Cotton Genomics and Genetics 2025, Vol.16, No.4, 173-183 http://cropscipublisher.com/index.php/cgg 177 not a panacea, but it is much more efficient than traditional blind irrigation and fertilization. On the one hand, it helps to stabilize production, and on the other hand, it can reduce waste, especially in the consumption of nitrogen fertilizer and water resources. With less investment, carbon emissions will naturally decrease. 5.3 Phenotyping and digital tools for carbon-efficient trait expression No one wants to squat in the fields to measure traits, and the current method of selecting breeding materials by eye has long been out of date. In order to find cotton with high carbon utilization efficiency more quickly, researchers now prefer to use high-throughput phenotyping and digital tools to "do the job for them". These tools can link genotypes and phenotypic performances, use algorithms to make predictions, and no longer rely on repeated field trials (Wang et al., 2022). Efficiency is naturally improved, and the breeding process is more accurate. In particular, those machine learning technologies save a lot of effort when screening target traits. In addition to laboratory analysis, field monitoring is also becoming smart. Some remote sensing platforms and visualization tools can already monitor the growth status of cotton in real time, such as leaf color, plant height, and even photosynthesis intensity. This information can be directly fed back to managers, and the timing of adjusting water and fertilizer strategies will be more accurate (Cooper and Messina, 2021). From a technical point of view, this dynamic monitoring will undoubtedly help to bring carbon efficiency to a higher level. 6 Microbiome and Root-Associated Traits in Carbon Sequestration 6.1 Genomic basis of cotton-root architecture and its role in soil carbon dynamics Roots are not only used to see whether cotton is stable, they also have an impact on how much soil carbon can be retained. For example, the deeper the roots grow and the more they spread, the more contact they have with the soil. How organic matter is decomposed and how carbon sinks are all inseparable from the participation of roots. Details such as root length, thickness, and branching structure may seem insignificant, but they are actually becoming more and more important in breeding (Rossi et al., 2020). Now many studies are also trying to "pick" out varieties with better root characteristics. Some are identified through genomic means, while others are directly modified by genetic engineering. The purpose is actually the same-to let cotton leave more carbon in the soil, not only will it grow well, but the land will also be able to support it (Srivastava and Yetgin, 2024). 6.2 Breeding for microbiome compatibility to promote carbon fixation In addition to the characteristics of the roots themselves, its "friends around" are also critical. Especially the fungi and bacteria living around the roots of cotton, they sometimes work harder than the plants themselves to help fix carbon. Some fungi, such as mycorrhizal fungi, and certain specific bacterial groups promote organic carbon accumulation by interacting with roots or root secretions (Wang et al., 2024). But there are problems. Not all cotton gets along with these microorganisms. If we can make cotton roots more attractive to these "good bacteria" through breeding, the carbon sequestration effect may be further improved. Of course, it's easier said than done. If we really want to "regulate" the microbiome, we still have to rely on a large number of experiments to verify the feasibility and stability, otherwise it is easy to have a situation where the ideal is very beautiful but the effect is unstable (Clemmensen et al., 2013; Song et al., 2020). 6.3 Integration of rhizosphere functional genomics into cotton selection programs In recent years, functional genomic methods have also begun to be used in cotton breeding. Researchers no longer focus on gene sequences, but begin to study what the roots secrete, what the microbial communities in the soil look like, and how these data correspond to plant traits (Panchal et al., 2022; Liu et al., 2022). These seemingly fragmented studies are actually pieced together a roadmap for "carbon sequestration cotton" bit by bit. If this information can be integrated into the breeding process, in the future it may be possible to select varieties that have a tacit understanding between roots and microorganisms, high yields, and low carbon. This is not only for yield, but also to allow agriculture itself to contribute to emission reduction (Fierer and Walsh, 2023). 7 Life Cycle Assessment (LCA) and Genomic Strategy Integration 7.1 Genomics-informed LCA models for cotton systems At present, there are not many studies that specifically combine genomic strategies with cotton life cycle assessment (LCA), which can almost be said to be blank. But then again, it is not completely clueless. In other
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