Table 3 Mean prediction accuracies for the different environments of wheat data sets 4 and 5 for GBLUP and GK methods, and three models including a single-environment (model (1)) and two multi-environment models (models (2) and (3))
GBLUPGK
EnvironmentModel (1)Model (2)Model (3)Model (1)Model (2)Model (3)
Wheat data set 4a
 E10.473 (0.052)0.501 (0.041)0.601 (0.033)0.482 (0.040)0.612 (0.041)0.616 (0.042)
 E20.414 (0.063)0.517 (0.049)0.588 (0.041)0.401 (0.051)0.584 (0.047)0.587 (0.044)
 E30.510 (0.052)0.588 (0.044)0.609 (0.044)0.524 (0.039)0.613 (0.038)0.613 (0.039)
 E40.448 (0.054)0.550 (0.037)0.611 (0.043)0.440 (0.045)0.603 (0.045)0.607 (0.044)
Wheat data set 5b
 E10.561 (0.035)0.585 (0.036)0.583 (0.036)0.614 (0.038)0.637 (0.032)0.637 (0.032)
 E20.445 (0.051)0.457 (0.040)0.458 (0.040)0.488 (0.046)0.517 (0.037)0.518 (0.037)
 E30.628 (0.037)0.630 (0.027)0.632 (0.026)0.687 (0.026)0.688 (0.030)0.688 (0.030)
 E40.360 (0.046)0.592 (0.042)0.608 (0.040)0.415 (0.043)0.630 (0.037)0.630 (0.037)
 E50.312 (0.055)0.576 (0.036)0.596 (0.035)0.330 (0.047)0.597 (0.038)0.597 (0.038)
  • 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 = 0.342; E1 vs. E3= –0.054; E1 vs. E4 = 0.311; E2 vs. E3 = 0.328; E2 vs. E4 = 0.414; E3 vs. E4 = 0.223.

  • b Empirical phenotypic correlation between environments: E1 vs. E2 = 0.166; E1 vs. E3 = 0.30; E1 vs. E4= –0.10; E1 vs. E5= –0.010; E2 vs. E3= –0.033; E2 vs. E4 = 0.122; E2 vs. E5 = 0.035; E3 vs. E4= –0.091; E3 vs. E5 = 0.023; E4 vs. E5 = 0.546.