Estimating Heritabilities and Breeding Values for Real and Predicted Milk Production in Holstein Dairy Cows with Artificial Neural Network and Multiple Linear Regression Models
Subject Areas : Camel
M.
Nosrati
1
(Department of Animal Science, Faculty of Animal Science and Fishery, Sari Agricultural Sciences and Natural Resources University, Sari, Iran)
S.H.
Hafezian
2
(Department of Animal Science, Faculty of Animal Science and Fishery, Sari Agricultural Sciences and Natural Resources University, Sari, Iran)
M.
Gholizadeh
3
(Department of Animal Science, Faculty of Animal Science and Fishery, Sari Agricultural Sciences and Natural Resources University, Sari, Iran)
Keywords: Artificial Neural Network, milk production, generation interval, milk prediction, multi-ple linear regression,
Abstract :
The success of a dairy herd depends on milk production. Prediction of future records can reduce recording time, accelerate the computation of genetic evaluations, decrease generation interval, and increase genetic progress. Multiple linear regression (MLR) is the most common prediction method. However, artificial neural networks (ANN) can handle complex linear and non-linear functions to solve a wide range of prediction problems. In this study, MLR and ANN models were applied to the prediction of 305-day milk production in the first and second lactations of dairy cows using variables related to milk production, test-day records and estimated breeding values (EBVs). The 305-day first lactation records were also used to predict 305-day second lactation records. ANN and MLR predictions were compared in terms of accuracy and efficiency. Dairy records from 7856 dairy cows in two herds were used in this research. The best ANN model was a multilayer perceptron with a back-propagation learning algorithm. Results showed that ANN and MLR predicted values were acceptable. However, ANN prediction accuracies for 305-day milk production in the first and second lactations were higher than those of MLR. Correlation coefficients between real and predicted 305-day milk production records in the first and second lactations ranged from 0.88 to 0.96 for ANN and from 0.66 to 0.89 for MLR. Adding test-day records and EBVs for 305-day milk production in the first lactation to the set of independent variables used to predict 305-day milk production in the second lactation increased more the prediction efficiency of ANN than MLR. Thus, ANN could be used to decrease the interval between collecting records and computing animal breeding values. In addition, real data and ANN-predicted data from the first lactation were used to compute EBVs. The correlation between EBVs with real and predicted data was 0.93. Results suggested that ANN could be useful for predicting complex traits using high dimensional genomic information.
Abbasi A.R., Bahreini Behzadi M.R. and Talebi M.A. (2016). Prediction of some carcass characteristics from body measurements using linear regression and artificial neural network methods in Lori-Bakhtiari sheep. J. Rumin. Res. 3, 1-20.
Adamowski J. and Chan H.F. (2011). A wavelet neural network conjunction model for groundwater level forecasting. J. Hydrol. 407(1), 28-40.
Adesh K., Sharma R.K. and Kasana H.S. (2007). Prediction of first lactation 305-day milk yield in Karan Fries dairy cattle using ANN modeling. Appl. Soft Comput. 7, 1112-1120.
Ali-Jafari Z. (2016). Modeling of milk production curve in Holstein dairy cows using artificial neural network and genetic algorithm. MS Thesis. Shahid Bahonar Univ., Kerman, Iran.
Ashton Q. (2013). Advances in Machine Learning Research and Application. Nova Publishers, Atlanta, Geogia.
Bahreini Behzadi M.R. (2015). Comparison of different growth models and artificial neural network to fit the growth curve of Lori-Bakhtiari sheep. J. Rumin. Res. 3, 125-148.
Bahreini Behzadi M.R. and Aslaminejad A.A. (2010). A comparison of neural network and nonlinear regression predictions of sheep growth. J. Anim. Vet. Adv. 9, 2128-2131.
Boichard D., Ducrocq V. and Fritz S. (2015). Sustainable dairy cattle selection in the genomic era. J. Anim. Breed. Genet. 132(2), 135-143.
Boniecki P., Lipiński M., Koszela K. and Przyby J. (2013). Neural prediction of cows’ milk yield according to environment temperature. African J. Biotechnol. 12, 4707-4712.
Chaturvedi S., Yadav R.L., Gupta A.K. and Sharma A.K. (2013). Life time milk amount prediction in dairy cows using artificial neural networks. Int. J. Res. Res. Rev. 5, 1-6.
Ghaderi Zefrehei M., Bahreini Behzadi M.R., Fayaz M.R. and Sharifi S. (2016). Association between calving interval and productive traits in dairy cattle over different inseminations using artificial neural network. J. Rumin. Res. 3(4), 169-187.
