Monte-Carlo Simulation of Technological Risks in Pig Farm

Authors

  • Szilvia Szőke University of Debrecen, Faculty of Applied Economics and Rural Development, Institute of Economic Analitical Methodology and Applied Informatics, H-4032 Debrecen, Böszörményi út 138.
  • Lajos Nagy University of Debrecen, Faculty of Applied Economics and Rural Development, Institute of Economic Analitical Methodology and Applied Informatics, H-4032 Debrecen, Böszörményi út 138.
  • Péter Balogh University of Debrecen, Faculty of Applied Economics and Rural Development, Institute of Economic Analitical Methodology and Applied Informatics, H-4032 Debrecen, Böszörményi út 138.

Keywords:

pig production, computer simulation

Abstract

The study was based on data from an Ltd.’s 1100-swine farm. Our aims were to study the operation and expected results of the farm’s operation in 2009. The significant independent variables, their ranges and probability distributions, and the correlation between them were inputs to the model. The values of the variables were produced using a random number generator. The computer simulation was performed using @Risk (Palisade Corporation) software. The study concentrates on the factors affecting the number of offspring (piglets). Model inputs were the mating, mortality and farrowing rates; the costs and the income values based on these rates have been analysed as the output data of the model. The results indicate that there is a modest correlation between the fattening pig price and per unit profit (Spearmann’s rank correlation coefficient: ρ = 0.585); the strongest correlation (-0.73 ≥ ρ ≥ -0.76) is between the fodder prices and per unit profit. The 10,000 model runs yielded the mean is 905 million HUF of the total cost, the mean value is 1005 million HUF of the total income of the swine farm. In case of the total profit: the mean is 101 million HUF. The probability of the loss in farm’s operation is 13.02 percent considering the above mentioned model settings.

Author Biography

  • Szilvia Szőke, University of Debrecen, Faculty of Applied Economics and Rural Development, Institute of Economic Analitical Methodology and Applied Informatics, H-4032 Debrecen, Böszörményi út 138.

    corresponding author
    szilvia@agr.unideb.hu

References

Bácskai, T., Huszti, E., Meszéna, Gy., Miko, Gy., Szép, J. (1976): A gazdasági kockázat mérésének eszközei. Közgazdasági és Jogi Könyvkiadó : Budapest

Buzás, Gy. (2000): A gazdasági kockázat kezelése, biztosítás In: Mezőgazdasági üzemtan I. In: Buzás Gy., Nemessályi Zs., Székely Cs., Mezőgazdasági Szaktudás Kiadó: Budapest, 434–457.p

Drimba, P., Ertsey, I. (2008): Elméleti és módszertani alapok. A kockázat forrásai, kockázatelemzési és becslési módszerek. in: Hatékonyság a mezőgazdaságban (Elmélet és gyakorlat) szerk.: Szűcs I., Farkasné F.M., Agroinform Kiadó : Budapest 280–295. p. ISBN 978-963-502-889-4

Drimba, P. (1998): A kockázat figyelembe vétele a mezőgazdasági döntési modellekben. PhD értekezés, Debrecen

Ertsey, I., Kovács, S., Csipkés, M., Nagy, L. (2008): Malomipari beruházások kockázat- és gazdaságossági vizsgálata Magyarországon. Hagyományok és új kihívások a menedzsmentben”. Nemzetközi Konferencia, Debrecen, 5. p.

Evans, M., Hastings, N., Peacock, B. (2000): Triangular Distribution. In Statistical Distributions, 3rd ed. New York : Wiley, 187–188. p.

Hajdú O. (2003):Többváltozós statisztikai számítások. Központi Statisztikai Hivatal. 215. p. ISBN 963-215-600-5

Hardaker, J. B., Huirne, R. B. M., Anderson, J. R. (1997): Coping with Risk in Agriculture. CAB International, New York

Jorgensen, E. (2000): Monte Carlo simulation models: Sampling from the joint distribution of “State of Nature”-parameters. In: Van der Fels-Klerx, I.; Mourits, M. (eds). Proceedings of the Symposium on “Economic modelling of Animal Health and Farm Management”, Farm Management Group, Dept. of Social Sciences, Wageningen University, 73–84. p.

Kovács, S., Ertsey, I., Balogh, P. (2007): An improved simulation for modelling foraging of laying hens Proceedings of the third scientific conference on Rural Development, Akdemia, Kaunas Region Lithuania, 320–325. p.

Moksony F. (2006): Gondolatok és adatok. Társadalomtudományi elméletek empirikus ellenőrzése. Aula Kiadó : Budapest 205. p. ISBN 978-963-200-100-5.

Mun, J. (2004): Applied risk analysis. John Wiley&Sons, Inc., 91–94. p.

Niemi, J. K., Sevón-Aimonen, M., Pietola, K., Stalder, K. J. (2010): The value of precision feeding technologie for grow-finish swine. In: Livestock Science. 129. 1–3. 13–23. https://doi.org/10.1016/j.livsci.2009.12.006

Palisade (2005): @RISK advanced risk analysis for spreadsheets. Version 4.5. Palisade Corporation 22, 116. p.

Russel, R. S., Taylor, B. W. (1998): Operations Management, Focusing on quality and competitiveness, New Jersey : Prentice Hall, 610–613. p.

Vose, D. (2006): Risk analysis. John Wiley&Sons Ltd. : New York 418. p.

Watson, H. (1981): Computer Simulation in Business. Whiley : New York

Winston W. L. (1997): Operations Research Applications and Algorithms, Wadswoth Publishing Company, 863–870. p.

Winston, W. L. (2001): Financial modells using simulation simulation and optimization. Palisade Corporation : Newfield 379. p.

Winston, W. L. (2006): Financial modells using simulation simulation and optimization. Palisade Corporation : Newfield 505. p.

Published

2010-12-15

How to Cite

Szőke, S., Nagy, L., & Balogh, P. (2010). Monte-Carlo Simulation of Technological Risks in Pig Farm. Acta Agraria Kaposváriensis, 14(3), 183-194. https://journal.uni-mate.hu/index.php/aak/article/view/1983

Most read articles by the same author(s)