Simulation of fluxes of greenhouse gases from European grasslands using the DNDC model

Agriculture, Ecosystems and Environment 121 (2007) 186–192 www.elsevier.com/locate/agee

Simulation of fluxes of greenhouse gases from European grasslands using the DNDC model P.E. Levy *, D.C. Mobbs, S.K. Jones, R. Milne, C. Campbell, M.A. Sutton Centre for Ecology and Hydrology, Bush Estate, Penicuik, Midlothian EH26 0QB, UK Available online 23 January 2007

Abstract Agricultural management of grasslands results in sequestration and emission of greenhouse gases (GHGs, particularly CO2, N2O and CH4). Here, we used a process-based model (DNDC) to estimate the fluxes of the major GHGs from grasslands at 0.58 resolution across Europe, and combined these to produce a spatially explicit estimate of the total global warming potential (GWP, expressed in CO2 equivalents). The DNDC model [Li, C., Frolking, S., Crocker, G.J., Grace, P.R., Klir, J., Korchens, M., Poulton, P.R., 1997. Simulating trends in soil organic carbon in long-term experiments using the DNDC model. Geoderma 81, 45–60] simulates carbon and nitrogen cycling in agroecosystems at a sub-daily time step and consists of four interacting submodels: soil and climate (including water flow and leaching), plant growth, decomposition, and denitrification. Input data sets for grassland area, climate, nitrogen deposition, and soil properties were collated. The typical current grassland management regime was established for ten biogeographical regions on the basis of questionnaires sent to national experts, and used to derive model input data. A 20-year simulation was carried out using DNDC for each site. Simple estimates of methane emissions from grazing livestock were made according to the IPCC Tier 1 method. Most grassland areas are net sources for GHGs in terms of total global warming potential—the beneficial effect of sequestering carbon in soil is outweighed by the emissions of N2O from soil and (predominantly) CH4 emissions from livestock. The net effect of European grasslands on GWP (emission of 23 Tg C year 1) corresponds to a 2.5% increase on the EU-15 fossil fuel CO2 emissions (907 Tg C year 1). # 2006 Elsevier B.V. All rights reserved. Keywords: Carbon dioxide; Nitrous oxide; Methane; Global warming potential; Grassland management; Livestock emissions

1. Introduction Grasslands cover approximately 140 million ha within Europe west of the Urals, accounting for 18% of the land area in the EU-15 (FAO, 2004). Agricultural management of grasslands results in sequestration and emission of greenhouse gases (GHGs, particularly carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4)). Across this wide area, there is considerable potential to adopt management practices which mitigate GHG emissions, if the effect of these practices on biogeochemical cycling is understood. Because of their extensive fibrous root systems, grassland soils contain substantial amounts of organic carbon and are a potentially important carbon sink. Even modest changes in * Corresponding author. Tel.: +44 131 445 4343; fax: +44 131 445 3943. E-mail address: [email protected] (P.E. Levy). 0167-8809/$ – see front matter # 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.agee.2006.12.019

carbon inputs could result in significant and long-lived sequestration. Several studies indicate that European grasslands tend to sequester carbon at present (Soussana et al., 2004), but uncertainties are high (Janssens et al., 2003). Globally, soils account for 65% of the total emissions of N2O (Kroeze et al., 1999), and grassland soils comprise an important fraction of this. The application of nitrogen fertilisers is a major contributor to increased N2O emissions (Dobbie et al., 1999). Two mechanisms are principally responsible for N2O emissions from soils: microbial nitrification and denitrification. Nitrification is an aerobic process, performed by both autotrophic and heterotrophic organisms, although it is thought that most nitrification is carried out by autotrophic bacteria (Conrad, 1996). Denitrification occurs in anaerobic sites in the soil and is carried out by a wide range of mainly heterotrophic, but also

