Genotype by environment interaction analysis of grain yield and yield components in provitamin A maize

Selecting superior genotypes for varietal release and commercial use is a key breeding objective. The use of appropriate statistical methods to analyze the complex genotype by environment interaction (GEI) phenomena may increase the efficiency in selecting superior genotypes.

Agyeman, A.; Ewool, M.

2021

English

Selecting superior genotypes for varietal release and commercial use is a key breeding objective. The use of appropriate statistical methods to analyze the complex genotype by environment interaction (GEI) phenomena may increase the efficiency in selecting superior genotypes. The objectives of this study were to evaluate the performance of maize (Zea mays L.) genotypes in diverse environments and identify and recommend high-yielding and stable genotypes for farmer adoption. Seven provitamin A maize varieties, including checks, were evaluated in 11 environments from 2016 to 2018. A linear mixed model analysis was performed using restricted maximum likelihood (REML) and the best linear unbiased prediction (BLUP) methods to estimate genotype, environment and genotype by environment interaction (GEI) variance components; and predict genetic values. Genetic correlations were also calculated to describe relationships across multiple environments. Genotype main effect plus genotype by environment interaction (GGE) biplot analysis was performed using BLUP estimates. The effects of genotype, and environment and GEI were significant (P< 0.05) for grain yield. Both the mixed model and the GGE biplot identified PVA SYN 21 (G2) as a superior genotype on the basis of its mean performance. The GGE biplot analysis revealed that this cultivar was also the most stable genotype across the 11 environments. The mixed model analysis allowed for an efficient selection of superior provitamin A maize genotypes.