Abstract
Maize (Zea mays L.), a vital crop for global food and economic security, faces intensifying biotic and abiotic stresses driven by climate change, including drought, heat, and erratic rainfall. This review synthesizes emerging biotechnology-driven strategies designed to enhance maize resilience under these shifting environmental conditions. We present an integrated framework that encompasses CRISPR/Cas9 and next-generation genome editing, Genomic Selection (GS), Environmental Genomic Selection (EGS), and multi-omics platforms—spanning transcriptomics, proteomics, metabolomics, and epigenomics. These approaches have significantly deepened our understanding of complex stress-adaptive traits and genotype-by-environment interactions, revealing precise targets for breeding climate-resilient cultivars. Furthermore, we highlight enabling technologies such as high-throughput phenotyping, artificial intelligence (AI), and nanoparticle-based gene delivery—including novel in planta and transformation-free protocols—that are accelerating translational breeding. Despite these technical breakthroughs, barriers such as genotype-dependent transformation efficiency, regulatory landscapes, and implementation costs in resource-limited settings remain. Bridging the gap between laboratory innovation and field deployment will require coordinated policy support and global collaboration. By integrating molecular breakthroughs with practical deployment strategies, this review offers a comprehensive roadmap for developing sustainable, climate-resilient maize varieties to meet future agricultural demands.
| Original language | English |
|---|---|
| Article number | 161 |
| Journal | Biology |
| Volume | 15 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jan 2026 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 13 Climate Action
Keywords
- AI-powered phenotyping
- CRISPR/Cas9
- Zea maysL
- abiotic stress
- biotechnology policy
- climate change
- climate resilience
- genomic selection
- multi-omics
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