Maize Genomics and Genetics 2025, Vol.16, No.6, 304-315 http://cropscipublisher.com/index.php/mgg 307 directly incorporates the interaction term between genotype and environment into the model and uses random effects or kernel methods to depict the mutual influence between genetics and environment. Such a design can take into account both the main effect and environmental specific deviations simultaneously, and is particularly useful for those traits that are strongly influenced by the environment. The idea of the reaction norm model is somewhat different. It pays more attention to the "expression curve" of genotypes under environmental gradients, that is, how traits respond when different environmental conditions change continuously. This model has unique advantages in describing phenotypic plasticity and has been proven in practice to effectively improve the accuracy of corn grain yield prediction. There is also the factor analysis (FA) model-it does not directly model each environmental effect, but rather decomposes genotype-environment interactions through latent factors to extract the main sources of variation. Due to its high computational efficiency, the FA model is particularly suitable for large-scale multi-environment experiments, and it can simultaneously simulate additive effects, dominant effects and their interactions with the environment (Krause et al., 2020). 4 Sources and Structure of Environmental Variation 4.1 Characterization of spatial and temporal environmental variability In multi-environment tests (METs), the environment is never constant. The changes can be quite drastic in different locations and different years. The performance differences of hybrid corn often stem from these spatial and temporal heterogeneity. Geographical conditions, soil types and local climates, these factors jointly shape spatial variations. Weather fluctuations and differences in agricultural management constitute unstable factors at the time level. Some long-term large-scale experimental data indicate that variables such as temperature, rainfall, vapor pressure deficit, and relative humidity often vary significantly between different locations and years (Yue et al., 2022). These fluctuations can trigger complex genotype-environment (G×E) interactions, making it difficult for the same hybrid to show consistent performance in different regions (Figure 1). Figure 1 Biplot for the principal component analysis between environmental variables
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