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Table 2 Wheat datasets – three classes

From: Genome-enabled prediction using probabilistic neural network classifiers

       Number of individuals Number of individuals Number of individuals
Data set Agronomic management Site in Mexico Year Number of SNP markers Total number of individuals Upper Upper Middle Middle Lower Lower
       15 % 30 % 40 % 70 % 15 % 30 %
GY-1 Drought-bed Cd. Obregon 2009 1717 306 46 92 119 211 49 95
GY-2 Drought-bed Cd. Obregon 2010 1717 306 47 92 122 213 46 92
GY-3 Drought-flat Cd. Obregon 2010 1717 263 39 80 104 185 39 79
GY-4 Full irrigation-bed Cd. Obregon 2009 1717 304 46 92 120 212 46 92
GY-5 Full irrigation-bed Cd. Obregon 2010 1717 306 46 94 118 214 46 94
GY-6 Heat-bed Cd. Obregon 2010 1717 306 46 94 120 214 46 92
GY-7 Full irrigation-flat Cd. Obregon 2010 1717 263 39 79 105 185 39 79
DTH-1 Drought-bed Cd. Obregon 2009 1717 306 53 100 93 197 56 113
DTH-2 Drought-bed Cd. Obregon 2010 1717 306 50 93 117 198 58 96
DTH-3 Drought-flat Cd. Obregon 2010 1717 263 40 86 77 177 46 100
DTH-4 Full irrigation-bed Cd. Obregon 2009 1717 306 59 107 107 173 74 92
DTH-5 Full irrigation-bed Cd. Obregon 2010 1717 306 47 101 105 207 52 100
DTH-6 Toluca Toluca 2009 1717 306 122 122 75 93 91 109
DTH-7 El Batan El Batan 2009 1717 306 66 104 101 175 65 101
DTH-8 Small observation plot Cd. Obregon 2009 1717 301 58 101 100 182 61 100
DTH-9 Small observation plot Cd. Obregon 2010 1717 263 45 100 76 173 45 87
DTH-10 Agua Fria Agua Fria 2010 1717 261 49 81 93 125 87 87
  1. Environment code of 12 combinations of sites in Mexico, agronomic management, and year for two wheat traits (grain yield, GY, and days to heading, DTH) from [11]. Number of markers, total number of individuals, number of individuals in the upper 15 and 30 % classes, in the middle 40 and 70 % classes, and in the lower 15 and 30 % classes from the empirical cumulative distribution