Use of different selection indices for improving the genetic gain in maize (Zea mays L.)

Abstract

Plant breeding relies heavily on artificial selection for improving grain yield through various yield contributing traits. The selection of complex quantitative traits like maize grain yield is difficult due to less heritability and a greater influence of the environment. The present study was made using 25 maize inbred lines aimed at identifying yield-attributing traits and identifying simultaneous selection models based on discriminant functions. The expected genetic gain for grain yield when all the studied traits were included in simultaneous selection was higher (51.86) than that of selecting grain yield alone (33.96). There were four traits that made up the ideal discriminant function: grain yield, kernels per row, 100-grain weight and cob length which had 49.57 percent relative efficiency and 155.29 percent genetic advance. The relative efficiency of selection considering grain yield alone was at 106.38%, but when five (X1, X2, X3, X5, and X6) and six traits were simultaneously considered the efficiency increased to 160.37 and 162.47%. Based on the ideal discriminant function among the genotypes G17 was selected as the best inbred line with the highest selection score of 66.57 followed by G20 (65.19) and G22 (65.01). Whereas, G23 was the last with 17.05 selection score