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Title:   Author  item
                                            Malone, Robert

                                                         - *Rob*item
                                            Ma, Liwangitem
                                            Karlen, Douglasitem
                                            Meade, Terryitem
                                            Meek, Daviditem
                                            Heilman, Philip

                                                         - *Phil*item
                                            KANWAR, R - IA STATE UNIVERSITYitem
                                            Hatfield, Jerry
  Submitted to: Geoderma
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 5/7/2007
                                    Publication  Date: 7/19/2007
                                    Citation: Malone, R.W., Ma, L., Karlen, D.L., Meade, T.G., Meek, D.W., Heilman, P., Kanwar, R.S., Hatfield, J.L. 2007. Empirical analysis and prediction of nitrate loading and crop yield for a corn-soybean rotations. Geoderma. 140:223-234.Interpretive Summary: Crop yield and nitrate nitrogen losses through subsurface drainage are determined by multiple climatic and management variables; however the interactive affect of these variables is not well understood.  Simple equations that predict nitrate loading and crop yield as a function of important variables may improve our understanding of agricultural systems.  Therefore, we developed regression equations to predict crop yield, nitrate concentration, drainage volume, and nitrate loading from a corn and soybean rotation in response to rainfall amount, N source, N rate, and timing of N application in northeastern Iowa.  The regression equations were then used to analyze nitrate leaching and crop yield under a variety of N management and climate scenarios.  The regression equations improve our understanding of variable interactions on nitrate leaching, offer a simple method to quantify potential N losses from Midwestern corn-soybean rotations, and are a step toward development of easy to use N management tools.  This work will initially help scientists develop easy to use N management tools.  Also, this work will eventually help decision-makers and farmers design farming practices that reduce nitrate leaching to shallow groundwater and tile drains while maintaining crop production goals.
                                Technical Abstract: Nitrate nitrogen losses through subsurface drainage and crop yield are determined by multiple climatic and management variables; however the interactive affect of these variables is not well understood.  Our objective is to predict crop yield, nitrate concentration, drainage volume, and nitrate loading from a corn (Zea mays L.) and soybean (Glycine max (L.) Merr.) rotation in response to rainfall amount, N source, N rate, and timing of N application in northeastern Iowa, U.S.A.  Fourteen years of data (1990-2003) from a long-term study near Nashua, Iowa, were used to develop multivariate polynomial regression equations describing these variables.  The regression equations described over 85% loading, 69% drainage, 60% yield, and 48% nitrate concentration variation.  Predicted nitrate loadings for a two-year rotation under average soil and climatic conditions with 150 kg/ha N were 24, 26, 33, and 29 kg/ha nitrate N for late-spring swine manure, early-spring swine manure, fall-applied swine manure, and early-spring UAN-N fertilizer, respectively.  Predicted corn yields were 10132 and 9925 kg/ha for the swine manure and UAN sources; soybean yield was 3707 kg/ha.  Timing of application (i.e., fall or spring) did not significantly affect corn yield and soybean yield was not affected by timing or amount of application.  These results confirm other research suggesting that manure application can result in less nitrate leaching than UAN (e.g., 26 vs. 29 kg/ha), and that spring application reduces nitrate leaching compared to fall application (e.g., 24 vs. 33 kg/ha).  The regression equations improve our understanding of variable interaction on nitrate leaching, offer a simple method to quantify potential N losses from Midwestern corn-soybean rotations, and are a step toward development of easy to use N management tools.


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