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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorSalah, Albert
dc.contributor.authorBeek, Milan van
dc.date.accessioned2023-08-11T00:02:37Z
dc.date.available2023-08-11T00:02:37Z
dc.date.issued2023
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/44639
dc.description.abstractThe Dutch Landrace goat (Nederlandse Landgeit in Dutch) is a goat species that has lived in the Netherlands since the 17th century. From around 1906 until 1910 goat farmers cross-bred the original Dutch Landrace goat with other goat species, causing the original genes to be lost. In this paper, a data pipeline is designed to assess the coat colour phenotype of the original Dutch Landrace goat from old paintings. This is done to answer the research questions: What colour(s) does the coat of the Dutch Landrace goat consist of? and What pattern(s) does the coat of the Dutch Landrace goat consist of? This research has shown that the colours black, brown and white occur in respectively 33,3%, 34,4% and 63,4% of goats. The three most common colour combinations are ’white’, ’brown and white’ and ’black and white’. Regarding coat patterns, it has been found that 58,3% of all observed goats contain one of four reoccurring patterns. These patterns are ’completely white’ (32,0%), ’dorsal stripe’ (13,3%), ’completely brown’ (9,3%) and ’black head with white body’ (4,0%). These findings give goat breeders consensus about the historic appearance of the Dutch Landrace goat, so breeding standards can be formed to which can be bred. Additionally, although a limited amount of data could be used, the results of this research and the database that has been constructed could form a basis for further research.
dc.description.sponsorshipUtrecht University
dc.language.isoEN
dc.subjectAssessment of the Coat Colour Phenotype of the Original Dutch Landrace goat, using a Machine Learning approach
dc.titleAssessment of the Coat Colour Phenotype of the Original Dutch Landrace goat, using a Machine Learning approach
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.courseuuApplied Data Science
dc.thesis.id21646


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