Delineating Soybean Mega-Environments Across State Lines: A Statistical Learning Approach to Multi-State Official Variety Trial Analysis

The current state-centric analysis of Official Variety Trials (OVTs) restricts the identification of stable performance zones across political boundaries. This study employed multivariate statistical learning techniques to delineate soybean (Glycine max L.) “mega-environments” using yield data from 2269 varieties collected across seven U.S. states (2019–2022). Utilizing Quadratic Discriminant Analysis (QDA), Principal Component Analysis (PCA), and Agglomerative Hierarchical Clustering (AHC), we examined the edaphoclimatic factors influencing yield stability. QDA classified over 79% of environments into distinct temporal categories, highlighting significant inter-annual climatic variability driven by Growing Degree Days (GDD) and latitude. PCA distinguished broad climatic drivers (PC1) from localized soil texture constraints (PC2). AHC identified optimal production clusters that frequently diverged from geographic proximity, indicating that distant sites often share more critical yield-determining factors than neighboring counties. By operationalizing these latent environmental patterns, this study provides a data-driven framework for cross-state environmental zoning that can support more precise variety placement once genotype performance has been evaluated within these zones.