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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.

We argue that animal-breeding schemes need well-designed breeding plans to maximise long-term genetic gains from genomic information. Genomic information has been implemented in livestock breeding schemes with ad hoc breeding plans, suggesting that the potential benefits of genomic information are not being fully exploited. Breeding schemes need well-designed breeding plans to exploit the benefits of genomic information for two reasons. First, there are several components of breeding schemes with genomic information that impact on long-term genetic gains. Second, these components interact, which implies that breeding schemes need to optimise components simultaneously in order to maximise long-term genetic gains. Designing breeding plans that optimise components simultaneously is a complex task. In more cases than not, breeding schemes, their components, and interactions between these components do not allow optimum breeding plans to be designed by mere reasoning. We recommend using decision frameworks to design breeding plans for schemes that use genomic information: testing sound hypotheses by designing and executing controlled experiments using decision tools, such as mathematical-statistical models. These decision frameworks enable us to design optimum breeding plans by providing an objective and theoretical basis to make and validate breeding decisions, enabling us to understand the underlying mechanisms of breeding schemes with genomic information, and allowing us to test the practical implementation of breeding decisions against theoretical models. Genomic information is an exciting prospect for animal breeding, and there is clearly an important role for breeding plans that maximise long-term genetic gains in breeding schemes using genomic information. (C) 2014 Elsevier B.V. All rights reserved.

The world is faced with the challenge to meet the increasing demand for livestock products while conserving animal genetic resource diversity and maintaining environmental integrity. Genetic improvement of local breeds can help to improve the livelihood of the livestock keepers, to increase the production of animal products and to conserve genetic diversity. Implementing breeding schemes in developing countries has proven to be very difficult. The objective of this paper is to discuss the role of reproductive technologies for the creation and dissemination of genetic improvement in livestock populations in developing countries. In the paper opportunities are discussed for implementing breeding schemes which minimize the need for extensive pedigree and performance recording. It is shown that genetic progress can be generated in a small population. Community-based breeding schemes offer a good starting point for involving farmers in improving local breeds. Artificial insemination to exchange genetic material between communities offers an opportunity to increase the rate of genetic improvement while restricting the rate of inbreeding. Furthermore, artificial insemination is a promising technique for dissemination of genetic gain to producers at a relatively low cost. Opportunities to use semen sexing in a crossbreeding scheme are presented. It is concluded that tailor-made solutions and long-term commitment are needed in order to meet the needs of farmers to increase their livelihoods and to meet the needs of the growing population of consumers.

Reproductive techniques have a major impact on the structure of breeding programmes, the rate of genetic gain and dissemination of genetic gain in populations. This manuscript reviews the impact of reproductive technologies on the underlying components of genetic gain and inbreeding with special reference to the role of female reproductive technology. Evaluation of alternative breeding schemes should be based on genetic gain while constraining inbreeding. Optimum breeding schemes can be characterised by: decreased importance of sib information; increased accuracy at the expense of intensity; and a factorial mating strategy. If large-scale embryo cloning becomes feasible, this will have a small impact on the rate of genetic gain but will have a large impact on the structure of breeding programmes.

We studied different genetic models and evaluation systems to select against a genetic disease with additive, recessive or polygenic inheritance in genetic conservation schemes. When using optimum contribution selection with a restriction on the rate of inbreeding (ΔF) to select against a disease allele, selection directly on DNA-genotypes is, as expected, the most efficient strategy. Selection for BLUP or segregation analysis breeding value estimates both need 1–2 generations more to halve the frequency of the disease allele, while these methods do not require knowledge of the disease mutation at the DNA level. BLUP and segregation analysis methods were equally efficient when selecting against a disease with single gene or complex polygene inheritance, i.e. knowledge about the mode of inheritance of the disease was not needed for efficient selection against the disease. Smaller schemes or schemes with a more stringent restriction on ΔF needed more generations to halve the frequency of the disease alleles or the fraction of diseased animals. Optimum contribution selection maintained ΔF at its predefined level, even when selection of females was at random. It is argued that in the investigated small conservation schemes with selection against a genetic defect, control of ΔF is very important.