Nourollah Ahmadi

The paper analyzes genetic adaptation to climate change in rice landraces over time, identifying selection footprints in flowering time regulatory genes.

This study evaluates genomic prediction models for arsenic tolerance and grain yield in rice using multi-environment and multi-trait approaches, finding improved predictive accuracy with advanced models.

This study compares genomic selection and QTL-based breeding for reducing arsenic accumulation in rice, finding genomic prediction more effective for genetic gain.

This study improves genomic prediction for drought tolerance in rice by using trait-specific markers and multi-environment models, achieving higher predictive ability than traditional methods.

This study uses genomic prediction with genotype by environment interaction to improve rice breeding for adaptation to alternate wetting and drying, showing multi-environment models outperform single-environment models.