Molecular Plant Breeding 2025, Vol.16, No.1, 24-34 http://genbreedpublisher.com/index.php/mpb 28 In conclusion, the adoption of modern breeding strategies, including MAS and GS, has significantly advanced the development of mechanization-friendly traits in maize. These approaches offer a more efficient and precise means of selecting for complex traits, ultimately contributing to the improvement of maize varieties suited for mechanical adaptation. 6 Challenges in Breeding for Mechanization-Friendly Traits 6.1 Limitations of current breeding practices Current breeding practices in maize often focus on yield and disease resistance, with less emphasis on traits that facilitate mechanization. Traditional breeding methods are time-consuming and labor-intensive, making it difficult to rapidly develop varieties that meet the specific needs of mechanized farming. Additionally, the reliance on phenotypic selection can be inefficient due to the complex nature of mechanization-friendly traits, which are often influenced by multiple genes and environmental factors (Su et al., 2017; Liu et al., 2019). 6.2 Genetic complexities and trait interactions Mechanization-friendly traits in maize, such as uniform plant height, ear placement, and stalk strength, are controlled by complex genetic interactions. These traits often exhibit pleiotropy, where a single gene affects multiple traits, complicating the breeding process (Hou et al., 2024). For instance, the genetic basis of yield-related traits involves numerous quantitative trait loci (QTL) and single nucleotide polymorphisms (SNPs), which interact in intricate ways (Romay et al., 2013; Yang et al., 2014; Zhang et al., 2020). The identification and manipulation of these genetic components require advanced genomic tools and techniques, such as genome-wide association studies (GWAS) and genomic prediction, to unravel the underlying genetic architecture (Ertiro et al., 2020; Sethi et al., 2023). 6.3 Environmental factors affecting trait expression Environmental factors play a significant role in the expression of mechanization-friendly traits. Variability in soil type, climate, and management practices can lead to inconsistent trait expression, making it challenging to select and stabilize these traits across different environments. Studies have shown that traits like kernel size and nitrogen use efficiency are highly influenced by environmental conditions, which can mask the genetic potential of the plants (Liu et al., 2019; Ertiro et al., 2020; Chen et al., 2021). This environmental dependency necessitates multi-environment trials and sophisticated statistical models to accurately assess and select for mechanization-friendly traits (Yuan et al., 2019). By addressing these challenges through the integration of advanced genomic tools and multi-environment testing, breeding programs can more effectively develop maize varieties that are well-suited for mechanized agriculture. 7 Advanced Genomic Techniques in Trait Mapping 7.1 Use of genome-wide association studies (GWAS) in trait identification Genome-wide association studies (GWAS) have become a cornerstone in identifying genetic loci associated with complex traits in maize. By leveraging high-density SNP markers, GWAS can dissect the genetic architecture of various agronomic traits. For instance, a study expanded an association panel to 513 inbred lines and identified numerous loci for traits such as plant height using both mixed linear models (MLM) and the Anderson-Darling (A-D) test, highlighting the utility of GWAS in maize genetics and breeding (Yang et al., 2014). Another study utilized a large maize SNP array for diversity analysis and high-density linkage mapping, demonstrating the array's effectiveness in genetic mapping and validating the B73 reference genome (Ganal et al., 2011). Additionally, GWAS has been instrumental in understanding the genetic basis of yield-related traits, as evidenced by the identification of 138 SNPs associated with these traits in maize (Zhang et al., 2020). These examples underscore the power of GWAS in linking genotypic variations to phenotypic differences, thereby facilitating the identification of candidate genes for breeding programs (Figure 2) (Xiao et al., 2017; Shikha et al., 2021).
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