Forecasting Grain Sorghum Yields Using Probability Functions
ABSTRACT CONDITIONED probability functions were devel-oped using a dynamic crop growth simulation model. One hundred and twenty sets of seasonal yield and daily crop growth data were simulated. These data sets were used to develop conditional probability functions for selected days during any growing season. The probability functions were conditioned on two state variables: leaf area and available soil water. Cumulative distribution functions computed from the probability functions were used to forecast yields at each of the selected dates as the season progressed. Forecast accuracy improved as the season progressed. Combining a deterministic crop growth model with stochastic weather data provides a realistic method of yield forecasting.