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Data from: Global pattern and drivers of nitrogen saturation threshold of grassland productivity

Citation

Peng, Yunfeng; Chen, Han; Yang, Yuanhe; Chen, Han Y.H. (2020), Data from: Global pattern and drivers of nitrogen saturation threshold of grassland productivity, Dryad, Dataset, https://doi.org/10.5061/dryad.c59zw3r4s

Abstract

  1. Ecosystem productivity usually exhibits first increase and then saturated response to increasing nitrogen (N) additions, yet the broad-scale pattern and potential drivers of the N saturation threshold are little investigated.
  2. By synthesizing N addition experiments with at least four N-input levels from the global grasslands, we applied the quadratic-plus-plateau model to fit the aboveground net primary productivity (ANPP)N rate relationship, and estimated the saturation threshold for N rate (critical N rate, NCR) and maximum ANPP (ANPPmax) from the inflection point where ANPP no longer statistically increased with N rate for individual experiments. Based on these estimations, we investigated the spatial pattern and driving factors of NCR and ANPPmax.
  3. The mean NCR and ANPPmax were 15.0 and 477.0 g m-2 year-1, respectively, but varied greatly across single-site experiments. Management strategies (e.g., biomass harvest, different N forms and addition frequency) minimally influenced both parameters. Structural equation models demonstrated that the spatial difference in NCR and ANPPmax were mainly explained by aridity index, and soil carbon (C)/N ratio availability also predicted the variation in NCR.
  4. Given that grasslands are important not only for the trend and variability of the land C sink but also for the maintenance of pasture yield, the pattern and controls of NCR and ANPPmax, as revealed by the current study, are crucial for constructing robust predictions of C sink capacity and improving N fertilizer management in grasslands.

Funding

National Natural Science Foundation of China, Award: 317,705,213,182,500,000,000,000

Youth Innovation Promotion Association of the Chinese Academy of Sciences, Award: 2018106