Table 4.
Model comparison of linear and non-linear ANN models
| Linear ANN | Non linear ANN | Best non-linear ANN | |
|---|---|---|---|
| r | r | r | |
| German Fleckvieh bulls | |||
| Milk yield DYD | 0.68 (0.0007) | 0.52 (0.0016) | 0.68 (0.0008) |
| Protein yield DYD | 0.68 (0.0006) | 0.53 (0.0011) | 0.67 (0.0005) |
| Fat yield DYD | 0.66 (0.0005) | 0.56 (0.0008) | 0.65 (0.0005) |
| Holstein-Friesian bulls | |||
| Milk yield DYD | 0.60 (0.0006) | 0.53 (0.0011) | 0.58 (0.0008) |
| Protein yield DYD | 0.59 (0.0009) | 0.50 (0.0013) | 0.57 (0.0009) |
| Fat yield DYD | 0.57 (0.0009) | 0.51 (0.0010) | 0.56 (0.0009) |
| Holstein-Friesian cows | |||
| Milk yield YD | 0.47 (0.0031) | 0.44 (0.0040) | 0.47 (0.0027) |
| Protein yield YD | 0.37 (0.0033) | 0.35 (0.0039) | 0.35 (0.0032) |
| Fat yield YD | 0.46 (0.0037) | 0.39 (0.0049) | 0.47 (0.0028) |
Compared are linear and non-linear ANN with 1 neuron in hidden layer and G matrix as input to the network and best non-linear ANN. DYD = Daughter yield deviation, YD = Yield deviation, r = average Pearson correlation coefficient of the cross-validation runs, variance of cross-validation runs is shown in brackets.