Maize Genomics and Genetics 2025, Vol.16, No.6, 316-324 http://cropscipublisher.com/index.php/mgg 322 "implementation" of these achievements in breeding often still needs to overcome several practical problems. For instance, the phenotypic effects of these QTLS are mostly small, the genotype-environment interaction is complex, and their expression in different contexts is often unstable. Not to mention that some seemingly "useful" heat tolerance loci, once lacking verification or not superimposed with other key stress resistance genes, are difficult to support genetic gain in actual breeding work. If only traditional field screening is relied upon, these problems may be magnified. But now, the combination approach integrating high-throughput phenotypic platforms, quantification of environmental factors, and genomic data is helping researchers more accurately locate the key sites that control filaments development and the flower-filament interval (ASI) under high-temperature stress. Especially on the basis of multi-environment trials, precise measurements of traits such as ASI and flowering period can enable GWAS and genomic selection techniques to more accurately infer the action regions and genetic contributions of QTLS. It should be noted that when environmental data is used alone, it is of little help in yield prediction. However, once it is linked with phenotypic and genomic data, it often significantly improves the accuracy of climate adaptability prediction. There is still a long way to go from discovery to actual application. The implementation of heat-resistant breeding achievements does not rely on breakthroughs in a single discipline, but rather on the joint efforts of various experts. Whether they are geneticists, breeding experts, physiological researchers, data analysis teams or field managers, collaboration is the key. At present, molecular tools such as double haploid and gene editing, in combination with precise phenotypic platforms, have been gradually integrated into some national and international cooperation projects, accelerating the promotion of climate-adapted corn in high-risk areas. However, to truly establish an efficient and sustainable climate breeding system, investment, data sharing mechanisms and regional capacity building remain indispensable fundamental conditions. Acknowledgments We would like to express our gratitude to the reviewers for their valuable feedback, which helped improve the manuscript. 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 Begcy K., Nosenko T., Zhou L., Fragner L., Weckwerth W., and Dresselhaus T., 2019, Male sterility in maize after transient heat stress during the tetrad stage of pollen development, Plant Physiology, 181: 683-700. https://doi.org/10.1104/pp.19.00707 Bista P., Thapa S., Rawal S., Dhakal D., and Joshi D., 2022, Agro-morphological characterization and estimation of genetic parameters of spring maize hybrids in the inner plains of far-west nepal, International Journal of Agronomy, 1: 4806266. https://doi.org/10.1155/2022/4806266 Borrás L., and Vitantonio-Mazzini L., 2018, Maize reproductive development and kernel set under limited plant growth environments, Journal of Experimental Botany, 69: 3235-3243. https://doi.org/10.1093/jxb/erx452 Budhlakoti N., Kushwaha A., Rai A., Chaturvedi K., Kumar A., Pradhan A., Kumar U., Kumar R., Juliana P., Mishra D., and Kumar S., 2022, Genomic selection: a tool for accelerating the efficiency of molecular breeding for development of climate-resilient crops, Frontiers in Genetics, 13: 832153. https://doi.org/10.3389/fgene.2022.832153 Cerrudo D., Cao S., Yuan Y., Martínez C., Suarez E., Babu R., Zhang X., and Trachsel S., 2018, Genomic selection outperforms marker assisted selection for grain yield and physiological traits in a maize doubled haploid population across water treatments, Frontiers in Plant Science, 9: 366. https://doi.org/10.3389/fpls.2018.00366 Chu Y., Lee Y., Gomez-Cano F., Gomez-Cano L., Zhou P., Doseff A., Springer N., and Grotewold E., 2024, Molecular mechanisms underlying gene regulatory variation of maize metabolic traits, The Plant Cell, 36: 3709-3728. https://doi.org/10.1093/plcell/koae180 Djalović I., Kundu S., Bahuguna R., Pareek A., Raza A., Singla-Pareek S., Prasad P., and Varshney R., 2023, Maize and heat stress: physiological, genetic, and molecular insights, The Plant Genome, 17(1): e20378. https://doi.org/10.1002/tpg2.20378 Dong X., Guan L., Zhang P., Liu X., Li S., Fu Z., Tang L., Qi Z., Qiu Z., Jin C., Huang S., and Yang H., 2021, Responses of maize with different growth periods to heat stress around flowering and early grain filling, Agricultural and Forest Meteorology, 303: 108378.
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