Growth curve analysis in different generations of Boer x Central Highland goats using alternative estimation models

cg.contactzeleke.t2007@gmail.comen_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.centerInternational Livestock Research Institute - ILRIen_US
cg.contributor.centerAmhara Regional Agricultural Research Institute - ARARIen_US
cg.contributor.centerAmhara Regional Agricultural Research Institute, Debre Birhan Agricultural Research Center - ARARI-DBARCen_US
cg.contributor.centerAmhara Regional Agricultural Research Institute, Sirinka Agricultural Research Center - ARARI - SARCen_US
cg.contributor.centerBahir Dar University, College of Agriculture and Environmental Science - BDU - CAESen_US
cg.contributor.centerAndasa Livestock Research Centeren_US
cg.contributor.crpResilient Agrifood Systems - RAFSen_US
cg.contributor.funderAmhara Regional Agricultural Research Institute - ARARIen_US
cg.contributor.initiativeSustainable Animal Productivityen_US
cg.contributor.project-lead-instituteInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.coverage.countryETen_US
cg.coverage.countryMLen_US
cg.coverage.countryUGen_US
cg.coverage.countryVNen_US
cg.coverage.regionEastern Africaen_US
cg.coverage.regionWestern Africaen_US
cg.coverage.regionSouth-Eastern Asiaen_US
cg.creator.idGetachew, Tesfaye: 0000-0002-0544-6314en_US
cg.identifier.doihttps://dx.doi.org/10.1371/journal.pone.0293493en_US
cg.identifier.doihttps://dx.doi.org/10.1371/journal.pone.0293493.s001en_US
cg.isijournalISI Journalen_US
cg.issn1932-6203en_US
cg.journalPLoS ONEen_US
cg.subject.actionAreaResilient Agrifood Systemsen_US
cg.subject.agrovocrisk mitigationen_US
cg.volume18en_US
dc.contributorKefale, Alemuen_US
dc.contributorDeribe, Belayen_US
dc.contributorTilahun, Mekonnenen_US
dc.contributorLakew, Mesfinen_US
dc.contributorAlebachew, Getachewen_US
dc.contributorBelayneh, Negusen_US
dc.contributorZegeye, Asresen_US
dc.contributorYizengaw, Liuelen_US
dc.contributorAlemayehu, Kefyalewen_US
dc.contributorGetachew, Tesfayeen_US
dc.contributorKebede, Damitieen_US
dc.contributorTaye, Mengistieen_US
dc.contributorGizaw, Solomonen_US
dc.creatorTesema, Zelekeen_US
dc.date.accessioned2024-01-22T17:27:51Z
dc.date.available2024-01-22T17:27:51Z
dc.description.abstractGrowth curve analysis can help to optimize the management, determine nutritional requirements, predict the weight of animals at a specific age, and to select highly productive animals. Therefore, this study aimed to find the best-fitted nonlinear functions to provide a specific shape of the growth curve from birth to yearling age in different generations of Boer x Central Highland goats. Gompertz, Logistic, Brody, Von Bertalanffy, Monomolecular, Negative exponential, and Richards models were evaluated to quantify their ability to describe the biological growth curve. Root mean square error (RMSE), Bayesian information criterion (BIC), adjusted coefficient of determination (AdjR2), and Akaike’s information criterion (AIC) were used to evaluate the goodness of fit and flexibility of the models. Data were analyzed using the nonlinear regression procedure of SAS. High AdjR2 and lower AIC, BIC, and RMSE values are indicators of best-fitted model. The best-fitting model for the first filial generation (F1), second filial generation (F2), and male goats’ growth data was Brody function, whereas the Richards model, followed by Brody, best described the growth of third filial generation (F3) and female goats. The values of parameter A (asymptotic weight) for F1, F2, F3, female, and male goats based on the Brody model were 30.5±1.32, 28.2±1.38, 24.4 ±1.04, 27.8±0.94, and 29.8±1.32 kg for F1, F2, F3, female, and male goats, respectively. As per the best-fitted growth function, the asymptotic weight tended to reduce when the filial generation increased. The asymptotic weight for male goats was higher than for female goats. F1 had a slightly small value of parameter K, followed by F2 and F3. Both males and females had similar maturity rates. Based on the Brody function, the correlation between maturation rate and mature weight was high (-0.98, P<0.001). The correlation estimates for A-B and B-K were 0.27 and -0.15, respectively. Brody was best fitted for most goat categories, although Richards, followed by Brody, was best fitted for female and F3 goats. Besides, Brody could be better than Richards due to the ease of interpretation, convergence, and applicability for a small sample size. Therefore, the Brody function can predict the mature body weight, maturation rate, and growth rate of Boer x Central Highland goats and be used to formulate breeding and management strategies for profitable goat farming.en_US
dc.formatPDFen_US
dc.identifierhttps://mel.cgiar.org/reporting/downloadmelspace/hash/68e6ea802815d393926da2d74816bd39/v/eb8d30b61bb2f94fea106db569bc680ben_US
dc.identifier.citationZeleke Tesema, Alemu Kefale, Belay Deribe, Mekonnen Tilahun, Mesfin Lakew, Getachew Alebachew, Negus Belayneh, Asres Zegeye, Liuel Yizengaw, Kefyalew Alemayehu, Tesfaye Getachew, Damitie Kebede, Mengistie Taye, Solomon Gizaw. (10/11/2023). Growth curve analysis in different generations of Boer x Central Highland goats using alternative estimation models. PLoS ONE, 18.en_US
dc.identifier.statusOpen accessen_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/69094
dc.languageenen_US
dc.publisherPlos Oneen_US
dc.rightsCC-BY-4.0en_US
dc.sourcePLoS ONE;18,en_US
dc.subjectcrossbreedingen_US
dc.subjectindigenous goatsen_US
dc.titleGrowth curve analysis in different generations of Boer x Central Highland goats using alternative estimation modelsen_US
dc.typeJournal Articleen_US
dcterms.available2023-11-10en_US
dcterms.issued2023-11-10en_US
mel.impact-factor3.7en_US

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