Maize Genomics and Genetics 2025, Vol.16, No.6, 304-315 http://cropscipublisher.com/index.php/mgg 315 Yue H., Olivoto T., Bu J., Li J., Wei J., Xie J., Chen S., Peng H., Nardino M., and Jiang X., 2022, Multi-trait selection for mean performance and stability of maize hybrids in mega-environments delineated using envirotyping techniques, Frontiers in Plant Science, 13: 1030521. https://doi.org/10.3389/fpls.2022.1030521 Yue H., Olivoto T., Bu J., Wei J., Liu P., Wu W., Nardino M., and Jiang X., 2025, Assessing the role of genotype by environment interaction as determinants of maize grain yield and lodging resistance, BMC Plant Biology, 25: 120. https://doi.org/10.1186/s12870-025-06158-w Zhang H., Yin L., Wang M., Yuan X., and Liu X., 2019, Factors affecting the accuracy of genomic selection for agricultural economic traits in maize, cattle, and pig populations, Frontiers in Genetics, 10: 189. https://doi.org/10.3389/fgene.2019.00189 Zhang Z., Wang X., Zhang Y., Zhou K., Yu G., Yang W., Li F., Guan X., Zhang X., Yang Z., Xu C., and Xu Y., 2025, SPDC-HG: an accelerator of genomic hybrid breeding in maize, Plant Biotechnology Journal, 23: 1847-1861. https://doi.org/10.1111/pbi.70011 Zhao Y.M., Bao Y., Zhou L., Zhang B.H., and Wang W.J., 2025, Gene mapping of mechanization-friendly traits in maize based on SNP markers and breeding for mechanical adaptation, Molecular Plant Breeding, 16(1): 24-34 http://dx.doi.org/10.5376/mpb.2025.16.0003 Zou Q., Tai S., Yuan Q., Nie Y., Gou H., Wang L., Li C., Jing Y., Dong F., Yue Z., Rong Y., Fang X., and Xiong S., 2025, Large-scale crop dataset and deep learning-based multi-modal fusion framework for more accurate G×E genomic prediction, Computers and Electronics in Agriculture, 230: 109833. https://doi.org/10.1016/j.compag.2024.109833
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