Classification and Comparison of Eight Different Growth Curve Methods for Pekin Duck
ABSTRACT Background As in all livestock species, growth is the most important trait. Growth is an increase in size (height, length, weight) with advancing age and growth curve models provide a visual assessment of growth as a function of time. These models can be used for predicting body weight for...
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| Format: | Article |
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Wiley
2025-07-01
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| Series: | Veterinary Medicine and Science |
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| Online Access: | https://doi.org/10.1002/vms3.70476 |
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| author | Hasan Önder Mine Yılmaz Mustafa Şahin Uğur Şen Sibel Bozkurt Kadir Erensoy İsmail Gök Tolga Tolun Ahmet Uçar |
| author_facet | Hasan Önder Mine Yılmaz Mustafa Şahin Uğur Şen Sibel Bozkurt Kadir Erensoy İsmail Gök Tolga Tolun Ahmet Uçar |
| author_sort | Hasan Önder |
| collection | DOAJ |
| description | ABSTRACT Background As in all livestock species, growth is the most important trait. Growth is an increase in size (height, length, weight) with advancing age and growth curve models provide a visual assessment of growth as a function of time. These models can be used for predicting body weight for a specific age from a dimensional perspective. Objective In this study, we compared Brody, Gompertz, Logistic, Gamma, Schnute, Richards, Negative Exponential and von Bertalanffy models on the body weight of Pekin ducks raised in Türkiye using 109 female and 110 male birds for 10 weeks of age. Methods Ducks were reared with a feeding program identical to standard commercial practices. Growth models were fitted to the data of the average growth curve and for the individual growth curves. Parameters were estimated using the SAS 9.0 statistical package program, Proc Nlin procedure and Gauss‐Newton algorithm were used to model the curves. Results 3‐D clustering results showed that the models clustered in three clusters both female and male Pekin ducks. According to the goodness of fit criteria such as mean square prediction error, coefficient of determination, adjusted coefficient of determination, accuracy factor, bias factor, Durbin–Watson value, Akaike Information Criteria, corrected Akaike Information Criteria, Bayesian Information Criteria and standard error of the regression and interpretation of the growth curves and clustering hierarchical 3‐D dendrograms, the Schnute and Richards models, were found to be a suggestible model for 70 days growth of both female and male Pekin ducks, but the Richards model can be more recommendable due to calculation easiness. |
| format | Article |
| id | doaj-art-1fcd69cbfc2c4bf8976906d2634e345d |
| institution | Kabale University |
| issn | 2053-1095 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Veterinary Medicine and Science |
| spelling | doaj-art-1fcd69cbfc2c4bf8976906d2634e345d2025-08-20T03:31:21ZengWileyVeterinary Medicine and Science2053-10952025-07-01114n/an/a10.1002/vms3.70476Classification and Comparison of Eight Different Growth Curve Methods for Pekin DuckHasan Önder0Mine Yılmaz1Mustafa Şahin2Uğur Şen3Sibel Bozkurt4Kadir Erensoy5İsmail Gök6Tolga Tolun7Ahmet Uçar8Faculty of Agriculture Department of Animal Science Ondokuz Mayis University Samsun TurkeyFaculty of Agriculture Department of Animal Science Ondokuz Mayis University Samsun TurkeyFaculty of Agriculture, Department of Agricultural Biotechnology Kahramanmaraş Sütçü İmam University Kahramanmaraş TurkeyFaculty of Agriculture, Department of Department of Agricultural Biotechnology Ondokuz Mayis University Samsun TurkeyFaculty of Agriculture, Department of Animal Science Dicle University Diyarbakır TurkeyFaculty of Agriculture Department of Animal Science Ondokuz Mayis University Samsun TurkeyFaculty of Agriculture, Department of Bioengineering Kahramanmaraş Sütçü İmam University Kahramanmaraş TurkeyFaculty of Agriculture, Department of Bioengineering Kahramanmaraş Sütçü İmam University Kahramanmaraş TurkeyFaculty of Agriculture, Department of Animal Science Ankara University Ankara TurkeyABSTRACT Background As in all livestock species, growth is the most important trait. Growth is an increase in size (height, length, weight) with advancing age and growth curve models provide a visual assessment of growth as a function of time. These models can be used for predicting body weight for a specific age from a dimensional perspective. Objective In this study, we compared Brody, Gompertz, Logistic, Gamma, Schnute, Richards, Negative Exponential and von Bertalanffy models on the body weight of Pekin ducks raised in Türkiye using 109 female and 110 male birds for 10 weeks of age. Methods Ducks were reared with a feeding program identical to standard commercial practices. Growth models were fitted to the data of the average growth curve and for the individual growth curves. Parameters were estimated using the SAS 9.0 statistical package program, Proc Nlin procedure and Gauss‐Newton algorithm were used to model the curves. Results 3‐D clustering results showed that the models clustered in three clusters both female and male Pekin ducks. According to the goodness of fit criteria such as mean square prediction error, coefficient of determination, adjusted coefficient of determination, accuracy factor, bias factor, Durbin–Watson value, Akaike Information Criteria, corrected Akaike Information Criteria, Bayesian Information Criteria and standard error of the regression and interpretation of the growth curves and clustering hierarchical 3‐D dendrograms, the Schnute and Richards models, were found to be a suggestible model for 70 days growth of both female and male Pekin ducks, but the Richards model can be more recommendable due to calculation easiness.https://doi.org/10.1002/vms3.704763‐D clusteringgoodness of fitgrowth curvePekin duck |
| spellingShingle | Hasan Önder Mine Yılmaz Mustafa Şahin Uğur Şen Sibel Bozkurt Kadir Erensoy İsmail Gök Tolga Tolun Ahmet Uçar Classification and Comparison of Eight Different Growth Curve Methods for Pekin Duck Veterinary Medicine and Science 3‐D clustering goodness of fit growth curve Pekin duck |
| title | Classification and Comparison of Eight Different Growth Curve Methods for Pekin Duck |
| title_full | Classification and Comparison of Eight Different Growth Curve Methods for Pekin Duck |
| title_fullStr | Classification and Comparison of Eight Different Growth Curve Methods for Pekin Duck |
| title_full_unstemmed | Classification and Comparison of Eight Different Growth Curve Methods for Pekin Duck |
| title_short | Classification and Comparison of Eight Different Growth Curve Methods for Pekin Duck |
| title_sort | classification and comparison of eight different growth curve methods for pekin duck |
| topic | 3‐D clustering goodness of fit growth curve Pekin duck |
| url | https://doi.org/10.1002/vms3.70476 |
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