Genetic and phenotypic parameters were estimated for lamb growth traits for the Dorper sheep in semi-arid Kenya using an animal model. Data on lamb growth performance were extracted from available performance records at the Sheep and Goats Station in Naivasha, Kenya. Growth traits considered were body weights at birth (BW0, kg), at 1 month (BW1, kg), at 2 months (BW2, kg), at weaning (WW, kg), at 6 months (BW6, kg), at 9 months (BW9, kg) and at yearling (YW, kg), average daily gain from birth to 6 months (ADG(0-6), gm) and from 6 months to 1 year (ADG(6-12), gm). Direct heritability estimates were, correspondingly, 0.18, 0.36, 0.32, 0.28, 0.21, 0.14, 0.29, 0.12 and 0.30 for BW0, BW1, BW2, WW, BW6, BW9, YW, ADG(0-6) and ADG(6-12). The corresponding maternal genetic heritability estimates for body weights up to 9 months were 0.16, 0.10, 0.10, 0.19, 0.21 and 0.18. Direct-maternal genetic correlations were negative and high ranging between -0.47 to -0.94. Negative genetic correlations were observed for ADG(0-6)-ADG(6-12), BW2-ADG(6-12), WW-ADG(6-12) and BW6-ADG(6-12). Phenotypic correlations ranged from 0.15 to 0.96. Maternal effects are important in the growth performance of the Dorper sheep though a negative correlation exists between direct and maternal genetic effects. The current study has provided important information on the extent of additive genetic variation in the existing flocks that could now be used in determining the merit of breeding rams and ewes for sale to the commercial flocks. The estimates provided would form the basis of designing breeding schemes for the Dorper sheep in Kenya. Implications of the study to future Dorper sheep breeding programmes are also discussed.

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.

Dairy cattle breeding programs in developing countries are constrained by minimal and erratic pedigree and performance recording on cows on commercial farms. Small-sized nucleus breeding programs offer a viable alternative. Deterministic simulations using selection index theory were performed to determine the optimum design for small-sized nucleus schemes for dairy cattle. The nucleus was made up of 197 bulls and 243 cows distributed in 8 non-overlapping age classes. Each year 10 sires and 100 dams were selected to produce the next generation of male and female selection candidates. Conception rates and sex ratio were fixed at 0.90 and 0.50, respectively, translating to 45 male and 45 female candidates joining the nucleus per year. Commercial recorded dams provided information for genetic evaluation of selection candidates (bulls) in the nucleus. Five strategies were defined: nucleus records only [within-nucleus dam performance (DP)], progeny records in addition to nucleus records [progeny testing (PT)], genomic information only [genomic selection (GS)], dam performance records in addition to genomic information (GS+DP), and progeny records in addition to genomic information (GS+PT). Alternative PT, GS, GS+DP, and GS+PT schemes differed in the number of progeny per sire and size of reference population. The maximum number of progeny records per sire was 30, and the maximum size of the reference population was 5,000. Results show that GS schemes had higher responses and lower accuracies compared with other strategies, with the higher response being due to shorter generation intervals. Compared with similar sized progeny-testing schemes, genomic-selection schemes would have lower accuracies but these are offset by higher responses per year, which might provide additional incentive for farmers to participate in recording.

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In developing countries minimal and erratic performance and pedigree recording impede implementation of large-sized breeding programs. Small-sized nucleus programs offer an alternative but rely on their economic performance for their viability. We investigated the economic performance of 2 alternative small-sized dairy nucleus programs [i.e., progeny testing (PT) and genomic selection (GS)] over a 20-yr investment period. The nucleus was made up of 453 male and 360 female animals distributed in 8 non-overlapping age classes. Each year 10 active sires and 100 elite dams were selected. Populations of commercial recorded cows (CRC) of sizes 12,592 and 25,184 were used to produce test daughters in PT or to create a reference population in GS, respectively. Economic performance was defined as gross margins, calculated as discounted revenues minus discounted costs following a single generation of selection. Revenues were calculated as cumulative discounted expressions (CDE, kg) × 0.32 (€/kg of milk) × 100,000 (size commercial population). Genetic superiorities, deterministically simulated using pseudo-BLUP index and CDE, were determined using gene flow. Costs were for one generation of selection. Results show that GS schemes had higher cumulated genetic gain in the commercial cow population and higher gross margins compared with PT schemes. Gross margins were between 3.2-and 5.2-fold higher for GS, depending on size of the CRC population. The increase in gross margin was mostly due to a decreased generation interval and lower running costs in GS schemes. In PT schemes many bulls are culled before selection. We therefore also compared 2 schemes in which semen was stored instead of keeping live bulls. As expected, semen storage resulted in an increase in gross margins in PT schemes, but gross margins remained lower than those of GS schemes. We conclude that implementation of small-sized GS breeding schemes can be economically viable for developing countries.

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