Empirical comparisons of feed-forward connectionist and conventional regression models for prediction of first lactation 305-day milk yield in Karan Fries dairy cows

Abstract

In this paper, two connectionist models are proposed based on different learning paradigms, viz., back propagation neural networks (BPNN) and radial basis function neural networks (RBFNN) to predict the first lactation 305-day milk yield (FLMY305) in Karan Fries (KF) dairy cattle. Also, a conventional multiple linear regression (MLR) model is developed for the prediction. In this study, all the models have been developed using a scientifically determined optimum dataset of representative breeding traits of the cattle. The prediction performances of the connectionist models are compared with that of the conventional model. This study shows that the RBFNN model performs relatively better than the MLR model. However, the BPNN model performs more or less in the close vicinity of the conventional MLR model. Hence, it is inferred that the connectionist models have potential as an alternative to the conventional models for predicting FLMY305 in KF cattle.

Development of Lifetime Milk Yield Equation Using Artificial Neural Network in Holstein Friesian Cross Breddairy Cattle and Comparison with Multiple Linear Regression Model

“…The analysis of lifetime milk yield (LTMY) is important for various reasons. It is helpful to select genetically superior bulls 4,5 . Milk yield prediction also helps in the selection of animals, which leads to optimal breeding strategies and increased annual genetic progress 6 .…”

“…Few studies focus on the capability of ANN to predict LTMY, first lactation 305-day milk yield (FL305DMY), fat and protein concentration of milk, etc. [4][5][6]8,[16][17][18][19] . Some studies showed that prediction of milk yield by using ANN model was more accurate than Wood's model 16,20 as well as by the MLR method [17][18][19]21 .…”

Examples of the Use of Data Mining Methods in Animal Breeding

“…In the species like cattle, in which the generation interval is approx. 5 years, every method that can contribute to the milk yield prediction in cows before the completion of lactation will speed up the process of bull identification and increase genetic progress [46]. In the cited work [47], the input variables in the neural models were the evaluation results from the first four test-day milkings, mean milk yield of a barn, lactation length, calving month, lactation number, proportion of Holstein-Friesian genes in animal genotype.…”

Artificial Neural Network versus Multiple Regression Analysis for Prediction of Lifetime Milk Production in Sahiwal Cattle

“…ANN approach needs specified algorithm to be transformed by a computer program (Grzesiak et al, 2003). The application of ANN in animal sciences and especially in animal breeding data is scanty (Grzesiak et al, 2003;Sharma et al, 2006;Hosseinia, 2007). Therefore, the present investigation was undertaken to predict lifetime milk production on the basis of first lactation traits by MRA and ANN approach and to compare their effectiveness for prediction of lifetime milk production in Sahiwal cattle.…”