Analysis of Genotype x Environment Interaction for Grain Yield in Maize Hybrids

Abstract

Maize is an important staple crop for millions of people in Sub-Saharan Africa. The crop grows in a wide range of environmental conditions. The present study was performed to analyze the genotype-by-environment (G×E) interaction for grain yield of fourty two single cross hybrids grown in three environments located at different agro-ecological zones of Kenya. The maize genotypes were grown in randomized complete block design with three replicates in 2006 and 2007. Grain yield data of the single cross hybrids was analysed using the additive main effects and multiplicative interaction (AMMI) and GGE biplot methods. The additive main effects and multiplicative interaction model (AMMI) analysis of variance (additive main effects) showed significant effects for genotypes, environment and the genotype by environment interaction. The total proportion contributed by environment and G x E interaction accounted for 78% and 10% of the total variation. The results also showed that 64.5% of the total variation was contributed by the environment while the genotype and interaction was associated with 20.7% and 10.3% of the total variation, respectively. The most stable genotype in the high yielding group in this study was CML312/TZMI 711 (X20), followed by genotypes TZMI 102/CML384 (designated as X33), and CML312/TZMI 712 (X21). The genotype CML312/TZMI 711 (designated as X20) could be used for broad selection since it was found to be the most ideal genotypes with both high mean yield and high stability. In the biplot analysis, X20 was also close to the origin, had the shortest vector from the ATC and with large PC1 scores (high mean yield) and small (absolute) PC2 scores (high stability) implying it had the least contribution to genotype by environment interaction (GEI). Through the stability analysis, the superior genotypes identified could also be used as references for genotype evaluation and also included in further testing in both early and later stages of selection.

Stability Analysis for Grain Yield and Quality Parameters in QPM (Zea mays L.) Inbred Line Crosses

“…conditions were also reported by Agrwal et al., (2000), Dodiya and Joshi (2003), Nirala and Jha (2003), Abera et al, (2004), Kumar and Singh (2004), Kaundal and Sharma (2006) Nadagoud et al, (2012), Shiri (2013), Nzuve et al, (2013), Kamutando et al, (2013). Anley et al, (2013), Tiwari et al, (2014) and Bisawas et al., (2014).…”

“…The significant G x E interaction for various traits were also reported by Sharma and Saikia (2000), Dodiya and Joshi (2003), Singh et al, (2003), Abera et al, (2006), Admassu et al, (2008), Zaidi et al, (2008), Rahman et al, (2010), Arulselvi and Selvi (2010), Beyene et al, (2011), Shiri (2013, Nzuve et al, (2013), Kamutando et al, (2013) and Sserumaga et al, (2016).A perusal of stability parameters for grain yield per plant revealed that out of 67 genotypes 59 genotypes (18 parents, 37 hybrids and 4 checks) exhibited non-significant deviation from regression (S2di) and are as such predictable for this trait. Parental line L6 exhibited non-significant deviation from regression (S2di) and regression coefficient greater than unity (bi >1) with higher mean values than the population mean and would remain stable for grain yield per plant in favourable environments.…”

Determining behaviour of maize genotypes and growing environments using AMMI statistics

“…The similar observations were also noted earlier by Arulselvi and Selvi (2010) and Khaldun (2012). Nzuve et al (2013), Jha et al (2013), Abuali et al (2014) and Kumar et al (2014) also observed significant variance due to G, E and GxE interaction however the magnitude of SS due to environment was noted to be higher than the G and GxE.…”

GGE biplot analysis for yield performance of grain Amaranth genotypes across different environments in western India

“…Moreover, by means of this method, it was apparently established that high and stable yields could be attained only in specific environment. Other researchers observed that GGE biplot analysis is a suitable device for detecting experimental environments to select best genotypes in field crops as well (Balestre et al, 2009;Ilker et al, 2009;Oliveira et al, 2010;Tonk et al, 2011;Khalil et al, 2011;Beyene et al, 2011, Nzuve et al, 2013Mortazavian et al, 2014;Miah et al, 2015).…”