Genetic parameter estimation and breeding plans for the South African dairy goat herd
Thesis (PhD (Animal Sciences))--University of Stellenbosch, 2005.
Milk production records of all grade and registered Saanen dairy goats from the Milk Recording and Performance Testing Scheme of the Animal Improvement Institute of the Agricultural Research Council of South Africa and pedigree information of these animals from SA Studbook were analyzed to obtain specific genetic parameters. Records of goats with lactations exceeding 60 days in milk were used. A sufficient number of records only became available from 1985 onwards. Reproduction records were determined from milk recording data. The number of milk production records for the British Alpine and Toggenburg breeds was too small to warrant a genetic evaluation. In total, 3190 lactation records of 1413 Saanen does were available for the initial analysis. First and second parity records, 1190 and 775 records, respectively, were subjected to a separate genetic analysis. Milk production records (2319) of one commercial herd providing more than 70% of all the records in the national herd, were also subjected to a separate genetic analysis. The fixed effects identified as having a significant (P<0.05) effect on all traits studied were production year, age of dam, lactation length, parity number, herds (owners) and year of birth. Although some significant interactions were found, they were ignored as their effects were very small. Additive genetic variances and heritability estimates were obtained by ASREML procedures fitting three models. Estimates were generally in accordance with values found in the literature although estimates for fat and protein percentage were lower than expected when compared to dairy cow data. This could be explained by pedigree information lacking in the data set. The h2 estimate for milk yield using all records, first parity records, second parity records and records from a commercial herd were 0.21±0.05, 0.32±0.08, 0.20±0.10 and 0.31±0.06, respectively. Heritability estimates for fat percentage showed a large variation and were 0.19±0.05, 0.67±0.08, 0.34±0.12 and 0.12±0.05, respectively for similar data sets as previously mentioned. In contrast to this protein percentage varied little between data sets and were 0.30±0.06, 0.32±0.00, 0.24±0.11 and 0.28±0.07, respectively. Genetic and phenotypic correlations among production traits were positive and high for all data sets. As for dairy cows, milk fat and protein percentages were negatively related to milk yield. Genetic correlations between milk fat and protein percentages were positive and moderate to high. Increasing milk volume would have a negative effect on fat and protein percentages although it would increase fat and protein yields. Reproduction parameters, i.e. age at first kidding (AFK), age at last kidding (ALK), productive life (PL) and number of lactations (NL) were derived from milk recording data. Mean values for these parameters were 457±171 days, 1046±718 days, 19.3±13.9 months and 2.24±1.37 kiddings, respectively. Kidding interval had no genetic basis and is controlled by management. Heritability estimates were in accordance with literature values and were 0.25±0.04, 0.28±0.04, 0.08±0.04 and 0.05±0.03 for AFK, ALK, PL and NL, respectively. The genetic correlation between AFK and ALK was as expected positive and high, i.e. 0.61±0.10, although the correlation between AFK and PL was negative indicating similar to dairy cows that PL is shortened by a later AFK. The genetic trend for milk, fat and protein yield were positive, although it did not differ from zero. Large variations were observed between years (R2 <0.13). Genetic trends for fat and protein percentages were positive and negative (P<0.05), respectively. These trends are in contrast to trends observed in other countries such as France, The Netherlands and the USA where positive trends were generally observed. This may indicate a higher selection emphasis on milk yield parameters or more complete data sets in terms of pedigree information. The dairy goat industry in South Africa should address some of the problems that were encountered in the analysis of the data. These include factors such as a large number of small herds, many short lactations, a large number of animals lacking production data linked to pedigree information, incomplete pedigrees, few does that have completed three or more lactations, little genetic ties between herds and a small number of progeny for bucks. Some organizational and logistic issues concerning pedigree and milk recording need to be addressed by the South African Milch Goat Society to enable the accurate estimation of the genetic merit of animals in the national herd.