Forecasts of global crop yields prior to planting have generally been single values, based on entirely past trends. Regression analysis testing a combination of data from ENSO (El Nino/Southern Oscillation) and ARMA models suggests that yield forecasting errors can be reduced, generating more normal distributions of these errors.
