Table 2 Mean prediction accuracies for the different environments of wheat data set 1, maize data set 2, and wheat data set 3 for GBLUP and GK methods, and three models including a single-environment (model (1)) and two multi-environment models (models (2) and (3))
EnvironmentGBLUPGK
Model (1)Model (2)Model (3)Model (1)Model (2)Model (3)
Wheat data set 1a
 E10.500 (0.056)0.512 (0.043)0.543 (0.044)0.577 (0.043)0.575 (0.036)0.606 (0.037)
 E20.474 (0.048)0.635 (0.042)0.720 (0.031)0.477 (0.056)0.685 (0.030)0.713 (0.029)
 E30.370 (0.056)0.592 (0.045)0.694 (0.031)0.422 (0.053)0.685 (0.030)0.699 (0.028)
 E40.447 (0.047)0.501 (0.040)0.525 (0.034)0.511 (0.044)0.555 (0.044)0.572 (0.040)
Maize data set 2b
 E10.558 (0.038)0.603 (0.043)0.624 (0.045)0.583 (0.042)0.644 (0.037)0.645 (0.037)
 E20.507 (0.049)0.567 (0.055)0.575 (0.054)0.542 (0.056)0.581 (0.057)0.582 (0.057)
 E30.508 (0.051)0.517 (0.045)0.525 (0.046)0.568 (0.044)0.577 (0.044)0.578 (0.044)
Wheat data set 3c
 E10.529 (0.044)0.603 (0.033)0.617 (0.031)0.557 (0.040)0.625 (0.033)0.631 (0.035)
 E20.622 (0.045)0.706 (0.031)0.716 (0.029)0.642 (0.030)0.688 (0.033)0.689 (0.034)
 E30.452 (0.051)0.506 (0.045)0.512 (0.043)0.523 (0.048)0.547 (0.041)0.551 (0.041)
 E40.493 (0.046)0.504 (0.041)0.507 (0.039)0.511 (0.042)0.508 (0.053)0.510 (0.052)
  • SDs are given in parentheses. The highest prediction accuracies for each environment in each data set are shown in boldface. GBLUP, genomic best linear unbiased predictors: GK, Gaussian kernel.

  • a Empirical phenotypic correlation between environments: E1 vs. E2= −0.019; E1 vs. E3= −0.19; E1 vs. E4= −0.12; E2 vs. E3 = 0.661; E2 vs. E4 = 0.411; E3 vs. E4 = 0.388.

  • b Empirical phenotypic correlation between environments: E1 vs. E2 = 0.388; E1 vs. E3 = 0.262; E 2 vs. E3 = 0.153.

  • c Empirical phenotypic correlation between environments: E1 vs. E2 = 0.527; E1 vs. E3 = 0.253; E1 vs. E4 = 0.259; E2 vs. E3 = 0.340; E2 vs. E4 = 0.328; E3 vs. E4 = 0.22.