Adaptabilidade, estabilidade e capacidade combinatória de híbridos topcrosses de milho na segunda safra - Gomes, Rodrigo (2019)
The growth of corn production in the second season, which currently corresponds to about 70% of the area sown in Brazil, has driven the market change and led to the increase in demand for hybrids adapted to the reality of the environments, imposing changes in strategies in the production programs. improvement. The objectives of this work were to evaluate and identify promising progenies for the generation of new hybrids with high productive potential in second crop environments. Thirty-three inbred lines from different heterotic groups were evaluated in topcross crosses with two elite testers (one adapted to summer environments and one adapted to second crop conditions), totaling 66 topcross hybrids. The grain yield data (kg ha-1) were obtained at 9 locations in the second harvest period, six in Paraná (1-Toledo, 2-Palotina, 3-Assis Chateaubriand, 4-Campo Mourão, 5-Sertanópolis and 6-São Jorge do Ivaí) and three in Mato Grosso do Sul (7-Caarapó, 8-Itaporã and 9-Douradina), in 2017. An analysis of variance and partial diallel (lines vs. testers) was performed. Genotype stability was evaluated by the methodologies of Schmidt and Cruz (2005) and AMMI (ZOBEL et al.,1988) GGE biplot analysis. There was a significant effect of genotypes (with average yield of 8,650 kg ha-1), environments and GE interaction. The highest average yield was recorded in environment 6 (10,180 kg ha-1), and the lowest average was in environment 4 (6,880 kg ha1). The heritability of grain yield of hybrids with tester 2 was greater than 50% in environments 2, 3, 6, 7, 8 and 9, with emphasis on environment 9, where heritability was 90.27%. There was no significant difference for testers' CGC, but there was significant difference for progeny GCA, as well as specific combining ability (SCA). There was a significant effect of the environments, as well as the interactions topcrosses x environments, GCA testers x environments, GCA progenies x environments and SCA x environments. Tester 1 (summer) positively contributed to CGC in environments 6, 7, 8, and 9. Tester 2 (second crop) positively contributed to CGC in environments 1, 2, 3, 4, and 5, where hybrids with this tester also had higher averages than tester 1. Progenies 21, 22, 31, and 32 had the most favorable CGC estimates in most environments. Among the topcross hybrids, which responded with higher average yields in the evaluation environments, HS045, HS065, HS066 and HS055 stood out, among which HS045 and HS066 were the most stable.