Search

Farmers, food supply-chain entities, and policymakers need a simple but robust indicator to demonstrate progress toward reducing nitrogen pollution associated with food production. We show that nitrogen balance--the difference between nitrogen inputs and nitrogen outputs in an agricultural production system--is a robust measure of nitrogen losses that is simple to calculate, easily understood, and based on readily available farm data. Nitrogen balance provides farmers with a means of demonstrating to an increasingly concerned public that they are succeeding in reducing nitrogen losses while also improving the overall sustainability of their farming operation. Likewise, supply-chain companies and policymakers can use nitrogen balance to track progress toward sustainability goals. We describe the value of nitrogen balance in translating environmental targets into actionable goals for farmers and illustrate the potential roles of science, policy, and agricultural support networks in helping farmers achieve them. [ABSTRACT FROM AUTHOR]

Recent observations indicate that long-term N additions can slow decomposition, leading to C accumulation in soils, but this process has received limited consideration by models. To address this, we developed a model of soil organic matter (SOM) dynamics to be used with the PnET model and applied it to simulate N addition effects on soil organic carbon (SOC) stocks. We developed the model's SOC turnover times and responses to experimental N additions using measurements from the Harvard Forest, Massachusetts. We compared model outcomes to SOC stocks measured during the 20th year of the Harvard Forest Chronic Nitrogen Amendment Study, which includes control, low (5 g N m yr) and high (15 g N m yr) N addition to hardwood and red pine stands. For unfertilized stands, simulated SOC stocks were within 10 % of measurements. Simulations that used measured changes in decomposition rates in response to N accurately captured SOC stocks in the hardwood low N and pine high N treatment, but greatly underestimated SOC stocks in the hardwood high N and the pine low N treatments. Simulated total SOC response to experimental N addition resulted in accumulation of 5.3-7.9 kg C per kg N following N addition at 5 g N m yr and 4.1-5.3 kg C per kg N following N addition at 15 g N m yr. Model simulations suggested that ambient atmospheric N deposition at the Harvard Forest (currently 0.8 g N m yr) has led to an increase in cumulative O, A, and B horizons C stocks of 211 g C m (3.9 kg C per kg N) and 114 g C m (2.1 kg C per kg N) for hardwood and pine stands, respectively. Simulated SOC accumulation is primarily driven by the modeled decrease in SOM decomposition in the Oa horizon. [ABSTRACT FROM AUTHOR]

Ecosystem models are increasingly used to guide natural resource management policy decisions. In this study, we build on available agroecosystem policy modeling tools by testing two methodologies for applying the Denitrification-Decomposition (DNDC) model to naturally-drained, temperate grain cropping systems. We used long-term observations from the Rodale Institute Farming Systems Trial (FST) to validate the DNDC model for application to grain cropping systems on silty clay loam soils typical of mid-Atlantic farmlands. Based on modeling efficiency (EF), Theil’s Inequality ( U2), and correlation coefficient ( r) metrics, the DNDC model showed moderate fit between observations and simulations at annual time scales for drainage (EF = 0.34, U2 = 0.12, r = 0.74) and nitrate leaching (EF = −0.05, U2 = 0.4, r = 0.86). Replication of observed seasonal water flux and nitrate leaching trends were difficult to capture in model simulations, resulting in a weak fit between observations and simulations for drainage (EF = −1.2, U2 = 0.89, r = 0.28) and nitrate leaching (EF = −2.5, U2 = 2.1, r = 0.3). Our comparison of observations and model outcomes highlights the challenge of scaling up belowground fluxes to farm or watershed scales. Ecosystem model representation of water transport generally assumes highly homogeneous soil conditions. In contrast, data from lysimeter sampling represents a small percentage of the total study area and is unlikely to capture average soil field properties. Additionally, our Rodale work highlights the limitation of biogeochemistry models which use vertical mass movement to describe water drainage and nitrate leaching. The application of the DNDC model to the Rodale FST demonstrates that model studies are not a simple substitute for field observation. The predictive utility of model outcomes can only be broadened through rigorous testing against long-term field observations. [ABSTRACT FROM AUTHOR]

The importance of lotic systems as sinks for nitrogen inputs is well recognized. A fraction of nitrogen in streamflow is removed to the atmosphere via denitrification with the remainder exported in streamflow as nitrogen loads. At the watershed scale, there is a keen interest in understanding the factors that control the fate of nitrogen throughout the stream channel network, with particular attention to the processes that deliver large nitrogen loads to sensitive coastal ecosystems. We use a dynamic stream transport model to assess biogeochemical (nitrate loadings, concentration, temperature) and hydrological (discharge, depth, velocity) effects on reach-scale denitrification and nitrate removal in the river networks of two watersheds having widely differing levels of nitrate enrichment but nearly identical discharges. Stream denitrification is estimated by regression as a nonlinear function of nitrate concentration, streamflow, and temperature, using more than 300 published measurements from a variety of US streams. These relations are used in the stream transport model to characterize nitrate dynamics related to denitrification at a monthly time scale in the stream reaches of the two watersheds. Results indicate that the nitrate removal efficiency of streams, as measured by the percentage of the stream nitrate flux removed via denitrification per unit length of channel, is appreciably reduced during months with high discharge and nitrate flux and increases during months of low-discharge and flux. Biogeochemical factors, including land use, nitrate inputs, and stream concentrations, are a major control on reach-scale denitrification, evidenced by the disproportionately lower nitrate removal efficiency in streams of the highly nitrate-enriched watershed as compared with that in similarly sized streams in the less nitrate-enriched watershed. Sensitivity analyses reveal that these important biogeochemical factors and physical hydrological factors contribute nearly equally to seasonal and stream-size related variations in the percentage of the stream nitrate flux removed in each watershed. [ABSTRACT FROM AUTHOR]

