Genetic and phenotypic correlation of milk traits in Saanen goats of South Africa

Abstract

Initial analysis was conducted to test significance of dam parity, litter size, birth season, birth year, kidding season and kidding age on lactation milking performance of various milk production traits and components, as well as to calculate phenotypic correlation between dam kidding age and these traits. Analysis of variance (ANOVA) was carried out using 16 407 non-pedigreed lactation records to test for non-genetic significant effects, while Pearson’s correlation coefficients were calculated using Minitab software. The second analysis included 2 960 fully pedigreed lactation records that were analysed to estimate (co) variance components and direct heritability values for milk production and component traits applying uni-variate linear analysis, as well as genetic and phenotypic correlations between them using bi-variate linear analysis. Both analyses used secondary data of all grade and registered Saanen goats participating in the official Milk Recording and Performance Testing Scheme of the Animal Improvement Institute of the Agricultural Research Council of South Africa. From ANOVA, dam parity and year of birth significantly influenced (p < 0.05) all traits investigated, with better lactation milking performances estimated in 3rd parity groups and animals born during recent years respectively. Birth season only affected (p < 0.05) MY, urea and NR with animals born during spring season yielding a better lactation milking performance. Kidding season influenced (p < 0.05) all traits except PY and urea, with highest lactation milking performance estimated in animals kidding during spring season. All traits except FY and PY were significantly influenced (p < 0.05) by litter size, with multiple litter kidding groups yielding highest, while kidding age effects were not significant (p > 0.05) on NR, SCCI and urea. Pearson’s correlation estimations showed negative associations between kidding age (rp = -0.30, -0.004, - 0.057, -0.051, -0.015, -0.265 and -0.271 for urea, MY, FY, PY, LY, NR and P respectively) except for SCCI (rp= 0.189). From uni-variate and bi-variate linear analyses, direct heritability estimates ranged from moderate to high (h2 = 0.42 ± 0.03, 0.38 ± 0.03, 0.39 ± 0.03, 0.22 ± 0.03, 0.40 ± 0.03, 0.38 ± 0.03, 0.28 ± 0.05 and 0.20 ± 0.03 for MY, FY, PY, LY, Urea, NR, P and SCCI respectively), with MY having highest value. Genetic correlation estimates between MY and traits such as FY, PY, urea, NR and P were all high and positive indicating favorable correlated responses (rg =0.97, 0.94, 0.95, 0.99 and 0.74 respectively). Furthermore, phenotypic correlation estimates between MY and these traits except P (rp = 0.33) were close to their respective genetic xv correlation values (rp=0.95, 0.91, 0.92 and 0.92 for FY, PY, urea and NR respectively). Genetic correlation between MY and LY, and between MY and SCCI were not significant (p > 0.05), while phenotypic correlations between MY and these traits were significant (p <0.05), positive and low (rp=0.03 and 0.02 for LY and SCCI respectively). It was concluded that non-genetic factors determine to what extent the genetic potential of an animal is expressed thus, their inclusion in genetic evaluation models is crucial. Selecting for increased MY would increase herd lactation NR and improve lactation milking performance of other traits such as FY, PY and P. Selection against SCCI needs to be applied more in the population to avoid losses attributed to intra mammary infections