Machine learning model for maternal quality in sheep

dc.contributor.authorÖdevci, Bahadır
dc.contributor.authorEmsen, Ebru
dc.date.accessioned2021-02-19T19:32:40Z
dc.date.available2021-02-19T19:32:40Z
dc.date.issued2019
dc.description.abstractThis paper aims to identify determinant traits of ewes by measuring their impact on lamb survival. For that, we devised a machine learning model that correlates ewe traits to lamb survival, and figured out as to which ewe traits explain the correlation and hence help us to identify the better mother. In this study, we kept pregnant ewes under 24 h observation by two researchers starting approximately three days before expected parturition dates. We conducted the study using native and crossbreed lambs produced in high altitude and cold climate region. It is critical to note that parturation took place with minimum interruption unless there is a birth difficulty. Independent variables used in the machine learning model pertain to mother's behaviours during parturation, however, we also took into consideration factors like dam breed, dam body weight at lambing, age of dam, litter size at birth, lamb breed and sex. Lamb survival is a nominal output variable, hence we tried out several classification algorithms like Bayesian Methods, Artificial Neural Networks, Support Vector Machine and Tree Based Algorithms. Classification algorithms applied for lamb survival were Bayesian Methods, Artificial Neural Networks, Support Vector Machine and Trees. RandomForest algorithm was found best performer among tree algorithms. We were able to present tree visualisation for mothering ability with 80% accuracy rate and 0.43 Kappa Statistics. The result of the study shows that grooming behaviour is the first determinant mothering ability. If the grooming duration is longer than 15 minutes, then it is a good mother.en_US
dc.description.sponsorshipAgriculture and Food Development Authority (Teagasc),An Roinn Talmhaiochta, Bia agus Mara, Department of Agriculture, Food and the Marine,Dairymaster,et al.,SoundTalks,Zoetis Publisher: Organising Committee of the 9th European Conference on Precisionen_US
dc.identifier.citation0
dc.identifier.endpage73en_US
dc.identifier.isbn978-184170654-2
dc.identifier.scopus2-s2.0-85073712406en_US
dc.identifier.scopusqualityN/A
dc.identifier.startpage69en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12469/3966
dc.identifier.wosqualityN/A
dc.institutionauthorÖdevci, Bahadıren_US
dc.institutionauthorEmsen, Ebruen_US
dc.language.isoenen_US
dc.publisherOrganising Committee of the 9th European Conference on Precision Livestock Farming (ECPLF), Teagasc, Animal and Grassland Research and Innovation Centreen_US
dc.relation.journalPrecision Livestock Farming 2019 - Papers Presented at the 9th European Conference on Precision Livestock Farming,en_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLamb survivalen_US
dc.subjectMachine learningen_US
dc.subjectMaternal qualityen_US
dc.titleMachine learning model for maternal quality in sheepen_US
dc.typeBook Parten_US
dspace.entity.typePublication

Files