IDEAS home Printed from https://ideas.repec.org/a/caa/jnlage/v65y2019i7id319-2018-agricecon.html
   My bibliography  Save this article

Analysis of economic risk in potatoes cultivation

Author

Listed:
  • Milan Cizek

    (Potato Research Institute Havlíčkův Brod, Havlíčkův Brod, Czech Republic)

  • Miroslav Mimra

    (Department of Machinery Utilisation, Faculty of Engineering, Czech University of Life Sciences Prague, Prague, Czech Republic)

  • Miroslav Kavka

    (Department of Machinery Utilisation, Faculty of Engineering, Czech University of Life Sciences Prague, Prague, Czech Republic)

  • Jaroslav Humpal

    (Institute of Agricultural Economics and Information, Prague, Czech Republic)

Abstract

A number of variables influences potatoes growing, including natural conditions, used growing technologies and market conditions. The most important parameters for the production of potatoes crops are yield, farmer's price, subsidies and costs. All these parameters can change over time. This means that managers of farms must constantly assess the key parameters affecting the economic outturn and analyse the degree of risk of their achievement. This article analyses the economic risks of potatoes cultivation based on statistical data obtained over the last 10 years. The Monte Carlo stochastic simulation method was used to analyse the risk of gross profits. The results of the calculations confirmed the considerable variability and risk of growing potatoes in the climate conditions of the Czech Republic in general, and especially regarding the first early potatoes and potatoes for starch production.

Suggested Citation

  • Milan Cizek & Miroslav Mimra & Miroslav Kavka & Jaroslav Humpal, 2019. "Analysis of economic risk in potatoes cultivation," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 65(7), pages 331-339.
    • Handle: RePEc:caa:jnlage:v:65:y:2019:i:7:id:319-2018-agricecon
    DOI: 10.17221/319/2018-AGRICECON
    as

    Download full text from publisher

    File URL: http://agricecon.agriculturejournals.cz/doi/10.17221/319/2018-AGRICECON.html
    Download Restriction: free of charge
    File URL: http://agricecon.agriculturejournals.cz/doi/10.17221/319/2018-AGRICECON.pdf
    Download Restriction: free of charge
    File URL: https://libkey.io/10.17221/319/2018-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

    for a different version of it.

    References listed on IDEAS

    as
    1. Koenker, Roger & Zhao, Quanshui, 1996. "Conditional Quantile Estimation and Inference for Arch Models," Econometric Theory, Cambridge University Press, vol. 12(5), pages 793-813, December.
    2. J. Špička & J. Boudný & B. Janotová, 2009. "The role of subsidies in managing the operating risk of agricultural enterprises," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 55(4), pages 169-180.
    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. Zongwu Cai & Haiqiang Chen & Xiaosai Liao, 2020. "A New Robust Inference for Predictive Quantile Regression," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202002, University of Kansas, Department of Economics, revised Feb 2020.
    2. Yanlin Tang & Xinyuan Song & Zhongyi Zhu, 2015. "Variable selection via composite quantile regression with dependent errors," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 69(1), pages 1-20, February.
    3. Dingshi Tian & Zongwu Cai & Ying Fang, 2018. "Econometric Modeling of Risk Measures: A Selective Review of the Recent Literature," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201807, University of Kansas, Department of Economics, revised Oct 2018.
    4. Escanciano, J. Carlos & Olmo, Jose, 2010. "Backtesting Parametric Value-at-Risk With Estimation Risk," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 36-51.
    5. Michal Chocholousek & Zuzana Hlouskova & Tereza Simova & Jan Huml, 2021. "Agriculture farms product differentiation assessment in the Czech Republic," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(3), pages 81-89.
    6. Caporin, Massimiliano & Pelizzon, Loriana & Ravazzolo, Francesco & Rigobon, Roberto, 2018. "Measuring sovereign contagion in Europe," Journal of Financial Stability, Elsevier, vol. 34(C), pages 150-181.
    7. Monica Billio & Massimiliano Caporin & Loriana Pelizzon & Domenico Sartore, 2012. "CDS Industrial Sector Indices, credit and liquidity risk," Working Papers 2012_09, Department of Economics, University of Venice "Ca' Foscari".
    8. Zongwu Cai & Meng Shi & Yue Zhao & Wuqing Wu, 2020. "Testing Financial Hierarchy Based on A PDQ-CRE Model," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202011, University of Kansas, Department of Economics, revised Jul 2020.
    9. Wagner Piazza Gaglianone & Luiz Renato Lima & Oliver Linton & Daniel R. Smith, 2011. "Evaluating Value-at-Risk Models via Quantile Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 150-160, January.
    10. Cho, Jin Seo & Kim, Tae-hwan & Shin, Yongcheol, 2015. "Quantile cointegration in the autoregressive distributed-lag modeling framework," Journal of Econometrics, Elsevier, vol. 188(1), pages 281-300.
    11. Cai, Zongwu & Xu, Xiaoping, 2009. "Nonparametric Quantile Estimations for Dynamic Smooth Coefficient Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 371-383.
    12. Komunjer, Ivana, 2005. "Quasi-maximum likelihood estimation for conditional quantiles," Journal of Econometrics, Elsevier, vol. 128(1), pages 137-164, September.
    13. Gourieroux, C. & Jasiak, J., 2008. "Dynamic quantile models," Journal of Econometrics, Elsevier, vol. 147(1), pages 198-205, November.
    14. Zhenlin Yang & Liangjun Su, 2007. "Instrumental Variable Quantile Estimation of Spatial Autoregressive Models," Working Papers 05-2007, Singapore Management University, School of Economics.
    15. Haowen Bao & Zongwu Cai & Yuying Sun & Shouyang Wang, 2023. "Penalized Model Averaging for High Dimensional Quantile Regressions," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202302, University of Kansas, Department of Economics.
    16. Escanciano, J. Carlos, 2010. "Asymptotic Distribution-Free Diagnostic Tests For Heteroskedastic Time Series Models," Econometric Theory, Cambridge University Press, vol. 26(3), pages 744-773, June.
    17. Furno, Marilena, 2001. "LAD estimation with random coefficient autocorrelated errors," Computational Statistics & Data Analysis, Elsevier, vol. 36(4), pages 511-523, June.
    18. Yuzhi Cai & Guodong Li, 2018. "A novel approach to modelling the distribution of financial returns," Working Papers 2018-22, Swansea University, School of Management.
    19. Fahimah A. Al-Awadhi & Zoulikha Kaid & Ali Laksaci & Idir Ouassou & Mustapha Rachdi, 2019. "Functional data analysis: local linear estimation of the $$L_1$$ L 1 -conditional quantiles," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(2), pages 217-240, June.
    20. Mendes, Beatriz Vaz de Melo & Júnior, Antonio Marcos Duarte, 1999. "Robust Estimation for ARCH Models," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 19(1), May.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:caa:jnlage:v:65:y:2019:i:7:id:319-2018-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.