Urban landscapes contain a mix of land-use types with different patterns of nitrogen (N) cycling and export. We measured nitrate (NO(3)(-)) leaching and soil:atmosphere nitrous oxide (N(2)O) flux in four urban grassland and eight forested long-term study plots in the Baltimore, Maryland metropolitan area. We evaluated ancillary controls on these fluxes by measuring soil temperature, moisture, and soil:atmosphere fluxes of carbon dioxide on these plots and by sampling a larger group of forest, grass, and agricultural sites once to evaluate soil organic matter, microbial biomass, and potential net N mineralization and nitrification. Annual NO(3)(-) leaching ranged from 0.05 to 4.1 g N m(-2) yr(-1) and was higher in grass than forest plots, except in a very dry year and when a disturbed forest plot was included in the analysis. Nitrous oxide fluxes ranged from 0.05 to >0.3 g N m(-2) yr(-1), with few differences between grass and forest plots and markedly higher fluxes in wet years. Differences in NO(3)(-) leaching and N(2)O flux between forests and grasslands were not as high as expected given the higher frequency of disturbance and fertilization in the grasslands. Carbon dioxide flux, organic matter, and microbial biomass were as high or higher in urban grasslands than in forests, suggesting that active carbon cycling creates sinks for N in vegetation and soil in these ecosystems. Although urban grasslands export more N to the environment than native forests, they have considerable capacity for N retention that should be considered in evaluations of land-use change.

Agriculture in the United States (US) cycles large quantities of nitrogen (N) to produce food, fuel, and fiber and is a major source of excess reactive nitrogen (Nr) in the environment. Nitrogen lost from cropping systems and animal operations moves to waterways, groundwater, and the atmosphere. Changes in climate and climate variability may further affect the ability of agricultural systems to conserve N. The N that escapes affects climate directly through the emissions of nitrous oxide (N 2 O), and indirectly through the loss of nitrate (NO 3 -), nitrogen oxides (NO x ) and ammonia to downstream and downwind ecosystems that then emit some of the N received as N 2 O and NO x . Emissions of NO x lead to the formation of tropospheric ozone, a greenhouse gas that can also harm crops directly. There are many opportunities to mitigate the impact of agricultural N on climate and the impact of climate on agricultural N. Some are available today; many need further research; and all await effective incentives to become adopted. Research needs can be grouped into four major categories: (1) an improved understanding of agricultural N cycle responses to changing climate; (2) a systems-level understanding of important crop and animal systems sufficient to identify key interactions and feedbacks; (3) the further development and testing of quantitative models capable of predicting N-climate interactions with confidence across a wide variety of crop-soil-climate combinations; and (4) socioecological research to better understand the incentives necessary to achieve meaningful deployment of realistic solutions.

Bio-based energy is key to developing a globally sustainable low-carbon economy. Lignocellulosic feedstock production on marginally productive croplands is expected to provide substantial climate mitigation benefits, but long-term field research comparing greenhouse gas (GHG) outcomes during the production of annual versus perennial crop-based feedstocks is lacking. Here, we show that long-term (16 years) switchgrass (Panicum virgatum L.) systems mitigate GHG emissions during the feedstock production phase compared to GHG-neutral continuous corn (Zea mays L.) under conservation management on marginally productive cropland. Increased soil organic carbon was the major GHG sink in all feedstock systems, but net agronomic GHG outcomes hinged on soil nitrous oxide emissions controlled by nitrogen (N) fertilizer rate. This long-term field study is the first to demonstrate that annual crop and perennial grass systems respectively maintain or mitigate atmospheric GHG contributions during the agronomic phase of bioenergy production, providing flexibility for land-use decisions on marginally productive croplands.

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Successful hydrological model predictions depend on appropriate framing of scale and the spatial-temporal accuracy of input parameters describing soil hydraulic properties. Saturated soil hydraulic conductivity (K sat ) is one of the most important properties influencing water movement through soil under saturated conditions. It is also one of the most expensive to measure and is highly variable. The objectives of this research were (i) to assess the ability of Amoozemeters, wells, piezometers, and flumes to accurately represent K sat at a small catchment scale and (ii) to extrapolate K sat to a larger watershed based on available soil data and soil landscape models for simulating streamflow using the Distributed Hydrological Soil Vegetation Model. The mean K sat between Amoozemeters, wells, and flumes varied from 2.4 to 4.9 ´ 10 −7 m s −1 , and differences were not significant. Mixed trends in mean K sat for slope positions and soil series were observed. The strongest significant and consistent trend in mean K sat was observed for soil depth. The mean K sat decreased exponentially with depth, from 6.51 ´ 10 6 m s −1 for upper horizons to 2.37 ´ 10 −7 m s −1 for bottom horizons. Recognizing the significantly decreasing trend of K sat with soil depth and the lack of consistent trends between soils and slope positions for small catchments, K sat values were extrapolated from the small catchments occurring in Dillon Creek to another large watershed (Hall Creek) based on soil similarity and distribution. The Nash-Sutcliffe model overall efficiency of 0.52 indicated a good performance in simulating streamflows without model calibration. Combining K sat measurement methods in small catchments with an understanding of soil landscapes and soil distribution relationships allowed successful upscaling of localized soil hydraulic properties for streamflow predictions to larger watersheds.