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autotrophic organisms. Uptake of N2O by soils can occur but is relatively insignificant. Methane is produced by bacteria under anaerobic conditions, such as the intestinal tracts of ruminants, sewage digesters, groundwater and soil. In grasslands, CH4 emissions are dominated by enteric fermentation in domesticated ruminants and from manure. Soils may also act as a sink for methane: under aerobic conditions, CH4 can be oxidized to CO2 by methanotrophic bacteria. Our objective here was to estimate the fluxes of these major GHGs from grasslands at the European scale, using a processbased model (DNDC). Management scenarios which mitigate the impact of European grasslands on global warming can then be investigated. The individual GHG fluxes can be combined to produce a spatially explicit estimate of the total global warming potential across Europe. Global warming potential (GWP) is defined as the cumulative radiative forcing between the present and some defined later time, caused by a unit mass of gas emitted now, expressed relative to the reference gas CO2. The GWP aggregates the radiative impacts of different greenhouse gases into a uniform measure. Here, we use the IPCC values for GWP for N2O and CH4 (IPCC, 2001), equating 1 kg of these gases with 310 and 23 kg of CO2, respectively, over a 100-year time horizon.

2. Methods 2.1. The model The DNDC model (Version 8.3P, November 2004, Li et al., 1997) simulates carbon and nitrogen cycling in agroecosystems at a sub-daily time step. It consists of four interacting submodels: soil and climate (including water flow and leaching), plant growth, decomposition, and denitrification. The soil climate submodel simulates soil temperature and moisture profiles based on soil physical properties, daily weather, and plant water use. The plant growth submodel calculates daily water and nitrogen uptake by vegetation, root respiration, and plant growth and partitioning of biomass into

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grain, stalks and roots. Potential crop yield and biomass allocation are parameters; actual yield simulated by the model will generally be suboptimal due to limitations by climate, water and/or nitrogen availability. The decomposition submodel simulates daily decomposition, nitrification, ammonia volatilization, and carbon dioxide production by soil microbes. The denitrification submodel operates at an hourly time step to simulate denitrification and the production of nitric oxide, nitrous oxide, and dinitrogen. 2.2. Input data The region bounded by latitudes 348N and 728N and longitudes 6.58W and 35.58E was divided into 4025 halfdegree grid cells (approximately 50 km  50 km). The longterm average climate for each 0.58 by 0.58 site was obtained from the CRU Climatology (New et al., 1999). Monthly mean data for each site were used to generate a typical years daily data (Richardson and Wright, 1984; Geng et al., 1986) and this was used for each year of the simulation. N concentration in rainfall was obtained from EMEP (http:// www.emep.int/Model_data/model_data.html) and converted to the required half-degree resolution. As dry deposition of oxidized and reduced nitrogen is not represented explicitly in DNDC, this component was combined with wet deposition to obtain the correct total N deposition. The background atmospheric NH3 concentration was set to a constant value of 0.06 mg N m 3 for all sites. The background atmospheric CO2 concentration was set to 350 ppm. Organic carbon content and pH were obtained from ISRIC-WISE at half-degree resolution (Batjes, 1996). Values for the top 30 cm of soil were used. Clay fraction was obtained from the Global_Ecosystems_Database_Project (2000). Soil texture was calculated as function of clay fraction based on the method used within DNDC. Bulk density was estimated from soil organic carbon content. Crop parameters were taken from the default values provided by DNDC for perennial grassland. The grassland area in each 0.58 pixel is shown in Fig. 1a, obtained from the PELCOM (Pan-European Land Use and

Fig. 1. (a) Grassland area (ha) within each 0.58 pixel, obtained from the PELCOM database. (b) Biogeographical regions of Europe, based on European Environment Agency data. Grassland management was considered to be the same within each region. Zone names are given in Table 1. All maps use Albers equal-area conic projection.

