Assessment of yield stability of maize inbred lines in multi-environment trials

Abstract

Stable performance of maize hybrids at a specific growing region is critical for obtaining high and stable yield. The objectives of this study were to assess grain yield stability of fourteen maize inbred lines from five different diverse regions of Bangladesh during 2010 -2011 growing season (rabi) using genotype main effect plus genotype by environment interaction (GGE) biplot and to identify maize inbred lines that have both high mean yield and stable yield performance across test environments of Bangladesh. The GGE biplot method was effective in recognition that the inbreds G2 and G13 to be the most desirable ones for growing in Jessore (E1), G6 and G8 in Gazipur (E2), G3 in Rahmatpur (E3) and G1, G12, G14 and G9 in Debiganj (E5) region. The inbreds G5 and G7 had average grain yield and high stability, whereas the inbred line G10 had high grain yield and high stability. Besides, the inbred line G13 was the least stable across different environments. This technique can serve as a useful tool for recommendation of maize inbreds for specific growing regions taking into account the specificities of hybrids and growing conditions.

Genotype by environment interaction and yield stability analysis of open pollinated maize varieties using AMMI model in Afar Regional State, Ethiopia

“…The AMMI result also showed that the first and second Interaction Principal Component Axis (IPCA1 and IPCA2) explained about 86.2% of the interaction sum squares, indicating that the first two IPCA are sufficient to explain GEI in grain yield of maize genotypes. This result is in harmony with some previous findings (Nzuve et al, 2013;Kumar and Singh, 2015;Kumar et al, 2014;Miah and Uddin, 2016); they indicated that AMMI with only two interaction principal component axes was the best predictive model. IPCA1 captures about 91.0% of the interaction sum of squares and the rest 9% were captured by IPCA2.…”

“…The closer the genotypes to the center in AMMI2 biplot are assumed to be more stable than the genotypes far away from the center. AMMI model does not provide a quantitative stability measure and is indispensable to quantify and rank genotypes in terms of yield and stability; however, ASV quantifies and ranks genotypes (Kumar and Singh, 2015;Yong-Jian et al, 2010;Shiri, 2013;Sumathi and Govintharaj, 2017;Mortazavian et al, 2014;Miah and Uddin, 2016).…”

“…In most trails, the G x E interaction is witnessed and then modeled statistically and elucidated. Genotype x environment interaction adjusts the reasonable grain yield of genotypes in diverse environments and makes it hard to select the better ones (Miah and Uddin, 2016).…”

Stability on Maize Hybrids Based on GGE Biplot Graphical Technique

“…The G × E interaction is recorded in the most significant trials and then modelled statistically and clarified. GEI modifies the hybrids' good seed performance in various environments so that choosing the correct genotype is very important [6]. In meta-environments, Modeling G × E interaction facilitates explaining the stability of breeding materials.…”

Yield Stability Analysis of Maize (Zea mays L.) Hybrids Using Parametric and AMMI Methods

“…In most trails, the G × E interaction was witnessed and then modeled statistically and elucidated. Genotype × environment interaction adjusts the reasonable grain yield of genotypes in diverse environments and makes it hard to select the better ones [ 5 ]. Multilocation and multiyear trials can help identify superior and sustainable genotypes [ 6 ].…”