Multiple criteria decision-making process to derive consensus desired genetic gains for a dairy cattle breeding objective for diverse production systems

Abstract

Dairy cattle industries contribute to food and nutrition security and are a source of income for numerous households in many developing countries. Selective breeding can enhance efficiency in these industries. Developing dairy industries are characterized by diverse production and marketing systems. In this paper, we use weighted goal aggregating procedure to derive consensus trait preferences for different producer categories and processors. We based the study on the dairy industry in Kenya. The analytic hierarchy process was used to derive individual preferences for milk yield (MY), calving interval (CIN), production lifetime (PLT), mature body weight (MBW), and fat yield (FY). Results show that classical classification of production systems into large-scale and smallholder systems does not capture all differences in trait preferences. These differences became apparent when classification was based on productivity at the individual animal level, with high and low intensity producers and processors as the most important groups. High intensity producers had highest preferences for PLT and MY, whereas low intensity producers had highest preference for CIN and PLT; processors preferred MY and FY the most. The highest disagreements between the groups were observed for FY, PLT, and MY. Individual and group preferences were aggregated into consensus preferences using weighted goal programming. Desired gains were obtained as a product of consensus preferences and percentage genetic gains (G%). These were 2.42, 0.22, 2.51, 0.15, and 0.87 for MY, CIN, PLT, MBW, and FY, respectively. Consensus preferences can be used to derive a single compromise breeding objective for situations where the same genetic resources are used in diverse production and marketing circumstances.

Use of Ahp and Moora for Cattle Breeding: Karesi District of Balikesir Province Application

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