1. Assessment of Nitrogen Management and Cropping Systems in the Arkansas Delta Irrigation Systems * For additional information, a complete list of references, questions, reprints, or requests for new tools, please email Dr. Cal Shumway at CSHUMWAY@astate.edu and/or Dr. Jorge A. Delgado at: jorge.delgado@ars.usda.gov. It has been reported that nitrogen that is lost into the Arkansas Delta can be transported to the Mississippi River Basin, potentially contributing to the nitrate load to which the hypoxia problem in the Gulf of Mexico has been attributed. Developing effective methods of reducing these nitrogen losses requires the development and validation of new tools that can be used to conduct quick assessments of how management practices may affect nitrogen losses. Field studies were conducted in 2008 and 2009 to collect data to test the new NLEAP- GIS 4.2 model and its capability to simulate nitrogen dynamics for different cropping systems grown at Arkansas State University and at the Judd Hill Plantation, an environmentally sustainable farm. The first NLEAP-GIS 4.2 simulation for this region, which analyzed cotton, soybean and corn grown in the Arkansas Delta, showed that the model was able to accurately simulate nitrogen dynamics and the effects of management on residual soil nitrate (P < 0.01). The model indicated that nitrate leaching and N 2 O emissions from soybean and cotton systems are lower than those from corn systems. Model simulations also showed that the residual nitrate can range from 30 to 200 lb N /acre in the top 5 feet of soil, in agreement with measured values. The simulated values indicate a need to improve management practices and that rotations of soybeans and cotton with corn could play an important role in reducing nitrate leaching losses and N 2 O emissions in this region. Abstract Summary References Problem Figure 1. The NLEAP-GIS 4.2 driver. NLEAP-GIS 4.2 uses Microsoft Excel® 2010. Simulated residual nitrate after harvesting of soybeans was also lower than after harvesting of corn. There was a strong correlation between residual soil nitrate and nitrogen fertilizer input. The nitrate leaching for cotton over the growing season was much lower than the nitrate leaching for the corn over the growing season. Soybeans could be used to reduce the potential for nitrate leaching in this region. Delgado, J.A., C.M. Gross, H. Lal, H. Cover, P. Gagliardi, S.P. McKinney, E. Hesketh and M.J. Shaffer. 2010. A new GIS nitrogen trading tool concept for conservation and reduction of reactive nitrogen losses to the environment. Adv. Agron. 105:117-171. Delgado, J.A., and R.F. Follett, Eds. 2010. Advances in Nitrogen Management. 2010. SWCS, Ankeny IA (in print) Shaffer, M.J., J.A. Delgado, C. Gross, and R.F. Follett. Simulation processes for the Nitrogen Loss and Environmental Assessment Package (NLEAP). 2010. In J.A. Delgado and R.F. Follett (Eds.) Advances in Nitrogen Management for Water Quality. SWCS, Ankeny, IA (In print) Delgado, J.A., P. Gagliardi, M.J. Shaffer, H. Cover, E. Hesketh, J. Ascough and B. Daniel. New Tools to Assess Nitrogen Management for Conservation of Our Biosphere. 2010. In J.A. Delgado and R.F. Follett (Eds.). Advances in Nitrogen Management for Water Quality. SWCS, Ankeny IA (In print) Meisinger, J. J., and J. A. Delgado. 2002. Principles for managing nitrogen leaching. J. Soil Water Conserv. 57:485-498. Goolsby, D.A., W.A. Battaglin, B.T. Aulenbach, and R.P. Hooper. 2001. Nitrogen input to the Gulf of Mexico. J. Environ. Qual. 30:329–336. Rabalais, N.N., R.E. Turner and W.J. Wiseman, Jr. 2002. Gulf of Mexico Hypoxia, A.K.A. “The Dead Zone”. Annu. Rev. Ecol. Syst. 33:235–63. Turner, R.E., and N.N. Rabalais. 2003. Linking Landscape and Water Quality in the Mississippi River Basin for 200 Years. BioScience 53(6):563-572. Randall, G.W., J.A. Delgado, and J.S. Schepers. 2008. Nitrogen management to protect water resources. In J. S. Schepers (ed.) ‘‘Nitrogen in Agriculture’’ Monograph 49; Nitrogen in Agricultural Systems, SSSA, Madison, WI, pp. 907-940. Disclaimer: Manufacturers’ names are necessary to report factually on available data, however the USDA neither guarantees nor warrants the standard of the product; and the use of a given name by the USDA does not imply approval of that product to the exclusion of others that may be suitable. The views expressed here are those of the authors and do necessarily reflect those of the US Department of Agriculture or the Economic Research Service. Cal Shumway 1, Jorge A. Delgado 2, Theodis Bunch 3, LeRoy Hansen 4 and Marc Ribaudo 4 1 Arkansas State University, Jonesboro, Arkansas; University of Arkansas Agricultural Experiment Station; 2 USDA-ARS-Soil Plant Nutrient Research Unit, Fort Collins, Colorado; 3 USDA-NRCS, National Water Management Center, Little Rock, Arkansas; 4 USDA-ERS, Washington, DC Hypoxia is an environmental problem that can be observed in bodies of water such as the Gulf of Mexico (Goolsby et al. 2001; Rabalais et al. 2002). The hypoxia issue observed in the Gulf of Mexico has been linked to the load and transport of nutrients, such as N, which is delivered to the Gulf via the Mississippi River (Rabalais et al. 2002; Turner and Rabalais 2003). Although some reports suggest that the transport