PBG 12. Population x Population Performance of Selected Philippine Maize (Zea mays L.) Accessions using Best Linear Unbiased Prediction (BLUP) (2018)
Best Linear Unbiased Prediction (BLUP) was used to evaluate the capacity and effectiveness of classifying maize accessions into heterotic groups. Fifty-four Philippine maize accessions were used and crossed to six broad testers, and were evaluated in three locations. However, only one location was considered for further analysis. Two heterotic groups, HetA and HetB, were identified, which are represented by testers, Pop 62 and IPB Var 13, respectively. Population accessions were divided into 4 categories namely, predictor cross (PC), lowest predicted untested hybrids (LPUH), top predicted untested hybrids (TPUH) and overall, and were evaluated in two locations for validation trial. La Granja site showed highest correlation from PC set of hybrids (0.32-0.36), followed by LPUH and TPUH, with overall correlation ranging from 0.26 to 0.28. Tranca site responded differently with TPUH (0.21-0.25) obtaining highest correlation, followed by PC and LPUH, with combined correlation ranging from 0.30 to 0.33. Using Simple Matching, the selection efficiency for La Granja, Tranca and combined locations were 14.47%, 34.21% and 53.95%, respectively. Rank correlation values between training and validation trials for Het A and Het B populations were 0.40 and 0.60, respectively. BLUP did not show high efficacy in predicting the yield of the untested crosses. This can be due to the fact that the entries used were populations, and the possible GxE interaction between training set and validation trials. However, BLUP was effective in selecting the top performing crosses, and early generation testing can be done for selected entries. It is recommended to use for materials which have undergone complete cycles of improvement and/or extracted inbred lines from each population to fully utilize the efficacy of BLUP. Heterotic grouping can be enhanced by conducting more trials in major com areas to better understand the environmental adaptability and diversity of the populations.