Improving the reproductive management of smallholder dairy cattle and the effectiveness of artificial insemination services using an integrated approach: A summary

Economic development is steadily progressing in Africa. One of the many consequences of this phenomenon is an increased demand for food arising from animal agriculture. This increased demand for animal products creates the possibility for a greater dispersion of economic resources, which to this point has been largely confined to urban areas. Thus, the opportunity for poverty alleviation in rural areas exists, but obstacles to this process must be removed. A high priority of the Member States (MS) of the African Regional Cooperative Agreement (AFRA) is thus to support research and the adoption of technologies that can help overcome these obstacles. Ironically, population expansion, although increasing demand, can have a net negative effect on livestock farming, by restricting the amount of land available for raising animals. Therefore, one major focus on increasing production of animal products must be the increase in productivity per animal and per unit of land. Improvement of nutrition is an important strategy for improving the output of livestock production and results can be obtained in the short-term. On the other hand, selective breeding is a highly effective and sustainable approach for increasing animal productivity in the long-term. Reproductive technologies such as artificial insemination (AI) allow single animals to have multiple progeny, reducing the number of parent animals required and allowing for significant increases in the intensity of selection, and proportional increases in genetic improvement of production. However, in order to benefit from the advantages of AI, farmers must detect the oestrus periods of their cows accurately, ensure that insemination is done at the correct time in relation to the onset of oestrus and detect any cows that later return to oestrus, so that they can be re-inseminated without delay. Even when these conditions are satisfied, optimum conception rates (CRs) will only be achieved if the quality of semen used is good and the AI technicians have adequate training and skills in the procedures for handling semen and performing inseminations. Although AI is used in many African countries, the above factors together with other socio-economic considerations specific to smallholder production systems, together with inadequate infrastructure for the efficient delivery of AI services, have often resulted in poor success rates. If these constraints can be overcome, not only would the farmers and service providers benefit, but the technology would also become more widely adopted and national goals of improving livestock production will be achieved faster. This will contribute to better food security and alleviation of rural poverty. Any attempt to improve the efficiency of AI has to be based on an understanding of the most important causes for failure under each specific production system. The traditional methods used for this rely on accurate recording and analysis of reproductive events such as oestrus, services, pregnancies and calvings. However, records are rarely kept by smallholders and, even when available, do not allow an assessment of the importance of factors such as efficiency and precision of oestrus detection by the farmers or incorrect timing of insemination. The application of radioimmunoassay (RIA) to measure the hormone progesterone, which is produced by a transient structure called the corpus luteum in the ovaries, in samples of blood or milk collected from cows at specific times in relation to AI provides a powerful tool for studies on reproductive efficiency and AI. It can determine whether, among other things, farmers are detecting oestrus accurately, AI has been done at the correct time, or the cow has not conceived and is likely to have returned to oestrus again. The advantage of the progesterone test is that non-pregnant animals can be accurately identified at an early stage, and action taken to observe them closely for heat and to get them mated again at the correct time. Furthermore, it can be used to assess the effectiveness of AI services, to identify de ficiencies and to monitor the results of interventions aimed at overcoming these deficiencies