GENETIC IMPROVEMENT OF CROP YIELD AND STRESS RESISTANCE THROUGH MOLECULAR BREEDING TECHNIQUES

Abstract

Crop productivity is increasingly threatened by climate change and multiple abiotic stresses that significantly reduce agricultural sustainability and food security worldwide. Molecular breeding and genomics-assisted crop improvement strategies have emerged as effective approaches for developing high-yielding and stress-resilient crop varieties. The present study evaluated quantitative agronomic traits and their significance in molecular breeding applications using a rice genotype–phenotype dataset containing quantitative trait information and SNP-based genomic data. Quantitative trait analysis, correlation analysis, Principal Component Analysis, and machine learning-based predictive modeling were performed to assess phenotypic variability and yield-associated trait relationships. The results demonstrated substantial phenotypic diversity among rice accessions, particularly for grain morphology, plant architecture, and reproductive traits. Correlation analysis revealed significant positive associations among several agronomic traits, while PCA identified plant architecture and grain morphology as major contributors to phenotypic variation. A Random Forest regression model was further developed to predict grain weight using agronomic traits, where grain width and grain length emerged as the most influential predictors of yield-associated performance. The findings highlight the importance of integrating quantitative trait analysis, predictive modeling, and molecular breeding approaches for improving crop yield and adaptive performance. The study also demonstrates the potential application of computational and genomics-assisted breeding frameworks in developing climate-resilient rice cultivars. Overall, the integration of machine learning and molecular breeding strategies may contribute substantially to sustainable crop improvement and future agricultural productivity.