TY - JOUR
T1 - Long-Term Shifts in U.S. Nitrogen Sources and Sinks Revealed by the New TREND-Nitrogen Data Set (1930–2017)
AU - Byrnes, D. K.
AU - Van Meter, K. J.
AU - Basu, N. B.
N1 - Funding Information:
We thank Linea Miller, Megan Jordan, Kathryn Starratt, Nicole Khun, Sara Dechant, and Caitlyn Watt for assistance with the data compilation. The authors have no competing interests to declare. The present work was financially supported by an NSERC Discovery Grant and by an Ontario Early Researcher Award, both awarded to N. B. B. and by startup funds for K. J. V. from the University of Illinois at Chicago. D. K. B was partially funded by the Queen Elizabeth II Graduate Scholarship in Science and Technology (QEII‐GSST).
Publisher Copyright:
© 2020. American Geophysical Union. All Rights Reserved.
PY - 2020/9/1
Y1 - 2020/9/1
N2 - 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.
AB - 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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U2 - 10.1029/2020GB006626
DO - 10.1029/2020GB006626
M3 - Article
AN - SCOPUS:85091529846
SN - 0886-6236
VL - 34
JO - Global Biogeochemical Cycles
JF - Global Biogeochemical Cycles
IS - 9
M1 - e2020GB006626
ER -