SIMPLE LINEAR AND MULTIPLE REGRESSION ANALYSES OF MORPHOLOGICAL TRAITS ON BODY WEIGHT IN FEMALE DORPER SHEEP LAMBS

  • Lebelo J. Selala School of Agricultural and Environmental Sciences, Department of Agricultural Economics and Animal Production University of Limpopo
  • Thobela L. Tyasi School of Agricultural and Environmental Sciences, Department of Agricultural Economics and Animal Production University of Limpopo https://orcid.org/0000-0002-3519-7806

Аннотация

Regression is a very influential statistical technique that uses independent variables to describe the dependent variable. The present study was performed to determine the best-fitted model from different regression techniques to predict body weight (BW) from morphological traits viz. heart girth (HG), rump height (RH), body length (BL), withers height (WH) and sternum height (SH). Twenty-eight female Dorper sheep lambs at birth were used for data collection. The simple and multiple regression were used for data analysis. The coefficients of determination (R2) and mean square error (MSE) were used to determine the best-fitted regression model. The results indicated that in simple regression the best fitted regression model for estimation of BW in female Dorper lamb was the model including BL (R2 = 0.79, MSE = 1.43), in multiple regression, the best fitted regression model for estimation of body weight in female Dorper lamb was model including HG, RH, BL, WH, SH (R2 = 0.89, MSE = 0.90) in the study. The results of simple regression suggest that BL can truly estimate the body weight in female Dorper sheep lambs. Multiple regression findings suggest that two or more morphological traits can truly estimate body weight in the female Dorper lambs. The study will help the farmers to accurately predict the body weight of the Dorper sheep lambs using the morphological traits.

Скачивания

Данные скачивания пока не доступны.

Литература

Karim G.M., Karym C.M. Prediction of body weight from body dimensions in Karadi sheep. Journal of Zankoy Sulaimani. JZS, 2ndInt. Conference of Agricultural Science. 2018. https://doi.org/10.17656/jzs.10660

Melesse A., Banerjee S., Lakew A., Mersha F., Hailemariam F., Tsegaye S., Makebo T. Variations in linear body measurements and establishing prediction equations for live weight of indigenous sheep populations of southern Ethiopia. Journal of Animal Science, 2013, vol. 2(1), pp. 15-25.

Taye M., Yilma M., Rischkowsky B., Dessie T., Okeyo M., Mekuriaw G., Haile A. Morphological characteristics and linear body measurements of Doyogena sheep in Doyogena district of SNNPR, Ethiopia. African Journal of Agricultural Research, 2016, vol. 11(48), pp. 4873-4885. https://doi.org/10.5897/AJAR2016.11826

Raja T.V., Venkatachalapathy R.T., Kannan A., Bindu K.A. Determination of best-fitted regression model for prediction of body weight in attappady black goats. Global Journal of Animal Breeding and Genetics, 2013, vol. 1 (1), pp. 020-025.

Tropal M., Yildiz N., Esenbuba N., Aksakal V., Macit M., Ozdemir M. Determination of best-fitted regression inodel for estimation of body weight in Awassi sheep. Journal of Applied Animal Research, 2003, vol. 23(2), pp. 201-208. https://doi.org/10.1080/09712119.2003.9706422

Mathapo M.C., Tyasi T.L. Prediction of Body Weight of Yearling Boer Goats from Morphometric Traits using Classification and Regression Tree. American Journal of Animal and Veterinary Sciences, 2021, vol. 16 (2), pp. 130-135. https://doi.org/10.3844/ajavsp.2021.130.135

Tyasi T.L., Mathye N.D., Danguru L.W., Rashijane L.T., Mokoena K., Makgowo K.M., Mathapo M.C., Molabe K.M., Bopape P.M., Maluleke D. Correlation and path analysis of body weight and biometric traits of Nguni cattle breed. Journal of Advanced Veterinary and Animal Research, 2020, vol. 7(1), pp. 148–55. http://doi.org/10.5455/javar.2020.g404


Просмотров аннотации: 75
Загрузок PDF: 45
Опубликован
2021-10-29
Как цитировать
Selala, L., & Tyasi, T. (2021). SIMPLE LINEAR AND MULTIPLE REGRESSION ANALYSES OF MORPHOLOGICAL TRAITS ON BODY WEIGHT IN FEMALE DORPER SHEEP LAMBS. Siberian Journal of Life Sciences and Agriculture, 13(5), 367-372. https://doi.org/10.12731/2658-6649-2021-13-5-367-372
Раздел
Сельскохозяйственные исследования