Genomic Insight–Enabled Economic Forecasting for Sustainable Agri-Biotech Markets

Abstract

The unstable climate-induced yields, rapid pathogen evolution, fluctuating commodity prices, and increasing demand for high-performing, genetically improved crops are contributing to unprecedented uncertainty in global agri-biotech markets. Economic forecasting tools rely on historical market patterns and conventional yield statistics while ignoring the genomic traits that fundamentally determine crop resilience, input efficiency, and long-term productivity, thus showing structural limitations. Decreased sustainability across the value chain, economic decisions made without accounting for the genetic architecture that drives agricultural performance, and inaccurate market predictions and misaligned investments are the major real-time problems created by this disconnect. The integration of genomic selection indices, trait heritability, genotype–environment interactions, molecular breeding values, and sequence-based performance markers into advanced AI-driven economic models is the observation of this study, a genomic insight–enabled economic forecasting framework that highlights this gap. Enabling early identification of profit instability, resilience gaps, and market opportunities by linking economic variables to genomic predictors in Simulation experiments improves forecasting precision by 12–18% compared to traditional models. Connecting genomic performance metrics to value-chain economics for the framework supports decision-making for gene-edited crops, microbial biofertilizers, and next-generation biotechnology products, and the study concludes with greatly enhanced prediction accuracy, strengthens resilience planning, and provides a scientifically grounded, future-ready approach to sustainable agri-biotech market development using economic forecasting by incorporating genomic insights.