GGE Biplot Analysis of Genotype-by-environment Interactions for Melon Fruit Yield and Quality Traits

Abstract

<jats:p>The stability of yield and quality traits in nine orange-fleshed melon (<jats:italic>Cucumis melo</jats:italic> L.) genotypes was studied over nine environments in south-central Texas (College Station, Uvalde, and Weslaco) over 3 years (2010, 2011, and 2012). Besides yield traits, fruit -quality components such as soluble solids content (SSC), β-carotene, and fruit firmness were also measured. Data were subjected to the combined analysis of variance and trait stability by GGE Biplot. The significant genotype-by-location interactions for yield traits demonstrated the potential to develop location-specific cultivars. However, the temporal fluctuations in productivity emphasized the need to select for stability over several years in potential cultivars for the target environments. Cultivar Mission was confirmed as the most stable and average performing genotype for marketable yield and quality traits at all locations. Uvalde was identified as the ideal location for selecting generally adapted genotypes for south-central Texas. Biplot analysis indicated that Orange Dew was the highest mean performing genotype for SSC. The hybrid Oro Duro, followed by TAMU 146, ranked highest for mean and stability of β-carotene content, but it ranked lowest for fruit firmness. TAMU Orange Casaba exhibited specific adaptation, producing the highest mean fruit yield at Weslaco, while Journey had the highest fruit yield at College Station and Uvalde. Understanding of genotype-by-environment interactions for multiple traits in melon is critical for developing cultivars with high mean performance and stability in target growing environments.</jats:p>

GGE Biplot Analysis of Genotype × Environment Interaction and Yield Stability in Bambara Groundnut

“…In this study, none of the accessions were recorded to be adaptable and stable in the environments tested. [42]. This ability gives it an edge over the AMMI biplot analysis [43].…”

“…GGE biplot allows environment evaluation based on the discriminating ability and representativeness of the GGE view [42]. This ability gives it an edge over the AMMI biplot analysis [43].…”

Mega-environment analysis to assess adaptability, stability, and genomic predictions in grain sorghum hybrids

“…Although MET primarily focus on identifying superior genotypes for target environments, established breeding programs also apply MET to generate relevant information about tested environments (Gauch and Zobel, 1997;Yan et al, 2000;Yan and Kang, 2002;Laffont et al, 2013;Gauch, 2013;Yan, 2015Yan, , 2016. This analysis can be useful to identify mega-environments (ME) across a wider production area (Rakshit et al 2012;Nielsen and Vigil 2018;Dalló et al 2019;Sharma et al 2020;Ansarifard et al 2020) A common breeding approach for multinational commercial breeding programs is to cross elite germplasm adapted to diverse target environments (Cooper et al, 2014). Studies have demonstrated the potential advantages of utilizing this strategy in hybrid crops (Podlich and Cooper, 1998;Technow et al, 2020).…”

Inheritance of beta-carotene content in melon

“…The beta-carotene content levels in these parents were 6.56 and 0.15 µg g -1 , respectively, which are lower values than those observed in the present work. It is underlined that beta-carotene content is influenced by the genotype, the stage of fruit development and environmental conditions (Sharma et al, 2020;Singh et al, 2021). Clayberg (1992) checked epistasis between the genes gf and wf for the genotypic combinations (wf + _/gf + _) and (wf + _/gfgf ) to produce fruit with orange mesocarp, as well as for combinations (wfwf / gf + _) to produce fruit with white mesocarp, and the combination (wfwf/gfgf ) which is responsible for the green color.…”