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Using Artificial Neural networks and Optimal Scaling Model to Forecast Agriculture Commodity Price: An Ecological-economic Approach


  • Roberto Louis Forestal
  • Shih-Ming Pi


This research paper employs input-output pricing model based on ecological-economic approach to investigate the impacts of internal factors as well as external forces on agriculture commodities. To empirically test our model, we select two different methodologies such as the optimal scaling regression with nonlinear transformations and feedforward artificial neural networks. Our sample includes data related to price of agriculture and energy commodities (cocoa, coffee and crude oil), production of crops and livestock, emissions of greenhouse gases (GHG) from agriculture from 1961 to 2019. Results find a bidirectional relationship between cocoa price and coffee price explaining by the fact that commodity-dependent countries often use kindred production landscapes and similar supply chain management when dealing with coffee and cocoa. Therefore, effect of supply side shocks may be transmitted from one market to another. We also present evidence that greenhouse gas emissions have strong effect on commodity price, thus we encourage an integrated approach including both concrete technological and proactive managerial measures in order to mitigate global warming impacts on the food system. We believe that these findings will be of interest to commodity producers, asset managers and academics who look a better understanding of the dynamics of commodity markets. JEL classification numbers: C50, Q02, Q57.

Suggested Citation

  • Roberto Louis Forestal & Shih-Ming Pi, 2021. "Using Artificial Neural networks and Optimal Scaling Model to Forecast Agriculture Commodity Price: An Ecological-economic Approach," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 11(3), pages 1-3.
  • Handle: RePEc:spt:admaec:v:11:y:2021:i:3:f:11_3_3

