heterozygosity and information index, was also significantly reduced in the conservation flocks. The average relatedness amongst the conservation flocks was high, whilst it remained low for the field populations. There was also significant evidence for population differentiation between field and conservation populations. Fst estimates for conservation flocks were moderate to high with a maximum reached between VD_C and VD_F (0.285). However, Fst estimates for field population were excessively low between the NN_C and EC_F (0.007) and between EC_F and OV_F (0.009). The significant population differentiation of the conservation flocks from their geographically correlated field populations of origin is further supported by the analysis of molecular variance (AMOVA), with 10.51 % of genetic diversity ascribed to population differences within groups (FSC = 0.106). The results suggest that significant genetic erosion has occurred within the conservation flocks due to inbreeding, pronounced effects of random drift and selection. It might be necessary to introduce new breeding individuals from the respective field populations in order to increase the effective population sizes of the conservation flocks and counter the effects of genetic erosion.Electronic supplementary materialThe online version of this article (doi:10.1007/s11250-016-1030-9) contains supplementary material, which is available to authorized users.

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