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Comparison of linear and non-linear methods in prediction of second parity milk yield in dairy cattle based on first parity production data

Published online by Cambridge University Press:  23 November 2017

Pouria Hosseinnia ,
Mohamad-Ali Edriss ,
Mehdi Edrisi  and
Hamid-Reza Rahmani
Pouria Hosseinnia
Affiliation:
Hamid-Reza Rahmani Isfahan University of Technology, Isfahan, Islamic Republic of Iran
Mohamad-Ali Edriss*
Affiliation:
Hamid-Reza Rahmani Isfahan University of Technology, Isfahan, Islamic Republic of Iran
Mehdi Edrisi
Affiliation:
Hamid-Reza Rahmani Isfahan University of Technology, Isfahan, Islamic Republic of Iran
Hamid-Reza Rahmani
Affiliation:
Hamid-Reza Rahmani Isfahan University of Technology, Isfahan, Islamic Republic of Iran
*
Email:[email protected]
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Extract

Milk production in dairy cattle controls by various linear and non-linear factors, and dairy production is influenced by interaction between genetic and environmental agents (Kominakis et al., 2002). Many current models in prediction dairy production assume a linear relationship between dependent and independent data. Artificial Neural Network (ANN) can be used to implement a linear or non-linear relationship for prediction. ANN is made up of set of neurons, to consider the process input data and the corresponding output; to elicit non-linear relationship between input and output data. The parallel processing in ANN is specifying close acquaintance for this system (Lacroix et al., 1995). The objective of this study was prediction of second parity milk yield (MY) based on first parity records by ANN, and comparing with current linear model like multiple linear regression (MLR).

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Proceedings of the British Society of Animal Science , Volume 2008 , 2008 , pp. 202
Copyright
Copyright © The British Society of Animal Science 2008

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References

Grzesiak, W., Blaszczyk, P. and Lacroix, R. 2006. Computer and Electronic in Agriculture. 54, 69–83.CrossRefGoogle Scholar
Kominakis, A.P., Abas, Z., Maltaris, I. and Rogdakis, E. 2002. Computer and Electronic in Agriculture. 35, 35–48.CrossRefGoogle Scholar
Lacroix, R., Wade, K.M., Kok, R. and Hayes, J. F. 1995. Trans. ASAE, 38, 1573–1579.CrossRefGoogle Scholar