A comparison of neural network and multiple regression predictions for 305-day lactation yield using partial lactation records | Zendy

Wilhelm Grzesiak | Zendy; R. Lacroix | Zendy; Janusz Wójcik | Zendy; Piotr Błaszczyk | Zendy

Open Access

A comparison of neural network and multiple regression predictions for 305-day lactation yield using partial lactation records

Author(s) -

Wilhelm Grzesiak,

R. Lacroix,

Janusz Wójcik,

Piotr Błaszczyk

Publication year - 2003

Publication title -

canadian journal of animal science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.377

H-Index - 64

eISSN - 1918-1825

pISSN - 0008-3984

DOI - 10.4141/a02-002

Subject(s) - lactation , regression , artificial neural network , yield (engineering) , linear regression , herd , regression analysis , ice calving , statistics , zoology , mathematics , biology , computer science , artificial intelligence , pregnancy , genetics , materials science , metallurgy

Milk yield predictions based on artificial neural etworks and multiple regression were studied. The 305-d lactation yield predictions were based on milk yield of the first 4 test days. Average 305-d milk production of the herd, number of days in milk and month of calving. The predictions made with either the neural network or the multiple regression model did not differ (P > 0.05) from the values estimated with the current Polish dairy cattle evaluation system. The neural network model may be alternative method of predicting these traits. Key words: Artificial neural networks, multiple linear regression, milk yield prediction, test day data

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