Maize Genomics and Genetics 2025, Vol.16, No.6, 304-315 http://cropscipublisher.com/index.php/mgg 308 Later, researchers proposed the concept of "giant environment"-that is, grouping regions with similar climate and soil patterns into one category. Such a classification is not merely a categorization game, but rather helps breeders more effectively locate the adaptation zones of varieties and explain the response patterns of different genotypes under environmental changes. 4.2 Role of environmental covariates and envirotyping in MEGP In genomic prediction, environmental factors are often regarded as "background noise", but this is not the case. Environmental covariates such as climate and soil, once systematically integrated into the model, often significantly enhance the predictive performance. Environmental typing is precisely for this purpose-it collects long-term meteorological and soil data to identify the similarities and differences in structures among different environments. In the MEGP model, the usage of environmental covariates is not fixed. They can be directly incorporated into the model or first transformed through some dimensionality reduction methods, such as principal component analysis, before being used. The truly valuable aspect lies in the fact that these variables enable the model to better "understand" the impact of environmental changes on trait performance. For instance, when both environmental covariates and their interactions with genetic markers are taken into account in the model, the prediction accuracy of traits such as grain yield and drought resistance often improves significantly, especially in regions with large differences in climatic conditions. Furthermore, the addition of environmental typing enables the model to handle the selection tasks of multiple traits and multiple environments simultaneously, making it easier for breeders to identify those hybrids that are both stable and adaptable (Yue et al., 2025). 4.3 Challenges in quantifying genotype-by-environment interactions Theoretically, the interaction between genotype and environment (G×E) can be characterized by models; But in reality, this matter is far more complicated than imagined. The problem often lies in the data-there are too many genetic and environmental variables, the structure is too complex, and the quality of data from different sources varies greatly. For a model to be effective, it is first necessary to ensure that the training environment is sufficiently similar to the target environment; otherwise, the prediction results will "deviate" (Rogers and Holland, 2021). Some methods do offer assistance, such as dimensionality reduction techniques like the response specification model or kernel methods, which can simplify the problem to a certain extent. However, not all environmental factors contribute equally to G×E, and the differences in sensitivity among different traits can also interfere with the results. What is more troublesome is that incomplete weather records or the absence of soil data often reduce the accuracy of quantitative analysis. Despite this, with the development of environmental typing, high-throughput phenotypic analysis and machine learning, these obstacles are being gradually weakened. Nowadays, researchers have been able to identify G×E interaction patterns more precisely and are more likely to find stable corn hybrids in complex environments. 5 Case Study: Application of MEGP in a Multi-Year Hybrid Maize Trial 5.1 Study background: experimental design, germplasm, and environments In corn breeding research, to truly understand the potential of a hybrid, one or two years of field trials alone are often insufficient. Continuous assessment in multiple environments and over multiple years can better reveal the complex nature of the interaction between genotype and environment (G×E). A large-scale study in recent years did exactly this-it tested thousands of hybrid varieties at different locations and in different years, hoping to find those materials that remained stable in a variable environment. One of the experiments is particularly typical: researchers constructed 2,126 hybrid combinations based on 475 inbred lines and used over 9,000 SNP markers for genotyping. These hybrid varieties were arranged to be planted continuously for two years in 34 environments in the two major corn-producing areas of China (Wang et al., 2025),
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