IDEAS home Printed from https://ideas.repec.org/a/caa/jnlage/v67y2021i11id128-2021-agricecon.html
   My bibliography  Save this article

The butterfly effect in the price of agricultural products: A multidimensional spatial-temporal association mining

Author

Listed:
  • Yan Guo

    (College of Information and Engineering, Sichuan Agricultural University, Yaan, China
    The Lab of Agricultural Information Engineering, Sichuan Key Laboratory, Yaan, China)

  • Xiaonan Hu

    (College of Information and Engineering, Sichuan Agricultural University, Yaan, China
    The Lab of Agricultural Information Engineering, Sichuan Key Laboratory, Yaan, China)

  • Zepeng Wang

    (College of Information and Engineering, Sichuan Agricultural University, Yaan, China)

  • Wei Tang

    (College of Information and Engineering, Sichuan Agricultural University, Yaan, China)

  • Deyu Liu

    (College of Information and Engineering, Sichuan Agricultural University, Yaan, China)

  • Yunzhong Luo

    (College of Information and Engineering, Sichuan Agricultural University, Yaan, China)

  • Hongxiang Xu

    (College of Information and Engineering, Sichuan Agricultural University, Yaan, China)

Abstract

With the advent of the era of big data, data mining methods show their powerful information mining ability in various fields, seeking the association information hidden in the data, which is convenient for people to make scientific decisions. This paper analyses the butterfly effect in the agricultural product industry chain from the perspective of producer and consumer by using multidimensional time and space theory and proposes a new price forecasting method. We consider that the price change of agricultural products is not only affected by the balance of market supply and demand but also by the factors of time and space. Taking the pig industry chain of Sichuan Province as an example, this paper explores and excavates the data from 2010 to 2020 in the time dimension. Interestingly, we found that the price changes in pork in the market are generally highly correlated with the prices of slaughtered pigs, piglets a few weeks ago and the prices of multiple feed a few months ago. Based on the precise time-space factors, we improved the price forecasting model, greatly improved the accuracy of price prediction, and proved the effectiveness of multidimensional spatiotemporal association mining. The research in this paper is helpful to establish a brand-new agricultural product price prediction theory, which is of great significance to the development of the agricultural economy and global poverty alleviation.

Suggested Citation

  • Yan Guo & Xiaonan Hu & Zepeng Wang & Wei Tang & Deyu Liu & Yunzhong Luo & Hongxiang Xu, 2021. "The butterfly effect in the price of agricultural products: A multidimensional spatial-temporal association mining," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(11), pages 457-467.
    • Handle: RePEc:caa:jnlage:v:67:y:2021:i:11:id:128-2021-agricecon
    DOI: 10.17221/128/2021-AGRICECON
    as

    Download full text from publisher

    File URL: http://agricecon.agriculturejournals.cz/doi/10.17221/128/2021-AGRICECON.html
    Download Restriction: free of charge
    File URL: http://agricecon.agriculturejournals.cz/doi/10.17221/128/2021-AGRICECON.pdf
    Download Restriction: free of charge
    File URL: https://libkey.io/10.17221/128/2021-AGRICECON?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Piotr Borawski & Marta Guth & Wojciech Truszkowski & Dagmara Zuzek & Aneta Beldycka-Borawska & Bartosz Mickiewicz & Elzbieta Szymanska & Jayson Kennedy Harper & James William Dunn, 2020. "Milk price changes in Poland in the context of the Common Agricultural Policy," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 66(1), pages 19-26.
    2. Marcelo Chan Fu Wei & José Paulo Molin, 2020. "Soybean Yield Estimation and Its Components: A Linear Regression Approach," Agriculture, MDPI, vol. 10(8), pages 1-13, August.
    3. Dejan Živkov & Jovan Njegić & Marko Pećanac, 2019. "Multiscale interdependence between the major agricultural commodities," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 65(2), pages 82-92.
    4. Xue-Bo Jin & Xing-Hong Yu & Xiao-Yi Wang & Yu-Ting Bai & Ting-Li Su & Jian-Lei Kong, 2020. "Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System," Sustainability, MDPI, vol. 12(4), pages 1-18, February.
    5. Krzysztof Drachal, 2019. "Analysis of Agricultural Commodities Prices with New Bayesian Model Combination Schemes," Sustainability, MDPI, vol. 11(19), pages 1-23, September.
    6. Ga-Ae Ryu & Aziz Nasridinov & HyungChul Rah & Kwan-Hee Yoo, 2020. "Forecasts of the Amount Purchase Pork Meat by Using Structured and Unstructured Big Data," Agriculture, MDPI, vol. 10(1), pages 1-14, January.
    7. Taghizadeh-Hesary, Farhad & Rasoulinezhad, Ehsan & Yoshino, Naoyuki, 2019. "Energy and Food Security: Linkages through Price Volatility," Energy Policy, Elsevier, vol. 128(C), pages 796-806.
    8. Marc F. Bellemare, 2015. "Rising Food Prices, Food Price Volatility, and Social Unrest," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(1), pages 1-21.
    Full references (including those not matched with items on IDEAS)

