Forecasting Grain Sorghum Yields Using Simulated Weather Data and Updating Techniques

A methodology was developed by which SORGF, a grain sorghum growth-simulation model, could be used to forecast crop status during the growing season. The methodology utilizes simulated weather data, generated by a Markov chain model, as input to the grain sorghum model. The modeled status of sorghum plants may be updated at any time during the growing season with actual plant status observed in the field. Ap-plication of the methodology was demonstrated by forecasting date of physiological maturity (PM) and head dry weight at PM for grain sorghum crops grown in 10 fields in Central Texas during 1976.