Food security is a major challenge in sub-Saharan Africa. This insecurity is attributed to low agricultural productivity, high population growth rate, inefficient systems and policies of food distribution, poverty, and low income.
As one of the measures to address food insecurity, governments in collaboration with Non-Governmental Organisations are putting efforts to provide dairy goats to farmers, and this has stimulated the demand for dairy goats in sub-Saharan Africa. The demand for dairy goats is on the rise necessitating effective dairy goat breeding programmes which can only be effectively implemented if different breeding scenarios and strategies are simulated.
Purpose
D.L.M. Gore, T.O. Okeno, T.K. Muasya, and J.N. Mburu from Egerton University sought to understand whether incorporating reproductive technologies and genomic selection in the current dairy goat breeding programmes would generate a higher response to selection compared to the use of natural mating in the current conventional breeding programme as used in the tropics.
Procedure
Two breeding schemes with three breeding strategies were simulated with the first scheme being the conventional breeding scheme which represented the current dairy goat-breeding programme and alternative schemes incorporated genomic selection. For each scheme, the breeding strategies evaluated were; a conventional scheme using natural mating, AI-Liquid semen and AI-Frozen semen.
Measures on gains and returns on investment of the different strategies used
The study found that conventional and genomic AI-Liquid semen schemes were superior compared to all other strategies in terms of annual genetic gain, returns and profit per doe per year. Also, the implementation of the genomic breeding scheme generated additional improvement across the three mating strategies in all the parameters measured compared to the conventional scheme. The optimal nucleus size ranged between 14 % and 16 %.
In conclusion, the study demonstrated that the adoption of reproductive technologies such as AI would optimize response to selection in dairy goat breeding programs in the tropics. The response to selection in such breeding programmes could be maximized in combination with genomic selection.
The study is published in the Small Ruminant Research Journal has been supported by the United States Agency for International Development (USAID) through Borlaug Higher Education for Agricultural Research and Development Programme (BHEARD Programme), Centre of Excellence for Livestock Innovation and Business (CoELIB) and Tatton Agriculture Park (TAP), Egerton University.
