期刊
POULTRY SCIENCE
卷 94, 期 4, 页码 772-780出版社
ELSEVIER
DOI: 10.3382/ps/pev031
关键词
egg production; phenotypic network; prediction model; non-parametric model; Bayesian networks
资金
- Wisconsin Agriculture Experiment Station
- COBB-Vantress, Inc. (Siloam Springs, AR)
- National Council of Scientific and Technological Development (CNPq)
- State of Minas Gerais, Brazil [FAPEMIG/APQ-00739-11]
The prediction of total egg production (TEP) potential in poultry is an important task to aid optimized management decisions in commercial enterprises. The objective of the present study was to compare different modeling approaches for prediction of TEP in meat type quails (Coturnix coturnix coturnix) using phenotypes such as weight, weight gain, egg production and egg quality measurements. Phenotypic data on 30 traits from two lines (L1, n = 180; and L2, n = 205) of quail were modeled to predict TEP. Prediction models included multiple linear regression and artificial neural network (ANN). Moreover, Bayesian network (BN) and a stepwise approach were used as variable selection methods. BN results showed that TEP is independent from other earlier expressed traits when conditioned on egg production from 35 to 80 days of age (EP1). In addition, the prediction accuracy was much lower when EP1 was not included in the model. The best predictive model was ANN, after feature selection, showing prediction correlations of r = 0.792 and r = 0.714 for L1 and L2, respectively. In conclusion, machine learning methods may be useful, but reasonable prediction accuracies are obtained only when partial egg production measurements are included in the model.
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