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Land Cover Monitoring) database (http://www.geo-informatie.nl/projects/pelcom/public/index.htm). This was compared with FAO data (FAO, 2004) for permanent pasture at country level. Certain anomalies were apparent, particularly in Austria, Croatia, Czech Republic, Denmark, Poland and Turkey, with PELCOM generally under-representing grassland area. Therefore, within each country, the grassland area from PELCOM was multiplied by a correction factor to give agreement with the permanent pasture area from FAO at national scale. Thus, at both European and national scale, we use the grassland area from FAO, but the relative spatial distribution is taken from PELCOM. Fig. 1b shows the biogeographical divisions within Europe, based on a dataset provided by the European Environment Agency (http:// dataservice.eea.eu.int/dataservice/metadetails.asp?id=308), giving the delineations used under implementation the Habitats Directive (92/43/EEC). In each biogeographical region, the typical current grassland management regime was established on the basis of questionnaires sent to participants in the EU GREENGRASS project and to other national grassland experts for different grassland types. These were translated into the farm management parameter files for the DNDC model (Table 1). Of the 4025 possible land pixels in the grid shown in Fig. 1, only 3822 have associated data for all input datasets. Furthermore, only 3378 of these sites contain grassland, according to the PELCOM dataset. A 20-year simulation was carried out using DNDC for each of these 3378 sites. Outputs are presented from the final year of the simulation. As a control run, the same simulation was performed but with no active grassland management (no tillage, grazing, cutting, fertilising or manure application).

dataservice.eea.eu.int/dataservice/metadetails.asp?id=755). These data were only available at country scale, so livestock was assumed to be distributed uniformly over the grasslands within each country. Emission factors for beef cattle, sheep and horses were taken from IPCC (1997). Emission factors for dairy cattle were taken from Baggott et al. (2005) following the IPCC Tier 2 procedure (IPCC, 1997), which take into account the age structure of the cattle population. A weighted mean emission factor for all cattle was used, based on the relative fractions in the European dairy and beef herds (84 and 16%, respectively, EEA).

3. Results Fig. 2a shows the model prediction of net CO2 uptake by European grasslands. (Here, we consider only sequestration in the soil, and ignore any apparent sink in the above-ground plant biomass, as this will be returned to the atmosphere by animal or microbial respiration within 1 year, and the fate of harvested products is not accounted for in the model.) Most areas of Europe are predicted to act as a sink for CO2. This is due to the inputs of carbon to the system (from litter and manure application) exceeding the losses of carbon through harvesting and soil respiration. The pattern somewhat follows that of the biogeographical regions (which dictate management)—the Alpine, Atlantic and Mediterranean regions have consistently high sinks because they have the highest carbon inputs through manure addition and plant litter. The pattern is heavily modified by other factors (Fig. 3). Fig. 2b shows the emissions of N2O predicted by the model. This reveals high values in the Atlantic region, which has the highest rate of nitrogen fertiliser and manure application. Again, this pattern is strongly confounded by the distribution of nitrogen deposition, temperature and soil texture (Fig. 3). Uptake of methane by grassland soils is very small over most areas of Europe (Fig. 2c). The highest uptake rates are estimated in Scandinavia and along the Atlantic coast. However, relationships with individual variables are unclear (Fig. 3). Fig. 2d shows that methane emissions from livestock are of the order of 200 times

2.3. Enteric emissions of methane from livestock Although the indirect effects of grazing animals are represented, the DNDC model does not represent enteric CH4 emissions directly from livestock. We therefore used the IPCC (1996) Tier 1 method, as a simple empirical method to estimate this component. Livestock numbers for each country, originally compiled by the FAO, were obtained from the European Environment Agency (http://

Table 1 Summary of biogeographical zone characteristics of typical grassland management used as inputs to the DNDC model, derived from questionnaire responses from European grassland experts Zone 1 2 3 4 5 6 7 8 9

Name Alpine Anatolian Arctic Atlantic Boreal Continental Mediterranean Pannonian Steppic

No. fertiliser applications

Fertiliser (kg N ha

2 0 0 3 2 2 1 1 0

75 0 0 170 200 70 50 25 0

1

year 1)

No. manure applications

Manure (kg N ha

3 0 0 2 1 3 2 0 0

95 0 0 120 34 27 59 0 0

1

year 1)

Total N applied (kg N 1 ha 1 year)

