Genetic Progress in 20 Years of Reciprocal Recurrent Selection in Maize

Júnior, José Arantes Ferreira; Gonçalves, Gabriel Moreno Bernardo; Crevelari, Jocarla Ambrosim; Souza, Yure Pequeno de; Gonçalves, Vivane Mirian Lanhellas; Viana, Flávia Nicácio; FiorioVettorazzi, Julio Cesar; Pereira, Messias Gonzaga

doi:10.35418/2526-4117/v2n1a2

Cited by 1 publication

(2 citation statements)

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“…Pinto, Lima Neto and Souza Junior (2000) obtained a selection gain of 7.2% with recurrent selection using half-Thus, the optimization of the number of repetitions is related to the reduction in phenotypic variance, regardless of the number of progenies used. The use of an adequate number of repetitions is of fundamental importance for reducing the experimental error and the phenotypic variance and, consequently, increasing the experimental precision Ferreira;Oliveira, 2012;Gordón-Mendoza;Camargo-Buitrago, 2015).…”

Section: Resultsmentioning

confidence: 99%

“…In maize breeding, heterosis can be exploited using reciprocal recurrent selection (RRS), a method in which additive and nonadditive effects are maximized to improve the hybridization between two populations of different heterotic groups (Comstock;Robinson;Harvey, 1949;Carena;Miranda Filho, 2010;Yu et al, 2020). Through RRS, improved populations are used as a source of parental lines for the production of high-performance hybrids (Betran;Hallauer, 1996;Kolawole et al, 2018;Ferreira Júnior et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Number of progenies and repetitions for reciprocal full-sib recurrent selection programs in maize

et al. 2021

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For a reciprocal recurrent selection (RRS) program to succeed, it is essential to maintain genetic variability throughout the selection cycles and to obtain accurate estimates of genetic parameters, which in turn are directly related to the number of progenies and repetitions evaluated. This study evaluates the potential of maize progenies of the sixth cycle of RRS and proposes, using simulation methods, the ideal combination of the number of progenies and repetitions to employ in reciprocal full-sib recurrent selection. A total of 163 full-sib progenies were evaluated in a randomized block design with six repetitions. Based on the yield data, analysis of variance was carried out, and different scenarios were simulated using the Monte Carlo chain method. These scenarios varied in the number of repetitions (two, four, and six) and progenies (30 to 163). The contrast between progenies and controls was significant, revealing the potential of the progenies of the sixth cycle of RRS. The high magnitude of the selective accuracy (0.77) was reflected in high estimates of heritability, which allowed for efficient phenotypic selection, obtaining selection gains of 14.07%. From the estimates of phenotypic and genotypic variance, heritability, accuracy, and standard error, it was found that a repetition number above two results in drastic changes in the estimates of these parameters; however, with the use of 130 progenies, these estimates tend to stabilize, implying that a high number of progenies does not interfere decisively in the quality of most parameters, except for the limits of maximum and minimum variation.

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