CRISPR-CAS9 AND EMERGING GENOME EDITING STRATEGIES IN RICE: A DATASET-DRIVEN ROADMAP FOR FUTURE BREEDING

Rice (Oryza sativa L.) feeds over half of the global population but is increasingly threatened by climate change, evolving diseases, soil toxicity, and the pressure to boost yields. Traditional breeding cannot respond fast enough to these challenges, making genome editing a powerful alternative. This study brings together recent advances in CRISPR-based tools including Cas9, base and prime editors, Cas12a, transcriptional regulators, and structural genome engineering into a curated dataset that tracks nuclease type, promoter choice, editing efficiency, and phenotypic outcomes. The analysis shows that Cas9 knockouts remain the most widely used and efficient approach, while emerging editors provide finer control through single-nucleotide substitutions, targeted insertions, and multiplexed edits. Ubiquitin promoters consistently drive higher editing success, and key targets such as OsSWEET14, IPA1, and DEP1 are strongly linked to improvements in yield, stress tolerance, and disease resistance. By combining structured datasets with machine learning, it becomes possible to predict which editing strategies will succeed, helping breeders design edits more rationally rather than by trial and error. Major hurdles remain such as variable efficiency, limited HDR performance, off-target effects, and cultivar-specific delivery but the integration of molecular precision with data-driven prediction points to a future where rice breeding is faster, more resilient, and more sustainable.