No. cuts year 1

170 0 0 290 234 97 109 25 0

2 0 0 2 2 2 1 0 0

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Fig. 2. Global warming potential of GHG emissions from European grasslands predicted by the DNDC model. Units are CO2 equivalents in kg C(ha grassland) 1 year 1. Positive values indicate a net emission of GHGs, negative values indicate a net uptake. Model estimates are shown for (a) CO2, (b) N2O, (c) soil CH4 exchange, (d) enteric emissions of CH4 from livestock, (e) total global warming potential excluding enteric emissions of CH4 from livestock, and (f) total global warming potential including enteric emissions of CH4 from livestock.

greater than methane uptake by soil. As the same emission factors were used across all countries, the pattern in Fig. 2d reflects only country scale variation in livestock density, weighted by the relative numbers of cattle, sheep and horses. Fig. 2e and Table 2 show the combined effect of simulated GHG emissions on GWP, excluding the contribution of methane from livestock. These show that most grassland areas are net sinks for GHGs in these terms. The largest sinks are in the boreal and Mediterranean regions, and the source areas are mainly in the Atlantic region. The overall GWP sink of 21 Tg C[CO2 equivalent] year 1 is a result of the sequestration of carbon in soil, which outweighs the emissions of N2O. The uptake of methane is relatively insignificant in comparison. When methane emissions from livestock are included, the result is reversed to give a net source of 23 Tg C[CO2 equivalent] year 1 (Table 2). Almost all areas become a net source of GHGs when animals are

included, with the exception of Scandinavia (Fig. 2f). Overall, methane emissions from livestock are almost 50% larger than the CO2 sink in the soil. In the control run with no management, both the CO2 and N2O fluxes are reduced to around a third. The methane uptake is only slightly reduced. The net effect is that grasslands are a smaller sink, 27% of that in the current management scenario excluding livestock.

4. Discussion Based on mathematical inversion of atmospheric CO2 concentrations and known fossil fuel sources, studies suggest that there is a terrestrial sink for CO2 within Europe (e.g. Bousquet et al., 2000). Janssens et al. (2003), using the CESAR soil carbon model, estimated a grassland

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Fig. 3. Scatter plots of DNDC model outputs (change in soil organic carbon (dSOC) and fluxes of N2O and CH4 from the soil) against model inputs, for the 3378 sites across Europe. The output units are kg C or N(ha grassland) 1 year 1. Model inputs are: Biogeographical region (see Table 1 for key); rainfall (mm); mean monthly maximum temperature (Tmax, 8C); mean monthly minimum temperature (Tmin, 8C); annual mean solar radiation (MJ m 2 day 1); annual mean vapour pressure (kPa); NO3 + NH4 in rainfall (mg l 1); soil texture (1 = sandy, 11 = clay); bulk density (g cm3); pH; soil organic carbon (SOC, kg C kg 1 in top 5 cm); soil clay content (fraction); nitrogen deposition (Ndep, g m 2 year 1); plant biomass at the end of the simulation (g m 2); external organic input (InputC, g m 2 year 1).

carbon sink of 101 Tg C year 1 over a similar region of Europe. This is three times larger than our estimate, but they note that the standard deviation in their estimate is 133 Tg C year 1. Smith et al. (2005), using the Roth-C Table 2 Summary of GHG emissions from European grasslands predicted by this application of the DNDC modela GHG

Simulation Current management Flux

CO2 N2O CH4 (excl. livestock) CH4 (incl. livestock) Total (excl. livestock) Total (incl. livestock)

GWPb

111.95 0.11 0.04

30.5 9.6 0.2

7.70

44.1 21.1

Control (no management) Flux 32.93 0.04 0.03

GWP 9.0 3.6 0.2

5.6

23.2

a Fluxes are in units of Tg of gas year 1. Positive values indicate an emission to the atmosphere. b GWP = global warming potential, in units of Tg C[CO2 equivalent] year 1.

