AI-Driven Dairy Export Strategy: Predictive Analytics, Market Intelligence, and Strategic Prioritization

AI-Driven Dairy Export Strategy: Predictive Analytics, Market Intelligence, and Strategic Prioritization

Majidian and Pishbahar (2026) develop an integrated framework combining machine learning and multi-criteria decision-making to evaluate and optimize Iran’s dairy export potential. Using a 20-year dataset (2003–2022), the study applies four machine learning algorithms—Bagging Regression, CatBoost, Gradient Boosting, and XGBoost—to forecast dairy export values. CatBoost achieves the highest predictive performance (R² = 0.93), while SHAP analysis identifies population, economic size, and trade potential as the most influential drivers of export performance. Furthermore, the entropy-weighted TOPSIS approach ranks Türkiye, Iraq, and Pakistan as the three most attractive destinations for Iran’s dairy exports.

The findings highlight three strategic priorities for strengthening export competitiveness: predictive intelligence, through advanced machine learning and explainable AI; market prioritization, through systematic evaluation of economic and trade potential; and trade development, through improved logistics, regional integration, and market diversification. Overall, this study demonstrates how integrating artificial intelligence with multi-criteria decision-making can transform agricultural export planning from conventional approaches into a more precise, transparent, and evidence-based strategic process, supporting economic diversification and sustainable growth beyond oil dependence.

Reference: Majidian, M., & Pishbahar, E. (2026). Applying Machine Learning to Strategic Market Prioritization in the Dairy Export Sector. Journal of Agricultural Science and Technology, 28(6), 1257–1272.