Linking agricultural crop management and air quality models for regional to national-scale nitrogen assessments

While nitrogen (N) is an essential element for life, human population growth and demands for energy, transportation and food can lead to excess nitrogen in the environment. A modeling framework is described and implemented to promote a more integrated, process-based and systemlevel approach to the estimation of ammonia (NH3) emissions which result from the application of inorganic nitrogen fertilizers to agricultural soils in the United States. The United States Department of Agriculture (USDA) Environmental Policy Integrated Climate (EPIC) model is used to simulate plant demand-driven fertilizer applications to commercial cropland throughout the continental US. This information is coupled with a process-based air quality model to produce continental-scale NH3 emission estimates. Regional cropland NH3 emissions are driven by the timing and amount of inorganic NH3 fertilizer applied, soil processes, local meteorology, and ambient air concentrations. Initial fertilizer application often occurs when crops are planted. A statelevel evaluation of EPIC-simulated, cumulative planted area compares well with similar USDA reported estimates. EPICannual, inorganic fertilizer application amounts also agree well with reported spatial patterns produced by others, but domain-wide the EPIC values are biased about 6 % low. Preliminary application of the integrated fertilizer application and air quality modeling system produces a modified geospatial pattern of seasonal NH3 emissions that improves current simulations of observed atmospheric particle nitrate concentrations. This modeling framework provides a more dynamic, flexible, and spatially and temporally resolved estimate of NH3 emissions than previous factor-based NH3 inventories, and will facilitate evaluation of alternative nitrogen and air quality policy and adaptation strategies associated with future climate and land use changes. 1 Background and introduction Nitrogen (N) is an essential element required for the growth and maintenance of all biological tissues, but human population growth and increased demands for energy, transportation and food have lead to dramatic increases in N production (Galloway et al., 2008). While beneficial in N-limited systems, excess N associated with these trends can adversely impact both terrestrial and aquatic ecosystems (Lovett and Tear, 2008). In addition to implications for ecosystem health and sustainability, atmospheric ammonia (NH3) gas will neutralize atmospheric acids, most notably sulfuric and nitric acid, to form ammonium (NH+4 ) aerosols, a major constituent of fine particulate matter (PM2.5) (Nenes et al., 1999), which can negatively impact human health (Pope and Dockery, 2006), reduce visibility and affect atmospheric radiative forcing (Hertel et al., 2011). The USEPA Science Advisory Board (United States Environmental Protection Agency, 2011) and the European Nitrogen Assessment (Sutton et al., 2011) emphasize the need for integrated, multimedia and transdisciplinary approaches to communicate effectively the risks associated with key societal threats from excess reactive nitrogen. Linking an agro-ecosystem model that includes cropland management decisions with a regional air-quality model to simulate continental-scale, bi-directional Published by Copernicus Publications on behalf of the European Geosciences Union. 4024 E. J. Cooter: Linking agricultural crop management and air quality models NH3 fluxes marks a significant step forward towards a more systems-level framework for N assessment. The 2008 United States Environmental Protection Agency (EPA) National Emissions Inventory (NEI) (http://www.epa. gov/ttn/chief/eiinformation.html) estimates that 83 % of US NH3 emissions are associated with commercial crop and livestock production. Ammonia emissions originating from soils receiving commercial N fertilizer applications account for 33 % of all agricultural NH3 emissions. This inventory was developed from a combination of emission factors and inverse modeling (Gilliland et al., 2006) that assumes unidirectional emission from soil and vegetation canopies; however, NH3 is known to exhibit bi-directional behavior (Sutton et al., 1995), and recent studies suggest that inclusion of bi-directional NH3 behavior will alter regional nitrogen budget simulations in ways that are important for ecosystem and human health (Dennis et al., 2010). The bi-directional (i.e., compensation point) approach described in Sutton et al. (1998) and Nemitz et al. (2001) employs a resistance-based flux model that compares the equilibrium concentrations of NH+4 and NH3 in leaf apoplast to ambient NH3 air concentrations. Cooter et al. (2010) confirm that this same paradigm can simulate the measured magnitude and temporal variability of post-application inorganic fertilizer NH3 emissions from grain-corn soils in the US southern Coastal Plain. This approach promises to improve current unidirectional, factor-based inventories, but its national-scale implementation is challenging. The foremost