Neural Computing & Applications

, Volume 15, Issue 3–4, pp 359–365 | Cite as

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

Original Article

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.

Keywords

Back-propagation networks Connectionist models Dairy production Karan Fries cows Prediction Radial basis function networks 305-day milk yield 

Notes

Acknowledgements

The authors wish to thank the Director, National Dairy Research Institute, (ICAR), Karnal, India for granting permission to use the livestock data for this study; and In-charge, Computer Centre, NDRI, Karnal for providing preliminary data analysis facilities for this study. Also, thanks are due to Dr. A. K. Chakarvarty, Principal Scientist, and Dr. S. S. Lathwal, Scientist (SS), Dairy Cattle Breeding Division, NDRI, Karnal for their valuable advice for carrying out the data collection and preliminary analysis for this study. Last but not the least, the authors are indebted to the anonymous reviewer(s) for the critical review that has greatly helped in the improvement of this paper as well as in the future directions of our research work.

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Copyright information

© Springer-Verlag London Limited 2006

Authors and Affiliations

  • Adesh K. Sharma
    • 1
    • 2
  • R. K. Sharma
    • 1
  • H. S. Kasana
    • 1
  1. 1.School of Mathematics and Computer ApplicationsThapar Institute of Engineering and Technology (Deemed University)PatialaIndia
  2. 2.Computer Applications in Agriculture, Computer CentreNational Dairy Research InstituteKarnalIndia

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