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    1. Serletis, Apostolos & Xu, Libo, 2019. "The ethanol mandate and crude oil and biofuel agricultural commodity price dynamics," Journal of Commodity Markets, Elsevier, vol. 15(C), pages 1-1.
    2. Bohl, Martin T. & Siklos, Pierre L. & Stefan, Martin & Wellenreuther, Claudia, 2020. "Price discovery in agricultural commodity markets: Do speculators contribute?," Journal of Commodity Markets, Elsevier, vol. 18(C).
    3. Melita Rozman Cafuta, 2015. "Open Space Evaluation Methodology and Three Dimensional Evaluation Model as a Base for Sustainable Development Tracking," Sustainability, MDPI, vol. 7(10), pages 1-23, October.
    4. Ahmadi, Maryam & Bashiri Behmiri, Niaz & Manera, Matteo, 2016. "How is volatility in commodity markets linked to oil price shocks?," Energy Economics, Elsevier, vol. 59(C), pages 11-23.
    5. Natanelov, Valeri & Alam, Mohammad J. & McKenzie, Andrew M. & Van Huylenbroeck, Guido, 2011. "Is there co-movement of agricultural commodities futures prices and crude oil?," Energy Policy, Elsevier, vol. 39(9), pages 4971-4984, September.
    6. Paschke, Raphael & Prokopczuk, Marcel, 2010. "Commodity derivatives valuation with autoregressive and moving average components in the price dynamics," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2742-2752, November.
    7. Hannon, Bruce, 2001. "Ecological pricing and economic efficiency," Ecological Economics, Elsevier, vol. 36(1), pages 19-30, January.
    8. Mekbib G. Haile & Tesfamicheal Wossen & Kindie Tesfaye & Joachim von Braun, 2017. "Impact of Climate Change, Weather Extremes, and Price Risk on Global Food Supply," Economics of Disasters and Climate Change, Springer, vol. 1(1), pages 55-75, June.
    9. Wang, Yudong & Wu, Chongfeng & Yang, Li, 2014. "Oil price shocks and agricultural commodity prices," Energy Economics, Elsevier, vol. 44(C), pages 22-35.
    10. Felix Akrofi-Atitianti & Chinwe Ifejika Speranza & Louis Bockel & Richard Asare, 2018. "Assessing Climate Smart Agriculture and Its Determinants of Practice in Ghana: A Case of the Cocoa Production System," Land, MDPI, vol. 7(1), pages 1-21, March.
    11. Fiala, Nathan, 2008. "Meeting the demand: An estimation of potential future greenhouse gas emissions from meat production," Ecological Economics, Elsevier, vol. 67(3), pages 412-419, October.
    12. Willems, S.J.W. & Fiocco, M. & Meulman, J.J., 2017. "Optimal scaling for survival analysis with ordinal data," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 155-171.
    13. Kaufmann, Robert K. & Cleveland, Cutler J., 1995. "Measuring sustainability: needed--an interdisciplinary approach to an interdisciplinary concept," Ecological Economics, Elsevier, vol. 15(2), pages 109-112, November.
    14. Yongli Zhang & Sanggyun Na, 2018. "A Novel Agricultural Commodity Price Forecasting Model Based on Fuzzy Information Granulation and MEA-SVM Model," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-10, November.
    15. Céline Louche & Timo Busch & Patricia Crifo & Alfred Marcus, 2019. "Financial Markets and the Transition to a Low-Carbon Economy: Challenging the Dominant Logics," Post-Print hal-02016756, HAL.
    16. Mahdavi, Saeid & Zhou, Su, 1997. "Gold and commodity prices as leading indicators of inflation: Tests of long-run relationship and predictive performance," Journal of Economics and Business, Elsevier, vol. 49(5), pages 475-489.
    17. Aurélien Bruel & Jakub Kronenberg & Nadège Troussier & Bertrand Guillaume, 2019. "Linking Industrial Ecology and Ecological Economics: A Theoretical and Empirical Foundation for the Circular Economy," Journal of Industrial Ecology, Yale University, vol. 23(1), pages 12-21, February.
    18. John Wei-Shan Hu & Yi-Chung Hu & Ricky Ray-Wen Lin, 2012. "Applying Neural Networks to Prices Prediction of Crude Oil Futures," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-12, August.
    19. Eleni Zafeiriou & Garyfallos Arabatzis & Paraskevi Karanikola & Stilianos Tampakis & Stavros Tsiantikoudis, 2018. "Agricultural Commodities and Crude Oil Prices: An Empirical Investigation of Their Relationship," Sustainability, MDPI, vol. 10(4), pages 1-11, April.
    20. Md Rafayet Alam & Scott Gilbert, 2017. "Monetary policy shocks and the dynamics of agricultural commodity prices: evidence from structural and factor†augmented VAR analyses," Agricultural Economics, International Association of Agricultural Economists, vol. 48(1), pages 15-27, January.
    21. Atems, Bebonchu & Melichar, Mark, 2019. "Do Global Crude Oil Market Shocks Have Differential Effects On Us Regions?," Macroeconomic Dynamics, Cambridge University Press, vol. 23(5), pages 1978-2008, July.
    22. Onil Banerjee & Martin Cicowiez & Mark Horridge & Renato Vargas, 2016. "A Conceptual Framework for Integrated Economic-Environmental Modelling," CEDLAS, Working Papers 0202, CEDLAS, Universidad Nacional de La Plata.
    23. Greenwood, Jeremy & Hercowitz, Zvi & Huffman, Gregory W, 1988. "Investment, Capacity Utilization, and the Real Business Cycle," American Economic Review, American Economic Association, vol. 78(3), pages 402-417, June.
    24. Kausik Chaudhuri, 2001. "Long-run prices of primary commodities and oil prices," Applied Economics, Taylor & Francis Journals, vol. 33(4), pages 531-538.
    25. Angela J. Black & Olga Klinkowska & David G. McMillan & Fiona J. McMillan, 2014. "Forecasting Stock Returns: Do Commodity Prices Help?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(8), pages 627-639, December.
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    More about this item


    Agriculture commodity; Input-output pricing model; Ecological-economic approach; Artificial neural networks; Optimal scaling regression.;
    All these keywords.

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q57 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Ecological Economics


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