IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i7p1006-d860509.html
   My bibliography  Save this article

Identifying the Determinants of Regional Raw Milk Prices in Russia Using Machine Learning

Author

Listed:
  • Svetlana Kresova

    (Department of Agricultural Markets, University of Hohenheim, 70599 Stuttgart, Germany)

  • Sebastian Hess

    (Department of Agricultural Markets, University of Hohenheim, 70599 Stuttgart, Germany)

Abstract

In this study, official data from Russia’s regions for the period from 2015 to 2019 were analysed on the basis of 12 predictor variables in order to explain the regional raw milk price. Model training and hyperparameter optimisation were performed with a spatiotemporal cross-validation technique using the machine learning (ML) algorithm. The findings of the study showed that the RF algorithm had a good predictive performance Variable importance revealed that drinking milk production, income, livestock numbers and population density are the four most important determinants to explain the variation in regional raw milk prices in Russia.

Suggested Citation

  • Svetlana Kresova & Sebastian Hess, 2022. "Identifying the Determinants of Regional Raw Milk Prices in Russia Using Machine Learning," Agriculture, MDPI, vol. 12(7), pages 1-18, July.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:7:p:1006-:d:860509
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/7/1006/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/12/7/1006/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    2. Barry K. Goodwin, 1992. "Multivariate Cointegration Tests and the Law of one Price: A Clarification and Correction," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 14(2), pages 337-338.
    3. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    4. Hugo Storm & Kathy Baylis & Thomas Heckelei, 2020. "Machine learning in agricultural and applied economics," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 47(3), pages 849-892.
    5. Carvalho, Glauco Rodrigues & Bessler, David & Hemme, Torsten & Schröer-Merker, Eva, 2015. "Understanding International Milk Price Relationships," 2015 Annual Meeting, January 31-February 3, 2015, Atlanta, Georgia 196692, Southern Agricultural Economics Association.
    6. Saltzman, Bennett & Yung, Julieta, 2018. "A machine learning approach to identifying different types of uncertainty," Economics Letters, Elsevier, vol. 171(C), pages 58-62.
    7. Barry K. Goodwin, 1992. "Multivariate Cointegration Tests and the Law of One Price in International Wheat Markets," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 14(1), pages 117-124.
    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. Sergei Kharin & Zuzana Kapustova & Ivan Lichner, 2023. "Price transmission between maize and poultry product markets in the Visegrád Group countries: What is more nonlinear, egg or chicken?," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 69(12), pages 510-522.

