Multi-environment trials of spring barley genotypes (<i>Hordeum vulgare</i> L.) in the final stage of breeding process

Hudzenko, V. M.; Поліщук, Тетяна Петрівна; Сардак, М. О.; Buniak, Nataliia; Ищенко, В. А.

doi:10.5958/0975-928x.2019.00183.2

Cited by 1 publication

(1 citation statement)

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“…PC1 and PC2 together accounted for 95.62% of Genotype + Genotype x Environment variation for grain yield. The results are presented in different sections Hudzenko et al, (2019) reported that analysis of data generated in multi environment trials using GGE biplot method have been theoretically substantiated and practically implemented by various studies and they signified the usage of this method for selecting the "best" spring barley breeding lines in the final stage of breeding process. Fig 3. is a basic bi plot that explains how environments and genotypes were scattered.…”

Section: Fig 1 Map View Of Test Environmentsmentioning

confidence: 90%

GGE Biplot analysis for yield stability in multi-environment trials of hybrid rice (Oryza sativa L.) in Northern India

Chandrashekhar

Babu²,

Jeyaprakash³

et al. 2020

EJPB

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This study was carried out to evaluate the performance of rice cultivars for grain yield stability performance analysis and wide adaptation by GGE biplot method. An experiment was conducted to evaluate 8 rice (Oryza sativa L.) hybrids, 2 check varieties for their stability at 6 different locations, viz Patna, Purnia, Lucknow, Gosaiganj, Barabanki and Prayagraj during rainy 2018 season representing different agroclimatic zones of Uttar Pradesh and Bihar. GGE biplot methodology was utilised to find out the grain yield performance and stability of rice cultivars examined over six environments. GGE biplot analysis considers both genotype (G) and GE interaction effects and graphically displays Genotype Environment interaction in a two-way table. GGE biplot is an effective method based on principal component analysis (PCA) to fully explore multi environment data. The significant Genotype by Environment interaction effects for yield infers that genotypes reaction was different over different environments, indicating that the genotype selection must be specific to the growing conditions. Based on the analyses, genotypes H2, H3 and H5 were high yielding and highly stable genotypes. Hybrid H6 at Environment 4 and hybrid H4 at Environment 3 performed well. Environments E1, E2, E5 and E6 were suitable evaluating environments for this set of rice cultivars.

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Section: Fig 1 Map View Of Test Environmentsmentioning

confidence: 90%