of nitrates from tributaries from Illinois, Iowa, and Minnesota contribute greater loads of nitrates, there are also losses from the Arkansas Delta that get transported to the Mississippi River and eventually to the Gulf of Mexico. Several researchers have reported that we could improve current best management practices and reduce the potential for N losses from agricultural systems and tile systems across watersheds to decrease the potential N loads to bodies of water such as the Gulf of Mexico (Meisinger and Delgado 2002; Randal et al. 2008). Approach Field studies were conducted during 2008 and 2009 at Arkansas State University and the research station at Judd Hill Plantation. At each site a series of plots (20.9 m 2 ) were established for cotton, corn, and soybean cropping systems, and residual soil nitrates were monitored by collecting soil samples in the top five feet of each plot at one-foot increments. The % soil organic matter (SOM) was also measured for the surface soil sample of each plot. Yields were collected at harvest for each plot and plant samples were collected to measure the N content at harvest. All above-ground plant compartments were collected and brought into the laboratory immediately after collection and were dried at 55 o C, ground, and analyzed for total C and N content with an automated C/N analyzer. To determine the initial and final inorganic nitrate (NO 3 -N) and ammonia (NH 4 -N) content a soil core was taken for the initial sample before planting and a final soil sample was collected after harvesting in each plot. Each plot was sampled in 0.3 m intervals down to 1.5 m. Each soil sample was kept in cool, sealed bags until they were brought to the laboratory, where they were dried and sent to the laboratory of ASU for inorganic N analysis. Figure 3. Measured and simulated residual soil nitrate after harvest from corn, soybean and cotton plots grown in studies conducted at Arkansas State University and Judd Hill Plantation. (Units are in lb N/acre. To convert values to kg N /ha, multiply by 1.12.) Figure 4. The new NLEAP-GIS 4.2 model was used to simulate the effects of nitrogen management practices on N 2 O emissions from different agroecosystems in Arkansas over a one-year period. (Units are in lb N/acre. To convert to kg N /ha, multiply by 1.12.) Residual Soil Nitrate y = 0.90x + 23.1 R 2 = 0.66 0 50 100 150 200 250 300 050100150200250300 Measured Residual NO 3 -N (lb N/acre) Simulated Residual NO 3 -N (lb N/acre) Month Simulated N 2 O Emissions 0 2 4 6 8 10 12 123456789101112 Simulated N 2 O (lb N/acre) Soybean Corn Cotton Figure 5. Nitrate leaching simulated by the new NLEAP-GIS 4.2 versus fertilizer applied to different agroecosystems in Arkansas. y = 0.30x + 21.3 R2R2 = 0.45 0 20 40 60 80 100 120 050100150200250300 Nitrogen Fertilizer Applied (lb N/acre) Simulated N Leaching (lb NO 3 -N/acre) Nitrate Leaching The new NLEAP-GIS 4.2 model was able to simulate the residual nitrate in the top 1.5 m of soil. The simulated values were significantly correlated with observed values (Figure 3; P<0.01). The model conducted an accurate evaluation of the soil nitrate dynamics under different cropping systems (corn, cotton and soybean) for different years and management scenarios. Simulated losses of reactive nitrogen, such as nitrate leaching losses, were significantly correlated with N fertilizer inputs (Figure 5; P<0.01). Simulated nitrate leaching was also correlated with the sum of N fertilizer inputs and initial soil nitrate content at planting. This strongly suggests a need to credit this initial source of nitrogen in nitrogen budgets if losses of reactive nitrogen are going to be minimized across this region. Results from these studies are in agreement with Meisinger and Delgado (2002), which described the management principles needed to reduce nitrate leaching, including better synchronization of nitrogen inputs with nitrogen sinks and better water management. This is the first evaluation of this new model’s ability to assess the region of the Mississippi River Basin watershed of Arkansas, and it suggests that the tool can be used to assess nitrogen management practices in this region. The residual soil nitrate after cotton harvest was lower than after corn harvest. A set of separate soil samples was also collected at 0.3 m intervals down to 1.5 m at planting to measure bulk densities. Dried soil samples were extracted with 2N KCl, and the NO 3 -N and NH 4 -N contents were determined calorimetrically by an automated flow injection analysis. To collect all needed management information, the irrigation, N fertilizer application, planting, harvesting, cultivation, and other agricultural management practices were recorded. Climatic data such as daily rain amount, evapotranspiration (ET), and maximum and minimum air temperature at the site were collected from the nearest weather station. Data needed for model calibration/validation and technology transfer efforts were entered into the NLEAP- GIS model and simulations were conducted. Simulated outputs for NO 3 -N in the soil profile were correlated to observed NO 3 -N in the soil profile for each plot. Figure 2. The NLEAP-GIS 4.2 has GIS capabilities and can interact with different GIS software programs.