Denitrification is known as an important pathway for nitrate loss in agroecosystems. It is important to estimate denitrification fluxes to close field and watershed N mass balances, determine greenhouse gas emissions (N2O), and help constrain estimates of other major N fluxes (e.g., nitrate leaching, mineralization, nitrification). We compared predicted denitrification estimates for a typical corn and soybean agroecosystem on a tile drained Mollisol from five models (DAYCENT, SWAT, EPIC, DRAINMOD-N II and two versions of DNDC, 82a and 82h), after first calibrating each model to crop yields, water flux, and nitrate leaching. Known annual crop yields and daily flux values (water, nitrate-N) for 1993–2006 were provided, along with daily environmental variables (air temperature, precipitation) and soil characteristics. Measured denitrification fluxes were not available. Model output for 1997–2006 was then compared for a range of annual, monthly and daily fluxes. Each model was able to estimate corn and soybean yields accurately, and most did well in estimating riverine water and nitrate-N fluxes (1997–2006 mean measured nitrate-N loss 28 kg N ha−1 year−1, model range 21–28 kg N ha−1 year−1). Monthly patterns in observed riverine nitrate-N flux were generally reflected in model output ( r2 values ranged from 0.51 to 0.76). Nitrogen fluxes that did not have corresponding measurements were quite variable across the models, including 10-year average denitrification estimates, ranging from 3.8 to 21 kg N ha−1 year−1 and substantial variability in simulated soybean N2 fixation, N harvest, and the change in soil organic N pools. DNDC82a and DAYCENT gave comparatively low estimates of total denitrification flux (3.8 and 5.6 kg N ha−1 year−1, respectively) with similar patterns controlled primarily by moisture. DNDC82h predicted similar fluxes until 2003, when estimates were abruptly much greater. SWAT and DRAINMOD predicted larger denitrification fluxes (about 17–18 kg N ha−1 year−1) with monthly values that were similar. EPIC denitrification was intermediate between all models (11 kg N ha−1 year−1). Predicted daily fluxes during a high precipitation year (2002) varied considerably among models regardless of whether the models had comparable annual fluxes for the years. Some models predicted large denitrification fluxes for a few days, whereas others predicted large fluxes persisting for several weeks to months. Modeled denitrification fluxes were controlled mainly by soil moisture status and nitrate available to be denitrified, and the way denitrification in each model responded to moisture status greatly determined the flux. Because denitrification is dependent on the amount of nitrate available at any given time, modeled differences in other components of the N cycle (e.g., N2 fixation, N harvest, change in soil N storage) no doubt led to differences in predicted denitrification. Model comparisons suggest our ability to accurately predict denitrification fluxes (without known values) from the dominant agroecosystem in the midwestern Illinois is quite uncertain at this time. [ABSTRACT FROM AUTHOR]

We modeled the expected range of seasonal and annual N2O flux from temperate, grain agroecosystems using Monte Carlo sampling of N2O flux field observations. This analysis is complimentary to mechanistic biogeochemical model outcomes and provides an alternative method of estimating N2O flux. Our analysis produced a range of annual N2O gas flux estimates with mean values overlapping with results from an intermodel comparison of mechanistic models. Mean seasonal N2O flux was 1–4% of available N, while median seasonal N2O flux was less than 2% of available N across corn, soybean, wheat, ryegrass, legume, and bare fallow systems. The 25th–75th percentile values for simulated average annualized N2O flux rates ranged from 1 to 12.2 kg N ha−1 in the conventional system, from 1.3 to 8.8 kg N ha−1 in the cover crop rotation, and from 0.8 to 9.3 kg N ha−1 in the legume rotation. Although these modeling techniques lack the seasonal resolution of mechanistic models, model outcomes are based on measured field observations. Given the large variation in seasonal N gas flux predictions resulting from the application of mechanistic simulation models, this data-derived approach is a complimentary benchmark for assessing the impact of agricultural policy on greenhouse gas emissions. [ABSTRACT FROM AUTHOR]

Denitrification, the anaerobic reduction of nitrogen oxides to nitrogenous gases, is an extremely challenging process to measure and model. Much of this challenge arises from the fact that small areas (hotspots) and brief periods (hot moments) frequently account for a high percentage of the denitrification activity that occurs in both terrestrial and aquatic ecosystems. In this paper, we describe the prospects for incorporating hotspot and hot moment phenomena into denitrification models in terrestrial soils, the interface between terrestrial and aquatic ecosystems, and in aquatic ecosystems. Our analysis suggests that while our data needs are strongest for hot moments, the greatest modeling challenges are for hotspots. Given the increasing availability of high temporal frequency climate data, models are promising tools for evaluating the importance of hot moments such as freeze-thaw cycles and drying/rewetting events. Spatial hotspots are less tractable due to our inability to get high resolution spatial approximations of denitrification drivers such as carbon substrate. Investigators need to consider the types of hotspots and hot moments that might be occurring at small, medium, and large spatial scales in the particular ecosystem type they are working in before starting a study or developing a new model. New experimental design and heterogeneity quantification tools can then be applied from the outset and will result in better quantification and more robust and widely applicable denitrification models. [ABSTRACT FROM AUTHOR]