MET
Multi-Environment Trials (GxE, AMMI, Finlay–Wilkinson)
Decompose genotype-by-environment interaction so you can ship cultivars that hold up across locations.
How it works
Our MET module ingests phenotype data measured across multiple environments and decomposes the variance into main effects and GxE interaction. AMMI (Additive Main effects and Multiplicative Interaction) extracts the dominant interaction patterns. Finlay–Wilkinson regression characterizes stability: a slope near 1 means a genotype adapts proportionally to environment quality, while flatter slopes identify broadly stable winners. We also report Wricke's ecovalence and Shukla's stability variance.
Formula
AMMI: y_ij = μ + g_i + e_j + Σ_k λ_k γ_ik δ_jk + ε_ij. Finlay–Wilkinson: y_ij = a_i + b_i·μ_j + ε_ij.
What you get
- ▸GxE heatmap of genotype × environment yields
- ▸AMMI biplot (PC1 vs PC2 of the interaction matrix)
- ▸Finlay–Wilkinson slopes and intercepts per genotype
- ▸Wricke ecovalence and Shukla stability variance rankings
When to use it
- ▸You have multi-location or multi-year trial data
- ▸You want to release cultivars with predictable performance
- ▸You're zoning your variety portfolio by mega-environment
References
Run MET on your data
Open the module and upload a CSV.