Trends in future N2O emissions due to land use change.

Better insight in the possible range of future N2O emissions can help to construct mitigation and adaptation strategies and to adapt land use planning and management to climate objectives. The Dutch fen meadow landscape is a hotspot of N2O emission due to high nitrogen inputs combined with moist peat soils due to land use change. Socio-economic developments in the area are expected to have major impacts on N2O emission. The goals of this study are to estimate changes in N2O emissions for the period 2006-2040 under three different scenarios for the Dutch fen meadow landscape (rural production, rural fragmentation, and rural multifunctionality) and to quantify the share of different emission sources. Three scenarios were constructed and quantified based on the Story-And-Simulation approach. The rural production and the rural fragmentation scenarios are characterized by globalization and a market-oriented economy; in the rural production scenario dairy farming has a strong competitive position in the study region, while under the rural fragmentation scenario agriculture is declining. Under the rural multifunctionality scenario, the global context is characterized by regionalization and stronger regulation toward environmental issues. The N2O emission decreased between 2006 and 2040 under all scenarios. Under the rural production scenario, the N2O emission decreased by 7%. Due to measures to limit peat mineralization and policies to reduce agricultural emissions, the rural multifunctionality scenario showed the largest decrease in N2O emissions (44%). Under the rural fragmentation scenario, in which the dairy farming sector is diminished, the emission decreased by 33%. Compared to other uncertainties involved in N2O emission estimates, the uncertainty due to possible future land use change is relatively large and assuming a constant emission with time is therefore not appropriate.

Effect of land cover data on nitrous oxide inventory in fen meadows.

Landscape representations based on land cover databases differ significantly from the real landscape. Using a land cover database with high uncertainty as input for emission inventory analyses can cause propagation of systematic and random errors. The objective of this study was to analyze how different land cover representations introduce systematic errors into the results of regional N2O emission inventories. Surface areas of grassland, ditches, and ditch banks were estimated for two polders in the Dutch fen meadow landscape using five land cover representations: four commonly used databases and a detailed field map, which most closely resembles the real landscape. These estimated surface areas were scaled up to the Dutch western fen meadow landscape. Based on the estimated surface areas agricultural N2O emissions were estimated using different inventory techniques. All four common databases overestimated the grassland area when compared to the field map. This caused a considerable overestimation of agricultural N2O emissions, ranging from 9% for more detailed databases to 11% for the coarsest database. The effect of poor land cover representation was larger for an inventory method based on a process model than for inventory methods based on simple emission factors. Although the effect of errors in land cover representations may be small compared to the effect of uncertainties in emission factors, these effects are systematic (i.e., cause bias) and do not cancel out by spatial upscaling. Moreover, bias in land cover representations can be quantified or reduced by careful selection of the land cover database.