Using Neural Network Classifier Approach for Statistically Forecasting Extreme Corn Yield Losses in Eastern United States

Abstract

This paper presents a statistical method for forecasting extreme corn yield losses caused by weather extremes. A neural network classifier approach is tested over the Eastern United States (time series of 35 years) to detect extreme yield losses for corn from weather-related information. We first developed a methodology to rank a series of climate-based predictors according to the accuracy with which they classify extreme from nonextreme yield losses. The classification methodology is adapted in order to be trained with a limited number of extreme cases. Using four weather predictors—the average temperature in July and August, and the SPEI (Standardized Precipitation-Evapotranspiration Index) in June and July—71% of the extreme cases are well classified by this statistical model. Furthermore, the neural network output represents a good yield severity index and can provide an early quantitative warning for extreme yield anomalies.