Table 4.
Dataset 2 EYT 2014–2015.
| Models and methods |
Models and methods |
|||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| BRR | GBLUP | GK | PK | SK | BRR | GBLUP | GK | PK | SK | |
|
|
|
|||||||||
| Scenario | Without G × E (WI) | With G × E (I) | ||||||||
| DTHD | ||||||||||
| MT | 7.03 | 7.02 | 6.70 | 6.72 | 10.72 | 6.03 | 5.99 | 5.16 | 5.20 | 10.89 |
| MT_P | 6.99 | 6.99 | 6.67 | 6.71 | 10.53 | 5.96 | 5.94 | 5.08 | 5.22 | 10.62 |
| DTMT | ||||||||||
| MT | 6.58 | 6.57 | 6.40 | 6.45 | 9.03 | 5.72 | 5.71 | 5.15 | 5.35 | 9.12 |
| MT_P | 6.42 | 6.43 | 6.25 | 6.32 | 8.70 | 5.58 | 5.59 | 5.07 | 5.29 | 8.75 |
Average mean squared error (MSE) prediction, across environments for five model-methods: BRR, Bayesian ridge regression; GBLUP, genomic best linear unbiased predictor; GK, Gaussian kernel; PK, polynomial kernel; SK, sigmoidal kernel without G × E (WI) and with G × E (I) for two scenarios (MT and MT_P) and two traits (DTHD, days to heading and DTMT, days to maturity). Boldface indicates model-method with the lowest MSE for the scenario.