Dynamic Programming Model of the Corn Production Decision Process With Stochastic Climate Forecasts (Information, Illinois)
Mjelde, James William
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https://hdl.handle.net/2142/69866
Description
Title
Dynamic Programming Model of the Corn Production Decision Process With Stochastic Climate Forecasts (Information, Illinois)
Author(s)
Mjelde, James William
Issue Date
1985
Department of Study
Agricultural Economics
Discipline
Agricultural Economics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Economics, Agricultural
Abstract
This study addresses two major issues which have surfaced recently in the Agricultural Economics literature: (1) the need to model crop production in a dynamic framework, and (2) the need for a better understanding of the economics of information. To address these issues a stochastic dynamic programming model of a single year's corn production process in east-central Illinois is developed. Improvements in information which are valued by the model are various climate forecast designs.
Development of the dynamic programming model entailed the synthesis of a crop growth simulation model and a nitrogen-climate interaction model to obtain a synthetic data set used in estimation of a corn production function. The dynamic programming model contains eight stages of production within a single year. As many as sixty decision alternatives are available to the decision maker at some of the stages.
The results of this study indicate that there is a potential for both perfect and imperfect forecasts to have value to a corn producer. The value of any climate forecast is depended not only on the economic scenario (corn price, input costs and interest rate), but also the design of the climate forecast. Design parameters considered are lead time, accuracy of the forecasts, weather parameters to be included in the forecast, identification of the most important periods, spatial resolution, number of climatic conditions to be forecast, and length of the period which is being forecast. Evaluation of these design parameters provides a better understanding of the relationships between the determinants of information value and the expected value of the information.
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