Xiaojing Yang , Huijuan Xu , Xiaoyan Chen

Modern Agriculture Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, Zhejiang, China
Author    Correspondence author
Cotton Genomics and Genetics, 2025, Vol. 16, No. 4   doi: 10.5376/cgg.2025.16.0017
Received: 12 May, 2025    Accepted: 23 Jun., 2025    Published: 15 Jul., 2025

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Yang X.J., Xu H.J., and Chen X.Y., 2025, Study on carbon reduction planting strategy of cotton based on genomic information, Cotton Genomics and Genetics, 16(4): 173-183 (doi: 10.5376/cgg.2025.16.0017)

Cotton production is a major contributor to agricultural greenhouse gas emissions due to high input requirements and land-use intensity. In response, genomic technologies have emerged as transformative tools for developing sustainable, low-carbon cotton farming systems. This review systematically explores the carbon footprint of conventional cotton cultivation and evaluates how genomic insights can be leveraged to mitigate emissions. We examine gene networks linked to carbon use efficiency, stress tolerance, and nutrient utilization, and discuss the application of marker-assisted selection, genomic prediction, and gene editing to breed low-carbon cultivars. Additionally, the integration of genomics with precision agronomic practices and root microbiome research is addressed for enhancing carbon sequestration. A life cycle assessment (LCA) framework is proposed to align genomic strategies with environmental impact metrics, and a regional case study from Xinjiang demonstrates measurable benefits of such an integrated approach. Ultimately, this review underscores the potential of genomic innovation to guide carbon-reduction planting strategies, paving the way for climate-resilient and environmentally responsible cotton production.

Cotton genomics; Carbon footprint; Low-carbon breeding; Precision agriculture; Life cycle assessment