Abstract

Direct nitrous oxide (N2O) emissions from fertilized soils are generally estimated using emission factors. However, the emission factors for N2O emission of applied slurry are not well quantified. The effect of slurry application technique on N2O emission was quantified in field experiments in the Netherlands in order to derive N2O emission factors for (shallow) injected and surface-applied cattle and pig slurries. Fluxes of N2O were measured using a closed flux chamber technique and a photo-acoustic infra-red gasmonitor. Fluxes of N2O were measured 64–83 times on grassland on sandy and clay soils and maize land on sandy soil, in the period 2007–2009. There were large differences in total N2O emission between the years, and differences between treatments were not consistent over the years and sites. The average emission factor of all treatments and years (n=35) was 0.9% of the N applied, which is close to the default IPCC emission factor of 1%. However, the range in emission was large, i.e. from −0.2% to 7.0%. The average emission factor for grassland was 1.7% of the N applied for calcium ammonium nitrate (CAN), 0.4% for shallow injected cattle slurry, and 0.1% for surface-applied cattle slurry. For maize land, the average emission factor for CAN was 0.1% of the N applied, for injected cattle slurry 0.9% and for surface-applied cattle slurry 0.4%. The emission factors for pig slurry applied to maize land were higher than for cattle slurry; 3.6% for injected pig slurry and 0.9% for surface-applied pig slurry. Increasing the N application rate on maize land resulted in higher emission factors for CAN, injected cattle slurry, and injected pig slurry. Concluding, on both grassland and maize land (shallow) injection of slurry increased the average emission factor of N2O in comparison to surface application. Differentiation of N2O emission factors which takes specific factors into account, such as N type and rate and application technique, can improve the quantification of N2O emission from agricultural soils and is needed to derive most efficient options for mitigation.

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