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Innovation in livestock genetic improvement

Innovation in livestock genetic improvement

Islam, Md Mofakkarul, Renwick, Alan, Lamprinopoulou, Chrysa and Klerkx, Laurens (2013) Innovation in livestock genetic improvement. EuroChoices, 12 (1). pp. 42-47. ISSN 1478-0917 (Print), 1746-692X (Online) (doi:

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The application of genetic selection technologies in livestock breeding offers unique opportunities to enhance the productivity, profitability and competitiveness of the livestock industry. However, there is a concern that the uptake of these technologies has been slower in the sheep and beef sectors in comparison to the dairy, pig and poultry sectors. This article discusses how an agricultural innovation systems perspective can help identify the dynamics of technology uptake in the livestock sector using the adoption of Estimated Breeding Values (EBVs) in sheep production in Scotland as a case study. Five major (systemic) challenges were identified: a weakly integrated sheep supply chain (market structure failure); the presence of a powerful faction antagonistic towards EBVs (network failure); a challenging policy environment (hard institutional failure); a dismantled and weak advisory service with regard to EBVs (capabilities failure); and an outdated and inflexible data management system (infrastructure failure). Whilst efforts are being made to address the barriers individually it is argued that a more holistic approach is needed to innovation. The findings are shown to have wider implications for innovation within the agricultural sector of the European Union.

Item Type: Article
Uncontrolled Keywords: innovation system, livestock, genetic improvement, Scotland
Subjects: S Agriculture > S Agriculture (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Natural Resources Institute
Faculty of Engineering & Science > Natural Resources Institute > Livelihoods & Institutions Department
Last Modified: 10 Jul 2020 12:03
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None

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