Merging Genes, Models, and Climate: A New Approach to Predicting Rice Flowering

Integrating Genetics, Modeling, and Climate Data: A Breakthrough Method for

In a groundbreaking advance that fuses traditional crop modeling, genomic science, and machine learning, researchers have unveiled a sophisticated approach to predicting rice flowering time with unprecedented accuracy and robustness. This novel method integrates three established rice growth simulation models—ORYZA, CERES-Rice, and RiceGrow—with genome-wide association studies (GWAS), single nucleotide polymorphism (SNP)-based genomic prediction…

Merging Genes, Models, and Climate: A New Approach to Predicting Rice Flowering

A research team used flowering data from 169 rice genotypes--each with over 700,000 SNP markers--across multiple environments to develop a robust framework for phenotypic prediction.