Genotype by environment interactions and phenotypic stability analysis for yield and yield components in parental lines of pearl millet (Pennisetum glaucum [L.] R. Br)

Abstract

Genotype by environment interactions and phenotypic stability analysis for yield and yield components in parental lines of pearl millet (Pennisetum glaucum [L.] R. Br)Twenty-four parental lines of pearl millet and a seed parent (ZATIB) as check were evaluated in five different locations in northern Nigeria to determine their yield levels and stability across the environments. Identification of stable parental line(s) will improve the performance of resulting pearl millet hybrids. Location and genotype effects were highly significant (P<0.05) for all the parameters sampled while interaction between locations and genotypes were significant (P<0.05) for stand count, days to 50% flowering, downy mildew score, panicle length and grain yield (kg ha -1 ). Estimates of environmental index showed that Samaru was the best performing environment while Bagauda and Panda were the poorest grain yielding environments. Most of the lines were adapted to high rainfall environment of Samaru while others showed specific adaptation to low rainfall locations; indicating the possibility of developing specific lines adapted to low and high rainfall areas. Mean grain yield ranged from 504.8 (kg ha -1 ) for G3 (20A-2) to 1920 (kg ha -1 ) for G24 (75B-3). G10, G14 and G15 were found suitable for favorable conditions with predictable performance as they gave high mean grain yield along with above average responsiveness (bi>1) and non-significant deviation from regression line while G13, G17 and G18 were considered suitable for poor environments. Regression coefficient and deviation from regression indicated that G23 and G24 (75A-3 and 75B-3) and ZATIB were most stable in performance across the test environments.

Genotype by environment interaction and grain yield stability of Ethiopian black seeded finger millet genotypes.

“…Degu and Acc BKFM0024 scored IPCA values deviating from zero (-0.6818 and 0.5276) (Table 3) and lower grain yield, indicating that these genotypes are unstable and are environment sensitive. Several researchers reported results that are in agreement with the present study, that genotypes with low grain yield and IPCA value deviated zero are not stable and are highly environmental sensitive, (Yuksel et al, 2002;Asnake Worku et al, 2013;Ezeaku et al, 2014).…”

“…Environmental conditions during different seasons significantly influenced grain yield, indicating that environments and genotypes were significant variable. Similar results were reported by Kebede et al (2016) and Kebede et al (2018), in brown seeded finger millets; Ezeaku et al (2014) in pearl millet and Dagnachew et al (2014) in Triticale. The IPCA scores of genotypes in AMMI analysis are an indication of the adaptability over environments and association between genotypes and environments (Gauch and Zobel, 1996;Alberts, 2004).…”

Evaluation of Genotype x Environment Interaction and Stability of Grain Yield and Related Yield Components in Pearl Millet (Pennisetum glaucum (L.) R.Br.)

“…In addition, Gebre [26] reported that rainfall pattern is one of the factors that he noted as being a major source of variable performance of improved genotypes (hybrids and OPVs as case in this study). The environments being important in genotype performance has also been reported in several pearl millet studies [27,28,29,15].…”

Analysis of Genotype by Environment Interaction of Improved Pearl Millet for Grain Yield and Rust Resistance

“…Rainfall pattern is one of the factors also reported by Gebre (2014) as being a source of variable performance of improved genotypes. The environments being important in genotype performance has also been reported in several pearl millet studies (Ezeaku et al, 2014;Misra et al, 2010;Gupta et al, 2013). The ANOVA adequately identified GEI as a significant source of variation but it is not able to explore the nature (Matus-Cadiz et al, 2003) of the GEI which may mask the true performance of genotypes in certain environments (Crossa, 1990) and thus the need to explore more methods; for which case GGE biplot was adopted.…”