Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine Volume 2012, Article ID 127130, 9 pages doi:10.1155/2012/127130 Research Article Prediction of Breeding Values for Dairy Cattle Using Artificial Neural Networks and Neuro-Fuzzy Systems Saleh Shahinfar, 1 Hassan Mehrabani-Yeganeh, 1 Caro Lucas, 2 Ahmad Kalhor, 2 Majid Kazemian, 2 and Kent A. Weigel 3 1 Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran 2 Center of Excellence: Control and Intelligent Processing, School of Electrical and Computer Engineering, University of Tehran, Iran 3 Department of Dairy Science, University of Wisconsin-Madison, Madison, WI 53706, USA Correspondence should be addressed to Saleh Shahinfar, shahinfar@wisc.edu Received 12 May 2012; Revised 9 August 2012; Accepted 9 August 2012 Academic Editor: Chunmei Liu Copyright © 2012 Saleh Shahinfar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV) of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP) with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production. 1. Introduction Machine learning techniques, such as decision trees and artificial neural networks (ANN), are used increasingly in agriculture, because they are quick, powerful, and flexible tools for classification and prediction applications, particu- larly those involving nonlinear systems [1]. These techniques have been used for detection of mastitis [2], detection of estrus [3], and discovery of reasons for culling [1]. Decision trees and related methods have also been used for analysis of lactation curves [4], interpretation of somatic cell count data [5], and assessment of the efficiency of reproductive management [6, 7]. In addition, ANN have been used for the prediction of total farm milk production [8], prediction of 305-day milk yield [9, 10], and detection of mastitis [11, 12]. Fuzzy logic, which involves classification of variables into fuzzy sets with degrees of membership between 0 and 1, has recently found its way into agricultural research [13, 14]. Applications have included development of decision- support systems for analyzing test-day milk yield data from Dairy Herd Improvement (DHI) programs [15]. Detection of mastitis and estrus from automated milking systems [16, 17], and definition of contemporary groups for the purpose of genetic evaluation [18]. A key challenge in the use of fuzzy sets is the development of appropriate membership functions (MF). Due to the relative simplicity of building ANN, these may be used to reduce the time and computational burden associated with MF determination. In fact, developments in neural network-driven fuzzy control suggest that these technologies may be quite complementary