Anthropogenic sources of reactive nitrogen have local and global impacts on air and water quality and detrimental effects on human and ecosystem health. This article uses the Nitrogen Footprint Tool (NFT) to determine the amount of nitrogen (N) released as a result of institutional consumption. The sectors accounted for include food (consumption and upstream production), energy, transportation, fertilizer, research animals, and agricultural research. The NFT is then used for scenario analysis to manage and track reductions, which are driven by the consumption behaviors of both the institution itself and its constituent individuals. In this article, the first seven completed institution nitrogen footprint results are presented. The Nitrogen Footprint Tool Network aims to develop footprints for many institutions to encourage widespread upper-level management strategies that will create significant reductions in reactive nitrogen released to the environment. Energy use and food purchases are the two largest sectors contributing to institution nitrogen footprints. Ongoing efforts by institutions to reduce greenhouse gas emissions also help to reduce the nitrogen footprint, but the impact of food production on nitrogen pollution has not been directly addressed by the higher education sustainability community. The Nitrogen Footprint Tool Network found that institutions could reduce their nitrogen footprints by optimizing food purchasing to reduce consumption of animal products and minimize food waste, as well as by reducing dependence on fossil fuels for energy.

Technological advances have allowed in situ monitoring of soil water content in an automated manner. These advances, along with an increase in large-scale networks monitoring soil water content, stress the need for a robust calibration framework that ensures that soil water content measurements are accurate and reliable. We have developed an approach to make consistent and comparable soil water content sensor calibrations across a continental-scale network in a production framework that incorporates a thorough accounting of uncertainties. More than 150 soil blocks of varying characteristics from 33 locations across the United States were used to generate soil-specific calibration coefficients for a capacitance sensor. We found that the manufacturer's nominal calibration coefficients poorly fit the data for nearly all soil types. This resulted in negative (91% of samples) and positive (5% of samples) biases and a mean root mean square error (RMSE) of 0.123 cm 3 cm −3 (1s) relative to reference standard measurements. We derived soil-specific coefficients, and when used with the manufacturer's nominal function, the biases were corrected and the mean RMSE dropped to ±0.017 cm 3 cm −3 (±1s). A logistic calibration function further reduced the mean RMSE to ±0.016 cm 3 cm −3 (±1s) and increased the range of soil moistures to which the calibration applied by 18% compared with the manufacturer's function. However, the uncertainty of the reference standard was notable (±0.022 cm 3 cm −3 ), and when propagated in quadrature with RMSE estimates, the combined uncertainty of the calibrated volumetric soil water content values increased to ±0.028 cm 3 cm −3 regardless of the calibration function used.Abbreviations: DPHP, dual-pulse heat probe; FDR, frequency-domain reflectometry; NEON, National Ecological Observatory Network; NMM, neutron moisture meter; NSF, National Science Foundation; PRT, platinum resistance thermometer; RMSE, root mean square error; TDR, time-domain reflectometry.Soil moisture is an important driver of numerous biogeophysical processes at scales ranging from the aggregate to the globe. The vertical and lateral flow of water through the soil determines patterns of eluviation and illuviation, making them central to soil pedogenesis, and control the flux of solutes within the soil profile and across the terrestrial aquatic interface, with implications for the transport of nutrients and pollutants (Kaiser et al., 2004) including dissolved organic matter (Burns et al., 2016;Kalbitz et al., 2000). Dissolved organic matter is a significant component of the global C budget, and the flux of dissolved organic matter within soils and into water bodies has implications for the global C cycle (Battin et al., 2008). Additionally, soil moisture status is important for the decomposition of soil organic matter and the form in which C is respired (e.g., CO 2 or CH 4 ) (Davidson et al. 2008). Soil moisture is also a determinant of ecosystem structure, sensible and latent heat fluxes, water balance, and local climate (Koster...

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The release of reactive nitrogen contributes to its accumulation in the environment, causing a variety of harmful effects. To measure Dickinson College's contribution to nitrogen pollution, and quantify the potential to reduce its contribution, we calculated the college's nitrogen footprint and simulated the effects of selected nitrogen mitigation measures. The analysis was obtained using the Nitrogen Footprint Tool, developed at the University of Virginia. Food production is by far the largest contributor to Dickinson's footprint, followed by heat and power. Transportation, sewage, and groundskeeping contribute relatively small amounts. Breaking food down into different food categories, meat and fish is the largest source of nitrogen, accounting for two-thirds of the food footprint. Simulations of individual mitigation measures showed that measures targeting food are the most impactful for reducing the college's nitrogen footprint. Two policy scenarios that combine multiple measures, one representing moderate action and the other more aggressive action, were also analyzed. They are projected to reduce Dickinson's footprint by roughly 15 and 25 percent, respectively, while reducing operating costs. Achieving these reductions would require substantial changes in dietary choices by members of the campus community.