Optimization of Dairy Cattle Breeding Programs with Genotype by Environment Interaction in Kenya

Peter K. Wahinya (), Gilbert M. Jeyaruban, Andrew A. Swan and Julius H. J. van der Werf
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Peter K. Wahinya: Animal Genetics & Breeding Unit, University of New England, Armidale, NSW 2351, Australia
Gilbert M. Jeyaruban: Animal Genetics & Breeding Unit, University of New England, Armidale, NSW 2351, Australia
Andrew A. Swan: Animal Genetics & Breeding Unit, University of New England, Armidale, NSW 2351, Australia
Julius H. J. van der Werf: School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia

Agriculture, 2022, vol. 12, issue 8, 1-10

Abstract: Genotype by environment interaction influences the effectiveness of dairy cattle breeding programs in developing countries. This study aimed to investigate the optimization of dairy cattle breeding programs for three different environments within Kenya. Multi-trait selection index theory was applied using deterministic simulation in SelAction software to determine the optimum strategy that would maximize genetic response for dairy cattle under low, medium, and high production systems. Four different breeding strategies were simulated: a single production system breeding program with progeny testing bulls in the high production system environment (HIGH); one joint breeding program with progeny testing bulls in three environments (JOINT); three environment-specific breeding programs each with testing of bulls within each environment (IND); and three environment-specific breeding programs each with testing of bulls within each environment using both phenotypic and genomic information (IND-GS). Breeding strategies were evaluated for the whole industry based on the predicted genetic response weighted by the relative size of each environment. The effect of increasing the size of the nucleus was also evaluated for all four strategies using 500, 1500, 2500, and 3000 cows in the nucleus. Correlated responses in the low and medium production systems when using a HIGH strategy were 18% and 3% lower, respectively, compared to direct responses achieved by progeny testing within each production system. The JOINT strategy with one joint breeding program with bull testing within the three production systems produced the highest response among the strategies using phenotypes only. The IND-GS strategy using phenotypic and genomic information produced extra responses compared to a similar strategy (IND) using phenotypes only, mainly due to a lower generation interval. Going forward, the dairy industry in Kenya would benefit from a breeding strategy involving progeny testing bulls within each production system.

Keywords: genotype by environment; breeding strategies; selection index; response (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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