Does fuel price volatility induce price instability in the agricultural commodity supply chain? Empirical evidence from Ghana

Amoah Osei, Awura-Abena; Etuah, Seth; Nimoh, Fred; Owusu Asante, Bright; Abunyuwah, Isaac; Oppong Mensah, Nicholas

doi:10.1016/j.jafr.2024.101216

Journal of Agriculture and Food Research

2024

DOI: 10.1016/j.jafr.2024.101216

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Does fuel price volatility induce price instability in the agricultural commodity supply chain? Empirical evidence from Ghana

Awura-Abena Amoah Osei,

Seth Etuah,

Fred Nimoh

et al.

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2024

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Cited by 2 publications

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Hybrid deep learning model for vegetable price forecasting based on principal component analysis and attention mechanism

Chen,

Cai,

et al. 2024

Phys. Scr.

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With the aim of enhancing the accuracy of current models for forecasting vegetable prices and improving market structures, this study focuses on the prices of bell peppers at the Nanhuanqiao Market in Suzhou. In this paper, we propose a hybrid Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) model for vegetable price forecasting based on Principal Component Analysis (PCA) and Attention Mechanism (ATT). Initially, we utilized the Pearson correlation coefficient to filter out the factors impacting prices. Then, we applied PCA to reduce dimensionality, extracting key price features. Next, we captured local sequence patterns with CNN, while handling time-series features with GRU. Finally, these outputs were integrated via ATT to generate the final prediction. Our results indicate that the hybrid CNN-GRU model, enhanced by PCA and ATT, achieved a Root Mean Square Error (RMSE) as low as 0.16. This performance is 11.11%, 11.11%, and 15.79% better than that of the PCA-CNN, PCA-GRU, and CNN-GRU-ATT models, respectively. Furthermore, in order to prove the effectiveness of our proposed model, the proposed model is compared with the state-of-the-art models and classical machine learning algorithms under the same dataset, the results indicate that our proposed hybrid deep learning model based on PCA and ATT shows the best performance. Consequently, our model offers a valuable reference for vegetable price prediction.

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Hybrid deep learning model for vegetable price forecasting based on principal component analysis and attention mechanism

Chen,

Cai,

et al. 2024

Phys. Scr.

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Editorial: Increasing resilience and adaptability to climate change of vulnerable groups in agriculture

Etuah,

Adams,

Osei Mensah

et al. 2024

Front. Sustain. Food Syst.

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Does fuel price volatility induce price instability in the agricultural commodity supply chain? Empirical evidence from Ghana

Cited by 2 publications

References 41 publications

Hybrid deep learning model for vegetable price forecasting based on principal component analysis and attention mechanism

Hybrid deep learning model for vegetable price forecasting based on principal component analysis and attention mechanism

Editorial: Increasing resilience and adaptability to climate change of vulnerable groups in agriculture

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