GGE Biplot Multi-Environment User Guide

1. INTRODUCTION

The GGE Biplot Multi-Environment Module implements Yan's GGE (Genotype main effect + Genotype-by-Environment interaction) biplot methodology. In multi-location trials across agriculture, industrial testing, and multi-site clinical trials, evaluating treatment main effects together with interaction effects is essential because treatment selection is based on combined performance across target environments.

GGE biplots center dataset columns by environment means and apply Singular Value Decomposition (SVD) to render two-dimensional biplot views that visually answer key research questions regarding mega-environment partitioning and site evaluation.

Primary Analytical Capabilities:

Which-Won-Where Polygon View: Draws a convex polygon surrounding outer treatment markers and perpendicular sector vectors to divide testing locations into distinct mega-environments and identify winning factors for each sector.
Mean vs. Stability View (AEC Axis): Constructs an Average Environment Coordinate (AEC) horizontal axis to rank overall mean performance alongside a perpendicular vertical axis measuring stability across environments.
Evaluation of Environments: Renders environment vector lengths (measuring discriminative power) and angles with the AEC axis (measuring representativeness of target locations).
Evaluation of Treatments (Ideal Entry): Identifies the "Ideal Treatment" located at the center of concentric circles combining high mean performance and zero interaction variability.
Multiple Centering & Scaling Options: Supports Tester Centering (G+GE), Double Centering (GE only), and Environment-focused (Scaling 1), Treatment-focused (Scaling 2), or Symmetric (Scaling 3) biplot scaling.

2. AVAILABLE OPTIONS & SETTINGS

The control panel and top header controls provide complete configuration for biplot views, centering, scaling, and trait selection:

Control / Parameter	Description	Statistical Purpose	When to Select / Set
Primary Factor Column (Treatment)	Categorical column identifying treatment entries or sample entities.	Defines primary factor levels evaluated across environments.	Required. Map to your treatment factor column.
Environment Column (Location / Site)	Categorical column identifying trial locations, testing sites, or environmental conditions.	Defines environmental testing sites for biplot modeling.	Required. Map to your environment/site column.
Target Quantitative Trait	Selects continuous numeric response measurement column.	Computes GGE SVD decomposition and biplots for the selected trait.	Required. Select a quantitative outcome trait column.
Centering Mode	Selects data centering: `Tester Centering (G+GE)`, `Double Centering (GE only)`, or `No Centering`.	Tester centering evaluates both treatment main effects and interactions (standard GGE).	Keep at Tester Centering (G+GE) for standard GGE biplot evaluation.
Biplot Scaling Mode	Selects scaling: `Scaling 1 (Environment-Focused)`, `Scaling 2 (Treatment-Focused)`, or `Scaling 3 (Symmetric)`.	Scaling 1 preserves environment relationships; Scaling 2 preserves treatment relationships; Scaling 3 balances both.	Use Scaling 2 to compare treatments; use Scaling 1 to evaluate environment relationships.
Biplot View Mode	Toggles display mode: `Which-Won-Where`, `Mean vs Stability`, `Discrimination vs Representative`, or `Ranking Treatments`.	Renders specific biplot geometry overlays for decision-making.	Select Which-Won-Where to identify mega-environments.

3. INPUT DATA FORMAT REQUIREMENT

Datasets must follow a tidy tabular structure (.xlsx or .csv). Each row represents an individual plot or trial unit containing treatment labels, environment site tags, and outcome traits:

Replicate	Treatment_Group	Environment_Site	Response_Metric_1	Response_Metric_2
Rep_1	Treatment_01	Location_Alpha	124.50	18.20
Rep_2	Treatment_01	Location_Alpha	127.10	18.90
Rep_1	Treatment_01	Location_Beta	110.40	15.80
Rep_2	Treatment_01	Location_Beta	112.80	16.30
Rep_1	Treatment_02	Location_Alpha	145.80	23.40
Rep_2	Treatment_02	Location_Alpha	148.20	24.10

4. STATISTICAL FOUNDATIONS & METRICS (PLAIN TEXT DEFINITIONS)

The mathematical concepts behind GGE biplots are defined in plain text below:

Environment Column Centering

Plain Text Definition:

Subtracting the average environment mean from each cell value in a given location column, removing environmental main effects while retaining both treatment main effects and factor-by-environment interactions.

