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ABSTRACT Agricultural trade is essential for Africa's economic development; however, its performance is hindered by macroeconomic volatility and institutional weaknesses. This study examines how macroeconomic instability and institutional quality influence agricultural trade across 35 African countries from 1995 to 2023. We employed Fixed and Random Effects models complemented by machine learning algorithms, and validated through the Panel‐Corrected Standard Errors and Generalized Method of Moments models, the analysis captures both linear and nonlinear dynamics. The results indicate that inflation and exchange rate volatility exert negative effects on agricultural trade, while institutional quality displays a positive direct impact. However, the interaction terms reveal that institutional quality moderates macroeconomic influences: its interactions with inflation and exchange rate volatility significantly enhance trade performance. Conversely, the combined effect of inflation and exchange rate volatility is negative, underscoring their compounded adverse implications. Among machine learning models, the Random Forest algorithm achieves the highest predictive accuracy, with feature importance highlighting institutional quality as key determinants. The findings emphasize the critical role of institutional strengthening, and macroeconomic stability, in enhancing Africa's agricultural trade resilience and competitiveness.
Abdullahi et al. (Mon,) studied this question.