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Abstract
Forecasts of global crop yields prior to planting have generally been single values, based entirely on past trends. Regression analysis testing a combination of data from ENSO (El Niño/Southern Oscillation) and ARMA models suggests that yield forecasting errors can be reduced, generating more normal distributions of these errors.
Keywords: El Niño, ENSO, forecasting crop yields, long range weather forecasting, agricultural modeling, food security, risk management