IDEAS home Printed from https://ideas.repec.org/a/ags/areint/322724.html
   My bibliography  Save this article

Forecasting agricultural commodity price using different models: a case study of widely consumed grains in Nigeria

Author

Listed:
  • Sanusi, Olajide I.
  • Safi, Samir K.
  • Adeeko, Omotara
  • Tabash, Mosab I.

Abstract

Purpose. This study highlights the specific and accurate methods for forecasting prices of commonly consumed grains or legumes in Nigeria based on data from January 2017 to June 2020. Methodology / approach. Different models that include autoregressive integrated moving average (ARIMA), artificial neural networks (ANN), seasonal decomposition of time series by loess method (STLM), and a combination of these three models (hybrid model) were proposed to forecast the sample grain price data. This study uses price data on widely consumed grains, such as white maize, local rice, imported rice, and white beans, in Nigeria from January 2017 to June 2020. Results. Our result indicates that ARIMA is the best applicable model for white maize and imported rice because it is well fitted to stationary data, as demonstrated in the sample period. The STLM is more appropriate in forecasting white beans. As white beans are highly seasonal in Nigeria, it further explains why the STLM model fits better in forecasting prices. The production of local rice is inconsistent in Nigeria because of erratic rainfall and stiff competition from the importation of rice from other countries. Therefore, and consistent with the analysis, the hybrid model is the best model applicable to local rice because it captures varying trends exhibited in the data. Originality / scientific novelty. This study suggests most accurate forecasting techniques for specific agricultural commodities in sub-Saharan African countries. It considers forecasting prices of commonly consumed grains and legumes in Nigeria and traded worldwide, such as imported rice, local rice, beans, and maize. Practical value / implications. The study highlights the importance of appropriate forecasts for policymakers, producers, and consumers to enhance better decision making and serve as an underlying incentive to guide the allocation of financial resources to the agricultural sector, which determines the structure and degree of sectoral growth.

