Abstract. 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 (NH 3 ) 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 NH 3 emission estimates. Regional cropland NH 3 emissions are driven by the timing and amount of inorganic NH 3 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 NH 3 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 NH 3 emissions than previous factor-based NH 3 inventories, and will facilitate evaluation of alternative nitrogen and air quality policy and adaptation strategies associated with future climate and land use changes.

Abstract. Atmospheric ammonia (NH3) plays an important role in atmospheric aerosol chemistry. China is one of the largest NH3 emitting countries with the majority of NH3 emissions coming from agricultural practices, such as fertilizer application and livestock production. The current NH3 emission estimates in China are mainly based on pre-defined emission factors that lack temporal or spatial details, which are needed to accurately predict NH3 emissions. This study provides the first online estimate of NH3 emissions from agricultural fertilizer application in China, using an agricultural fertilizer modeling system which couples a regional air quality model (the Community Multi-scale Air Quality model, or CMAQ) and an agro-ecosystem model (the Environmental Policy Integrated Climate model, or EPIC). This method improves the spatial and temporal resolution of NH3 emissions from this sector. We combined the cropland area data of 14 crops from 2710 counties with the Moderate Resolution Imaging Spectroradiometer (MODIS) land use data to determine the crop distribution. The fertilizer application rates and methods for different crops were collected at provincial or agricultural region levels. The EPIC outputs of daily fertilizer application and soil characteristics were input into the CMAQ model and the hourly NH3 emissions were calculated online with CMAQ running. The estimated agricultural fertilizer NH3 emissions in this study were approximately 3 Tg in 2011. The regions with the highest modeled emission rates are located in the North China Plain. Seasonally, peak ammonia emissions occur from April to July. Compared with previous researches, this study considers an increased number of influencing factors, such as meteorological fields, soil and fertilizer application, and provides improved NH3 emissions with higher spatial and temporal resolution.

A bacterial primer set, known to produce a 542-bp amplicon specific for Bacteroides thetaiotaomicron, generated this product in PCR with 1 ng of extracted DNA from 92% of 25 human fecal samples, 100% of 20 sewage samples, and 16% of 31 dog fecal samples. The marker was not detected in 1 ng of fecal DNA from 61 cows, 35 horses, 44 pigs, 24 chickens, 29 turkeys, and 17 geese.

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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 system-level approach to the estimation of ammonia (NH₃) emissions resulting 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 fertilizer applied, local meteorology, and ambient air concentrations. An evaluation of EPIC-simulated crop management activities associated with fertilizer application at planting compared with similar USDA state-level event estimates shows temporally progressive spatial patterns that agree well with one another. EPIC annual 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 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

Models simulating crop growth offer an opportunity to fill important data gaps from experimental plots. Examples include incorporation of many weather conditions as well as alternative management strategies. The simulated data must be reliable, however. This study examined the application of the EPIC growth model to simulate corn (Zea mays L.) yield on the Southern High Plains when water stress was imposed on various dates in the growing season. The EPIC model produced simulated yield distributions with means not significantly (P = 0.05) different from those of the actual data in 2 of the 3 yr considered, and standard deviations of simulated yields similar to those of the actual yields for all 3 yr. Linear regressions of simulated yields on actual yields resulted in slope coefficients not significantly (P = 0.05) different from 1.0 for 2 of the 3 yr. Simulated yields explained 83, 86, and 72% of the variance in actual yields for the 3 yr of measured data. The EPIC model appears useful for further studies of irrigation scheduling for corn.

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Broiler litter, a mixture of poultry excreta and bedding material, is commonly used to fertilize grasslands in the southeastern USA. Previous work has shown that under certain situations, application of broiler (Gallus gallus domesticus) litter to grasslands may lead to elevated levels of phosphorus (P) in surface runoff. The EPIC simulation model may be a useful tool to identify those situations. This work was conducted to evaluate EPIC's ability to simulate event and annual runoff volume and losses of dissolved reactive phosphorus (DRP) from tall fescue (Festuca arundinacea Schreb.)-bermudagrass [Cynodon dactylon (L.) Pers.] paddocks fertilized with broiler litter. The EPIC simulations of event runoff volume showed a trend toward underestimation, particularly for runoff events >30 mm. On an annual basis, EPIC also tended to underestimate runoff, especially at runoff volumes > 100 mm. Both event and annual runoff estimations were strongly associated with observed values, indicating that model calibration could improve the simulation of surface runoff volume. The relationship between simulated and observed values of DRP loss was relatively poor on an event basis (r=0.65), but was stronger (r=0.75) on an annual basis. In general, EPIC tended to underestimate annual DRP losses. This underestimation was apparently caused by the lack of an explicit mechanism to model broiler litter on the soil surface. These results suggested that additional work on the EPIC P submodel would be warranted to improve its simulation of surface application of broiler litter to grasslands.