Prediction Of Egg Weight from Egg Quality Characteristics by Using Regression Analysis Methods in White Leghorn Chicken

Arjun  Alapatt; J. K.  Chaudhary; N. Shyamsana  Singh; T. C.  Tolenkhomba; Girin  Kalita; Jagan  Mohanarao G.

doi:10.5455/ijlr.20210929030220

Prediction Of Egg Weight from Egg Quality Characteristics by Using Regression Analysis Methods in White Leghorn Chicken

Authors

Arjun Alapatt Department of AGB, CVSc A.H., Central Agricultural University CAU Aizawl Mizoram
J. K. Chaudhary Department of AGB, CVSc A.H., Central Agricultural University CAU Aizawl Mizoram
N. Shyamsana Singh Department of AGB, CVSc A.H., Central Agricultural University CAU Aizawl Mizoram
T. C. Tolenkhomba Department of AGB, CVSc A.H., Central Agricultural University CAU Aizawl Mizoram
Girin Kalita Department of LPM, CVSc A.H., Central Agricultural University CAU Aizawl Mizoram
Jagan Mohanarao G. Department of VPB, CVSc A.H., Central Agricultural University CAU Aizawl Mizoram

DOI:

https://doi.org/10.5455/ijlr.20210929030220

Keywords:

Egg weight, Egg quality traits, Elastic net, LASSO, Multiple Linear Regression, Ridge Regression, White Leghorn

Abstract

The study was aimed at evaluating the performance of regression methods such as Multiple Linear Regression (MLR), Ridge Regression (RR), LASSO regression, and Elastic net in the prediction of egg weight from various egg quality characteristics such as shape index (SI), yolk height (YH), yolk index (YI), albumen height (AH), Haugh unit (HU), albumen index (AI), yolk ratio (YR), albumen ratio (AR), shell weight (SW) and shell thickness (ST). For this, 100 white leghorn eggs were collected and egg quality parameters were recorded. In order to compare the predictive performances of the assigned methods, the goodness of fit criteria such as the coefficient of determination (R2), Adjusted R2, Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE) were utilized. The highest Adj. The R2 value was obtained for the Elastic net and MLR methods. Considering the multicollinearity existent, the Elastic net was identified as the best performing model.

References

Celik, S., Sogut, B., Sengul, T., Eyduran, E. and Sengul, A. Y. (2016). Usability of CART algorithm for determining egg quality characteristics influencing fertility in the eggs of Japanese quail. Revista Brasileira de Zootecnia, 45(11): 645-649.

Ciftsuren, M.N. and Akkol, S. (2018). Prediction of internal egg quality characteristics and variable selection using regularization methods: ridge, LASSO and elastic net. Archives Animal Breeding, 61(3): 279-284.

Faridi, A., France, J. and Golian, A. (2013). Neural network models for predicting early egg weight in broiler breeder hens. Journal of Applied Poultry Research, 22(1):1-8.

Kantardzic, M. (2011). Data Mining: Concepts, Models, Methods and Algorithms. John Wiley & Sons, Inc., USA.

Khan, M.A., Tariq, M.M., Eyduran, E., Tatliyer, A., Rafeeq, M., Abbas, F., Rashid, N., Awan, M.A. and Javed, K. (2014). Estimating body weight from several body measurements in Harnai Sheep without multicollinearity problem. Journal of Animal and Plant Sciences, 24(1): 120-126.

Khan, M.K.I., Khatun, M.J. and Kibria, A.K.M.G. (2004). Study the quality of eggs of different genotypes of chickens under semi-scavenging system at Bangladesh. Pakistan Journal of Biological Sciences, 7(12): 2163-2166.

Lahmann, N.A. and Kottner, J. (2011). Relation between pressure, friction and pressure ulcer categories: a secondary data analysis of hospital patients using CHAID methods. International Journal of Nursing Studies, 48(12): 1487-1494.

Liswaniso, S., Qin, N., Tyasi, T. L., Chimbaka, I. M., Sun, X., and Xu, R. (2021). Use of Data Mining Algorithms Chaid and Cart in Predicting Egg Weight from Egg Quality Traits of Indigenous Free-Range Chickens in Zambia. Advances in Animal and Veterinary Sciences, 9(2): 215-220.

McDonald, G.C. (2009). Ridge regression. Wiley Interdisciplinary Reviews: Computational Statistics, 1(1): 93-100.

Okoro, V.M.O., Ravhuhali, K.E., Mapholi, T.H., Mbajiorgu, E.F. and Mbajiorgu, C.A. (2017). Comparison of commercial and locally developed layers’ performance and egg size prediction using regression tree method. Journal of Apllied Poultry Research, 26(4): 476-484.

Ranstam, J. and Cook, J.A. (2018). LASSO regression. British Journal of Surgery, 105(10): 1348-1348.

Sumashree, N., Sharanagouda, H., Udaykumar, N., Ramappa, K.T. and Naik, N. (2019). Study on physical properties of exotic and indigenous breed eggs. International Journal of Current Microbiology and Applied Sciences, 8(9):327-334.

Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B (Statistical Methodology), 67(2):301-320.

Salgado, P.R., Vazquez, C., Sesma, B., Mendoza, N.P., Marin, H.A., Hernandez, E.W and Ortega, B.J. (2021). Prediction of Egg Weight from External Egg Traits of Guinea Fowl Using Multiple Linear Regression and Regression Tree Methods. Brazilian Journal of Poultry Science, 23(3): 1-6.

Downloads

Published

28-02-2022

How to Cite

Alapatt, A. ., Chaudhary, J. K. ., Singh, N. S. ., Tolenkhomba, T. C. ., Kalita, G. ., & Mohanarao G., J. . (2022). Prediction Of Egg Weight from Egg Quality Characteristics by Using Regression Analysis Methods in White Leghorn Chicken . International Journal of Livestock Research, 12(2), 40–48. https://doi.org/10.5455/ijlr.20210929030220