Prediction of first lactation 305-day milk yield in Karan Fries dairy cattle using ANN modeling |
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| Affiliation: | 1. Université de Perpignan via Domitia, IMAGES EA4218, Building S, 52 Av. Paul Alduy, 66860 Perpignan Cedex, France;2. Biosensor Lab., Department of Chemistry, BITS, Pilani-K.K. Birla Goa Campus, Goa 403726, India;3. Universidad Autónoma de Guerrero, Academic Unit of Engineering, Chilpancingo, Guerrero, Mexico;2. Animal and Grassland Research and Innovation Center, Teagasc, Moorepark, Fermoy, Co. Cork, Ireland;1. D-TEC – Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT), Argentina;2. ISISTAN – UNICEN – CONICET, Tandil, Buenos Aires, Argentina;3. CIVETAN – UNICEN – CONICET – CICPBA, Tandil, Buenos Aires, Argentina;4. Faculty of Veterinary Sciences – UNICEN, Argentina;1. School of Mathematics and Statistics, University College Dublin, Belfield, Dublin 4, Ireland;2. Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, P61 P302 Ireland;2. School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Camden, NSW 2570, Australia;3. Animal Welfare Science Centre, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia |
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| Abstract: | In this paper, an artificial neural network (ANN) model is proposed to predict the first lactation 305-day milk yield (FLMY305) using partial lactation records pertaining to the Karan Fries (KF) crossbred dairy cattle. A scientifically determined optimum dataset of representative breeding traits of the cattle is used to develop the model.Several training algorithms, viz., (i) gradient descent algorithm with adaptive learning rate; (ii) Fletcher–Reeves conjugate gradient algorithm; (iii) Polak–Ribiére conjugate gradient algorithm; (iv) Powell–Beale conjugate gradient algorithm; (v) Quasi-Newton algorithm with Broyden, Fletcher, Goldfarb, and Shanno (BFGS) update; and (vi) Levenberg–Marquardt algorithm with Bayesian regularization; along with various network architectural parameters, i.e., data partitioning strategy, initial synaptic weights, number of hidden layers, number of neurons in each hidden layer, activation functions, regularization factor, etc., are experimentally investigated to arrive at the best model for predicting the FLMY305.Also, a multiple linear regression (MLR) model is developed for the milk-yield prediction. The performances of ANN and MLR models are compared to assess the relative prediction capability of the former model.It emerges from this study that the performance of ANN model seems to be slightly superior to that of the conventional regression model. Hence, it is recommended that the ANNs can potentially be used as an alternative technique to predict FLMY305 in the KF cattle. |
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