Empirical analysis and prediction of nitrate loading and crop yield for corn–soybean rotations

Nitrate nitrogen losses through subsurface drainage and crop yield are determined by multiple climatic and management variables. The combined and interactive effects of these variables, however, are poorly understood. Our objective is to predict crop yield, nitrate concentration, drainage volume, and nitrate loss in subsurface drainage from a corn (Zea mays L.) and soybean (Glycine max (L.) Merr.) rotation as a function of rainfall amount, soybean yield for the year before the corn–soybean sequence being evaluated, N source, N rate, and timing of N application in northeastern Iowa, U.S.A. Ten years of data (1994–2003) from a long-term study near Nashua, Iowa were used to develop multivariate polynomial regression equations describing these variables. The regression equations described over 87, 85, 94, 76, and 95% of variation in soybean yield, corn yield, subsurface drainage, nitrate concentration, and nitrate loss in subsurface drainage, respectively. A two-year rotation under average soil, average climatic conditions, and 125 kg N/ha application was predicted to loose 29, 37, 36, and 30 kg N/ha in subsurface drainage for early-spring swine manure, fall-applied swine manure, early-spring UAN fertilizer, and late-spring split UAN fertilizer (urea ammonium nitrate), respectively. Predicted corn yields were 10.0 and 9.7 Mg/ha for the swine manure and UAN sources applied at 125 kg N/ha. Timing of application (i.e., fall or spring) did not significantly affect corn yield. These results confirm other research suggesting that manure application can result in less nitrate leaching than UAN (e.g., 29 vs. 36 kg N/ha), and that spring application reduces nitrate leaching compared to fall application (e.g., 29 vs. 37 kg N/ha). The regression equations improve our understanding of nitrate leaching; offer a simple method to quantify potential N losses from Midwestern corn–soybean rotations under the climate, soil, and management conditions of the Nashua field experiment; and are a step toward development of easy to use N management tools. © 2007 Elsevier B.V. All rights reserved.