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

Evaluating and predicting agricultural management effects under tile drainage using modified APSIM

An accurate and management sensitive simulation model for tile-drained Midwestern soils is needed to optimize the use of agricultural management practices (e.g., winter cover crops) to reduce nitrate leaching without adversely affecting corn yield. Our objectives were to enhance the Agricultural Production Systems Simulator (APSIM) for tile drainage, test the modified model for several management scenarios, and then predict nitrate leaching with and without winter wheat cover crop. Twelve years of data (1990–2001) from northeast Iowa were used for model testing. Management scenarios included continuous corn and corn–soybean rotations with single or split N applications. For 38 of 44 observations, yearly drain flow was simulated within 50 mm of observed for low drainage (\u3c 100 mm) or within 30% of observed for high drain flow. Corn yield was simulated within 1500 kg/ha for 12 of 24 observations. For 30 of 45 observations yearly nitrate-N loss in tile drains was simulated within 10 kg N/ha for low nitrate-N loss (\u3c 20 kg N/ha) or within 30% of observed for high nitrate-N loss. Several of the poor yield and nitrate-N loss predictions appear related to poor N-uptake simulations. The model accurately predicted greater corn yield under split application (140–190 kg N/ha) compared to single 110 kg N/ha application and higher drainage and nitrate-N loss under continuous corn compared to corn/soybean rotations. A winter wheat cover crop was predicted to reduce nitrate-N loss 38% (341 vs. 537 kg N/ha with and without cover) under 41-years of corn-soybean rotations and 150 kg N/ha applied to corn. These results suggest that the modified APSIM model is a promising tool to help estimate the relative effect of alternative management practices under fluctuating high water tables

Spatial Variability Analysis: A First Step in Site-Specific Management

Small-scale spatial variability of selected soil-test parameters in two adjacent central Iowa fields is discussed. We used semivariance analysis to detect the distance to which parameters were correlated and to estimate the strength of each correlation. Distinct differences in spatial dependence patterns were observed for the two farming systems

Impact of Manure and N-Management Systems on Water Quality

Nitrate from fertilizers and manure application have been detected in the surface and ground water in many agricultural regions of the country including Iowa. The current practices of fertilizer application methods and rates are believed to be contributing significantly in the contamination of groundwater. Therefore, it is imperative that tillage and planting systems, regarded as best management practices for agricultural sustainability, minimize the potential for chemical runoff and leaching losses to groundwater with alternative chemical management systems. If the potential for contamination is not reduced by developing and successfully demonstrating the innovative nitrogen and pesticide management practices, the potential for contamination will remain and could result in additional regulations. Because of these concerns, researchers must develop alternative farming practices with the goals of reducing the input costs, and preserving the resource base for the sustainability of our agriculture and protecting the environment

Statistical Asynchronous Regression: Determining the Relationship Between two Quantities that are not Measured Simultaneously

We introduce the Statistical Asynchronous Regression (SAR) method: a technique for determining a relationship between two time varying quantities without simultaneous measurements of both quantities. We require that there is a time invariant, monotonic function Y = u(X) relating the two quantities, Y and X. In order to determine u(X), we only need to know the statistical distributions of X and Y. We show that u(X) is the change of variables that converts the distribution of X into the distribution of Y, while conserving probability. We describe an algorithm for implementing this method and apply it to several example distributions. We also demonstrate how the method can separate spatial and temporal variations from a time series of energetic electron flux measurements made by a spacecraft in geosynchronous orbit. We expect this method will be useful to the general problem of spacecraft instrument calibration. We also suggest some applications of the SAR method outside of space physics.Comment: 27 pages, 10 figures, stronger motivations and rewriting to make the paper more accessible to a general audience. in press in J. Geophys. Res. (Space Physics

AN AUTOREGRESSION MODEL FOR A PAIRED WATERSHED COMPARISON

Analysis of water quality data from a paired watershed design is needed to determine if a best fertilizer management practice reduces a specific water quality variable compared to a conventional fertilizer management practice. This study examines an existing recommended method of analysis for paired watershed designs, simple analysis of covariance (ANCOVA) on time aggregated data, then offers two autoregression analyses (AR) as alternatives. The first approach models the sequence of paired differences and estimates its 95% confidence band. The second approach develops individual watershed AR models then examines the joint 95% confidence interval about the predicted difference. A reliability analysis on the water quality data reveals that the data for the controlled watershed, i.e., the covariate, has a sizable measurement error, a factor that is not considered in the usual ANCOVA model. The AR methods avoid the measurement error and other inherent problems with the published recommended method. Graphically both AR analyses are similar and reveal three distinct trend phases: a period of continued similarity; a period of transition; and a period of sustained change. The model for the sequence of paired differences is the easier one of the two AR methods to use and interpret because its trend model of splined linear segments readily defines each response phase. Hence, we recommend it over the given alternatives. It offers water resources researchers an effective and readily adoptable analysis option

Multilocation Corn Stover Harvest Effects on Crop Yields and Nutrient Removal

Corn (Zea mays L.) stover was identified as an important feedstock for cellulosic bioenergy production because of the extensive area upon which the crop is already grown. This report summarizes 239 site-years of field research examining effects of zero, moderate, and high stover removal rates at 36 sites in seven different states. Grain and stover yields from all sites as well as N, P, and K removal from 28 sites are summarized for nine longitude and six latitude bands, two tillage practices (conventional vs no tillage), two stover-harvest methods (machine vs calculated), and two crop rotations {continuous corn (maize) vs corn/soybean [Glycine max (L.) Merr.]}. Mean grain yields ranged from 5.0 to 12.0 Mg ha−1 (80 to 192 bu ac−1). Harvesting an average of 3.9 or 7.2 Mg ha−1(1.7 or 3.2 tons ac−1) of the corn stover resulted in a slight increase in grain yield at 57 and 51 % of the sites, respectively. Average no-till grain yields were significantly lower than with conventional tillage when stover was not harvested, but not when it was collected. Plant samples collected between physiological maturity and combine harvest showed that compared to not harvesting stover, N, P, and K removal was increased by 24, 2.7, and 31 kg ha−1, respectively, with moderate (3.9 Mg ha−1) harvest and by 47, 5.5, and 62 kg ha−1, respectively, with high (7.2 Mg ha−1) removal. This data will be useful for verifying simulation models and available corn stover feedstock projections, but is too variable for planning site-specific stover harvest

Effect of tillage, crop rotation and innovative nitrogen and pesticide management practices on productivity, sustainability and water quality

Better nitrogen (N) management practices can improve nitrogen uptake and possibly reduce nitrogen leaching to groundwater. More efficient herbicide use can decrease or eliminate the herbicide leaching losses to water sources. In this project, the effects of seven N management practices on water quality were evaluated after collecting data from 40 experimental plots. Lower rates of N application and strip and hay cropping systems help produce lower concentrations of NO3-N in the shallow groundwater in comparison with the higher rate of N application. Also, banding of herbicides has resulted in lower herbicide losses to shallow groundwater. The use of the late spring NO3-N test (LSNT) and strip cropping show a great deal of promise to protect water quality