Biplot analysis for GxE interaction in wheat and barley trials

Title : Biplot analysis for GxE interaction in wheat and barley trials

Objectives

  1. Compare the Univariate (Parametric) estimates for GxE interaction
  2. Compute and compare the Univariate (Non Parametric) estimates for GxE interaction
  3. Compute the multivariate estimates of GxE interaction
  4. Compute AMMI to quantify the components
  5. Compare GGE  estimates with AMMI

Genotype by environment interaction (GxE interaction) is unevitable in coordinated trials as differential responses of genotypes have observed across environments. Several statistical models have been used to understand complex GxE interactions of different crops for identifying suitable genotypes. Usually analysis of variance (ANOVA), principal component analysis (PCA) and linear regression (LR) analysis are used to study multi-location data.  The popular procedure ANOVA can only describe the genotypic main effects being an additive model, while, PCA being a multiplicative model, does not describe the additive main effects. The additive main effects and multiplicative interaction (AMMI) model, explains GE interaction much effectively. The use of biplot methodology explains the complex GE interaction in a much simpler graphical manner.

The additive main effects and multiplicative interaction (AMMI) model, explains GE interaction much effectively. The use of biplot methodology explains the complex GE interaction in a much simpler graphical manner.

 

AMMI ANALYSIS

OF

BARLEY COORDINATED TRIALS

 

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 Name(s)  and designation of PI : Dr. Ajay Verma, Principal Scientist


GGE BIPLOT ANALYSIS

for

COORDINATED TRIALS : BARLEY

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