Integrating Phenotypic and Genotypic Approaches in Sunflower to Enhance Drought Tolerance: A Review

Zahra Noor, Humera Razzaq*, Asim Jamil and M. Ali Zia

Department of Plant Breeding and Genetics, University of Agriculture, Faisalabad, Pakistan

Abstract

Sunflower (Helianthus annuus L.), the world’s fourth most important oilseed crop, faces productivity challenges under drought conditions that are intensified by climate change. Drought causes a negative impact on sunflower physiology, such as photosynthesis, stomatal conductance, and osmotic control, leading to low biomass and oil production. In this review, the combination of phenotypic and genotypic methods of improving drought tolerance in sunflowers is emphasized. Under both experimental and field environments, researchers can pinpoint adaptive responses including enhanced root depth, increased proline content, improved leaf water retention and stable yield performance under varying stress conditions. These characteristics form the basis of the choice of drought-resilient genotypes using such indices as stress tolerance index (STI) and stress susceptibility index (SSI). On the genomic level, SSRs, SNPs and AFLPs have made it possible to conduct QTL mapping and genome-wide association studies (GWAS) so as to establish loci-specific to drought-responsive phenotypes. Regulatory gene families such as GASA, SAP, TIFY, transcription factors (bHLH, WRKY, NAC) have also been identified by transcriptomic and proteomic studies, which mediate abscisic acid signals, reactive oxygen species (ROS) regulation and metabolic adaptation. The combination of the phenotypic measurements with the multi-omics and bioinformatics tools will improve the accuracy of genomic selection (GS), marker-assisted backcrossing (MABC) and CRISPR/Cas9-based gene editing. Together with the introgression of alleles of wild Helianthus species, the approaches provide a way forward in expanding the genetic foundation and reinforcing the ability to withstand drought. The future opportunities concerned the use of AI-based phenomics and predictive modeling to shorten breeding periods and provide sustainable production. Phenotypic precision, genomic innovation and computational tools, therefore, converge to give a comprehensive approach of coming up with climate-smart, drought-resistant sunflower cultivars that can remain stable in yield conditions that are characterized by water scarcity.