From the food security prospective, the stability of agricultural production is as important as the magnitude of output. Food production is very much a function of climate, which in itself is unpredictable 1 . The Genotype-by-Environment Interaction (GEI) and stability of crop performance across environments are become more relevant issues as greater emphasis is placed on sustainable agricultural systems 2 . From the statistical point of view, the basic model of GEI is:
$$Response = \mu + E + G + GE$$
Here, \(\mu\) is the overall mean. This model contains E, which is typically many times larger than G and GE but is not relevant to cultivar evaluation. The GGE model allows the analysis to be focus only on the useful parts of the data (i.e. G and GE) in the Principle Components Analysis (PCA).
This web application calculates the GGE model using GGEBiplots package where input data presented with a two way table of means with genotypes in rows, where genotype names are set in the first column, and environments in columns, where environment names are set in the first row. ( Sample Data File )
References:
- Wittwer, S.H. 1998. The changing global environment and world crop production. J. Crop Prod., 1(1):291-299.
- Yan, W. 2002. Singular-Value Partitioning in Biplot Analysis of Multi environment Trial Data. Agron. J. 94:990-996.