Abstract
Abstract: The objective of this work was to identify corn (Zea mays) genotypes with forage potential and to evaluate the efficiency of testers to discriminate forage traits in topcrosses, considering the contribution of additive and nonadditive genes. The experiment was carried out in the 2015/2016 and 2016/2017 crop seasons, in a randomized complete block design with three replicates. Thirty S3 corn progenies were evaluated in topcrosses with the AG8025, P30B39, MLP102, 60.H23.1, and 70.H26.1 testers. The following traits were assessed: forage dry mass yield, neutral detergent fiber, acid detergent fiber, and forage dry mass degradability. Progenies 205.2, 159.6, and 199.2, in this order, presented the best performance for forage potential. Testers 60.H23.1 and 70.H26.1 better expressed the genetic variability between progenies. For all traits in both crop seasons, there is a predominance of the action of genes of nonadditive effects.
Highlights
Corn (Zea mays L.) is one of the main crops used for forage production (Grignani et al, 2007)
There was a greater amplitude of dry mass yield (DMY) averages for topcross hybrids with the 60.H23.1 and 70.H26.1 testers, and 16 out of the 20 topcrosses with higher averages were obtained from the crossing with these testers (Figure 2)
Testers 60.H23.1 and 70.H26.1 did not repeat this favorable result for neutral detergent fiber (NDF) and acid detergent fiber (ADF), since, for these traits, lower averages are desirable (Pirondini et al, 2015), as those obtained by the AG8025 and P30B39 testers
Summary
Corn (Zea mays L.) is one of the main crops used for forage production (Grignani et al, 2007). This, in most cases, restricts a more accurate identification of the potential of different testers in discriminating forage traits and of the action of genes of additive and nonadditive effects, so far little discussed in the literature (Aslam et al, 2017). There is still not a consensus on how to select the best tester to discriminate the genetic potential of progenies (Lobato-Ortiz et al, 2010). Many authors concluded that the choice of the best tester should be based on genetic merit, with a high frequency of favorable alleles, as well as on genetic factors related to additive and nonadditive actions (Lobato-Ortiz et al, 2010; Pirondini et al, 2015; Seye et al, 2019). Hallauer et al (2010) emphasized that the best tester is the one that classifies correctly the genetic merit of progenies based on estimates of genetic variance components, disregarding other information
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