Discipline: Keywords: across-country genetic evaluation, genetic connectedness, genetic progress.
South Africa and Kenya rank among the top milk producing countries in Africa, whereas Zimbabwe ranks in the middle. These countries are unique on the continent in that they implement dairy production breeding programmes. South Africa has the highest milk yield per cow, with opportunities to export to other countries. South Africa is also often used as the main reference point for dairy improvement in Africa because countries such as Kenya, Zimbabwe and Rwanda source South African animals to improve their national dairy herds. Kenya and Zimbabwe may also serve as reference points for genetic evaluations in other countries of Africa.
The potential benefit from across-country collaboration has been well demonstrated. Opportunities to exploit dairy genetic connectedness in across-country evaluations were illustrated for Nordic Red and Jersey are also available through the International Bull Genetic Evaluation Service (Interbull, Uppsala, Sweden). These evaluations are made possible through systematic genetic improvement programmes within country and collaboration between countries. South Africa is the only country from sub-Saharan Africa that currently participates in Interbull. The Interbull model, to a large extent, uses individual country genetic evaluations as the raw material for further evaluation and ranking of animals. But instead of using the Interbull model as a ‘blueprint’, the African scenario may well use individual performance data as the raw material for genetic evaluation, because currently few countries perform within-country genetic evaluations.
The hypothesis the study cited below is that joint genetic evaluation across countries in sub-Saharan Africa will generate more accurate genetic parameters of traits and estimated breeding values of dairy animals, and increase the relative rate of selection response compared with national genetic evaluations. The objectives of the study therefore were to, 1) calculate and compare within- and across-country genetic parameters for production and reproduction traits using data from Kenya, South Africa and Zimbabwe; 2) estimate and compare the breeding values of individual animals from within- and across-country genetic evaluations; and 3) quantify the predicted genetic gains from sire selection based on these genetic evaluations.
Genetic parameters were estimated for the 305-day milk yield in the first lactation and across five lactations, for age at first calving and for interval between first and second calving. Estimated breeding values (EBVs) of individual animals for these traits were calculated. There were records from 2 333, 25 208 and 5 929 Holstein cows in Kenya, South Africa and Zimbabwe, and 898 and 65 134 Jersey cows from Kenya and South Africa. Genetic gain from sire selection within and across countries was predicted. Genetic links between countries were determined from sires with daughters that had records in two or more countries, and from common ancestral sires across seven generations on both the maternal and paternal sides of the pedigree. Each country was treated as a trait in the across-country evaluation.
The results showed that genetic variance and heritability could not always be estimated within country, but were significantly different from zero in the across-country evaluation. In all three countries, there was greater genetic gain in all traits from an across-country genetic evaluation owing to greater accuracy of selection compared with within country. Kenya stood to benefit most from an across-country evaluation, followed by Zimbabwe, then South Africa.
It was concluded that an across-country breeding programme using joint genetic evaluation would be feasible, provided that there were genetic links across countries. Countries with limited yet genetically linked within-country data would benefit most from across-country evaluations of production and fitness traits. Across-country genetic evaluations could provide robust genetic information that could enhance genetic progress and optimize future breeding strategies in sub-Saharan Africa. As more data accumulates, the best approach would be to adapt more appropriate models that reflect the diverse production environments across sub-Saharan Africa with records from countries being treated as separate but correlated traits to account for genotype by environment interaction effectively.
O. Opoola, G. Banos, J.M.K. Ojango, R. Mrode, G. Simm, C.B. Banga, L.M. Beffa & M.G.G. Chagunda, 2020. Joint genetic analysis for dairy cattle performance across countries in sub-Saharan Africa. S.Afr. J. Anim. Sci. 50, 507-520.