Biplot Singular Value Decomposition (SVD)

Plain Text Definition:

Decomposing the environment-centered mean matrix into primary biplot component 1 (PC1) and component 2 (PC2) scores, allowing treatments and environments to be plotted simultaneously on a 2D scatter plane.

Average Environment Coordinate (AEC) Axis

Plain Text Definition:

A single directional reference line passing through the biplot origin and the average environment marker. Projections of treatment points onto this horizontal axis rank overall mean performance.

AEC Stability Line

Plain Text Definition:

A line perpendicular to the AEC horizontal axis passing through the origin. The length of a treatment's projection vector onto this vertical line measures its stability variability across environments (shorter projection = higher stability).

Convex Polygon & Mega-Environment Sectors

Plain Text Definition:

A polygon formed by connecting outer-most treatment points on the biplot. Perpendicular equality lines drawn from the origin divide the biplot into sectors; environments falling in a sector share a common winning treatment located at the vertex of that sector.

5. STEP-BY-STEP WORKFLOW

Upload Dataset: Open the sidebar panel and upload your multi-environment spreadsheet (.xlsx or .csv).
Map Factor Columns: Assign categorical dataset columns to Primary Factor (Treatment) and Environment (Location/Site).
Select Outcome Trait: Check a continuous response measurement column from the variable list.
Configure Centering & Scaling: Select Tester Centering (G+GE) and choose Scaling 2 (Treatment-Focused) to evaluate factor entries.
Run GGE Analysis: Click the bold Run Analysis button.
Switch Biplot Views: Toggle between Which-Won-Where (mega-environments), Mean vs Stability (AEC axis), Discrimination vs Representative (environment evaluation), and Ideal Treatment views.
Export Reports & Graphics: Download formatted reports in Excel (.xlsx), Word (.docx), PowerPoint (.pptx), or high-res biplot chart images.

6. SAMPLE RESULTS & INTERPRETATION

Below is an example of a GGE Biplot Component Decomposition & Environment Vector Table:

Biplot Component Axis	Eigenvalue / Variance	Proportion of G+GE Explained (%)	Cumulative Proportion (%)	Analytical Utility
Component 1 (PC1)	312.450	64.20 %	64.20 %	Correlates strongly with overall mean performance.
Component 2 (PC2)	124.800	25.60 %	89.80 %	Correlates strongly with interaction instability.
Residual Components	49.600	10.20 %	100.00 %	Unmodeled higher-order variance.

How to Read GGE Output:

High Cumulative Variance (89.80%): PC1 (64.20%) and PC2 (25.60%) explain nearly 90% of total G+GE variation, confirming biplot visual accuracy.
Which-Won-Where Sectors: Testing locations grouped inside a polygon sector indicate a unified mega-environment; the vertex treatment at the corner of that sector is the top performer for those sites.
Ideal Treatment Landmark: Treatments located nearest the center of the concentric target circles combine maximum mean performance with zero interaction instability.

7. BEST PRACTICES & TIPS

Environment Discriminative Power

Environments with long vector lengths from the origin possess high discriminative power to separate treatment entries; short vectors indicate non-discriminating test sites.

Minimum Location Requirement

GGE biplots require datasets evaluated across at least 3 testing locations or environments to render meaningful 2D polygon sectors.

Cite DATES in Research Papers

If you use the DATES GGE Biplot module for experimental data analysis in published scientific research, please cite it as follows:

@software{dates_app_2026, author = {DATES Development Team}, title = {DATES: Data Analysis and Trial Evaluation System}, year = {2026}, url = {https://dates-app.org}, note = {Multi-Environment Analysis — GGE Biplot Module} }