Hongpeng Wang , Minghua Li

Biotechnology Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, China
Author    Correspondence author
Maize Genomics and Genetics, 2025, Vol. 16, No. 6   doi: 10.5376/mgg.2025.16.0028
Received: 28 Sep., 2025    Accepted: 15 Oct., 2025    Published: 24 Nov., 2025

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Wang H.P., and Li M.H., 2025, Multi-environment genomic prediction models for hybrid maize performance, Maize Genomics and Genetics, 16(6): 304-315 (doi: 10.5376/mgg.2025.16.0028)

Hybrid maize breeding relies on accurate prediction of hybrid performance across diverse environments to enhance yield stability and adaptability. This study focused on developing and evaluating Multi-Environment Genomic Prediction (MEGP) models to improve the predictive accuracy of hybrid maize performance under variable environmental conditions. We first examined the principles of genomic prediction and the limitations of single-environment models before implementing MEGP frameworks that integrate genotype-by-environment (G×E) interactions through reaction norm and factor analytic approaches. Environmental variation was quantified using spatial and temporal covariates, while envirotyping provided additional insights into environmental effects on hybrid performance. A multi-year hybrid maize trial was conducted to assess the MEGP models, integrating genotypic, phenotypic, and environmental data. Results demonstrated that MEGP models significantly outperformed single-environment models in predictive accuracy and heritability estimates, highlighting their potential for more robust selection decisions. The study also explored the integration of high-throughput phenotyping, remote sensing, and machine learning techniques to further enhance model performance. Overall, MEGP models present a promising framework for accelerating hybrid maize breeding, improving climate resilience, and supporting global breeding networks through data-driven decision-making.

Genomic prediction; Hybrid maize; Multi-environment models; G×E interactions; Breeding optimization