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Section 1: Publication
Publication Type
Journal Article
Authorship
Byrnes, D. K., Van Meter, K. J., & Basu, N. B.
Title
Long-Term Shifts in US Nitrogen Sources and Sinks Revealed by the New TREND-Nitrogen Data Set (1930-2017)
Year
2020
Publication Outlet
Global Biogeochemical Cycles, 34(9), e2020GB006626
DOI
ISBN
ISSN
Citation
Byrnes, D. K., Van Meter, K. J., & Basu, N. B. (2020). Long-Term Shifts in US Nitrogen Sources and Sinks Revealed by the New TREND-Nitrogen Data Set (1930-2017). Global Biogeochemical Cycles, 34(9), e2020GB006626.
https://doi.org/10.1029/2020GB006626
Abstract
Reactive nitrogen (N) fluxes have increased tenfold over the last century, driven by increases in population, shifting diets, and increased use of commercial N fertilizers. Runoff of excess N from intensively managed landscapes threatens drinking water quality and disrupts aquatic ecosystems. Excess N is also a major source of greenhouse gas emissions from agricultural soils. While N emissions from agricultural landscapes are known to originate from not only current-year N input but also legacy N accumulation in soils and groundwater, there has been limited access to fine-scale, long-term data regarding N inputs and outputs over decades of intensive agricultural land use. In the present work, we synthesize population, agricultural, and atmospheric deposition data to develop a comprehensive, 88-year (1930–2017) data set of county-scale components of the N mass balance across the contiguous United States (Trajectories Nutrient Dataset for nitrogen [TREND-nitrogen]). Using a machine-learning algorithm, we also develop spatially explicit typologies for components of the N mass balance. Our results indicate a large range of N trajectory behaviors across the United States due to differences in land use and management and particularly due to the very different drivers of N dynamics in densely populated urban areas compared with intensively managed agricultural zones. Our analysis of N trajectories also demonstrates a widespread functional homogenization of agricultural landscapes. This newly developed typology of N trajectories improves our understanding of long-term N dynamics, and the underlying data set provides a powerful tool for modeling the impacts of legacy N on past, present, and future water quality.
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