Data-driven quantification of nitrogen enrichment impact on Northern Hemisphere plant biomass

Liu, Y., Piao, S., Makowski, D., Ciais, P., Gasser, T. ORCID:, Song, J., Wan, S., Peñuelas, J., et al. (2022). Data-driven quantification of nitrogen enrichment impact on Northern Hemisphere plant biomass. Environmental Research Letters 17 (7) e074032. 10.1088/1748-9326/ac7b38.

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Project: Effects of phosphorus limitations on Life, Earth system and Society (IMBALANCE-P, FP7 610028)


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The production of anthropogenic reactive nitrogen (N) has grown so much in the last century that quantifying the effect of N enrichment on plant growth has become a central question for carbon (C) cycle research. Numerous field experiments generally found that N enrichment increased site-scale plant biomass, although the magnitude of the response and sign varied across experiments. We quantified the response of terrestrial natural vegetation biomass to N enrichment in the Northern Hemisphere (>30° N) by scaling up data from 773 field observations (142 sites) of the response of biomass to N enrichment using machine-learning algorithms. N enrichment had a significant and nonlinear effect on aboveground biomass (AGB), but a marginal effect on belowground biomass. The most influential variables on the AGB response were the amount of N applied, mean biomass before the experiment, the treatment duration and soil phosphorus availability. From the machine learning models, we found that N enrichment due to increased atmospheric N deposition during 1993-2010 has enhanced total biomass by 1.1 ± 0.3 Pg C, in absence of losses from harvest and disturbances. The largest effect of N enrichment on plant growth occurred in northeastern Asia, where N deposition markedly increased. These estimates were similar to the range of values provided by state-of-the-art C-N ecosystem process models. This work provides data-driven insights into hemisphere-scale N enrichment effect on plant biomass growth, which allows to constrain the terrestrial ecosystem process model used to predict future terrestrial C storage.

Item Type: Article
Uncontrolled Keywords: carbon cycle; ecosystem process model; field experiment; machine-learning; nitrogen deposition
Research Programs: Advancing Systems Analysis (ASA)
Advancing Systems Analysis (ASA) > Exploratory Modeling of Human-natural Systems (EM)
Energy, Climate, and Environment (ECE)
Energy, Climate, and Environment (ECE) > Integrated Assessment and Climate Change (IACC)
Depositing User: Luke Kirwan
Date Deposited: 26 Jul 2022 06:02
Last Modified: 26 Jul 2022 06:02

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