Cotton Genomics and Genetics 2025, Vol.16, No.4, 173-183 http://cropscipublisher.com/index.php/cgg 180 environmental parameters, and even information about how farmers manage the land are all pulled in to calculate (Qin et al., 2025). The integration of these data makes the results look more realistic. But then again, most of the current research is still centered around yield and carbon flux. As for new data such as pan-genomics and multi-omics, although there is a lot of discussion, not many of them are actually integrated into the model. However, this is not difficult to understand. No matter how big the data is, how to use it is the key. If these genetic-level data can work well with the existing system, there is a chance to screen out those cotton varieties that are "naturally low-carbon and highly adaptable". This direction may be more efficient than simply adjusting the management method. Especially in the face of an increasingly unstable climate, it is far from enough to rely on feelings to control, and it is necessary to rely on data to "target" and make changes. In the final analysis, future breeding and management are more like "precision customization" rather than a one-size-fits-all approach. 9.2 Global collaboration for open-source carbon data in cotton If cotton production is to become greener and more sustainable, comprehensive, transparent, and accessible carbon footprint data is needed. Studies in China and the United States have shown that large-scale carbon accounting using life cycle assessment and statistical models is valuable (Huang et al., 2022). If more countries can work together to develop unified data collection methods, modeling steps, and reporting standards in the future, it will be easier to compare different regions and promote good low-carbon planting methods more quickly. 9.3 Policy implications and incentives for genomic-guided low-carbon farming In terms of policy, we should also encourage people to adopt more energy-saving and environmentally friendly planting methods, such as using genomics to select varieties, or carefully managing fertilizer and water resources. Many studies have shown that reasonably reducing the use of fertilizers and energy while improving efficiency is an effective way to reduce cotton carbon emissions (Singh et al., 2021). Policymakers can encourage farmers and companies to participate by funding relevant research, promoting data sharing, and establishing some low-carbon planting reward certification mechanisms. 10 Concluding Remarks Genomic technology is really hot now, especially tools like CRISPR/Cas that can "move genes", which have set off quite a wave in cotton breeding. To be honest, breeding a new cotton variety in the past was not only slow, but also easy to hit a wall. But the situation has begun to change. Especially with the transformation method that does not rely on genotypes, the efficiency has been improved all of a sudden, and many of the original stuck places have also loosened. However, editing technology alone is not enough. Researchers now simply integrate the data of genomes, transcriptomes, and metabolomes at once, with the goal of finding those "excellent" genes that are both carbon-saving and resistant to stress. With these clues, the direction of breeding is also clearer-breeding "all-rounder" cotton that can do things and has strong adaptability is no longer just an ideal (although it is still a little far from being fully realized). In addition to CRISPR, new tools such as base editing and primary editing have also been gradually added, and the breeding toolbox is getting fuller and fuller. Although it is a bit "dazzling", it is indeed practical, especially in improving carbon utilization efficiency and climate adaptability. These new technologies, together with the research on functional genomics and regulatory networks, are also gradually revealing the complex mechanisms behind cotton-such as how fibers develop, how to resist drought, how to save resources, etc. These basic knowledge are the roots of supporting "low-carbon and high-yield" cotton. Of course, to make these technologies truly come into play, it is not enough to rely on the scientific research circle alone. There must also be policy support, corporate participation, and data sharing. In recent years, sequencing, editing, and multi-omics technologies have indeed developed rapidly, but "fast technology" does not mean "smooth implementation". In the future, if you want to breed "climate-smart" cotton that can truly adapt to the future environment and emit less carbon, it will definitely not work alone. Scientific research, industry, and government must all work together to make this happen. Acknowledgments We are grateful to Dr. Xu for their assistance with the data analysis and helpful discussions during the course of this research.
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