Improving the bottom up N2O emission inventory for agricultural soils

U. Skiba , S. Jones, N. Cowan, D. Famulari, M. Anderson, J. Drewer, Centre for Ecology and Hydrology, Edinburgh, UK D. Chadwick, Rothamsted Research , North Wyke, UK

First steps on how to improve the national inventory of

agricultural soil N2O emissions

Conventional and new technologies were used to improve understanding the spatial and temporal variability of soil N2O fluxes:

1) static chambers to assess inter-annual variability (Fig 1)2) ‘fast’ chambers to assess spatial variability (Fig 2) 3) eddy covariance for field scale measurements (Fig 3)

Rational•Agricultural N2O emissions from soils account for 58% of the global anthropogenic N2O budget but have an uncertainty of > 400%.•Emissions are very variable in space and time, and most of our understanding of soil N2O fluxes is based on spot measurements using small chambers (Fig 1).• The IPCC Tier 1 Emission Factor approach, (for example EF1 = 1% of mineral N fertiliser is emitted as N2O-N) is unable to account for this variability.

Figure 2: The spatial variability of N2O was assessed using a ‘fast’ chamber approach on the same field as used in Fig 1. A static chamber was linked to a quantum cascade laser spectrometer and provided fluxes within 3 minutes at an accuracy of 20ppt. Largest fluxes were measured from the waterlogged area on the field. Knowledge of the spatial variability of N2O fluxes together with that of controlling variables (i.e. rainfall, temperature, soil physical and chemical properties) improves our understanding of soil N2O fluxes, and constrains the certainty of the measurement.

Figure 3: Technological improvements have provided user-friendly, high sensitive laser instruments capable of near continuous eddy covariance N2O flux measurements at the field scale (> 1ha). We recorded an almost immediate increase in N2O flux (green dots) when a rain event increased the soil water content (blue line) and soil temperature (brown line) in a grassland in Devon.

Acknowledgements: We wish to thank DEFRA and the Devolved Administrations for funding the projects on ‘Improving the UK’s agricultural greenhouse gas emission inventories’ and the EU for funding the Nitroeurope IP.

Figure 1: The above graph shows weekly to fortnightly N2O flux measurements by 8 static chambers on a grazed and fertilised grassland. N fertiliser stimulated N2O emissions at different rates depending on the amount of rainfall during this period. Emission factors were very different for the 4 years, and greater than the Tier 1 EF1 (1%). Different rainfall amounts during fertilisation periods explained ~50% of the inter-annual variability.

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Spatial Variability in N2O Emissions

The ‘fast chamber’.

Comparing eddy covariance and static chamber N2O fluxes in Devon

The QCL is situated in a mobile laboratory

The Aerodyne Quantum cascade laser spectrometer

Conclusions

4 years of static chamber measurements;

analysis of spatial variability of a 40*25 m

area using a ‘fast chamber’ method and

eddy covariance flux measurements at 10

Hz

demonstrated that PRECIPITATION is one of

the key variables to be considered in Tier 2

emission factor equations.

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N2O fluxes at Easter Bush grassland 2007 - 2010N2O loss as % of N fertiliser input

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red arrows: N fertiliser application