Additive Main Effect and Multiplicative Interaction Analysis of National Turfgrass Performance Trials

By J. S. Ebdon, Hugh G. Jr Gauch. 2002

361 citations recorded by the source corpus.

Subfields: Environmental Chemistry, Plant Science

View Paper: https://www.semanticscholar.org/paper/9a55114f512ad31100aefe5e674d18de2d9a808a

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In Environmental Chemistry

In Plant Science

Citations in the corpus

Additive main effect and multiplicative interaction analysis of grain yield of wheat varieties in Lithuania (2006, background): “…because it captures a large portion of the GEI sum of square, it clearly separates main and interaction effects that present agricultural researchers with different kinds of opportunities, and the model often provides agronomically meaningful interpretation of the data (Ebdon & Gauch, 2002).”
GGE biplot and AMMI analysis of barley yield performance in Iran (2017, background): “This effect reduces the association between phenotypic and genotypic values and complicates the selection of the best genotypes (Ebdon and Gauch 2002).”
GGE Biplot and AMMI Analysis of Yield Performance of Barley Genotypes across Different Environments in Iran (2014, background): “The genotypes close to ordinate expressed general adaptation, whereas the further genotypes depicted more specific adaptation to environments (Ebdon and Gauch, 2002).”
Genotype by environment interaction components underlying variations in root, sugar and white sugar yield in sugar beet (Beta vulgaris L.) (2018, methodology): “The aim of achieving maximum predictive accuracy is to balance underfitting real structure and overfitting spurious noise (Gauch and Zobel 1988; Ebdon and Gauch 2002a, b).”
The use of parametric and non-parametric measures for selecting stable and adapted barley lines (2018, background): “GE interaction effect decreases the association between genotypic and phenotypic values and also confuses the selection of the desirable genotypes (Ebdon and Gauch 2002).”
AMMI and SREG GGE biplot analysis for matching varieties onto soybean production environments in Ethiopia (2009, methodology): “This is because in the presence of GEI, yield is less predictable and cannot be interpreted based on genotype and environmental means alone (Ebdon and Gauch, 2002b; Voltas et al., 2002).”
Integrating different stability models to investigate genotype × environment interactions and identify stable and high-yielding barley genotypes (2019, background): “This interaction effect decreases the relationship between genotypic and phenotypic values and confuses the selection of desirable genotypes (Ebdon and Gauch 2002).”
GGE Biplot Analysis for Yield Stability in Multi-environment Trials of Promising Hybrid Rice (Oryza sativa L.) (2015, background): “Genotype × environment (GE) interaction refers to different ranking of genotypes across environments and may complement the selection process and recommendation of a genotype for a target environment (Ebdon and Gauch, 2002; Gauch, 2006).”