model with SRES scenario data, also produced current-day estimates in which grasslands could be either sources or sinks, given the uncertainty in model inputs. Allard et al. (2007) attempted to measure all these GHG fluxes at nine contrasting sites across Europe. The sign of our estimated CO2 sink is in agreement with their observations, but substantially smaller (424 cf. 1200 kg C ha 1 year 1). For N2O, the mean of our estimates (1.01 kg N ha 1 year 1) is very close to that of Flechard et al. (2007), who found a mean emission of 0.93 kg N ha 1 year 1. Boeckx and van Cleemput (2001) report a mean value of 5.6 kg N ha 1 year 1 for the EU-15 using the IPCC inventory method, even though this included arable croplands, which generally have lower N2O emissions due to soil compaction and higher crop N requirements (Velthof et al., 1996). Our value is larger than measured by Allard et al. (2007) (267 cf. 150 kg C[CO2 equivalent] ha 1 year 1), and is at the upper end of the 95% confidence limits on the observations. Summarising observational data reported in recent literature, Boeckx and van Cleemput (2001) estimated a mean methane oxidation rate of 1.9 kg C ha 1 year 1 for grasslands. This is five times higher than our mean value (0.4 kg C ha 1 year 1) so it seems likely that methane

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oxidation is underestimated in the model. However, even at the observed higher rates, this is still minor in comparison to the other GHG fluxes. Total methane emissions for this region in 2003 were 170 Tg C[CO2 equivalent] year 1 (UNFCCC, 2005) (but we note that this excludes Russia, Poland and Turkey, for which values are not reported). A precise breakdown of this value into its components is not readily available, but enteric emissions from livestock constitute roughly 30% of total methane emissions in Europe (AEA, 1998), giving a value of 51 Tg C[CO2 equivalent] year 1. This corresponds reasonably with our estimate (44 Tg C[CO2 equivalent] year 1), although the missing data in the UNFCCC estimate would substantially increase this value. Independent estimates of enteric methane emissions for 1995 from RIVM/TNO (2002), made with a similar method to ours, gave a somewhat larger value of 59 Tg C[CO2 equivalent] year 1. Some of this difference will be attributable to the 7% decrease in cattle numbers in Europe between 1995 and 2003. Allard et al. (2007) attempted to measure enteric emissions of methane directly using the SF6 tracer method at four sites, and obtained a mean value of 570 kg C[CO2 equivalent] ha 1 year 1. Our mean value (613 kg C[CO2 equivalent] ha 1 year 1) is of a similar magnitude, and suggests that the empirical emission factors are not unreasonable. Our results contrast with those of Allard et al. (2007) in that they estimate a larger CO2 sink and a smaller methane source, whereby the net GHG balance is a small sink. A larger CO2 sink is clearly within the bounds of uncertainty, and several factors which would influence the CO2 sink are not included here. Changes in climate and the increase in atmospheric CO2 concentration over the last century are not represented, although these effects may tend to counterbalance each other. The spatial distribution of grass production (and litter input) is likely to dominate uncertainty in modelled dSOC. Developing a database with detailed, spatially differentiated grass production data (from agricultural census or remote sensing approaches) would be a key effort to improve the modelled results. Most soil models, including DNDC, were developed for application to mineral soils. In highly organic soils, the factors controlling decomposition tend to be different, and such models give poor predictions under these conditions. Whilst a larger CO2 sink is possible, a smaller methane source is unlikely at European scale. The numbers of livestock are relatively well known and, although there is considerable variability in emission factors, the values used here are relatively conservative, and take into account the age structure of the dairy cattle population. We note that if the dairy cattle population in the rest of Europe has a markedly different age distribution from the UK, this would affect the true overall mean emission factor. However, the necessary data are not currently available to examine this. Our values exclude livestock which do not graze extensively on grasslands, most notably pigs and

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goats, but these two comprise only 1% of the livestock methane emission in the UK, so are unlikely to alter the estimate significantly.

5. Concluding remarks As the existence of the CO2 sink is uncertain, but the methane source from livestock is large and relatively certain, the latter may be a more appropriate target for mitigation options, by reducing livestock numbers or developing new feedstuffs which reduce methane production (van Nevel and Demeyer, 1996). Emissions of N2O are a significant part of the GWP budget. However, because of the complexity of the soil processes which determine nitrification and denitrification, the uncertainty is high, and options for mitigation are harder to envisage.

Acknowledgements This work formed part of the GREENGRASS project, funded by the European Commission DG Research 5th Framework Programme. This work also contributed to COST action 627.

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