Comparison of Agricultural Costs Prediction Approaches

DOI 10.7160/aol.2018.100101
No 1/2018, March
pp. 3-13

Hloušková, Z., Ženíšková, P. and Prášilová, M. (2018) “Comparison of Agricultural Costs Prediction Approaches", AGRIS on-line Papers in Economics and Informatics, Vol. 10, No. 1, pp. 3-13. ISSN 1804-1930. DOI 10.7160/aol.2018.100101.


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.


Time series analysis, exponential smoothing, ARIMA models, cost inputs in agriculture, Farm Accountancy Data Network (FADN).


  1. Allen, P. G. (1994) "Economic forecasting in agriculture", International Journal of Forecasting, Vol. 10, No.1, pp. 81-135. ISSN 0169-2070. DOI 10.1016/0169-2070(94)90052-3.
  2. Artl, J., Artlová, M. and Rubíková, E. (2002) "Analýza ekonomických časových řad s příklady"(in Czech), University of Economics Prague, Faculty of Informatics and Statistics, Prague, 147 s.ISBN 80-245-0307-7.
  3. Artlová, M. and Artl, J. (1995) "Grafické metody analýzy ekonomických časových řad" (in Czech),Statistika 32, CZSO, Prague, Vol. 11, pp. 483-493. ISSN 0322-788x.
  4. CZSO (2016a) "Indexy cen dodávek výrobků a služeb do zemědělství. Tabulka 1: Indexy cen vstupůdo zemědělství (stejné období předchozího roku = 100" (in Czech), CZSO, [Online]. Available: [Accessed: 5 Aug. 2017].
  5. CZSO (2016b) "Souhrnný zemědělský účet. Časové řady" (in Czech), CZSO, [Online]. Available: [Accessed: 5 Aug. 2017].
  6. Deppermann, A., Offermann, F., Puttkammer, J. and Grethe, H. (2016) "EU biofuel policies: Incomeeffects and lobbying decisions in the German agricultural sector", Renewable Energy, Vol. 87,pp. 259-265. ISSN 09601481. 0960-1481. DOI 10.1016/j.renene.2015.10.005.
  7. E, J., Bao, Y. and Ye, J. (2017) "Crude oil price analysis and forecasting based on variational modedecomposition and independent component analysis", Physica A: Statistical Mechanics and itsApplications, Vol. 484, pp. 412-427. ISSN 03784371. DOI 10.1016/j.physa.2017.04.160.
  8. Enders, W. and Holt, M. T. (2012) "Sharp Breaks or Smooth Shifts? An Investigation of the Evolutionof Primary Commodity Prices", American Journal of Agricultural Economics, Vol. 94, No. 3,pp. 659-673. DOI 10.1093/ajae/aar162.
  9. European Commission - EU FADN (2016) "EU Farm Economics Overview based on 2013 FADNdata", European Commission. [Online]. Available:[Accessed: 23 Aug. 2017].
  10. European Commission (2017) "Short-term outlook for EU agricultural markets in 2017 and 2018",European Commission. [Online]. Available: markets-and-prices/index_en.htm [Accessed: 25 Aug. 2017].
  11. European Commission (2016) "EU Agricultural Outlook, Prospects for agricultural markets andincome in the EU 2016-2026", European Commission. [Online]. Available: [Accessed: 25 Aug. 2017].
  12. Galbraith, G., Bakhshi, S., Kung, W. and Kjaer, P. (2011) "Incorporating a Farm-Level Balance SheetForecast into the Canadian Agricultural Dynamic Microsimulation Model", 3rd General Conferenceof the International Microsimulation Association, Stockholm, Sweden.
  13. Hamjah, M. A. (2014) "Rice Production Forecasting in Bangladesh: An Application Of Box-Jenkins ARIMA Model", Mathematical Theory and Modeling, Vol. 4, No. 4. [Online]. Available: [Accessed: 23 Aug. 2017].ISSN 2225-0522.
  14. Hanke, J. E. and Wichern, D. (2008) "Business Forecasting 9th Edition", Pearson Education Limited,576 p. ISBN 0132301202.
  15. Hloušková, Z., Lekešová, M., Harvilíková, M., Slížka, E. and Vrkočová, V. (2015) "Odhadekonomických výsledků v zemědělství na základě podnikových dat FADN s nízkou či nulovouznalostí faktického vývoje odhadovaného roku"(in Czech), Final report on results of investigationof Internal research project IVP No. 1281/2014, IAEI, Prague.
