Accurate forecasting of agricultural commodity prices is essential for managing market volatility, improving supply chain coordination, and supporting food security-related decision-making. Recent advances in deep learning have demonstrated strong potential for capturing nonlinear and temporal dependencies in commodity price dynamics. In this study, we propose a hybrid long short-term memory–multi-head attention (LSTM–MHA) framework for agricultural commodity price forecasting using global futures market data. The model is trained and evaluated on multivariate global commodity futures prices, reflecting internationally traded benchmark markets rather than region-specific domestic prices. While the empirical analysis is based on global data, the study is motivated by the relevance of international price movements for import-dependent regions, particularly West Africa, where global price transmission plays a critical role in domestic market dynamics. The experimental results demonstrate that the proposed model effectively captures short-term temporal dependencies and provides interpretable attention-based insights into lag relevance. An ablation study further highlights the trade-offs between forecasting accuracy and interpretability across different model configurations. The hybrid architecture combines the time-based pattern identification and weighting capabilities of multi-head attention with the sequential learning capabilities of LSTM. Mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the model’s performance. With an MSE of 0.0124, an RMSE of 0.1114, and an MAE of 0.1097, the model outperformed conventional models like ARIMA and standalone LSTM by three to four times in error reduction. The findings suggest that attention-enhanced deep learning models can serve as valuable analytical tools for understanding global price dynamics and informing policy analysis and risk management in West African agricultural markets.

The rapid expansion of digital trade presents transformative opportunities for South-South cooperation, particularly between China and West Africa. However, emerging new risks in technological, institutional, and sociocultural domains pose significant challenges to sustainable e-business collaboration. This study proposes a Pythagorean Fuzzy Analytic Hierarchy Process (PF-AHP) framework to evaluate and prioritize these risks under conditions of uncertainty and expert judgment ambiguity. By integrating fuzzy logic with AHP, the model effectively captures the vagueness and imprecision inherent in multi-criteria decision-making (MCDM). Expert assessments from professionals in digital trade, cybersecurity, and risk management were used to conduct pairwise comparisons across three primary risk categories: Technological, Institutional, and Sociocultural Risks. The results indicate that Institutional Risk (weight: 0.3488) holds the highest priority, followed by Sociocultural Risk (weight: 0.3268) and Technological Risk (weight: 0.3244), highlighting the critical influence of cultural alignment, consumer trust, and behavioral norms in cross-border digital collaboration. The PF-AHP approach enhances decision reliability by incorporating membership, non-membership, and indeterminacy degrees, offering a robust tool for risk assessment in complex digital supply chains. This research contributes to the discourse on equitable digital globalization and provides strategic insights for policymakers and stakeholders aiming to build inclusive, resilient, and mutually beneficial digital trade ecosystems between China and West Africa.

The rapid expansion of digital trade presents transformative opportunities for South-South cooperation, particularly between China and West Africa. However, emerging risks in technological, institutional, and sociocultural domains pose significant challenges to sustainable e-business collaboration. This study proposes a Pythagorean Fuzzy Analytic Hierarchy Process (PF-AHP) framework to evaluate and prioritize these risks under conditions of uncertainty and expert judgment ambiguity. By integrating fuzzy logic with AHP, the model effectively captures the vagueness and imprecision inherent in multi-criteria decision-making (MCDM). Expert assessments from professionals in digital trade, cybersecurity, and risk management were used to conduct pairwise comparisons across three primary risk categories: Technological, Institutional, and Sociocultural Risks. The results indicate that Sociocultural Risk holds the highest priority (weight: 0.3268), followed by Institutional Risk (0.3488) and Technological Risk (0.3244), highlighting the critical influence of cultural alignment, consumer trust, and behavioral norms in cross-border digital collaboration. The PF-AHP approach enhances decision reliability by incorporating membership, non-membership, and indeterminacy degrees, offering a robust tool for risk assessment in complex digital supply chains. This research contributes to the discourse on equitable digital globalization and provides strategic insights for policymakers and stakeholders aiming to build inclusive, resilient, and mutually beneficial digital trade ecosystems between China and West Africa.