Prioritising support for cost effective rare breed conservation using multi-criteria decision analysis

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Abstract

Farm Animal Genetic Resources (FAnGR) are threatened by breed homogenisation. Rare breeds may carry important genes that allow breeders to respond to global production challenges including climate change and emerging disease risk. Yet, exploration of approaches to improve cost-effectiveness of investments in farm animal genetic diversity has been limited. We employ multi-criteria decision analysis (MCDA) to investigate how rare breed incentive schemes can be rationalised. A performance matrix was used to assess 19 UK cattle native breeds at risk, in terms of diversity, marketability, and endangerment criteria, and an expert workshop was used to assign weights for prioritisation. The workshop participants suggested that criteria pertaining to diversity, marketability and endangerment should be weighted 30, 20, and 50%, respectively. A principal component analysis (PCA) on the criteria suggested that fewer criteria could be used to characterise breed status but that each criteria node contributed effectively in explaining variation in breed scores. Breed scores from the MCDA model were used in a hypothetical exercise to rationalise monetary investments across the case study breeds. The allocation of the hypothetical breed improvement fund (BIF) revealed that the greatest variation in the allocation of incentives occurred when marketability was weighted highest, while least variation occurred when endangerment received the highest weight. We suggest MCDA can support more targeted investments in diversity by considering the multiple factors that may be driving extinction risk in addition to the cultural and diversity attributes that compliment conservation.

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APA

Wainwright, W., Ahmadi, B. V., Mcvittie, A., Simm, G., & Moran, D. (2019). Prioritising support for cost effective rare breed conservation using multi-criteria decision analysis. Frontiers in Ecology and Evolution, 7(APR). https://doi.org/10.3389/fevo.2019.00110

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