Suggested Citation

  • Sanusi, Olajide I. & Safi, Samir K. & Adeeko, Omotara & Tabash, Mosab I., 2022. "Forecasting agricultural commodity price using different models: a case study of widely consumed grains in Nigeria," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 8(2), June.
    • Handle: RePEc:ags:areint:322724
    DOI: 10.22004/ag.econ.322724
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/322724/files/7_Sanusi_article.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.322724?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Periklis Gogas & Theophilos Papadimitriou & Emmanouil Sofianos, 2022. "Forecasting unemployment in the euro area with machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 551-566, April.
    2. Araujo, Claudio & Araujo-Bonjean, Catherine & Brunelin, Stéphanie, 2012. "Alert at Maradi: Preventing Food Crises by Using Price Signals," World Development, Elsevier, vol. 40(9), pages 1882-1894.
    3. Ferrara, Laurent & Karadimitropoulou, Aikaterini & Triantafyllou, Athanasios, 2021. "Commodity price uncertainty comovement: Does it matter for global economic growth?," Essex Finance Centre Working Papers 30945, University of Essex, Essex Business School.
    4. Kitenge, Erick M. & Morshed, A.K.M. Mahbub, 2019. "Price convergence among Indian cities: The role of linguistic differences, topography, and aggregation," Journal of Asian Economics, Elsevier, vol. 61(C), pages 34-50.
    5. Yu-Chin Chen & Kenneth S. Rogoff & Barbara Rossi, 2010. "Can Exchange Rates Forecast Commodity Prices?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(3), pages 1145-1194.
    6. Higgins, Nathaniel & Hintermann, Beat & Brown, Molly E., 2015. "A model of West African millet prices in rural markets," Food Policy, Elsevier, vol. 52(C), pages 33-43.
    7. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    8. Lowder, Sarah K. & Skoet, Jakob & Raney, Terri, 2016. "The Number, Size, and Distribution of Farms, Smallholder Farms, and Family Farms Worldwide," World Development, Elsevier, vol. 87(C), pages 16-29.
    9. Paul Dorosh & Steven Haggblade, 2003. "Growth Linkages, Price Effects and Income Distribution in Sub-Saharan Africa," Journal of African Economies, Centre for the Study of African Economies, vol. 12(2), pages 207-235, June.
    10. Fulgence Joseph Mishili & Joan Fulton & Musa Shehu & Saket Kushwaha & Kofi Marfo & Mustafa Jamal & Alpha Kergna & J. Lowenberg-DeBoer, 2009. "Consumer preferences for quality characteristics along the cowpea value chain in Nigeria, Ghana, and Mali," Agribusiness, John Wiley & Sons, Ltd., vol. 25(1), pages 16-35.
    11. Antonaci, Lavinia & Demeke, Mulat & Vezzani, Antonio, 2014. "The challenges of managing agricultural price and production risks in sub-Saharan Africa," ESA Working Papers 288979, Food and Agriculture Organization of the United Nations, Agricultural Development Economics Division (ESA).
    12. Huang, Wen & Huang, Zhuo & Matei, Marius & Wang, Tianyi, 2012. "Price Volatility Forecast for Agricultural Commodity Futures： The Role of High Frequency Data," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 83-103, December.
    13. Chris Ackello-Ogutu, 2011. "Managing Food Security Implications of Food Price Shocks in Africa," Journal of African Economies, Centre for the Study of African Economies, vol. 20(suppl_1), pages -141, May.
    14. Julius Manda & Arega D. Alene & Adane H. Tufa & Tahirou Abdoulaye & Alpha Y. Kamara & Olusoji Olufajo & Ousmane Boukar & Victor M. Manyong, 2020. "Adoption and Ex‐post Impacts of Improved Cowpea Varieties on Productivity and Net Returns in Nigeria," Journal of Agricultural Economics, Wiley Blackwell, vol. 71(1), pages 165-183, February.
    15. Wang, Jue & Wang, Zhen & Li, Xiang & Zhou, Hao, 2022. "Artificial bee colony-based combination approach to forecasting agricultural commodity prices," International Journal of Forecasting, Elsevier, vol. 38(1), pages 21-34.
    16. Itishree Pattnaik & Kuntala Lahiri-Dutt & Stewart Lockie & Bill Pritchard, 2018. "The feminization of agriculture or the feminization of agrarian distress? Tracking the trajectory of women in agriculture in India," Journal of the Asia Pacific Economy, Taylor & Francis Journals, vol. 23(1), pages 138-155, January.
    17. Taylor, Mykel R. & Dhuyvetter, Kevin C. & Kastens, Terry L., 2006. "Forecasting Crop Basis Using Historical Averages Supplemented with Current Market Information," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 31(3), pages 1-19, December.
    18. Jia-Lang Xu & Ying-Lin Hsu, 2022. "The Impact of News Sentiment Indicators on Agricultural Product Prices," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1645-1657, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kosar, Nataliia & Kuzo, Nataliia & Binda, Jacek & Hayvanovych, Nataliya & Pytulyak, Nataliya, 2022. "Modeling of the factors influencing the dairy market in Ukraine," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 8(3), September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Drachal, Krzysztof, 2019. "Forecasting prices of selected metals with Bayesian data-rich models," Resources Policy, Elsevier, vol. 64(C).
