We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Neural network and Bayesian-based prediction of breeding values in Beetal goat.
- Authors
Magotra, Ankit; Bangar, Yogesh C.; Yadav, A. S.
- Abstract
The estimation of breeding values is prime concern for animal breeders in order to achieve desired genetic progress of farm animals. However, current methods for estimating BV involve simultaneous selection of animal model which are computationally intensive and time-consuming. The present attempt was made to predict breeding values of weaning trait under artificial neural networks (ANN), Bayesian technique (BT), and multiple regression (MR) methods. The data records comprising year of birth, sex, type of birth, dam’s weight at kidding, birth weight, weaning weight, and estimated breeding values (BV) for weaning weight (under animal model) pertaining to 849 kids born to 37 sires and 237 dams between 2004 and 2019 were used in this study. All three methods, viz., ANN under multilayer perceptron (2 hidden layers), BT under Markov chain Monte Carlo (MCMC) approach, and MR under full model, were used for 75% training dataset initially and prediction model developed was applied on 25% test dataset. The initial analysis showed positive and significant (P < 0.01) relationship of BV with other variables which hinted that BV may be predicted with accuracy. Then, it was revealed from the results indicated that ANN, BT, and MR methods have similar accuracy (r = 0.86 to 0.87) for prediction of BV. However, ANN showed slightly higher but negligible model adequacy than BT and MR method. The prediction error under three methods was almost equal. The results indicated that these methods could be used as potential alternative for recurrent prediction of BV based on phenotypic data in order to optimize selection plans at young age in resourced population of Beetal goat.
- Publication
Tropical Animal Health & Production, 2022, Vol 54, Issue 5, p1
- ISSN
0049-4747
- Publication type
Article
- DOI
10.1007/s11250-022-03294-5