Direct human influence on atmospheric CO2 seasonality from increased cropland productivity

Journal name:
Nature
Volume:
515,
Pages:
398–401
Date published:
DOI:
doi:10.1038/nature13957
Received
Accepted
Published online

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.

At a glance

Figures

  1. Latitudinal patterns of increased crop production.
    Figure 1: Latitudinal patterns of increased crop production.

    Average gridded production values were summed over one-degree latitudinal bands for three-year intervals centred on 1965 and 2005 for maize (a), wheat (b), rice (c), soybeans (d) and MWRS (e).

  2. Attributing the enhanced seasonality.
    Figure 2: Attributing the enhanced seasonality.

    Annual contributions of Northern Hemisphere extratropical MWRS production to atmospheric CO2 seasonality SCO2,MWRS from 1961 to 2008 with 95% confidence intervals (quantiles from 106 iterations) (a), contributions to the total increase by crop (b), and by region (c; see Extended Data Fig. 5). a shows a linear fit with a slope of 14 Tg C yr−1.

  3. Increased production and seasonality.
    Figure 3: Increased production and seasonality.

    Geographic patterns of increases in Northern Hemisphere extratropical MWRS production (P) from 1961–2008 (left), and the resulting increase in forcing to atmospheric CO2 seasonality (right). Values are shown as sums within 1° × 1° grid cells for illustration, but analyses were conducted at 0.05° × 0.05° grid resolution. Cells with values <0.1 Tg C are not shown (see Extended Data Fig. 6).

  4. Calculating [Dgr]S.
    Extended Data Fig. 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).

  5. Harvest index and root:shoot ratio.
    Extended Data Fig. 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).

  6. Agricultural fluxes from FluxNET.
    Extended Data Fig. 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.

  7. Parameter distributions.
    Extended Data Fig. 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).

  8. Aggregation zones.
    Extended Data Fig. 5: Aggregation zones.

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

  9. Increased production and seasonality.
    Extended Data Fig. 6: Increased production and seasonality.

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

  10. Remotely sensed growing season length.
    Extended Data Fig. 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.)

Tables

  1. Global dry biomass production
    Extended Data Table 1: Global dry biomass production
  2. 2009-2011 mean MWRS dry biomass production
    Extended Data Table 2: 2009–2011 mean MWRS dry biomass production
  3. Proportion of NPP in CUP
    Extended Data Table 3: Proportion of NPP in CUP

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Author information

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

    • Navin Ramankutty

Affiliations

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

    • Josh M. Gray &
    • Mark A. Friedl
  2. Earth Systems Research Center, University of New Hampshire, Durham, New Hampshire 03824, USA

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

    • Eric A. Kort
  4. Institute on the Environment, University of Minnesota, Saint Paul, Minnesota 55108, USA

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

    • Christopher J. Kucharik
  6. Department of Geography, McGill University, Montreal, Quebec H3A 0B9, Canada

    • Navin Ramankutty

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.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

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

Author details

Extended data figures and tables

Extended Data Figures

  1. Extended Data Figure 1: Calculating ΔS. (278 KB)

    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).

  2. Extended Data Figure 2: Harvest index and root:shoot ratio. (109 KB)

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

  3. Extended Data Figure 3: Agricultural fluxes from FluxNET. (265 KB)

    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.

  4. Extended Data Figure 4: Parameter distributions. (151 KB)

    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).

  5. Extended Data Figure 5: Aggregation zones. (306 KB)

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

  6. Extended Data Figure 6: Increased production and seasonality. (338 KB)

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

  7. Extended Data Figure 7: Remotely sensed growing season length. (145 KB)

    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 Tables

  1. Extended Data Table 1: Global dry biomass production (358 KB)
  2. Extended Data Table 2: 2009–2011 mean MWRS dry biomass production (81 KB)
  3. Extended Data Table 3: Proportion of NPP in CUP (191 KB)

Additional data