Data-Driven Seed Quality Optimization as a Strategic Lever for Predictive Yield Stability and Food Security

Seed quality is a crucial but often overlooked factor influencing agricultural productivity, yield consistency, and food security. Conventional methods frequently evaluate seeds using singular measures, overlooking the intricate relationships among seed characteristics, environmental factors, and management strategies. This study introduces a data-driven methodology for optimizing seed quality, which integrates multidimensional seed attributes into a Seed Quality Optimization Index (SQOI) and incorporates it into machine learning-based yield prediction models. The methodology was assessed across various agroecological contexts utilizing a hybrid dataset that amalgamated actual and simulated seed, environmental, and yield data. Findings indicate that models including SQOI substantially exceed baseline and extended models in predictive accuracy (R² reaching 0.89) and diminish inter-annual yield variability by 20–30%. Sensitivity analysis determined seed vigor and germination rate as the most significant factors, whereas the optimization layer uncovered configurations that enhance yield stability across diverse environments. These data suggest that strategically enhanced seed quality serves as a stabilizing mechanism, reducing environmental risks and improving resilience. The framework connects yield stability outcomes to food security by tackling the dimensions of availability and stability, providing a scalable and replicable decision-support tool for farmers, seed producers, and policymakers. This study connects seed science, predictive modeling, and optimization, positioning seed quality as a measurable and proactive factor in sustainable agricultural systems.