Table 1.
The predictive abilities for the three selection criterion’s; CV2, CV1 and CV0 analyzed to predict grain yields under non-stress conditions with calibration sets viz., non-stress (NS) and Combined [All (stress and non-stress)] (CSN) datasets using seven models.
| Mixed models | Cross-validation scenarios | ||||||
|---|---|---|---|---|---|---|---|
| CV2 | CV1 | CV0 | |||||
| Calibration sets | CSN | NS | CSN | NS | CSN | NS | S → NS |
| M1: E + A | 0.276 | 0.286 | 0.184 | 0.227 | 0.200 | 0.171 | 0.127 |
| M2: E + A + AE | 0.348 | 0.328 | 0.292 | 0.290 | 0.209 | 0.176 | 0.132 |
| M3: E + GCA | 0.272 | 0.304 | 0.204 | 0.247 | 0.159 | 0.153 | 0.095 |
| M4: E + GCA + SCA | 0.304 | 0.338 | 0.225 | 0.277 | 0.188 | 0.193 | 0.106 |
| M5: E + GCA + SCA + GCA × E + SCA × E | 0.352 | 0.347 | 0.306 | 0.305 | 0.197 | 0.190 | 0.112 |
| M6: E + GCA + SCA + GCA × E + A × E | 0.352 | 0.348 | 0.304 | 0.309 | 0.184 | 0.187 | 0.086 |
| M7: E + GCA + A + GCA × E + A × E | 0.360 | 0.341 | 0.298 | 0.299 | 0.206 | 0.182 | 0.126 |
Also the extreme right column of the table contains the predicted grain yield under non-stress conditions using the stress (S) dataset in CV0 scenario. The highlighted values represent models harboring highest predictive abilities in each of the cases.