Use of Real-Time Multisensor Data to Assess the Relationship of Normalized Corn Yield with Monthly Rainfall and Heat Stress across the Central United States

Abstract

This study evaluated the suitability of rain estimates based on the National Weather Service (NWS) Weather Surveillance Radar-1988 Doppler (WSR-88D) network to estimate yield response to rainfall on a county scale and to provide real-time information related to crop stress resulting from deficient or excessive precipitation throughout the summer. The relationship between normalized corn yield and rainfall was examined for nine states in the central United States for 1997?99 and 2001?02. Monthly rainfall estimates were computed employing multisensor precipitation estimate (MPE) data from the National Centers for Environmental Prediction and quality-controlled (QC_Coop) and real-time (RT_Coop) NWS cooperative gauge data. In-season MPE rain estimates were found to be of comparable quality to the postseason QC_Coop estimates for predicting county corn yields. Both MPE and QC_Coop estimates were better related to corn yield than were RT_Coop estimates, presumably because of the lower density of RT_Coop gauges. Large corn yields typically resulted when May rain was less than 125 mm and July rain was greater than 50 mm. Low yields often occurred when July rainfall was less than 100 mm. For moderate July rains (50?100 mm), positive and negative normalized yields resulted. Parameterization of heat stress (number of July days > 32.2°C) improved the correlation between rainfall and normalized corn yield, particularly for years with the poorest yield-vs-rain relationship (1998 and 1999). For the combined analysis years, the multiple regression correlation coefficient was 0.56, incorporating May and July rainfall and July heat stress and explaining 31% of the variance of normalized corn yield. Results show that MPE rainfall estimates provide timely yield projections within the growing season.