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Direct human influence on atmospheric CO2 seasonality from increased cropland productivity

Nature volume 515, pages 398–401 (2014)Cite this article

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Abstract

Ground- and aircraft-based measurements show that the seasonal amplitude of Northern Hemisphere atmospheric carbon dioxide (CO2) concentrations has increased by as much as 50 per cent over the past 50 years1,2,3. This increase has been linked to changes in temperate, boreal and arctic ecosystem properties and processes such as enhanced photosynthesis, increased heterotrophic respiration, and expansion of woody vegetation4,5,6. However, the precise causal mechanisms behind the observed changes in atmospheric CO2 seasonality remain unclear2,3,4. Here we use production statistics and a carbon accounting model to show that increases in agricultural productivity, which have been largely overlooked in previous investigations, explain as much as a quarter of the observed changes in atmospheric CO2 seasonality. Specifically, Northern Hemisphere extratropical maize, wheat, rice, and soybean production grew by 240 per cent between 1961 and 2008, thereby increasing the amount of net carbon uptake by croplands during the Northern Hemisphere growing season by 0.33 petagrams. Maize alone accounts for two-thirds of this change, owing mostly to agricultural intensification within concentrated production zones in the midwestern United States and northern China. Maize, wheat, rice, and soybeans account for about 68 per cent of extratropical dry biomass production, so it is likely that the total impact of increased agricultural production exceeds the amount quantified here.

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Figure 1: Latitudinal patterns of increased crop production.
Figure 2: Attributing the enhanced seasonality.
Figure 3: Increased production and seasonality.

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Acknowledgements

This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (US Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowledge the financial support to the eddy covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, Max Planck Institute for Biogeochemistry, National Science Foundation, University of Tuscia, Université Laval and Environment Canada and US Department of Energy and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California - Berkeley, University of Virginia. This work was supported by NASA grant number NNX11AE75G and NSF grant numbers EF-1064614 and NSF EAR-1038818. Research support to D.K.R. was primarily provided by the Gordon and Betty Moore Foundation and the Institute on Environment at the University of Minnesota. We also acknowledge input and data provided by H. Graven and P. Patra.

Author information

Author notes

  1. Navin Ramankutty

    Present address: Present address: Liu Institute for Global Issues and Institute for Resources, Environment, and Sustainability, University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada.,

Authors and Affiliations

  1. Department of Earth and Environment, Boston University, Boston, 02215, Massachussetts, USA

    Josh M. Gray & Mark A. Friedl

  2. Earth Systems Research Center, University of New Hampshire, Durham, 03824, New Hampshire, USA

    Steve Frolking

  3. Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, 48109, Michigan, USA

    Eric A. Kort

  4. Institute on the Environment, University of Minnesota, Saint Paul, 55108, Minnesota, USA

    Deepak K. Ray

  5. Department of Agronomy and Nelson Institute Center for Sustainability and the Global Environment, University of Wisconsin-Madison, Madison, 53706, Wisconsin, USA

    Christopher J. Kucharik

  6. Department of Geography, McGill University, Montreal, Quebec H3A 0B9, Canada,

    Navin Ramankutty

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Contributions

J.M.G. led the design, analysis, and writing of the paper. J.M.G., S.F., N.R. and M.A.F. designed the analysis. E.A.K. provided the initial inspiration for the paper and guidance on interpreting atmospheric CO2 dynamics. C.J.K. contributed guidance on agronomic elements of the paper. D.K.R. provided the gridded MWRS data set. All authors edited and contributed to writing the paper.

Corresponding author

Correspondence to Josh M. Gray.

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Competing interests

The authors declare no competing financial interests.

Additional information

MWRS yield and harvested area data will be archived at http://www.earthstat.org and are available on request.

Extended data figures and tables

Extended Data Figure 1 Calculating ΔS.

Schematic diagram showing the CO2 seasonality difference (ΔS) for two time periods representing a baseline condition (t1) with CO2 seasonality St1 and a scenario where NEE is enhanced 35% (t2) with correspondingly higher seasonality, St2. Flux-derived daily NEE (a), cumulative NEE (b), and NEPCUP and NEPCRP (c). Note the assumption that annual NEP = 0 (b).

Extended Data Figure 2 Harvest index and root:shoot ratio.

Crop-specific and MWRS aggregate distributions of literature-reported values of harvest index (a) and R:S (b).

Extended Data Figure 3 Agricultural fluxes from FluxNET.

NEE and GPP for agricultural FluxNET sites used in this study to determine the θ and φ parameters. Shading corresponds to May, June, July, and August, the CUP at the latitudes of most agricultural production, and the CUP definition used throughout this study.

Extended Data Figure 4 Parameter distributions.

PERT distributions for all Monte-Carlo-varied parameters in this study. Shown are moisture fraction (a), harvest efficiency (b), R:S ratio (c), harvest index (d), CUP proportion of NPP (e), and CUP proportion of Rh (f).

Extended Data Figure 5 Aggregation zones.

Eco-climatic (top; from ref. 3) and aggregated production regions (bottom) used in this study.

Extended Data Figure 6 Increased production and seasonality.

Change in MWRS production (top) and SCO2,MWRS (bottom) over the period 1965–2005.

Extended Data Figure 7 Remotely sensed growing season length.

Mean ‘greenup’ and dormancy values from MODIS Land Cover Dynamics product (MCD12Q2) for pixels identified as agriculture (AG) and deciduous broadleaf forest (DBF) in the MODIS Land Cover product (MCD12Q1; classes 12 and 4, respectively) for 1° latitudinal bands in North America (a) (MODIS tiles are h11v03, h12v03, h13v03, h10v04, h11v04, h12v04, h09v05, h10v05, and h11v05), and for China (b) (MODIS tiles are h23v03, h24v03, h25v03, h26v04, h27v04, h26v05, and h27v05.)

Extended Data Table 1 Global dry biomass production
Full size table
Extended Data Table 2 2009–2011 mean MWRS dry biomass production
Full size table
Extended Data Table 3 Proportion of NPP in CUP
Full size table

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Gray, J., Frolking, S., Kort, E. et al. Direct human influence on atmospheric CO2 seasonality from increased cropland productivity. Nature 515, 398–401 (2014). https://doi.org/10.1038/nature13957

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