A Technical Manual for Stochastic Simulation of Crop Yield, Price, and Policy Outcomes

➩ Yield distributions are modeled non-parametrically. Detrended historical yield data and kernel-based simulation preserve observed yield variability, skewness, downside risk, and county-specific distributional features without imposing restrictive parametric assumptions.

➩ Price uncertainty captures market-based information. Alternative price measures, including futures-based prices, implied volatility, MYA prices, FSA projections, and other user-specified forecasts, can be incorporated to simulate price uncertainty under different information sets and policy scenarios.

➩ Joint yield-price distributions are constructed with copulas. Copula methods capture joint dependence among crop yields, crop prices, and regional outcomes while preserving flexible marginal distributions and high-dimensional correlation patterns across counties.

➩ Farm-level yield risk is calibrated with RMA insurance data. RMA Yield Protection data are used to calibrate farm-level yield volatility, linking county-level yield simulations to insurance-based measures of farm-level risk exposure and premium-implied yield uncertainty.

➩ Simulated outcomes are translated into policy payment distributions. Simulated yield and price realizations are used to compute crop insurance and farm safety net payments, allowing users to evaluate expected payments, downside protection, and distributional variation across stochastic production and market scenarios.

➩ The framework is modular for policy applications. Yield processing, price simulation, dependence modeling, farm-level calibration, and policy payment simulation are organized as separate components that can be modified, replaced, or extended across crops and regions.

Recommended Citation: Junkan Li, Shawn Arita, and Sandro Steinbach (2026). A Technical Manual for Stochastic Simulation of Crop Yield, Price, and Policy Outcomes. ARPC Report 2026–03. Agricultural Risk Policy Center, North Dakota State University.