JOURNAL ARTICLE
Advancements and challenges in bamboo breeding for sustainable development.
Published In: Tree Physiology, 2023, v. 43, n. 10. P. 1705 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Sun, Huayu; Wang, Jiangfei; Li, Hui; Li, Tiankuo; Gao, Zhimin 3 of 3
Abstract
This article provides a comprehensive review of bamboo breeding technologies, tracing their development through four eras—selective breeding, traditional breeding, molecular breeding, and design breeding—and emphasizing their role in enhancing bamboo as a sustainable, renewable resource. It highlights the challenges posed by bamboo's long vegetative growth and irregular flowering, which complicate breeding efforts, and details advances in genetic engineering methods such as Agrobacterium-mediated transformation, plant virus vectors, gene gun, polyethylene glycol (PEG)-mediated transformation, and CRISPR/Cas9 gene editing. The review underscores the importance of improving traits like fiber length, internode length, wall thickness, and lignin and cellulose content to meet growing demands for bamboo in papermaking, plastic substitution, construction, and other industries. It also identifies future priorities including expanding genomic resources, developing universal breeding technologies applicable across diverse bamboo species, and integrating omics and artificial intelligence to accelerate breeding for environmental adaptability and material quality, thereby supporting sustainable development and the global bamboo industry.
Additional Information
- Source:Tree Physiology. 2023/10, Vol. 43, Issue 10, p1705
- Document Type:Article
- Subject Area:Science
- Publication Date:2023
- ISSN:0829-318X
- DOI:10.1093/treephys/tpad086
- Accession Number:172915676
- Copyright Statement:Copyright of Tree Physiology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.