Integrating genomic tools and traditional breeding for climate-resilient cotton: A comprehensive review
Abstract
Cotton (Gossypium hirsutum L.) is a globally important fibre and cash crop, playing a crucial role in the agricultural and industrial economy. Climate change, including high and low temperatures, drought, and chilling stress, poses significant challenges to maintaining yield stability and fibre quality. The development of climate-resilient cultivars has become essential to ensure sustainable cotton production and secure the livelihoods of millions of farmers worldwide. This review presents both traditional and modern breeding strategies designed to enhance cotton’s resilience and productivity. It begins with an overview of traditional approaches such as mass and progeny selection. Modern tools like genome selection focus on traits associated with abiotic stress tolerance, fibre quality, and seed oil enhancement. Criteria for selection in breeding programs are discussed in detail, followed by the identification of candidate genes that can serve as targets for genome editing technologies. The integration of traditional breeding methods with modern genomic tools offers promising pathways for developing climate-resilient and high-yielding cotton varieties. Candidate genes for fibre yield, oil quality, and stress tolerance have been identified, paving the way for targeted genetic improvements. These advancements are vital for ensuring long-term cotton sustainability aimed at changing climatic conditions. The primary aim of this review is to provide a comprehensive synthesis of the advancements in cultivar selection strategies for cotton, with a special focus on breeding for climate resilience and yield stability.
Keywords
Full Text:
PDFDOI: https://doi.org/10.33865/ijcrt.007.01.1532
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 Muhammad Talha Ramzan, Laiba Razaq, Xiaoqing Zhang, Muhammad Saad Zia, Usama Yaseen, Muhammad Usama Munir Chaudhary, Muhammad Jaffer Ali, Muhammad Ammad khan

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
E-ISSN = 2707-5281