    2. Bermpei, Theodora & Ferrara, Laurent & Karadimitropoulou, Aikaterini & Triantafyllou, Athanasios, 2024. "Commodity currencies revisited: The role of global commodity price uncertainty," Journal of International Money and Finance, Elsevier, vol. 145(C).
    3. Krzysztof Drachal, 2022. "Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression," Energies, MDPI, vol. 16(1), pages 1-29, December.
    4. Xu, Kunliang & Niu, Hongli, 2023. "Denoising or distortion: Does decomposition-reconstruction modeling paradigm provide a reliable prediction for crude oil price time series?," Energy Economics, Elsevier, vol. 128(C).
    5. Krzysztof Drachal, 0000. "Choosing Parameters for Bayesian Symbolic Regression: An Application to Modelling Commodities Prices," Proceedings of Economics and Finance Conferences 14116014, International Institute of Social and Economic Sciences.
    6. Nyein Nyein Kyaw & Soojung Ahn & Sang Hyeon Lee, 2018. "Analysis of the Factors Influencing Market Participation among Smallholder Rice Farmers in Magway Region, Central Dry Zone of Myanmar," Sustainability, MDPI, vol. 10(12), pages 1-15, November.
    7. Xu, Kunliang & Wang, Weiqing, 2023. "Limited information limits accuracy: Whether ensemble empirical mode decomposition improves crude oil spot price prediction?," International Review of Financial Analysis, Elsevier, vol. 87(C).
    8. Priti Khatri & Prashant Kumar & Kaushlesh Singh Shakya & Marios C. Kirlas & Kamal Kant Tiwari, 2024. "Understanding the intertwined nature of rising multiple risks in modern agriculture and food system," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(9), pages 24107-24150, September.
    9. Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Umar, Muhammad, 2024. "Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting," International Review of Financial Analysis, Elsevier, vol. 94(C).
    10. Arulingam, Indika & Brady, G. & Chaya, M. & Conti, M. & Kgomotso, P. K. & Korzenszky, A. & Njie, D. & Schroth, G. & Suhardiman, Diana, 2022. "Small-scale producers in sustainable agrifood systems transformation," IWMI Reports 329171, International Water Management Institute.
    11. Arulingam, Indika & Brady, G. & Chaya, M. & Conti, M. & Kgomotso, P. K. & Korzenszky, A. & Njie, D. & Schroth, G. & Suhardiman, Diana, 2022. "Small-scale producers in sustainable agrifood systems transformation," IWMI Books, Reports H051435, International Water Management Institute.
    12. von der Gracht, Heiko A. & Hommel, Ulrich & Prokesch, Tobias & Wohlenberg, Holger, 2016. "Testing weighting approaches for forecasting in a Group Wisdom Support System environment," Journal of Business Research, Elsevier, vol. 69(10), pages 4081-4094.
    13. repec:prg:jnlcfu:v:2022:y:2022:i:1:id:572 is not listed on IDEAS
    14. Pantelis Agathangelou & Demetris Trihinas & Ioannis Katakis, 2020. "A Multi-Factor Analysis of Forecasting Methods: A Study on the M4 Competition," Data, MDPI, vol. 5(2), pages 1-24, April.
    15. Cabral, Joilson de Assis & Freitas Cabral, Maria Viviana de & Pereira Júnior, Amaro Olímpio, 2020. "Elasticity estimation and forecasting: An analysis of residential electricity demand in Brazil," Utilities Policy, Elsevier, vol. 66(C).
    16. De Baets, Shari & Harvey, Nigel, 2018. "Forecasting from time series subject to sporadic perturbations: Effectiveness of different types of forecasting support," International Journal of Forecasting, Elsevier, vol. 34(2), pages 163-180.
    17. Jellason, Nugun P. & Robinson, Elizabeth J. Z. & Ogbaga, Chukwuma C., 2021. "Agriculture 4.0: is sub-Saharan Africa ready?," LSE Research Online Documents on Economics 113550, London School of Economics and Political Science, LSE Library.
    18. Keenan, Michael & Fort, Ricardo & Vargas, Ricardo, 2024. "Shocked into side-selling? Production shocks and organic coffee farmers’ marketing decisions," Food Policy, Elsevier, vol. 125(C).
    19. Sbrana, Giacomo & Silvestrini, Andrea, 2020. "Forecasting with the damped trend model using the structural approach," International Journal of Production Economics, Elsevier, vol. 226(C).
    20. Shiu-Sheng Chen, 2014. "Forecasting Crude Oil Price Movements With Oil-Sensitive Stocks," Economic Inquiry, Western Economic Association International, vol. 52(2), pages 830-844, April.
    21. Bouckaert, Nicolas & Van den Heede, Koen & Van de Voorde, Carine, 2018. "Improving the forecasting of hospital services: A comparison between projections and actual utilization of hospital services," Health Policy, Elsevier, vol. 122(7), pages 728-736.

    More about this item

    Keywords

    Agribusiness; Financial Economics;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:areint:322724. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: http://are-journal.com/are .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.