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dc.contributor.authorGhaderi-Zefrehei, M.en
dc.contributor.authorRafeie, F.en
dc.contributor.authorBahreini Behzadi, M. R.en
dc.contributor.authorNazari, S.en
dc.contributor.authorMuhaghegh-Dolatabady, M.en
dc.contributor.authorSamadian, F.en
dc.contributor.authorMaxwell, Thomas M. R.en
dc.contributor.authorAmirpour Najafabadi, H.en
dc.date.accessioned2019-02-08T03:37:50Z
dc.date.available2018-04-22en
dc.date.issued2018-08-09en
dc.identifier.citationGhaderi-Zefrehei, M., Rafeie, F., Bahreini Behzadi, M. R., Nazari, S., Muhaghegh-Dolatabady, M., Samadian, F., Maxwell, T. M. R., & Amirpour Najafabadi, H. (2018). Simple hierarchical and general nonlinear growth modeling in sheep. Turkish Journal of Veterinary and Animal Sciences, 42(4), 326-334. doi:10.3906/vet-1711-69en
dc.identifier.issn1300-0128en
dc.identifier.urihttps://hdl.handle.net/10182/10480
dc.description.abstractDifferential equations and advanced statistical models have been used to predict growth phenomena. In the present study, general nonlinear growth functions such as von Bertalanffy, Gompertz, logistic, and Brody, along with hierarchical modeling were applied to investigate the phenotypic growth pattern of Iranian Lori-Bakhtiari sheep. Growth data from 1410 Lori- Bakhtiari lambs were used in the present study. The results showed that the Brody function outperformed the other three nonlinear growth functions. In addition, including hierarchical growth modeling results allowed the adoption of many random effect structures, suggesting that hierarchical growth modeling has a useful role in growth data modeling. This method provides an estimation of growth parameters based on individual animals, improving individual growth selection. The results suggest this approach for growth modeling. Combining the strength of individual growth modeling with general growth modeling, e.g., von Bertalanffy, Gompertz, logistic, and Brody would be deeply appealing in the future. In this regard, dealing with sheep growth phenomenon using pure mathematical models, i.e. grey system theory models that could be new powerful prediction tools for breeders and experts, has not been done yet. However, running the analysis on large datasets will require significantly higher computational power than is ordinarily available.en
dc.format.extent326-334en
dc.language.isoenen
dc.publisherScientific and Technological Research Council of Turkey (TÜBİTAK)en
dc.relationThe original publication is available from - Scientific and Technological Research Council of Turkey (TÜBİTAK) - https://doi.org/10.3906/vet-1711-69 - https://journals.tubitak.gov.tr/veterinary/issue.htm?id=3643en
dc.relation.urihttps://doi.org/10.3906/vet-1711-69en
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectgrowth functionsen
dc.subjecthierarchical modelingen
dc.subjectLori-Bakhtiari sheepen
dc.subjectnonlinear modelsen
dc.subjectprediction of growth phenomenaen
dc.subjectDairy & Animal Scienceen
dc.titleSimple hierarchical and general nonlinear growth modeling in sheepen
dc.typeJournal Article
lu.contributor.unitLincoln Universityen
lu.contributor.unitFaculty of Agriculture and Life Sciencesen
lu.contributor.unitDepartment of Agricultural Sciencesen
lu.contributor.uniten
lu.contributor.uniten
dc.identifier.doi10.3906/vet-1711-69en
dc.subject.anzsrc070202 Animal Growth and Developmenten
dc.subject.anzsrc07 Agricultural and Veterinary Sciencesen
dc.subject.anzsrc070105 Agricultural Systems Analysis and Modellingen
dc.subject.anzsrc0702 Animal Productionen
dc.relation.isPartOfTurkish Journal of Veterinary and Animal Sciencesen
pubs.issue4en
pubs.organisational-group/LU
pubs.organisational-group/LU/Agriculture and Life Sciences
pubs.organisational-group/LU/Agriculture and Life Sciences/AGSC
pubs.organisational-group/LU/Research Management Office
pubs.organisational-group/LU/Research Management Office/2018 PBRF Staff group
pubs.publication-statusPublisheden
pubs.publisher-urlhttps://journals.tubitak.gov.tr/veterinary/issue.htm?id=3643en
pubs.volume42en
dc.identifier.eissn1303-6181en
dc.rights.licenceAttributionen
lu.identifier.orcid0000-0001-9204-1667


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