  16. Hloušková, Z., Lekešová, M. and Slížka, E. (2014) "Microsimulation Model Estimating CzechFarm Income from Farm Accountancy Data Network Database", Agris on-line Papers in Economicsand informatics, Vol. 6, No 3, pp. 27-37. ISSN 1804-1930.
  17. Hsiao, C. (2014) "Analysis of panel data, Third edition", Cambridge University Press.ISBN 978-1-107-03869.
  18. Hyndman, R. J. and Athanasopoulos, G. (2013) „Forecasting: principles and practice“ OTexts:Melbourne, Australia. [Online]. Available: Accessed: [Accessed: 20 Aug.2017].
  19. Hyndman, R. J. and Kostenko, A. V. (2007) "Minimum sample size requirements for seasonalforecasting models", Foresight: the International Journal of Applied Forecasting, Vol. 6, pp. 12-15.
  20. Khalid, M., Sultana, M. and Zaidi, F. (2014) "Prediction of Agriculture Commodities PriceReturns Using ARMA and Wavelet", Journal of Natural Sciences Research, Vol. 4, No.23, 30 p. [Online]. Available:;jsessionid=E2A810D4DD0F4BC4E548439370CE8C35?doi=[Accessed: 20 Aug. 2017]. ISSN 2225-0921.
  21. Kostlivý, V., Fuksová, Z. and Dubec, J. (2017) "Farms Productivity Developments Basedon Malmquist Production Indices", Agris on-line Papers in Economics and Informatics, Vol. 9,No. 2, pp. 91 - 100. ISSN 1804-1930. DOI 10.7160/aol.2017.090208.
  22. Ishaque, M., Ziblim, S. (2013) "Use of Some Exponential Smoothing Models in Forecasting SomeFood Crop Prices in the Upper East Region of Ghana", Mathematical Theory and Modeling, Vol. 3,No. 7., pp. 16 - 27. [Online]. Available: [Accessed: 26 Aug. 2017]. ISSN 2225-0522.
  23. Labys, W. C. (2003) "New Directions in the Modeling and Forecasting of CommodityMarkets", Mondes en développement, Vol. 2, No. 122, pp. 3-19. [Online]. Available: [Accessed: 23 Aug.2017]. ISBN 9782804143022. DOI 10.3917/med.122.0003.
  24. Linden, A., Adams, J. L. and Roberts N. (2003) "Evaluating Disease Management ProgramEffectiveness, An Introduction to Time-Series Analysis", Deseas Management, Vol. 6, No. 4,pp. 243-55. ISSN 1093507X. DOI 10.1089/109350703322682559.
  25. Mošová, V. (2013) "Využití waveletů při analýze časových řad - 1. teoretická část"(in Czech),Economics Management Innovation, Vol. 5. [Online]. Available: [Accessed: 20 Jan. 2017]. ISSN 1805-353X.
  26. Natural Resources Institute Finland (2016) "EconomyDoctor", Natural Resources Institute Finland.[Online]. Available: [Accessed: 26 Aug. 2017].
  27. OECD, FAO (2017) "OECD-FAO Agricultural Outlook 2017-2026", OECD. [Online]. [Accessed: 26 Aug. 2017].
  28. Quiroga, S., Suárez, C., Fernández-Haddad, Z. and Philippidis, G. (2017) "Levelling the playingfield for European Union agriculture: Does the Common Agricultural Policy impact homogeneouslyon farm productivity and efficiency?", Land Use Policy, Vol. 68, pp. 179-188. ISSN 02648377. DOI 10.1016/j.landusepol.2017.07.057.
  29. Rizov, M., Pokrivcak, J., Ciaian, P. (2013) "CAP subsidies and productivity of the EU farms", Journalof Agricultural Economics, 64 (3), pp. 537-557. ISSN 0021857X. DOI 10.1111/1477-9552.12030.
  30. Rural Business Research (2016) "Farm Bussiness Survey", University of Cambridge, UnitedKingdom. [Online]. Available: [Accessed:20 Aug. 2017].
  31. Špička, J. (2014) "The Regional Efficiency of Mixed Crop and Livestock Type of Farmingand Its Determinants", Agris on-line Papers in Economics and Informatics, Vol. 6, No. 1, pp. 99-109.ISSN 1804-1930.
  32. Allen, P. G. (1994) "Economic forecasting in agriculture: Comment", InternationalJournal of Forecasting, Vol. 10. [Online]. Available: [Accessed: 20 Aug. 2017]. ISSN 0169-2070. DOI 10.1016/0169-2070(94)90052-3.
  33. USDA (2016) "Farm Sector Income Forecast", Economic Research Service. United StatesDepartment of Agriculture. [Online]. Available: [Accessed: 20 Aug.2017].

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