Genotype × Environment Interaction: A Comparison between Joint Regression Analysis and Weighted Biplot Models

Abstract

This work examines the obstacles presented by genotype-environment interaction (GEI) in plant breeding and the significance of accurate analysis in selecting superior genotypes. Although current models, such as the JRA and GGE biplot models, have their limitations, especially when dealing with multi-environment data with a single trait and several environments, our approach addresses the challenges of GEI in plant breeding. We introduce new models and conduct a comprehensive comparison with the existing ones. The inclusion of mega-environment analysis and the evaluation of individual test environments within each mega-environment adds depth to this study, aiming to provide a more nuanced understanding of the causes and effects of GEI. We intend to validate and test the proposed models on real-world datasets to assess their effectiveness and practical applicability in the field of plant breeding. Additionally, communicating the benefits and potential limitations of your proposed models will contribute to the broader understanding and adoption of improved methods for analyzing GEI in plant breeding. We conclude that joint use of the JRA and GGE Biplot models has proven effective in exploring genotype × environment interaction, particularly for multi-environment data (MET). JRA model provides a most robust and reliable representation of patterns in the data related to genotypes and environments.