Gorgulu O. (2012). Prediction of 305-day milk yield in Brown Swiss cattle using artificial neural networks. South African J. Anim. Sci. 42(3), 280-287.
Grzesiak W., Lacroix R., Wójcik J. and Blaszczyk P. (2003). A comparison of neural network and multiple regression pre-dictions for 305-day lactation yield using partial lactation records. Canadian J. Anim. Sci. 83, 307-310.
Guresen E., Kayakutlu G. and Daim, T.U. (2011). Using artificial neural network models instock market index prediction. Exp. Syst. Appl. 38, 10389-10397.
Hashemi A. and Nayebpour M. (2008). Estimation of genetic and phenotypic parameters for milk production in Holstein-Friesian cows. Res. J. Biol. Sci. 3(6), 678-682.
Hosseinia P., Edris M., Edriss M.A. and Nilforooshan M.A. (2007). Prediction of second parity milk yield and fat percentage of dairy cows based on first parity information using neural network system. J. Appl. Sci. 7, 3274-3279.
Ince D. and Sofu A. (2013). Estimation of lactation milk yield of Awassi sheep with artificial neural network modeling. Small Rumin. Res. 113, 15-19.
Karadas K., Tariq M., Tariq M.M. and Eyduran E. (2017). Measuring predictive performance of data mining and artificial neural network algorithms for predicting lactation milk yield in indigenous Akkaraman Sheep. Pakistan J. Zool. 49(1), 1-7.
Kasy M. and Kummer M. (2008). Market Entry in E-Commerce. The Networks, Electronic Commerce, and Telecommunications (NET) Institute. Available at: http://www.NETinst.org.
Khazaei J. and Nikosiar M. (2008). Approximating milk yield and milk fat and protein concentration of cows through the use of mathematical and artificial neural networks models. Pp. 123-129 in Proc. World Conf. Agric. Inf. IT/IAALD/AFITA WCCA 2008, Tokyo, Japan.
Miglior F., Fleming A., Malchiodi F., Brito L.F., Martin P. and Baes C.F. (2017). A 100-year review: Identification and genetic selection of economically important traits in dairy cattle. J. Dairy Sci. 100(12), 10251-10271.
Njubi D.M., Wakhungu J.W. and Badamana M.S. (2009). Milk yield prediction in Kenyan Holstein-Friesian cattle using computer neural networks. Livest. Res. Rural Dev. Available at: https://www.lrrd.cipav.org.co/lrrd21/4/njub21046.htm.
Njubi D.M., Wakhungu J.W. and Badamana M.S. (2010). Use of test-day records to predict first lactation 305-day milk yield using artificial neural network in Kenyan Holstein–Friesian dairy cows. Trop. Anim. Health Prod. 42, 639-644.
Nobari K., Bane H., Esmaeilkhanian S., Yousefi K. and Samiei R. (2019). Comparison of linear model and artificial neural network to prediction of milk yield using first recorded parity. J. Rumin. Res. 6(4), 89-100.
Park S.J., Hwang C.S. and Vlek P.L.G. (2005). Comparison of adaptive techniques to predict crop yield responseunder varying soil and land management conditions. Agric. Syst. J. 85, 59-81.
Pour Hamidi S., Mohammadabadi M.R., Asadi Foozi M. and Nezamabadi-Pour H. (2017). Prediction of breeding values for the milk production trait in Iranian Holstein cows applying artificial neural networks. J. Livest. Sci. Technol. 5(2), 53-61.
Qotbi A.A.A., Hossein Nia P., Seidavi A. and Ghovvati S. (2010). Predictions of semen production in ram using phenotypic traits by artificial neural network. African J. Biotechnol. 9(30), 4822-4825.
Safari R. (2016). The use of artificial neural networks in predicting the production of Holstein dairy cows. MS Thesis. University of Tabriz, Tabriz, Iran.
Shahinfar S., Mehrabani-Yeganeh H., Lucas C., Kalhor A., Kazemian M., Kent A. and Weigel K.A. (2012). Prediction of breeding values for dairy cattle using artificial neural networks and neuro-fuzzy systems. Comput. Math. Methods Med. 2012, 1-9.
Visentin G., McDermott A., McParland S., Berry D.P., Kenny O.A., Brodkorb A., Fenelon M.A. and De Marchi M. (2015). Prediction of bovine milk technological traits from mid-infrared spectroscopy analysis in dairy cows. J. Dairy Sci. 98(9), 6620-6629.