Prediction of first lactation milk geld of Boran cattle and their crosses with Holstein-Friesian in Central Ethiopia using multiple regression and principal components analysis
The data on 60 Ethiopian Boran and 428 Boran-Friesian crosses spread over 15 years (1990-2004) were used lo predict first lactation milk yield (FLYD) based on earlier expressed traits using multiple regression analysis and principal components analysis. In the prediction of FLYD in Ethiopian Boran c...
| Autores principales: | , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
2008
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/67297 |
| _version_ | 1855537889728790528 |
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| author | Haile, Aynalem Joshi, B.K. Ayalew, W. Tegegne, Azage Singh, A. Chakravarty, A.K. |
| author_browse | Ayalew, W. Chakravarty, A.K. Haile, Aynalem Joshi, B.K. Singh, A. Tegegne, Azage |
| author_facet | Haile, Aynalem Joshi, B.K. Ayalew, W. Tegegne, Azage Singh, A. Chakravarty, A.K. |
| author_sort | Haile, Aynalem |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The data on 60 Ethiopian Boran and 428 Boran-Friesian crosses spread over 15 years (1990-2004) were used lo predict first lactation milk yield (FLYD) based on earlier expressed traits using multiple regression analysis and principal components analysis. In the prediction of FLYD in Ethiopian Boran cattle, the step-wise regression found only AFCONC to be significantly associated with FLYD (R2=13.6%) in adjusted data. In the Boran-Fnesian crosses, Gain 2, BWT, AFS and UWT were significantly associated with FLYD (R2= 15.21%). The first principal component (PC1) out of 4 fitted composite variables was significantly associated with FLYD in Ethiopian Boran breed (Rk 13.29%). The third principal component (PC3) was more related to FLYD in Boran-Friesian crosses (R2= 8 1.83%) and inclusion of next important principal component PC1 improved accuracy of prediction to 13.76%. The rest of the components were significant and dropped. The results indicated that first lactation milk yield could be predicted directly from the early expressed growth and reproductive performance traits, though with lower accuracy, and transformation of original variables into principal components offers no additional advantage in terms of accuracy of prediction. |
| format | Journal Article |
| id | CGSpace67297 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2008 |
| publishDateRange | 2008 |
| publishDateSort | 2008 |
| record_format | dspace |
| spelling | CGSpace672972023-02-15T10:10:59Z Prediction of first lactation milk geld of Boran cattle and their crosses with Holstein-Friesian in Central Ethiopia using multiple regression and principal components analysis Haile, Aynalem Joshi, B.K. Ayalew, W. Tegegne, Azage Singh, A. Chakravarty, A.K. cattle breeds daries research The data on 60 Ethiopian Boran and 428 Boran-Friesian crosses spread over 15 years (1990-2004) were used lo predict first lactation milk yield (FLYD) based on earlier expressed traits using multiple regression analysis and principal components analysis. In the prediction of FLYD in Ethiopian Boran cattle, the step-wise regression found only AFCONC to be significantly associated with FLYD (R2=13.6%) in adjusted data. In the Boran-Fnesian crosses, Gain 2, BWT, AFS and UWT were significantly associated with FLYD (R2= 15.21%). The first principal component (PC1) out of 4 fitted composite variables was significantly associated with FLYD in Ethiopian Boran breed (Rk 13.29%). The third principal component (PC3) was more related to FLYD in Boran-Friesian crosses (R2= 8 1.83%) and inclusion of next important principal component PC1 improved accuracy of prediction to 13.76%. The rest of the components were significant and dropped. The results indicated that first lactation milk yield could be predicted directly from the early expressed growth and reproductive performance traits, though with lower accuracy, and transformation of original variables into principal components offers no additional advantage in terms of accuracy of prediction. 2008 2015-07-15T06:53:39Z 2015-07-15T06:53:39Z Journal Article https://hdl.handle.net/10568/67297 en Open Access Haile, A., Joshi, B.K., Ayalew, W., Tegegne, A., Singh, A. and Chakravarty, A.K. 2008. Prediction of first lactation milk geld of Boran cattle and their crosses with Holstein-Friesian in Central Ethiopia using multiple regression and principal components analysis. Indian Journal of'AnirnaE Sciences 78 (1): 66-69. |
| spellingShingle | cattle breeds daries research Haile, Aynalem Joshi, B.K. Ayalew, W. Tegegne, Azage Singh, A. Chakravarty, A.K. Prediction of first lactation milk geld of Boran cattle and their crosses with Holstein-Friesian in Central Ethiopia using multiple regression and principal components analysis |
| title | Prediction of first lactation milk geld of Boran cattle and their crosses with Holstein-Friesian in Central Ethiopia using multiple regression and principal components analysis |
| title_full | Prediction of first lactation milk geld of Boran cattle and their crosses with Holstein-Friesian in Central Ethiopia using multiple regression and principal components analysis |
| title_fullStr | Prediction of first lactation milk geld of Boran cattle and their crosses with Holstein-Friesian in Central Ethiopia using multiple regression and principal components analysis |
| title_full_unstemmed | Prediction of first lactation milk geld of Boran cattle and their crosses with Holstein-Friesian in Central Ethiopia using multiple regression and principal components analysis |
| title_short | Prediction of first lactation milk geld of Boran cattle and their crosses with Holstein-Friesian in Central Ethiopia using multiple regression and principal components analysis |
| title_sort | prediction of first lactation milk geld of boran cattle and their crosses with holstein friesian in central ethiopia using multiple regression and principal components analysis |
| topic | cattle breeds daries research |
| url | https://hdl.handle.net/10568/67297 |
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