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.
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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
Bokelmann W, Hirschauer N, Nagel UJ, Odening M (1996) Landwirtschaftliche Beratung in Brandenburg: Eine Evaluierung erster Erfahrungen. Margraf, Weikersheim
Brandes W (1974) Wie analysiere und plane ich meinen Betrieb?. Parey, Hamburg
Brekke KA, Moxnes E (2003) Do numerical simulation and optimization results improve management? Experimental evidence. J Econ Behav Org 50:117–131
Dent JB, Harrison SR, Woodford KB (1986) Farm planning with linear programming: concept and practice. Butterworths, Sydney
Gigerenzer G, Selten R (2001) Bounded rationality: the adaptive toolbox. MIT Press, Cambridge
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
Hardaker JB, Huirne RBM, Anderson JR, Lien G (2004) Coping with risk in agriculture, 2nd edn. CAB International, Wallingford
Hardaker JB, Pandey S, Patten LH (1991) Farm planning under uncertainty: a review of alternative programming model. Rev Marketing Agric Econ 59:9–22
Haykin S (1999) Neural network: a comprehensive foundation. Macmillan Publishing, New York
Hazell PBR, Norton RD (1986) Mathematical programming for economic analysis in agriculture. Macmillan Publishing, New York
Heady EO, Candler W (1958) Linear programming methods. Iowa State University Press, Ames
Hudson D, Coble K, Lusk J (2005) Consistency of risk premium measures. Agric Econ 33:41–49
Jorion P (1997) Value at Risk–the new benchmark for controlling market risk. McGraw-Hill, New York
Just RE, Pope RD (2003) Agricultural risk analysis: adequacy of models, data, and issues. Am J Agric Econ 85:1249–1256
Koekebakker S, Lien G (2004) Volatility and price jumps in agricultural futures prices–evidence from wheat options. Am J Agric Econ 86:1018–1031
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
Nelson RR, Winter SG (1982) An evolutionary theory of economic change. Harvard University Press, Cambridge
Pindyck RS, Rubinfeld DL (1998) Econometric models and economic forecasts. McGraw-Hill, Singapore
Rae AN (1994). Agricultural management economics: activity analysis and decision-making. CAB International, Wallingford
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
ZMP-Bilanz Getreide, Ölsaaten und Futtermittel (Zentrale Preis- und Marktberichtsstelle). Bonn, several years
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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.
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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
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DOI: https://doi.org/10.1007/s10100-007-0022-2