Bioscience Evidence 2024, Vol.14, No.3, 122-130 http://bioscipublisher.com/index.php/be 125 environments with mean temperatures ranging from 13 ℃ to 28 ℃. Another study by Wisser et al. (2019) focused on the adaptation of a tropical landrace of maize to a temperate environment, highlighting the importance of selecting diverse climatic conditions for experimental trials. Furthermore, Moradi et al. (2014) simulated climate change effects on maize growth and yield in Khorasan Razavi province of Iran, employing various General Circulation Models (GCMs) and scenarios to predict future climatic conditions. 3.2 Experimental design Field and greenhouse trials were arranged to evaluate the performance of selected maize varieties under different environmental conditions. For instance, Hallauer and Carena (2013) utilized stratified mass selection methodology for the adaptation of tropical and temperate populations to Iowa and North Dakota environments, screening up to 25 000 genotypes per population cycle. Similarly, Teixeira et al. (2014) conducted trials across a geographical range from Wisconsin to Puerto Rico to study the adaptation process of a tropical maize population subjected to recurrent selection for early flowering. Data collection focused on key traits such as flowering time, yield, and developmental patterns. For example, Choquette et al. (2023) jointly tested all selection lineages for the target trait (flowering time) and 23 other traits, modeling intergenerational shifts in a physiological reaction norm. Another study by Lafitte et al. (1997) measured rates of emergence, leaf appearance, anthesis, silking, final leaf number, grain yield, and yield components across different environments. Additionally, Tao and Zhang (2010) applied a super-ensemble-based probabilistic projection system to project maize productivity and evapotranspiration during the growing period, examining the relative contributions of various adaptation options. 3.3 Molecular biology methods DNA extraction and sequencing were performed to identify genetic variations associated with maize adaptation. For instance, Brandenburg et al. (2017) sequenced 67 genomes with an average sequencing depth of 18x, discovering over 22 million SNPs and identifying segments with high rates of heterozygosity (Figure 2). Another study by Camus-Kulandaivelu et al. (2006) genotyped collections of inbred lines and landraces for genome-wide simple sequence repeat (SSR) markers and a specific insertion/deletion in the Dwarf8 gene, which was associated with flowering time variation. Figure 2 Genome-wide patterns of nucleotide diversity of American and European samples (Adopted from Brandenburg et al., 2017)
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