    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. Kresova, Svetlana & Hess, Sebastian, 2021. "Determinants of Regional Raw Milk Prices in Russia," 61st Annual Conference, Berlin, Germany, September 22-24, 2021 317051, German Association of Agricultural Economists (GEWISOLA).
    2. Pippenger, John, 2020. "The Law Of One Price, Purchasing Power Parity And Exchange Rates: Setting The Record Straight," University of California at Santa Barbara, Economics Working Paper Series qt2n8899rp, Department of Economics, UC Santa Barbara.
    3. Arjan S. Gosal & Janine A. McMahon & Katharine M. Bowgen & Catherine H. Hoppe & Guy Ziv, 2021. "Identifying and Mapping Groups of Protected Area Visitors by Environmental Awareness," Land, MDPI, vol. 10(6), pages 1-14, May.
    4. Francesco Sartor & Jonathan P. Moore & Hans-Peter Kubis, 2021. "Plasma Interleukin-10 and Cholesterol Levels May Inform about Interdependences between Fitness and Fatness in Healthy Individuals," IJERPH, MDPI, vol. 18(4), pages 1-19, February.
    5. James O. Bukenya & Walter C. Labys, 2005. "Price Convergence on World Commodity Markets: Fact or Fiction?," International Regional Science Review, , vol. 28(3), pages 302-329, July.
    6. Faisal Alsayegh & Moh A Alkhamis & Fatima Ali & Sreeja Attur & Nicholas M Fountain-Jones & Mohammad Zubaid, 2022. "Anemia or other comorbidities? using machine learning to reveal deeper insights into the drivers of acute coronary syndromes in hospital admitted patients," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-15, January.
    7. Saleerut, Sriwena & Jatuporn, Chalermpon & Suvanvihok, Vasu & Wanaset, Apinya, 2020. "Price Adjustment of Oil Palm and Palm Oil in Thailand to the World Price of the Palm Oil Market," Asian Journal of Agriculture and Rural Development, Asian Economic and Social Society (AESS), vol. 10(02), January.
    8. Gil, Jose M. & Montañés, Antonio & Vásquez-González, Bernardo, 2023. "Are prices converging in the global sawnwood market?," Forest Policy and Economics, Elsevier, vol. 155(C).
    9. Franck M. Ramaharo & Michael Fitiavana Randriamifidy, 2023. "Determinants of renewable energy consumption in Madagascar: Evidence from feature selection algorithms," Working Papers hal-04262240, HAL.
    10. Pippenger, John, 2015. "Arbitrage and the Law of One Price: Setting the Record Straight," University of California at Santa Barbara, Economics Working Paper Series qt27t4q265, Department of Economics, UC Santa Barbara.
    11. Maximilian Heigermoser & Linde Götz & Miranda Svanidze, 2021. "Price formation within Egypt's wheat tender market: Implications for Black Sea exporters," Agricultural Economics, International Association of Agricultural Economists, vol. 52(5), pages 819-831, September.
    12. Sara Saadatmand & Khodakaram Salimifard & Reza Mohammadi & Alex Kuiper & Maryam Marzban & Akram Farhadi, 2023. "Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients," Annals of Operations Research, Springer, vol. 328(1), pages 1043-1071, September.
    13. Sriwena Saleerut & Chalermpon Jatuporn & Vasu Suvanvihok & Apinya Wanaset, 2020. "Price adjustment of oil palm and palm oil in Thailand to the world price of the palm oil market," Asian Journal of Agriculture and rural Development, Asian Economic and Social Society, vol. 10(2), pages 690-697, December.
    14. Tleubayev, Alisher & Jamali Jaghdani, Tinoush & Götz, Linde & Svanidze, Miranda, 2021. "The relationship between trade policies and macroeconomic adjustments in the Russian cheese market integration," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 22(3), pages 44-68.
    15. Suharno, S., 2018. "Spatial Market Integration and Price Transmission of Meat in Indonesia," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277371, International Association of Agricultural Economists.
    16. Sami Khedhiri, 2023. "The impact of COVID‐19 on agricultural market integration in Eastern Canada," Regional Science Policy & Practice, Wiley Blackwell, vol. 15(2), pages 371-386, April.
    17. Pippenger, John, 2022. "The Law Of One Price, Borders And Purchasing Power Parity," University of California at Santa Barbara, Economics Working Paper Series qt5b17d1dr, Department of Economics, UC Santa Barbara.
    18. Yu JIANG & Yu WANG, 2013. "Is China's domestic agricultural market influenced by price fluctuations of the world agricultural commodities in the short-run?," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 59(12), pages 578-589.
    19. Wei Fang & Ying Liu & Chun Xu & Xingguang Luo & Kesheng Wang, 2024. "Feature Selection and Machine Learning Approaches in Prediction of Current E-Cigarette Use Among U.S. Adults in 2022," IJERPH, MDPI, vol. 21(11), pages 1-14, November.
    20. Nanna Munck & Patrick Murigu Kamau Njage & Pimlapas Leekitcharoenphon & Eva Litrup & Tine Hald, 2020. "Application of Whole‐Genome Sequences and Machine Learning in Source Attribution of Salmonella Typhimurium," Risk Analysis, John Wiley & Sons, vol. 40(9), pages 1693-1705, September.

    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:gam:jagris:v:12:y:2022:i:7:p:1006-:d:860509. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.