Maize Genomics and Genetics 2024, Vol.15, No.2, 93-101 http://cropscipublisher.com/index.php/mgg 96 Similarly, another study analyzed the grain yield variation in 1918 maize hybrids across 65 testing environments. It was found that genetic-by-environment variances were more significant than genetic main effect variances, highlighting the importance of considering both additive and dominance relationships in modeling GEI patterns. This approach allows for better genomic prediction of hybrid performance across different environments (Zhang et al., 2019; Rogers et al., 2021). In Greece, the interaction of genotype by environment was also evident in multi-environment trials of maize hybrids. The study used principal components analysis (PCA) and additive main effects and multiplicative interaction (AMMI) analysis to evaluate the performance of four maize genotypes across five locations. The results showed that certain environments provided higher yields and better grain quality, indicating the importance of selecting specific genotypes for specific environments to optimize grain quality (Figure 1) (Katsenios et al., 2021). Figure 1 AMMI biplot presenting mean fiber content and the first interaction principal components axis (IPC1) for various genotypes in different environments (Adapted from Katsenios et al., 2021) Image caption: AMMI biplot presenting mean fiber content and the first interaction principal components axis (IPC1) of 4 genotypes (red) evaluated in 10 environments (blue) (Adapted from Katsenios et al., 2021) 4.2 Breeding for adaptation Breeding programs aiming to improve maize grain quality must consider the interaction between genetic and environmental factors. The identification of genotypes that perform well under specific environmental conditions is essential for developing stable and high-yielding hybrids. For example, a study on Spanish maize populations under stress conditions such as drought and cold found that commercial hybrids had higher yield and stability compared to most populations. The study emphasized the importance of considering climatic variables and genotypic traits like kernel depth and ear length in breeding programs (Svečnjak et al., 2007; Pok et al., 2009; Romay et al., 2010). Another study focused on the adaptation of maize genotypes to environments with varying nitrogen availability. The research identified morpho-physiological traits associated with better performance in low nitrogen environments, such as efficient canopy to sustain resource capture up to maturity. These findings highlight the need for breeding programs to consider specific traits that enhance adaptation to different environmental conditions (Cirilo et al., 2009).
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