Cotton Genomics and Genetics 2025, Vol.16, No.4, 173-183 http://cropscipublisher.com/index.php/cgg 176 level. Of course, the premise is that there must be a reliable reference genome and genetic markers linked to the target traits. With these "positioning points", breeders can save a lot of time in the test field and directly screen plants at the molecular level (Conaty et al., 2022). To put it bluntly, you don't have to wait for it to grow up to know where its potential is. This method is not only fast, it also improves efficiency, and is particularly suitable for breeding under conditions where resources are not so abundant. Low investment and high hit rate-the selected cotton not only has a stable yield, but also uses nutrients and water more reasonably. For carbon emission control, this is undoubtedly a more realistic breeding path. 4.2 Utilization of gene editing (e.g., CRISPR/Cas9) to reduce carbon cost of growth Now, gene editing technology, especially the CRISPR/Cas9 system, provides breeders with a way to directly modify genes. These genes are related to carbon metabolism, stress resistance, and the efficiency of fertilizer and pesticide use. Using this type of technology, researchers can precisely improve certain specific functions, such as making photosynthesis more efficient or making cotton less dependent on chemical fertilizers, so that the carbon emissions of the entire planting process can be reduced (Shahzad et al., 2022). The introduction of gene editing makes rapid and accurate genetic improvement possible, and also provides an important supplement to traditional breeding and molecular marker selection. 4.3 Integration of wild relatives and landraces with low-input resilience Not all cotton is shaped by modern breeding technology. Some old varieties and wild relatives of cotton may look "inconspicuous", but they actually contain a lot of valuable genetic resources. Traits such as drought resistance, insect resistance, and strong nutrient absorption capacity are common in them. They do not rely on high-intensity management, but can survive well under limited conditions. In terms of carbon emission reduction, the value of such varieties is becoming more and more important. Because the investment is not high and the yield is stable, it is naturally more in line with the current planting needs than those "high-consumption" varieties (Sreedasyam et al., 2024). Therefore, many breeding projects are now beginning to re-examine these local species and wild relatives, thinking about how to integrate their advantages into modern cotton lines. Of course, it is not easy to introduce these traits. It takes some technical means such as comparative genomics and gene introgression to introduce characteristics such as stress tolerance and energy saving without destroying the core advantages of the main varieties. The goal is clear: to cultivate cotton varieties that adapt to low investment and support sustainable development, and these resources are just the breakthrough. 5 Optimizing Agronomic Practices Through Genomic Prediction 5.1 Genotype-by-environment (G×E) interaction modeling for site-specific strategies The performance of cotton is often influenced by the interaction of genes and environment (G×E). Understanding this interaction is necessary to tailor management methods to different locations. Now, the development of environmental omics allows people to use genomic data and environmental information together to use more efficient methods to predict the performance of cotton in different environments. By incorporating the influence of G×E into the prediction model, breeders and agronomists can find the combination of cotton varieties that best suits a certain plot of land, so that higher-yield cotton can be grown locally with fewer resources (Gevartosky et al., 2021; De Coninck et al., 2016). This approach can help develop low-carbon planting methods that are more suitable for local conditions. 5.2 Precision irrigation and fertilization guided by genomic data Many times, fertilization and irrigation still rely on experience. But in recent years, the situation has changed. More and more studies have begun to try to apply genomic data to field management, especially in the field of precision irrigation and precision fertilization. It is not to say that genes alone can determine how much water to use and how much fertilizer to apply, but they can indeed provide some useful clues. Especially when these data are combined with multi-omics information and machine learning technology, the ability of predictive models has become much stronger. They can not only predict the performance of certain cotton varieties under specific water and fertilizer conditions based on different genotypes, but also take environmental factors into account. For example, deep learning models and multi-trait analysis models can now integrate phenotypic, genetic and climate data to assist decision-making (Bhatta et al., 2020; Tong and Nikoloski, 2020). Of course, this type of method is
RkJQdWJsaXNoZXIy MjQ4ODYzNA==