Cotton Genomics and Genetics 2025, Vol.16, No.4, 173-183 http://cropscipublisher.com/index.php/cgg 178 fields, such as corn or grain crops, some people have tried to enrich LCA models with genetic data, which provides a lot of ideas for cotton. For example, integrating new data such as genomics into LCA is not necessarily for "seeing further", but for "seeing more closely"-more realistic and closer to the situation in the field. Once the data source is updated, the transparency and traceability of the model will follow. In fact, some studies have significantly improved the reliability of LCA models by introducing new data streams (Löwgren et al., 2025). Therefore, although it has not really been implemented in the cotton field yet, this method can be used as a reference. In the future, if you want to evaluate the carbon efficiency of a cotton variety from seed sowing to harvesting and then to post-processing, genomic data will sooner or later be pulled in. 7.2 Environmental impact metrics aligned with carbon footprint reduction goals In the past, when we talked about LCA of cotton, we always focused on how much energy was used and how much material was invested. These are indeed important, but now it seems that these alone are obviously not enough. Emissions, waste, and even the treatment methods at the end of the entire life cycle should actually be taken into account (Löwgren et al., 2025). Especially if you want to evaluate new varieties bred using genomic methods, the indicators must be more specific. It is not enough to just look at inputs and outputs. You also have to ask: Can this cotton help the soil lock in more carbon? Can you use less nitrogen fertilizer? Can greenhouse gas emissions be lower than before? These are the measurements that are truly linked to carbon reduction goals. In the final analysis, what we need is a more practical environmental indicator system that can reflect the real environmental effects of different breeding strategies, rather than just the input-output ratio on paper. 7.3 Data integration for decision support in sustainable cotton farming If we talk about LCA now, it seems that we are "out of touch" if we don't mention data integration. Many systems are already using blockchain to track the entire life cycle of agricultural products. This approach is quite advanced and has indeed improved the transparency and credibility of the data. But on the other hand, genomic data is often "put aside." In fact, it shouldn't be like this. If cotton cultivation wants to become more environmentally friendly and low-carbon, it is far from enough to rely solely on the tracking process. Genetic information must also be integrated. As long as the data is well pieced together, the basis for both on-site decision-making and macro policies will be more solid. For farmers, this is a tool to help them make more timely adjustments; for policymakers, it is a yardstick for measuring green planting performance. In the final analysis, data integration must be deep enough to allow these gene-driven strategies to truly be implemented. 8 Case Study: Carbon-Reduced Cotton Cultivation in the Xinjiang Region 8.1 Application of carbon-efficient genotypes and molecular breeding tools Xinjiang does lack "hard evidence" in carbon-efficient molecular breeding. But this does not mean that the technology is backward. On the contrary, Xinjiang's planting system has been quietly changing, and the changes are not small. From the early optimization of light and heat utilization, to the later large-scale drip irrigation system, to the current mature efficient and simplified planting system, this "three-stage" development path has long been running (Feng et al., 2024). As for molecular breeding technology, it is now more of a supplementary means of intervention. Once combined with existing field technologies, such as selecting cotton varieties that perform well in water and fertilizer utilization and are easy to grow, overall carbon emissions may be further reduced. In other words, although technology is a piece of the puzzle, don't ignore the space behind it and the traditional agronomic methods. 8.2 Agronomic modifications based on genomic-environmental fit In the past, cotton planting required “experience” and “adaptability”, but now it is different. Xinjiang’s planting planning has begun to gradually introduce climate and environmental data, trying to plant each variety in the most suitable plot. It is not to say that changing the location will save resources, but if the combination is good, a lot of waste can actually be avoided (Figure 2) (Zhu et al., 2023). Methods such as drip irrigation, water-fertilizer integration, and mulching are already being used in many cotton-growing areas in Xinjiang, and the efficiency of water and fertilizer utilization has indeed increased. Moreover, once such methods become popular, it will not only be a matter of increasing production, but energy conservation and emission reduction will also become a “by
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