Table 4.
Best predicting variables of the control and drought PLSR models for the prediction of grain yield loss in the 292 rice accessions
| Control PLSR model | Drought PLSR model | ||||||
|---|---|---|---|---|---|---|---|
| Variable | Rank-product | GY loss | Sam-Flow | Variable | Rank-product | GY loss | Sam-Flow |
| r s | r s | r s | r s | ||||
| Galactaric acid | 32 | 0.09 | 0.00 | DHAR | 1 | –0.56 * | –0.06 |
| Erythritol | 64 | 0.41 * | 0.75 * | MDA | 1024 | 0.63 * | 0.23 |
| Adipic acid, 2-amino- | 9.841E+04 | 0.31 * | 0.11 | MDHAR | 5.905E+04 | –0.03 | 0.17 |
| Tryptophan | 7.680E+05 | 0.27 * | 0.73 * | TAC | 1.806E+07 | 0.08 | 0.07 |
| Allantoin | 2.540E+07 | 0.18 * | 0.18 | AO | 3.931E+07 | 0.22 * | –0.17 * |
Top five ranked predicting variables of the double cross-validated PLSR models for grain yield loss prediction based on control (left) and drought (right) values. Variables are ranked based on their rank-product value. Variables with the lower rank-product value are the ones with the larger discriminative power. rs, Spearman’s rho of correlation with grain yield loss (GY loss) and flowering at sampling (Sam-Flow). An asterisk indicates a significantly correlated variable.