challenge is development of fertilizer management information on the temporal and spatial scales needed to support the dynamic regional air quality models that are used to perform regionaland national-scale N budget analyses. This information should reflect a range of current and alternative farm management actions that will support analysis of N budget response to future policy and alternative climate conditions. In addition, since future climate may require innovative management adaptation strategies, these estimates must rely minimally on historical data (i.e., should be process-driven) and should respond to intra-annual, interannual and multidecadal weather and climate as well as land use and land cover changes. The discussion that follows describes the development of such a fertilizer simulation system, evaluates two key aspects of this system, and closes with an example of the integration of this information into a regional air quality model application with bi-directional ammonia flux. 2 The Agricultural Fertilizer Modeling System The primary objective of fertilizer application in the US is to maximize economic return related to commodity production. Cropand region-specific fertilizer management strategies are employed by farmers to meet this objective and so proper characterization of these strategies is critical. In addition, the post-application biogeochemical fate of the fertilizer is needed to properly link NH3 fertilizer application with evasion. Models that simulate the effect of both farm management practices and biogeochemical processes on soil nitrogen concentrations can be characterized as being process, empirical or semi-empirical process-based. Processbased models attempt to simulate processes at the most fundamental level and are extremely useful for basic research or exploratory site-specific studies that seek to better understand the nature of these processes. Empirical models simulate many of the same processes through parameterizations requiring less detailed input information. These models are appropriate for applications that ask broad, “whatif” questions. Semi-empirical process models use more detailed parameterizations based on process research, still support “what-if” scenario studies, but are detailed enough to highlight specific areas in need of additional process-level analysis. Given this characterization, the Environmental Policy Integrated Climate (EPIC) model was selected for this application. EPIC is a semi-empirical biogeochemical process model originally developed by the United States Department of Agriculture (USDA) in the early 1980’s to assess the effect of wind and water erosion on crop productivity (Williams et al., 1984, 2008). It is a daily time-step, field-scale model, where computational “fields” can extend up to 100 ha in area. In the beginning, EPIC’s focus was the characterization of the physical processes associated with erosion in order to simulate management solutions that maximize crop production while reducing soil and nutrient losses. Model options included characterization of various tillage practices, e.g., conventional, reduced-till, no-till, contour plowing, and engineering changes such as the construction of terraces and the installation of tile drainage. It included a heat unit-driven, aboveand below-ground plant growth model, soil hydrology and soil heat budgets for multiple soil layers of variable thickness. EPIC also contained an economic component that supported farm-firm economic budget analysis, including input costs, e.g., equipment amortization, fuel use/cost, supplemental nutrient cost and application, as well as production benefits in terms of biomass and yield. In the mid-2000’s, the soil organic matter model used in the CENTURY biogeochemical model was modified and incorporated into EPIC (Izaurralde et al., 2006; Parton et al., 1994; Vitousek et al., 1994). Details of these modifications and a description of N treatment is provided in Appendix A. Figure 1 illustrates the current EPIC biogeochemical configuration for N and carbon (C). As noted in Izaurralde et al. (2006), a unique aspect of EPIC is that it explicitly treats changes in the soil matrix (density, porosity and water retention) as well as changes in soil constituents, such as organic C, thereby allowing feedback mechanisms to operate. In this way, EPIC is well-suited for simulation of scenarios such as land use, land management and climate change in which soil moisture supply and soil matrix properties vary concurrently. Simulation output frequency is user-specified, Biogeosciences, 9, 4023–4035, 2012 www.biogeosciences.net/9/4023/2012/ E. J. Cooter: Linking agricultural crop management and air quality models 4025 Fig. 1. Biogeochemical components of the carbon and nitrogen budgets in EPIC. ranging from daily to annual summaries of biogeochemical process rates, nutrient pools and management activity. The current EPIC community code can be downloaded from http://epicapex.brc.tamus.edu. A relatively recent bibliography of EPIC publications is available at http://www.card. iastate.edu/environment/interactive-programs.aspx.