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

Abstract

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.

Keywords

• field experiment
• nitrogen deposition
• carbon cycle
• machine-learning
• ecosystem process model