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Sophisticated Program Planning Approaches Generate Large Benefits in High Risk Crop Farming

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  • Musshoff, Oliver
  • Hirschauer, Norbert

Abstract

Agricultural production relies to a great extent on biological processes in natural environments. In addition to volatile prices, it is thus heavily exposed to risks caused by the variability of natural conditions such as rainfall, temperature and pests. With a view to the apparently lacking support of risky farm production program decisions through formal planning models, the objective of this paper is to examine whether, and eventually by how much, farmers’ “intuitive” program decisions can be improved through formal statistical analyses and stochastic optimization models. In this performance comparison, we use the results of the formal planning approach that are generated in a quasi ex-ante analysis as a normative benchmark for the empirically observed ones. To avoid benchmark solutions that would possibly exceed the respective farmer’s risk tolerance, we limit the formal search to a subset of solutions that are second- degree stochastically dominant compared to the farmer’s own decision. We furthermore compare the suitability of different statistical (time series) models to forecast the uncertainty of single gross margins.

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Bibliographic Info

Paper provided by Agricultural Economics Society in its series 82nd Annual Conference, March 31 - April 2, 2008, Royal Agricultural College, Cirencester, UK with number 36865.

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Date of creation: 30 Mar 2008
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Handle: RePEc:ags:aes008:36865

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Related research

Keywords: stochastic optimization; stochastic processes; production risk; program planning; time series analysis; C1; C61; M11; Q12;

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  1. Hardaker, J. Brian & Patten, Louise H. & Pannell, David J., 1988. "Utility-Efficient Programming For Whole-Farm Planning," Australian Journal of Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 32(02-03).
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  9. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-72, June.
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  11. Rulon D. Pope, 2003. "Agricultural Risk Analysis: Adequacy of Models, Data, and Issues," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(5), pages 1249-1256.
  12. Brekke, Kjell Arne & Moxnes, Erling, 2003. "Do numerical simulation and optimization results improve management?: Experimental evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 50(1), pages 117-131, January.
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