Modeling the Korean Crop Market under the Yield Uncertainty Using the Stochastic Approach

Abstract

This study introduces a stochastic agricultural policy model to generate stochastic projection under uncertainty. To do that, we extract an uncertainty from the residual in the yield functions for rice, wheat, corn, soybean, and barley to consider the uncertainty of climate conditions. A copula function is used to reflect a correlation among variables. We then utilize a Monte Carlo simulation approach to generate 500 random draws representing residuals for the yield of individual crop in the future. We impose random draws into the stochastic model built based on the crop model in KREI-KASMO. Our stochastic projections show the possible range of prices that a deterministic model does not generate. These ranges imply that the possible range of prices under the uncertainty of climate conditions in the Korean crop market. Our study finds that the actual price of rice is out of the projected range, but soybean price is within the range of stochastic price projection. Our study provides the necessity of stochastic agricultural policy model and helps policy makers and market analysts who consider a future market.