Res. Agr. Eng., 2016, 62(3):113-121 | DOI: 10.17221/48/2015-RAE

Sensitivity analysis of key operating parameters of combine harvestersOriginal Paper

M. Kavka1, M. Mimra1, F. Kumhála2
1 Department of Machinery Utilization, Faculty of Engineering, Czech University of Life Sciences Prague, Prague, Czech Republic
2 Department of Agricultural Machines, Faculty of Engineering, Czech University of Life Sciences Prague, Prague, Czech Republic

The sensitivity analysis of key operating parameters on the average annual sub-profit in a group of three combine harvesters operating in companies providing agricultural services were analysed. Based on the results of the cost analysis, the following key operating parameters with the greatest influence on the costs were identified: the purchase price of the machine, the price of fuel, maintenance costs, personnel costs and annual performance. These parameters were used in the sensitivity analysis to investigate their effect on unit costs. Changing the above-mentioned parameters is calculated within ± 30% from their mean value. To perform a sensitivity analysis of the average annual sub-profit of combine harvesters, the unit price of mechanized work was additionally used. The results showed that greatest impact on both the average annual earnings of combines operation and on the changes in unit cost was those of the annual performance of the combine harvester, combine harvester purchase price and the cost of fuel. On the other hand, maintenance and personnel costs had a smaller influence concerning these changes of parameters.

Keywords: annual performance; costs optimization; unit costs; cost analysis; average annual sub-profit

Published: September 30, 2016  Show citation

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Kavka M, Mimra M, Kumhála F. Sensitivity analysis of key operating parameters of combine harvesters. Res. Agr. Eng. 2016;62(3):113-121. doi: 10.17221/48/2015-RAE.
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References

  1. Anderson W. (1988): Factors affecting machinery costs in grain production. American Society of Agricultural Engineers. St. Joseph: ASAE Paper No. 88-1057.
  2. Buckmaster D.R. (2003): Benchmarking tractor costs. Applied Engineering in Agriculture, 19: 151-154. Go to original source...
  3. Calcante A., Fontanini L. Mazzetto F. (2013): Coefficients of repair and maintenance costs of self-propelled combine harvesters in Italy. Agricultural Engineering International: CIGR Journal, 15: 141. Go to original source...
  4. Cross T.L., Perry G.M. (1996): Remaining value function for farm equipment. Applied Engineering in Agriculture, 12: 547-553. Go to original source...
  5. Delbridge T.A., Fernholz C., King R.P., Lazarus W. (2013): A whole-farm profitability analysis of organic and conventional cropping systems. Agricultural Systems, 122: 1-10. Go to original source...
  6. Fotr J. (1992): Jak hodnotit a snižovat podnikatelské riziko. 1st Ed. Prague, Management Press.
  7. Fotr J., Kislingerová E. (2009): Integrace rizika a nejistoty do investičního rozhodování a oceňování. Politická ekonomie, 57: 801-826. Go to original source...
  8. Fotr J., Souček M. (2005): Podnikatelský záměr a investiční rozhodování. Prague, Grada Publishing.
  9. Goodwin P., Wright G. (2004): Decision Analysis for Managerial Judgment. Chichester, John Wiley & Sons.
  10. Harrell R. (1987): Economic analysis of robotic citrus harvesting in Florida. American Society of Agricultural Engineers. ASAE Paper No. 30: 298-304. Go to original source...
  11. Kavka M. (1997): Využití zemědělské techniky v podmínkách tržního hospodářství. Prague, ÚZPI.
  12. Lovallo D., Kahneman D. (2003): Delusion of success: how optimism undermines executives' decision. Harvard Business Review. July-August 2003: 45-52.
  13. Mankind M.C., Steele R. (2005):Turning great strategy into great performance. Harvard Business Review, July-August 2005: 69-75.
  14. McCarl A.B., Kline D.E, Bender A.D. (1990): Improving on shadow price information for identifying critical farm machinery. American Journal of Agricultural Economics, 72: 582-588. Go to original source...
  15. Mun J. (2004): Applied Risk Analysis. New York, John Wiley & Sons.
  16. Mun J. (2006): Modelling Risk. Applying Monte Carlo Simulation, Real Options Analysis, Forecasting and Optimisation Techniques. Hoboken, John Wiley & Sons.
  17. O'Donnell J.C. (2012): Nonparametric estimates of the components of productivity and profitability change in U.S. agriculture. Journal of Agricultural Economics, 94: 873-890. Go to original source...
  18. Pelesaraei N., Abdi R., Rafiee S. (2013): Energy use pattern and sensitivity analysis of energyinputs and economical models for peanut production in Iran. International Journal of Agriculture and Crop Sciences, 5: 2193-2202.
  19. Rataj V. (2005): Projektovanie výrobných systémov: Výpočty a analýzy. Nitra, SPU.
  20. Rotz C.A. (1987): A standard model for repair costs of agricultural machinery. Applied Engineering in Agriculture, 3: 3-9. Go to original source...
  21. Rotz C.A. (1991): Bowers, W. Repair and maintenance cost data for agricultural equipment. American Society of Agricultural Engineers. St. Joseph: ASAE Paper No. 91-1531.
  22. Scholleová H. (2007): Hodnota flexibility. Reálné opce. Prague, C.H. BECK.
  23. Schoemaker J. (2002): Profiting from Uncertainty. Strategies for Succeeding No Matter What the Future Brings. New York, The Free Press.
  24. Yousif A.L., Dahab H.M., El Ramlawi R.H. (2013): Crop-machinery management system for field operations and farm machinery selection. Journal of Agricultural Biotechnology and Sustainable Development, 5: 84-90. Go to original source...

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