Maize Genomics and Genetics 2025, Vol.16, No.6, 304-315 http://cropscipublisher.com/index.php/mgg 313 decision-making systems, open-source environment typing processes, shared databases, and joint field trials are gradually breaking down regional and institutional barriers. High-quality phenotypic, genotypic and environmental data from different sources were collected, continuously enhancing the robustness and universality of the model. When the achievements of MEGP are further embedded in decision support systems, breeders and even policymakers can make judgments based on real data: which regions are more suitable for growing which varieties, how resources should be allocated, and how strategies for addressing climate risks should be adjusted. It can be foreseen that future corn breeding will no longer be a "single-point breakthrough", but a global collaborative networked system. MEGP may not change everything immediately, but it is becoming an indispensable supporting tool for modern breeding-a key path towards higher yields, more stable yields and more sustainable agriculture. Acknowledgments We would like to thank the anonymous reviewers for their detailed review of the draft. Their specific feedback helped us correct the logical loopholes in our arguments. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Adak A., Kang M., Anderson S., Murray S., Jarquín D., Wong R., and Katzfuss M., 2023, Phenomic data-driven biological prediction of maize through field-based high throughput phenotyping integration with genomic data, Journal of Experimental Botany, 74(17): 5307-5326. https://doi.org/10.1093/jxb/erad216 Alemu A., Åstrand J., Montesinos-López O., Sánchez J., Fernández-González J., Tadesse W., Vetukuri R., Carlsson A., Ceplitis A., Crossa J., Ortiz R., and Chawade A., 2024, Genomic selection in plant breeding: key factors shaping two decades of progress, Molecular Plant, 17(4): 552-578. https://doi.org/10.1016/j.molp.2024.03.007 Alves F., Galli G., Matias F., Vidotti M., Morosini J., and Fritsche‐Neto R., 2021, Impact of the complexity of genotype by environment and dominance modeling on the predictive accuracy of maize hybrids in multi-environment prediction models, Euphytica, 217: 37. https://doi.org/10.1007/s10681-021-02779-y Barreto C., Dias K., De Sousa I., Azevedo C., Nascimento A., Guimarães L., Guimarães C., Pastina M., and Nascimento M., 2024, Genomic prediction in multi-environment trials in maize using statistical and machine learning methods, Scientific Reports, 14: 1062. https://doi.org/10.1038/s41598-024-51792-3 Beyene Y., Gowda M., Olsen M., Robbins K., Pérez-Rodríguez P., Alvarado G., Dreher K., Gao S., Mugo S., Prasanna B., and Crossa J., 2019, Empirical comparison of tropical maize hybrids selected through genomic and phenotypic selections, Frontiers in Plant Science, 10: 1502. https://doi.org/10.3389/fpls.2019.01502 Costa-Neto G., Fritsche‐Neto R., and Crossa J., 2020, Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials, Heredity, 126: 92-106. https://doi.org/10.1038/s41437-020-00353-1 De Oliveira A., Resende M., Ferrão L., Amadeu R., Guimarães L., Guimarães C., Pastina M., and Margarido G., 2020, Genomic prediction applied to multiple traits and environments in second season maize hybrids, Heredity, 125: 60-72. https://doi.org/10.1038/s41437-020-0321-0 Ferrão L., Marinho C., Muñoz P., and Resende M., 2020, Improvement of predictive ability in maize hybrids by including dominance effects and marker × environment models, Crop Science, 60: 666-677. https://doi.org/10.1002/csc2.20096 Fritsche‐Neto R., Galli G., Borges K., Costa-Neto G., Alves F., Sabadin F., Lyra D., Morais P., De Andrade L., Granato Í., and Crossa J., 2021, Optimizing genomic-enabled prediction in small-scale maize hybrid breeding programs: a roadmap review, Frontiers in Plant Science, 12: 658267. https://doi.org/10.3389/fpls.2021.658267 Gao S., Yu T., Rasheed A., Wang J., Crossa J., Hearne S., and Li H., 2025, Fast-forwarding plant breeding with deep learning-based genomic prediction, Journal of Integrative Plant Biology, 67: 1700-1705. https://doi.org/10.1111/jipb.13914 Gevartosky R., Carvalho H., Costa-Neto G., Montesinos-López O., Crossa J., and Fritsche‐Neto R., 2021, Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical maize, BMC Plant Biology, 23: 10. https://doi.org/10.1186/s12870-022-03975-1 Guo T., Yu X., Li X., Zhang H., Zhu C., Flint-Garcia S., McMullen M., Holland J., Szalma S., Wisser R., and Yu J., 2019, Optimal designs for genomic selection in hybrid crops, Molecular Plant, 12(3): 390-401. https://doi.org/10.1016/j.molp.2018.12.022
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