Using phenotypic distribution models to predict livestock performance

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cg.authorship.typesCGIAR and advanced research instituteen_US
cg.contributor.affiliationWageningen University & Researchen_US
cg.contributor.affiliationInternational Livestock Research Instituteen_US
cg.contributor.crpLivestocken_US
cg.contributor.donorKoepon Foundationen_US
cg.coverage.countryEthiopiaen_US
cg.coverage.iso3166-alpha2ETen_US
cg.coverage.regionAfricaen_US
cg.coverage.regionEastern Africaen_US
cg.creator.identifierTadelle Dessie: 0000-0002-1630-0417en_US
cg.howPublishedFormally Publisheden_US
cg.identifier.doihttps://doi.org/10.1038/s41598-019-51910-6en_US
cg.isijournalISI Journalen_US
cg.issn2045-2322en_US
cg.issue1en_US
cg.journalScientific Reportsen_US
cg.reviewStatusPeer Reviewen_US
cg.subject.ilriANIMAL BREEDINGen_US
cg.subject.ilriBREEDSen_US
cg.subject.ilriCHICKENSen_US
cg.subject.ilriINDIGENOUS BREEDSen_US
cg.subject.ilriLIVESTOCKen_US
cg.subject.ilriPOULTRYen_US
cg.volume9en_US
dc.contributor.authorLozano Jaramillo, Mariaen_US
dc.contributor.authorWorku, Setegnen_US
dc.contributor.authorDessie, Tadelleen_US
dc.contributor.authorKomen, Hansen_US
dc.contributor.authorBastiaansen, John W.M.en_US
dc.date.accessioned2019-10-28T10:10:23Zen_US
dc.date.available2019-10-28T10:10:23Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/105534en_US
dc.titleUsing phenotypic distribution models to predict livestock performanceen_US
dcterms.abstractLivestock production systems of the developing world use indigenous breeds that locally adapted to specific agro-ecologies. Introducing commercial breeds usually results in lower productivity than expected, as a result of unfavourable genotype by environment interaction. It is difficult to predict of how these commercial breeds will perform in different conditions encountered in e.g. sub-Saharan Africa. Here, we present a novel methodology to model performance, by using growth data from different chicken breeds that were tested in Ethiopia. The suitability of these commercial breeds was tested by predicting the response of body weight as a function of the environment across Ethiopia. Phenotype distribution models were built using machine learning algorithms to make predictions of weight in the local environmental conditions based on the productivity for the breed. Based on the predicted body weight, breeds were assigned as being most suitable in a given agro-ecology or region. We identified the most important environmental variables that explained the variation in body weight across agro-ecologies for each of the breeds. Our results highlight the importance of acknowledging the role of environment in predicting productivity in scavenging chicken production systems. The use of phenotype distribution models in livestock breeding is recommended to develop breeds that will better fit in their intended production environment.en_US
dcterms.accessRightsOpen Accessen_US
dcterms.audienceScientistsen_US
dcterms.available2019-10-25en_US
dcterms.bibliographicCitationLozano-Jaramillo, M., Alemu, S.W., Dessie, T., Komen, H. and Bastiaansen, J.W.M. 2019. Using phenotypic distribution models to predict livestock performance. Scientific Reports 9:15371.en_US
dcterms.issued2019-10-25en_US
dcterms.languageenen_US
dcterms.licenseCC-BY-4.0en_US
dcterms.publisherSpringeren_US
dcterms.subjectanimal breedingen_US
dcterms.subjectchickensen_US
dcterms.subjectpoultryen_US
dcterms.subjectindigenous breedsen_US
dcterms.subjectlivestocken_US
dcterms.typeJournal Articleen_US

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African Chicken Genetic Gains (ACGG)
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