IDEAS home Printed from https://ideas.repec.org/a/ags/aolpei/276083.html
   My bibliography  Save this article

Comparison of Agricultural Costs Prediction Approaches

Author

Listed:
  • Hloušková, Z.
  • Ženíšková, P.
  • Prášilová, M.

Abstract

The paper submitted offers an assessment and comparison of three approaches to agricultural cost inputs short-term forecasting, that have been proposed as possible alternatives to tackle the problem. The data applied have been taken from the Czech Statistical Office and the Farm Accountancy Data Network data sources. The forecasts were prepared using time series analyses based on methods of exponential smoothing and Box-Jenkins methodology of autoregressive integrated process moving averages. The proposed change index numbers for the 2012, 2013 and 2014 years from three approaches were confronted with the real development of costs time series as it was found in the statistical FADN survey results. The main conclusion drawn pointed out that, for the purpose of economic income estimation based on the FADN database, the cost prediction approach based on the same database, i.e., on time series analysis of the FADN panel data, is the most applicable one. However, it is recommended, too, to use other approaches for crops protection products cost and labour cost development.

Suggested Citation

  • Hloušková, Z. & Ženíšková, P. & Prášilová, M., 2018. "Comparison of Agricultural Costs Prediction Approaches," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 10(01).
  • Handle: RePEc:ags:aolpei:276083
    DOI: 10.22004/ag.econ.276083
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/276083/files/363_agris-on-line-2018-1-hlouskova-zeniskova-prasilova.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.276083?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. Marian Rizov & Jan Pokrivcak & Pavel Ciaian, 2013. "CAP Subsidies and Productivity of the EU Farms," Journal of Agricultural Economics, Wiley Blackwell, vol. 64(3), pages 537-557, September.
    2. Walter Enders & Matthew T. Holt, 2012. "Sharp Breaks or Smooth Shifts? an Investigation of the Evolution of Primary Commodity Prices," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(3), pages 659-673.
    3. Deppermann, Andre & Offermann, Frank & Puttkammer, Judith & Grethe, Harald, 2016. "EU biofuel policies: Income effects and lobbying decisions in the German agricultural sector," Renewable Energy, Elsevier, vol. 87(P1), pages 259-265.
    4. E, Jianwei & Bao, Yanling & Ye, Jimin, 2017. "Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 412-427.
    5. Rob J. Hyndman & Andrey V. Kostenko, 2007. "Minimum Sample Size requirements for Seasonal Forecasting Models," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 6, pages 12-15, Spring.
    6. Allen, P. Geoffrey, 1994. "Economic forecasting in agriculture," International Journal of Forecasting, Elsevier, vol. 10(1), pages 81-135, June.
    7. Quiroga, Sonia & Suárez, Cristina & Fernández-Haddad, Zaira & Philippidis, George, 2017. "Levelling the playing field for European Union agriculture: Does the Common Agricultural Policy impact homogeneously on farm productivity and efficiency?," Land Use Policy, Elsevier, vol. 68(C), pages 179-188.
    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. Jana LOSOSOVÁ & Radek ZDENĚK & Jaroslav SVOBODA, 2020. "Tangible fixed assets in Czech small and middle-sized farms Abstract: The aim of this paper is to analyse the development, structure and efficiency of tangible fixed assets in the Czech farms and iden," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 11, pages 236-251, June.
    2. Khafagy, Amr & Vigani, Mauro, 2022. "Technical change and the Common Agricultural Policy," Food Policy, Elsevier, vol. 109(C).
    3. Giannakis, Elias & Bruggeman, Adriana, 2018. "Exploring the labour productivity of agricultural systems across European regions: A multilevel approach," Land Use Policy, Elsevier, vol. 77(C), pages 94-106.
    4. Radek Zdeněk & Jana Lososová, 2020. "Investments of Czech farms located in less favoured areas after EU accession," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 66(2), pages 55-64.
    5. Carl R. Zulauf & Scott H. Irwin, 1998. "Market Efficiency and Marketing to Enhance Income of Crop Producers," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 20(2), pages 308-331.
    6. Abdullah Mamun, 2024. "Impact of farm subsidies on global agricultural productivity," Agricultural Economics, International Association of Agricultural Economists, vol. 55(2), pages 346-364, March.
    7. Petrick, Martin & Kloss, Mathias, 2013. "Identifying Factor Productivity from Micro-data: The case of EU agriculture," Working papers 144004, Factor Markets, Centre for European Policy Studies.
    8. Nicola GALLUZZO, 2019. "An Assessment Of Rurality In Italian Farms And In Their Specialization Using A Quantitative Approach," Agricultural Economics and Rural Development, Institute of Agricultural Economics, vol. 16(1), pages 39-51.
    9. Petrick, Martin & Kloss, Mathias, 2013. "Synthesis Report on the Impact of Capital Use," Factor Markets Working Papers 169, Centre for European Policy Studies.
    10. Bazyli CZYZEWSKI & Katarzyna SMEDZIK-AMBROZY, 2017. "The regional structure of the CAP subsidies and the factor productivity in agriculture in the EU 28," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 63(4), pages 149-163.
    11. Matthias Klumpp, 2016. "To Green or Not to Green: A Political, Economic and Social Analysis for the Past Failure of Green Logistics," Sustainability, MDPI, vol. 8(5), pages 1-22, May.
    12. Dietz, Sarah N. & Aulerich, Nicole M. & Irwin, Scott H. & Good, Darrel L., 2009. "The Marketing Performance of Illinois and Kansas Wheat Farmers," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 41(01), pages 1-15, April.
    13. Wu, Wen-Ze & Zeng, Liang & Liu, Chong & Xie, Wanli & Goh, Mark, 2022. "A time power-based grey model with conformable fractional derivative and its applications," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    14. Chiaramonti, David & Goumas, Theodor, 2019. "Impacts on industrial-scale market deployment of advanced biofuels and recycled carbon fuels from the EU Renewable Energy Directive II," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    15. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    16. Otero, Jesús & Argüello, Ricardo & Oviedo, Juan Daniel & Ramírez, Manuel, 2018. "Explaining coffee price differentials in terms of chemical markers: Evidence from a pairwise approach," Economic Modelling, Elsevier, vol. 72(C), pages 190-201.
    17. Georgieva, Vanya & Guerov, Gueorgui & Blagoeva, Nadezhda, 2024. "Impact of economic and environmental factors on agricultural product pricing in the EU," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 10(4), December.
    18. repec:ers:journl:v:xxiv:y:2021:i:4b:p:397-409 is not listed on IDEAS
    19. Wang, Bin & Wang, Jun, 2021. "Energy futures price prediction and evaluation model with deep bidirectional gated recurrent unit neural network and RIF-based algorithm," Energy, Elsevier, vol. 216(C).
    20. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    21. Cró, Susana & Martins, António Miguel, 2017. "Structural breaks in international tourism demand: Are they caused by crises or disasters?," Tourism Management, Elsevier, vol. 63(C), pages 3-9.

    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:ags:aolpei:276083. 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: https://edirc.repec.org/data/fevszcz.html .

    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.