Plant Gene and Trait 2025, Vol.16, No.5, 194-205 http://genbreedpublisher.com/index.php/pgt 203 8 Conclusion, Implications, and Future Outlook 8.1 Breeding and horticultural implications The need for climate-resilient rose cultivars becomes more critical because of climate change. The stability of flower production under heat stress and photoperiod changes depends on four main molecular targets which include RcCO, RcFT, RcPIFs and RoKSN. The combination of marker-assisted selection with multi-omics data enables better thermotolerance prediction and CRISPR-based non-transgenic genome editing shortens breeding times and boosts public acceptance of new crop varieties. The strategies link sustainability to market competitiveness through their ability to meet consumer floral design needs while building business resilience. Scientists transform genetic and environmental data into sustainable garden designs through their application of horticultural practices. LED lighting systems that work with renewable heating and automated irrigation systems create climate-adaptive gardens which achieve optimal resource management. The Kew case study shows how synchronized flower displays create educational benefits and improve visitor experiences and conservation efforts which can serve as a model for worldwide adaptation programs. 8.2 Integrated conclusion Research on Rosa chinensis shows that RcCO–RcFT–RcSOC1 networks receive light and temperature signals which are detected by RcPHYA, RcCRY2, RcPIF4 and RcHsfA6. The expression of floral symmetry along with fragrance and longevity depends on how genetic makeup interacts with environmental conditions. The controlled cultivation systems which match environmental signals to genetic pathways produce synchronized and predictable flowering patterns that benefit both scientific research and commercial farming operations. 8.3 Future outlook and challenges The development of future breeding depends on uniting transcriptomic and epigenomic and metabolomic and phenomic data to generate predictive systems biology models. The implementation of machine learning and AI models for cultivar performance forecasting requires maintaining genetic diversity to prevent uniformity risks. The speed of commercialization depends on regulatory frameworks because fast approval of gene-edited ornamentals would enable companies to achieve global market leadership. Public acceptance requires successful public engagement to achieve its goals. The exact control methods used for rose flowering can serve as a model to improve other ornamental plants while maintaining molecular breeding and environmental control for sustainable horticulture under climate change conditions. Acknowledgments Thank supervisors, colleagues, and my family for their guidance and support during this research. 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. Reference Antoniou-Kourounioti R.L., Hepworth J., Heckmann A., Duncan S., Qüesta J., Rosa S., Säll T., Holm S., Dean C., and Howard M., 2018, Temperature sensing is distributed throughout the regulatory network that controls FLC epigenetic silencing in vernalization, Cell Systems, 7(6): 643-655. https://doi.org/10.1016/j.cels.2018.10.011 Chen Y., Lu J., Wang W., Fan C., Yuan G., Sun J., Liu J., and Wang C., 2023, Rose long noncoding RNA lncWD83 promotes flowering by modulating ubiquitination of the floral repressor RcMYC2L, Plant Physiology, 193(4): 2573-2591. https://doi.org/10.1093/plphys/kiad502 Cola G., Mariani L., Toscano S., Romano D., and Ferrante A., 2020, Comparison of greenhouse energy requirements for rose cultivation in Europe and North Africa, Agronomy, 10(3): 422. https://doi.org/10.3390/agronomy10030422 Gendron J.M., and Staiger D., 2023, New horizons in plant photoperiodism, Annual Review of Plant Biology, 74: 481-509. https://doi.org/10.1146/annurev-arplant-070522-055628 Guo X., Yu C., Luo L.E., Wan H., Li Y., Wang J., Sung T., Pan H., and Zhang Q., 2017, Comparative transcriptome analysis of the floral transition in Rosa chinensis ‘Old Blush’ and R. odorata var. gigantea, Scientific Reports, 7: 6068. https://doi.org/10.1038/s41598-017-05850-8
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