Agric. Econ. - Czech, 2019, 65(7):331-339 | DOI: 10.17221/319/2018-AGRICECON

Analysis of economic risk in potatoes cultivationOriginal Paper

Milan Cizek1, Miroslav Mimra2, Miroslav Kavka*,2, Jaroslav Humpal3
1 Potato Research Institute Havlíčkův Brod, Havlíčkův Brod, Czech Republic
2 Department of Machinery Utilisation, Faculty of Engineering, Czech University of Life Sciences Prague, Prague, Czech Republic
3 Institute of Agricultural Economics and Information, Prague, Czech Republic

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.

Keywords: break-even point; gross profit; Monte Carlo method; risk analysis

Published: July 31, 2019  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Cizek M, Mimra M, Kavka M, Humpal J. Analysis of economic risk in potatoes cultivation. Agric. Econ. - Czech. 2019;65(7):331-339. doi: 10.17221/319/2018-AGRICECON.
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Cizek M. (2014): Influence of potato growing technology on yield, realisation price, revenues and costs of production in the Czech Republic between 2010-2013 years. In: Goffart J.P., Rolot J.L., Demeulemeester K., Goeminne M. (eds): Proceedings 19th Triennial Conference of the European Association for Potato Research, Brussels, July 6-11, 2014.
    2. CSO (2018): Vývoj ploch a sklizní zemědělských plodin v letech 2006 až 2017. (Trends in sowing areas and crop harvests in 2006-2017). Czech Statistical Office. Available at: https://www.czso.cz/documents/10180/61431846/2701411801.pdf/49e7c45d-850f-4c7e-b237-1de9d0034beb?version=1.0 (accessed Aug 10, 2018). (in Czech)
    3. Dobele A., Vitols M. (2016): Economic assessment for the production of starch potatoes on farms in Latvia. In: Proceedings International Scientific Conference on Economic Science for Rural Development, Jelgava, Latvia, Apr 21-22, 2016: 214-220.
    4. Evans M., Hastings N., Peacock B. (2000): Statistical Distributions. 3rd Ed. John Wiley & Sons Inc., New York.
    5. Foltyn I., Kopecek P., Zednickova I., Vavra V. (2009): Profitability development of key Czech agricultural commodities in the period 2002-2006. Agricultural Economics - Czech, 55: 181-199. Go to original source...
    6. Gleissner W., Berger T. (2004a): Die Ableitung von Kapitalkostensätzen aus dem Risikoinventar eines Unternehmens. (Derivation of Cost of Capital from the Risk Inventory of a Company). UM - Unternehmensbewertung & Management, April, 2004: 143-147. (in German)
    7. Gleissner W., Berger T. (2004b): Auf nach Monte Carlo: Simulationsverfahren zur Risiko-Aggregation. (The Monte Carlo: simulation method for risk aggregation). Risknews, 1: 30-37. (in German) Go to original source...
    8. Hyndman R.J., Fan Y. (1996): Sample quantiles in statistical packages. American Statistician, 50: 361-365. Go to original source...
    9. IAEI (2018): Internal Materials. Institute of Agricultural Economics and Information, Prague.
    10. Koenker R., Zhao Q. (1996): Conditional Quantile estimation and inference for Arch Models. Econometric Theory, 12: 793-813. Go to original source...
    11. Koenker R., Hallock K. (2001): Quantile regression: An introduction. Journal of Economic Perspectives, 15: 143-156. Go to original source...
    12. Kroese D.P., Taimre T., Botev Z.I. (2011): Handbook of Monte Carlo Methods. John Wiley & Sons, New York. Go to original source...
    13. Marin F., Jones J.W., Boote K.J. (2017): A stochastic method for crop models: Including uncertainty in a sugarcane model. Agronomy Journal, 109: 483-495. Go to original source...
    14. PRI HB (2018): Internal Materials. Potato Research Institute Havlíčkův Brod, Havlíčkův Brod.
    15. Rayburn E.B. (2009): Estimating economic risk using Monte Carlo enterprise budgets. Forage and Grasslands, 7. Go to original source...
    16. Spicka J., Boudny J., Janotova B. (2009): The role of subsidies in managing the operating risk of agricultural enterprises. Agricultural Economics, 55: 169-179. Go to original source...
    17. Wolke T. (2008): Risikomanagement. Oldenburg Wissenschaftverlag GmBH, München. Go to original source...
    18. Zizka J. (2018): Situační a výhledová zpráva brambory. (Situation and outlook report of potatoes). Ministry of Agriculture of the Czech Republic, Prague. Available at: http://eagri.cz/public/web/file/588170/SVZ_Brambory_06_2018.pdf (accessed Aug 10, 2018). (in Czech)

    This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content