Table 1.

Dataset 1 EYT 2013–2014

		Models and methods					Models and methods
		BRR	GBLUP	GK	PK	SK	BRR	GBLUP	GK	PK	SK
Env.	Scenario	Without G × E (WI)					With G × E (I)
		DTHD
Bed5IR	MT	14.95	14.94	12.96	13.08	25.12	12.77	12.52	11.36	12.17	25.07
EHT	MT	31.32	31.30	29.51	29.86	42.57	27.77	27.19	23.17	24.43	42.62
Flat5IR	MT	8.68	8.59	8.61	8.62	9.36	5.97	5.92	6.49	7.26	7.85
LHT	MT	6.00	5.99	5.87	5.94	7.71	4.56	4.68	5.36	5.84	7.12
Bed5IR	MT_P	14.57	14.56	13.07	13.26	23.68	12.46	12.21	10.97	11.86	23.58
EHT	MT_P	26.06	26.09	24.63	24.98	34.09	24.50	23.75	20.45	20.89	34.58
Flat5IR	MT_P	9.12	9.09	9.09	9.18	9.96	6.25	6.29	6.75	7.62	8.45
LHT	MT_P	6.97	6.99	6.63	6.71	9.54	5.52	5.63	6.06	6.56	9.23
		DTMT
Bed5IR	MT	11.62	11.58	10.17	10.18	18.88	10.25	9.94	9.07	9.37	18.93
EHT	MT	26.21	26.22	24.72	24.89	35.73	23.81	23.55	19.81	20.35	37.19
Flat5IR	MT	8.92	8.88	9.37	9.45	8.35	6.58	6.58	7.58	8.30	6.64
LHT	MT	7.80	7.77	7.58	7.62	10.83	6.52	6.45	6.44	6.93	10.77
Bed5IR	MT_P	11.47	11.49	10.34	10.43	17.96	10.21	9.91	8.99	9.40	18.05
EHT	MT_P	19.56	19.61	18.38	18.58	26.16	18.94	18.69	15.41	15.38	27.49
Flat5IR	MT_P	9.68	9.66	10.02	10.10	9.55	7.19	7.16	7.96	8.78	7.89
LHT	MT_P	8.42	8.42	8.00	8.11	11.83	7.24	7.20	7.30	7.69	12.13

Average mean squared error (MSE) of prediction for five multitrait multienvironment 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) for four environments (Bed5IR, EHT, Flat5IR, LHT) and two traits (DTHD, days to heading and DTMT, days to maturity). Boldface indicates model-method with the lowest MSE for the environment.