Milk yield prediction in Kenyan Holstein- Friesian cattle using computer neural networks system

Citation:
Njubi DM, Wakhungu J, Badamana MS. "Milk yield prediction in Kenyan Holstein- Friesian cattle using computer neural networks system.". 2009.

Abstract:

An essential aspect of understanding any natural system is the ability to acquire knowledge through experience and to adapt to new situations. This study, investigates the use of back-propagation Artificial Neural Networks (ANN) (an artificial intelligence technique) approach to model and predict the performance of daughter first lactation milk yield in recorded dairy cattle herds in Kenya. Such prediction is a prerequisite to selection which ultimately leads to optimal breeding strategies and increased annual genetic progress. Data consisting of 6095 lactation records made by the Kenyan Holstein-Friesian cows from 76 officially milk recorded herds of 445 sires, 1956 dams and 2267 daughters and collected over the period 1988 -2005 were used to predict the first lactation performance of the female offspring based on recorded genetic traits of their parents using computer Neural Networks (NN). Weka and MATLAB softwares were used for NN analyses while SAS (2003) and Derivative free Restricted maximum likelihood (DFREML) (Meyer 1989) computer packages were used for statistical analyses. Different ANN were modeled and the best performing number of hidden layers and neurons and training algorithms retained. The performance of the ANN model in simulating daughter performance was compared with the industry default technique linear regression (LR) model. The best NN model had one hidden layer with 8 hidden nodes and tangent sigmoid transfer function for hidden layer. The correlation coefficients between the observed and the estimated daughter milk yield for the two estimation methods was generally high (>0.80). Including sire information resulted to more accurate predictions by a neural network as shown by reduced root mean square error. Generally more accurate prediction was obtained by a neural network approach than by linear regression. This suggests a non-linear relationship exists among the feature variables in the data and that these are learned by the hidden layer of the NN. Thus, prediction tests show that the ANN models used in this study

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