Improvement of the 15 N gas flux method for in situ measurement of soil denitrification and its product stoichiometry.

RATIONALE: Field measurement of denitrification in agricultural ecosystems using the 15 N gas flux method has been limited by poor sensitivity because current isotope ratio mass spectrometry is not precise enough to detect low 15 N2 fluxes in the presence of a high atmospheric N2 background. For laboratory studies, detection limits are improved by incubating soils in closed systems and under N2 -depleted atmospheres. METHODS: We developed a new procedure to conduct the 15 N gas flux method suitable for field application using an artificially N2 -depleted atmosphere to improve the detection limit at the given precision of mass spectrometry. Laboratory experiments with and without 15 N-labelling and using different flushing strategies were conducted to develop a suitable field method. Subsequently, this method was tested in the field and results were compared with those obtained from the conventional 15 N gas flux method. RESULTS: Results of the two methods were in close agreement showing that the denitrification rates determined were not biased by the flushing procedure. Best sensitivity for N2 + N2 O fluxes was 10 ppb, which was 80-fold better than that of the reference method. Further improvement can be achieved by lowering the N2 background concentration below the values established in the present study. CONCLUSIONS: In view of this progress in sensitivity, the new method will be suitable to measure denitrification dynamics in the field beyond peak events.

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations.

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.