Genomic Bayesian functional regression models with interactions for predicting wheat grain yield using hyper-spectral image data

Table 3 Prediction accuracy (average of the ten trn–tst random partitions of the Pearson correlation, APC) of models M11 and M13 for time-points 1–9 for each environment for 90CV when 90% of lines are missing in only one environment (standard error, SE)

Time-point	Drought		Irrigated		Reduced irrigation
Time-point	APC	SE	APC	SE	APC	SE
M11
1	0.142	0.017	0.244	0.019	0.175	0.013
2	0.197	0.016	0.231	0.027	0.145	0.019
3	0.307	0.018	0.422	0.013	0.315	0.022
4	0.248	0.014	0.347	0.020	0.238	0.019
5	0.298	0.016	0.394	0.016	0.259	0.015
6	0.415	0.006	0.459	0.011	0.310	0.022
7	0.589	0.008	0.528	0.010	0.257	0.020
8	0.422	0.009	0.411	0.014	0.198	0.024
9	0.604	0.009	0.422	0.007	0.313	0.020
M13
1	0.166	0.015	0.262	0.016	0.216	0.014
2	0.202	0.016	0.234	0.027	0.163	0.017
3	0.307	0.015	0.416	0.017	0.328	0.020
4	0.245	0.015	0.339	0.021	0.238	0.013
5	0.314	0.015	0.409	0.011	0.277	0.016
6	0.427	0.008	0.456	0.013	0.347	0.013
7	0.598	0.011	0.531	0.011	0.280	0.025
8	0.416	0.010	0.402	0.018	0.171	0.021
9	0.613	0.015	0.416	0.006	0.354	0.018

ISSN: 1746-4811