Genetic progress and economic benefit of sheep community-based breeding programmes out- and up-scaling options in Ethiopia
Mueller, Joaquin Pablo
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The expected genetic progress and economic benefit from implementing three sheep Community Based Breeding Programme (CBBP) out- and up-scaling strategies were investigated. Strategy 1 is to replicate average existing CBBPs, Strategy 2 is to increase the number of breeding males produced by average CBBPs and Strategy 3 is to intensify the use of breeding males with artificial insemination (AI). The strategies were modelled using Ethiopian Menz sheep field and market data and genetic progress and economic benefit were calculated using gene flow techniques. Different breeding program durations (T), planning horizons (H) and discount rates (r) were tested when breeding objective and selection criterion is six-month weight. Genetic progress expected in average existing CBBPs is 0.11 kg/year in the Nucleus and accumulated discounted economic benefit is 54560 $ with a return to investment of 5.2 when T = H = 20 years and r = 0.07. Thus, Strategy 1, replicating ongoing CBBPs, is highly beneficial. Strategy 2 was tested assuming 200, instead of 32, rams distributed to Base herds from the Nucleus and an additional cost of these rams of 10% their present market value, both assumptions considered realistic situations. Return to investment with this strategy results in 24.7 $ per $ invested. Additional cost of improved rams would need to be 2.5 times higher of current market value to make this strategy unprofitable. Strategy 3, taking advantage of AI, was tested in two ways, using fewer males to increase selection differential in nucleus (Case A) and increasing the number of females served with improved males in general flocks (Case B). Operational feasible AI programmes were modelled for both cases. Genetic progress of six-month weight in the Nucleus of a CBBP increase to 0.14 kg/year and return to investment results in 2.4 for Case A and 1.6 for Case B. Thus, Strategy 3 increases genetic progress by almost 30% of the expected genetic progress in average current CBBPs but return to investment is lower than in strategies without AI, this is due to substantial additional costs of AI programmes.