Maize Genomics and Genetics 2025, Vol.16, No.6, 304-315 http://cropscipublisher.com/index.php/mgg 309 with a wide range of climatic conditions and different soil types. Similar multi-environment trials are not uncommon-some cover dozens of locations and multiple years, mainly focusing on core agronomic traits such as grain yield and grain moisture content. The huge volume of data provides a solid foundation for model validation. 5.2 Implementation of MEGP model: data integration and model performance In these studies, the process of building the MEGP model is not simply "throwing data into the model". It integrates high-density genomic information with detailed environmental descriptions-including 19 climate variables such as temperature, radiation, sunshine duration, etc., as well as principal components extracted based on these variables (Figure 2) (He et al., 2025). To present the interaction between genetics and the environment more realistically, the research team did not use only one method. They tried several different modeling strategies: the traditional GBLUP framework, the response specification model, and some machine learning algorithms were all included. Figure 2 Dimensionality reduction of environmental parameters according to the development period of maize hybrids. a) The 36 development stage-environment windows (V0 to R6) for maize hybrids were defined based on the relationship between the developmental stages and growing degree days (GDD). b) Trends in GDD, day length (DL), photosynthetically active radiation (PAR), and precipitation (PRE) across 36 development stage-environment windows (Adopted from He et al., 2025)
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