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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Extended data figures and tables
Extended Data Figures
- 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).
- 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).
- 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.
- 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).
- Extended Data Figure 5: Aggregation zones. (306 KB)
Eco-climatic (top; from ref. 3) and aggregated production regions (bottom) used in this study.
- Extended Data Figure 6: Increased production and seasonality. (338 KB)
Change in MWRS production (top) and SCO2,MWRS (bottom) over the period 1965–2005.
- 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.)