TABLE 3.

Prediction performance of the Maize data set for each environment and across environments (Global) of each of the six models.

Model	Metric	E1		E2		E3		E4		Global
		Mean	SE	Mean	SE	Mean	SE	Mean	SE	Mean	SE
M1	MAE	0.2038	0.0024	0.4360	0.0122	0.2708	0.0035	0.5392	0.0088	0.3409	0.0047
M1	MSE	0.0700	0.0028	0.2991	0.0179	0.1125	0.0034	0.4670	0.0177	0.2059	0.0059
M1	NRMSE	0.8751	0.0259	0.9021	0.0077	0.9159	0.0135	0.9146	0.0196	0.8872	0.0147
M1	RMSE	0.2644	0.0051	0.5459	0.0166	0.3352	0.0050	0.6829	0.0129	0.4535	0.0066
M2	MAAPE	0.7672	0.0106	0.7787	0.0120	0.7734	0.0102	0.7580	0.0075	0.7565	0.0064
M2	MAE	0.2040	0.0067	0.4687	0.0144	0.2700	0.0060	0.5751	0.0166	0.3592	0.0072
M2	MSE	0.0713	0.0045	0.3460	0.0157	0.1131	0.0064	0.5281	0.0319	0.2336	0.0126
M2	NRMSE	0.9174	0.0119	0.9687	0.0057	0.9353	0.0163	0.9517	0.0070	0.9498	0.0040
M2	RMSE	0.2664	0.0083	0.5876	0.0134	0.3358	0.0095	0.7253	0.0225	0.4826	0.0127
M3	MAAPE	0.7861	0.0059	0.7829	0.0010	0.7871	0.0031	0.7870	0.0023	0.7852	0.0015
M3	MAE	0.2187	0.0054	0.4814	0.0130	0.2855	0.0049	0.6109	0.0151	0.3817	0.0069
M3	MSE	0.0847	0.0034	0.3701	0.0144	0.1287	0.0051	0.5861	0.0325	0.2603	0.0131
M3	NRMSE	1.0023	0.0027	1.0024	0.0031	0.9985	0.0010	1.0032	0.0013	1.0029	0.0014
M3	RMSE	0.2908	0.0059	0.6079	0.0119	0.3584	0.0070	0.7643	0.0215	0.5095	0.0126
M4	MAAPE	0.7450	0.0146	0.7615	0.0114	0.7432	0.0150	0.7418	0.0077	0.7444	0.0063
M4	MAE	0.2006	0.0053	0.4430	0.0100	0.2615	0.0069	0.5586	0.0119	0.3498	0.0034
M4	MSE	0.0678	0.0048	0.3073	0.0136	0.1070	0.0062	0.5041	0.0223	0.2215	0.0054
M4	NRMSE	0.8882	0.0082	0.9320	0.0076	0.9032	0.0173	0.9052	0.0076	0.8997	0.0042
M4	RMSE	0.2598	0.0091	0.5538	0.0122	0.3265	0.0096	0.7093	0.0157	0.4705	0.0058
M5	MAAPE	0.7853	0.0067	0.7601	0.0125	0.7600	0.0064	0.7275	0.0067	0.7483	0.0058
M5	MAE	0.2199	0.0033	0.4507	0.0074	0.2747	0.0086	0.5259	0.0089	0.3426	0.0060
M5	MSE	0.0796	0.0037	0.3269	0.0104	0.1166	0.0067	0.4500	0.0153	0.2099	0.0081
M5	NRMSE	0.9858	0.0087	0.9364	0.0111	0.9533	0.0223	0.8808	0.0063	0.9116	0.0065
M5	RMSE	0.2819	0.0065	0.5714	0.0091	0.3408	0.0100	0.6705	0.0113	0.4578	0.0089
M6	MAAPE	0.7980	0.0126	0.7792	0.0101	0.7819	0.0189	0.7681	0.0113	0.7747	0.0110
M6	MAE	0.2177	0.0075	0.4843	0.0157	0.2907	0.0094	0.5653	0.0200	0.3655	0.0112
M6	MSE	0.0798	0.0048	0.3775	0.0220	0.1398	0.0071	0.4992	0.0290	0.2396	0.0148
M6	NRMSE	0.9720	0.0214	1.0107	0.0114	1.0406	0.0199	0.9267	0.0249	0.9616	0.0215
M6	RMSE	0.2820	0.0084	0.6134	0.0180	0.3735	0.0096	0.7053	0.0212	0.4885	0.0155

Generalized boosted machines (M1), generalized linear models (M2), support vector machines (M3), random forest (M4), Bayesian regression models (M5) and deep neural networks (M6). The tuning process was done under the grid search framework. Mean is the average of the five partitions for each metric, SE denotes the standard error for each metric.