    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. Zhuo Chen & Bo Yan & Hanwen Kang, 2022. "Dynamic correlation between crude oil and agricultural futures markets," Review of Development Economics, Wiley Blackwell, vol. 26(3), pages 1798-1849, August.
    2. Tserenpurev Chuluunsaikhan & Ga-Ae Ryu & Kwan-Hee Yoo & HyungChul Rah & Aziz Nasridinov, 2020. "Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea," Agriculture, MDPI, vol. 10(11), pages 1-22, October.
    3. Abdoul G. Sam & Babatunde O. Abidoye & Sihle Mashaba, 2021. "Climate change and household welfare in sub-Saharan Africa: empirical evidence from Swaziland," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 13(2), pages 439-455, April.
    4. Bhattacharjee, Arnab & Pabst, Adrian & Mosley, Max & Szendrei, Tibor, 2022. "Outlook for the devolved nations, English regions, and UK households," National Institute UK Economic Outlook, National Institute of Economic and Social Research, issue 6, pages 37-55.
    5. Kalle Hirvonen & Bart Minten & Belay Mohammed & Seneshaw Tamru, 2021. "Food prices and marketing margins during the COVID‐19 pandemic: Evidence from vegetable value chains in Ethiopia," Agricultural Economics, International Association of Agricultural Economists, vol. 52(3), pages 407-421, May.
    6. Piotr Bórawski & Aneta Bełdycka-Bórawska & Zuzana Kapsdorferová & Tomasz Rokicki & Andrzej Parzonko & Lisa Holden, 2024. "Perspectives of Electricity Production from Biogas in the European Union," Energies, MDPI, vol. 17(5), pages 1-26, March.
    7. Čermák, Michal & Ligocká, Marie, 2022. "Could Exist a Causality Between the Most Traded Commodities and Futures Commodity Prices in the Agricultural Market?," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 14(4), December.
    8. Loretta Mastroeni & Alessandro Mazzoccoli & Greta Quaresima & Pierluigi Vellucci, 2021. "Wavelet analysis and energy-based measures for oil-food price relationship as a footprint of financialisation effect," Papers 2104.11891, arXiv.org, revised Mar 2022.
    9. Dixon, Peter, 2022. "Box B: How likely are we to see a major recession in 2022?," National Institute UK Economic Outlook, National Institute of Economic and Social Research, issue 6, pages 21-24.
    10. Gilles Dufrénot & William Ginn & Marc Pourroy, 2023. "ENSO Climate Patterns on Global Economic Conditions," AMSE Working Papers 2308, Aix-Marseille School of Economics, France.
    11. Rasoulinezhad, Ehsan & Taghizadeh-Hesary, Farhad & Yoshino, Naoyuki & Sarker, Tapan, 2019. "Russian Federation–East Asia Liquefied Natural Gas Trade Patterns and Regional Energy Security," ADBI Working Papers 965, Asian Development Bank Institute.
    12. Chen, Junyi & Kibriya, Shahriar & Bessler, David & Price, Edwin, 2018. "The relationship between conflict events and commodity prices in Sudan," Journal of Policy Modeling, Elsevier, vol. 40(4), pages 663-684.
    13. Jesus Crespo Cuaresma & Jaroslava Hlouskova & Michael Obersteiner, 2021. "Agricultural commodity price dynamics and their determinants: A comprehensive econometric approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1245-1273, November.
    14. Hussein, Mohamud & Law, Cherry & Fraser, Iain, 2021. "An analysis of food demand in a fragile and insecure country: Somalia as a case study," Food Policy, Elsevier, vol. 101(C).
    15. Carine Meyimdjui, 2017. "Food Price Shocks and Government Expenditure Composition: Evidence from African Countries," Working Papers halshs-01457366, HAL.
    16. Zhou Yujun & Baylis Kathy, 2020. "Effects of Stockholding Policy on Maize Prices: Evidence from Zambia," Journal of Agricultural & Food Industrial Organization, De Gruyter, vol. 18(1), pages 1-11, January.
    17. Dalheimer, Bernhard & Herwartz, Helmut & Lange, Alexander, 2021. "The threat of oil market turmoils to food price stability in Sub-Saharan Africa," Energy Economics, Elsevier, vol. 93(C).
    18. Tiziano Distefano & Guido Chiarotti & Francesco Laio & Luca Ridolfi, 2018. "Spatial distribution of the international food prices: unexpected randomness and heterogeneity," SEEDS Working Papers 0118, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Jan 2018.
    19. Gozgor, Giray & Paramati, Sudharshan Reddy, 2022. "Does energy diversification cause an economic slowdown? Evidence from a newly constructed energy diversification index," Energy Economics, Elsevier, vol. 109(C).
    20. Huawei, Tian, 2022. "Does gross domestic product, inflation, total investment, and exchanges rate matter in natural resources commodity prices volatility," Resources Policy, Elsevier, vol. 79(C).

    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:caa:jnlage:v:67:y:2021:i:11:id:128-2021-agricecon. 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: Ivo Andrle (email available below). General contact details of provider: https://www.cazv.cz/en/home/ .

    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.