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Determination of the effect of Genotype × Environment interaction on the trait seed cotton yield in upland cotton (G. hirsutum) genotypes using AMMI and GGE biplot methods

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Abstract Background Cotton is an important natural fiber crop worldwide that demands the attention of the textile industry worldwide. Seed cotton yield is a complex polygenic trait that is influenced by many genetic and environmental factors across locations and years. Results The present investigation was conducted in three consecutive environments to delineate the genotype × environment interaction and to assess the stability of seven cotton genotypes at the Regional Agricultural Research Station, Nandyal, during 2018-19, 2019-20 and 2020-21. Multivariate stability tests such as additive main effect and multiplicative interaction (AMMI) and genotype and genotype-by-environment interaction (GGE) models were employed to investigate the stability among cotton genotypes. The AMMI results revealed that the majority of the variation was explained by the sum of the squares of the environmental variables, followed by the sum of the squares of the genotypic variables and the sum of the GEIs for the majority of the traits studied. The first two interaction principal components explained the majority of the GEI in all traits under study. A two-dimensional GGE biplot generated using the first two principal components revealed that the GGE biplot explained 97.14% of the total variation, which was distributed as 83.73% and 13.41% of the sum of squares between principal components PC1 and PC2, respectively, for biometric trait seed cotton yield. Conclusions Based on which-won-where polygon, ideal genotype ranking of AMMI and GGE biplot analysis, genotype, G3 (NDLH 2035-5) was identified as having the highest yield and was most stable in all the test environments studied. However, low yielding but stable genotypes such as G4 (BGDS 1033) and G7 (Sivanandi) were also identified. Among the three environments studied, environment E1 (2018-19) was identified as the most discriminating and representative.
Title: Determination of the effect of Genotype × Environment interaction on the trait seed cotton yield in upland cotton (G. hirsutum) genotypes using AMMI and GGE biplot methods
Description:
Abstract Background Cotton is an important natural fiber crop worldwide that demands the attention of the textile industry worldwide.
Seed cotton yield is a complex polygenic trait that is influenced by many genetic and environmental factors across locations and years.
Results The present investigation was conducted in three consecutive environments to delineate the genotype × environment interaction and to assess the stability of seven cotton genotypes at the Regional Agricultural Research Station, Nandyal, during 2018-19, 2019-20 and 2020-21.
Multivariate stability tests such as additive main effect and multiplicative interaction (AMMI) and genotype and genotype-by-environment interaction (GGE) models were employed to investigate the stability among cotton genotypes.
The AMMI results revealed that the majority of the variation was explained by the sum of the squares of the environmental variables, followed by the sum of the squares of the genotypic variables and the sum of the GEIs for the majority of the traits studied.
The first two interaction principal components explained the majority of the GEI in all traits under study.
A two-dimensional GGE biplot generated using the first two principal components revealed that the GGE biplot explained 97.
14% of the total variation, which was distributed as 83.
73% and 13.
41% of the sum of squares between principal components PC1 and PC2, respectively, for biometric trait seed cotton yield.
Conclusions Based on which-won-where polygon, ideal genotype ranking of AMMI and GGE biplot analysis, genotype, G3 (NDLH 2035-5) was identified as having the highest yield and was most stable in all the test environments studied.
However, low yielding but stable genotypes such as G4 (BGDS 1033) and G7 (Sivanandi) were also identified.
Among the three environments studied, environment E1 (2018-19) was identified as the most discriminating and representative.

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