Skip to main content
Log in

Improved program planning with formal models? The case of high risk crop farming in Northeast Germany

  • Original Paper
  • Published:
Central European Journal of Operations Research Aims and scope Submit manuscript

Abstract

In this paper we explore whether the incorporation of systematic time series analyses and mathematical optimization procedures in the practical planning process has the potential to improve production program decisions. The cases of four German cash crop farms are investigated over six planning periods. In order to avoid solutions that simply exceed the farmer’s risk tolerance, the apparently accepted variance of the observed program’s total gross margin is used as an upper bound in the optimization. For each of the 24 planning occasions, the formal model is used to generate optimized alternative programs. The total gross margins that could have been realized if the formally optimized programs had been implemented are then compared to those that were actually realized. We find that the farmers could have increased their total gross margins significantly if—instead of using simple routines and rules of thumb—they had used adequate methods of statistical analysis combined with the formal optimization model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Adams RM, Menkhaus DJ, Woolery B (1980) Alternative parameter specification in E,V analysis: implications for farm level decision making. Western J Agric Econ 5:13–20

    Google Scholar 

  • Bokelmann W, Hirschauer N, Nagel UJ, Odening M (1996) Landwirtschaftliche Beratung in Brandenburg: Eine Evaluierung erster Erfahrungen. Margraf, Weikersheim

    Google Scholar 

  • Brandes W (1974) Wie analysiere und plane ich meinen Betrieb?. Parey, Hamburg

    Google Scholar 

  • Brekke KA, Moxnes E (2003) Do numerical simulation and optimization results improve management? Experimental evidence. J Econ Behav Org 50:117–131

    Article  Google Scholar 

  • Dent JB, Harrison SR, Woodford KB (1986) Farm planning with linear programming: concept and practice. Butterworths, Sydney

    Google Scholar 

  • Gigerenzer G, Selten R (2001) Bounded rationality: the adaptive toolbox. MIT Press, Cambridge

    Google Scholar 

  • Hanf CH (1991) Lineare Programmierung und landwirtschaftliche Beratung (Oder: Wird wertvolle Ausbildungszeit an der Universität vergeudet?). Betriebswirtschaftliche Mitteilungen der Landwirtschaftskammer Schleswig-Holstein 432:3–12

    Google Scholar 

  • Hardaker JB, Huirne RBM, Anderson JR, Lien G (2004) Coping with risk in agriculture, 2nd edn. CAB International, Wallingford

    Google Scholar 

  • Hardaker JB, Pandey S, Patten LH (1991) Farm planning under uncertainty: a review of alternative programming model. Rev Marketing Agric Econ 59:9–22

    Google Scholar 

  • Haykin S (1999) Neural network: a comprehensive foundation. Macmillan Publishing, New York

    Google Scholar 

  • Hazell PBR, Norton RD (1986) Mathematical programming for economic analysis in agriculture. Macmillan Publishing, New York

    Google Scholar 

  • Heady EO, Candler W (1958) Linear programming methods. Iowa State University Press, Ames

    Google Scholar 

  • Hudson D, Coble K, Lusk J (2005) Consistency of risk premium measures. Agric Econ 33:41–49

    Article  Google Scholar 

  • Jorion P (1997) Value at Risk–the new benchmark for controlling market risk. McGraw-Hill, New York

    Google Scholar 

  • Just RE, Pope RD (2003) Agricultural risk analysis: adequacy of models, data, and issues. Am J Agric Econ 85:1249–1256

    Article  Google Scholar 

  • Koekebakker S, Lien G (2004) Volatility and price jumps in agricultural futures prices–evidence from wheat options. Am J Agric Econ 86:1018–1031

    Article  Google Scholar 

  • LDS Brandenburg–Landesbetrieb für Datenverarbeitung und Statistik (2003) Information via fax

  • Mueller JA, Lemke F (2003) Self-organizing data mining: extracting knowledge from data. Trafford Publishing, British Columbia

    Google Scholar 

  • Nelson RR, Winter SG (1982) An evolutionary theory of economic change. Harvard University Press, Cambridge

    Google Scholar 

  • Pindyck RS, Rubinfeld DL (1998) Econometric models and economic forecasts. McGraw-Hill, Singapore

    Google Scholar 

  • Rae AN (1994). Agricultural management economics: activity analysis and decision-making. CAB International, Wallingford

    Google Scholar 

  • Verstegen JAAM, Huirne RBM, Dijkhuizen AA, Kleijnen JPC (1995) Economic value of management information systems in agriculture: a review of evaluation approaches. Comput Electron Agric 13:273–288

    Article  Google Scholar 

  • ZMP-Bilanz Getreide, Ölsaaten und Futtermittel (Zentrale Preis- und Marktberichtsstelle). Bonn, several years

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Oliver Mußhoff.

Additional information

The authors thank four anonymous referees and the Editors for helpful comments and suggestions.

Norbert Hirschauer thanks the German Research Foundation (DFG) for the opportunity to work on this paper during a research leave.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mußhoff, O., Hirschauer, N. Improved program planning with formal models? The case of high risk crop farming in Northeast Germany. cent.eur.j.oper.res. 15, 127–141 (2007). https://doi.org/10.1007/s10100-007-0022-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10100-007-0022-2

Keywords

JEL Classification

Navigation