Scholarly article on topic 'Estimating the effect of nitrogen fertilizer on the greenhouse gas balance of soils in Wales under current and future climate'

Estimating the effect of nitrogen fertilizer on the greenhouse gas balance of soils in Wales under current and future climate Academic research paper on "Earth and related environmental sciences"

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Regional Environmental Change

Academic research paper on topic "Estimating the effect of nitrogen fertilizer on the greenhouse gas balance of soils in Wales under current and future climate"

Reg Environ Change

DOI 10.1007/s10113-016-0958-7 CrossMark


Estimating the effect of nitrogen fertilizer on the greenhouse gas balance of soils in Wales under current and future climate

Mohamed Abdalla1 • Mark Richards1 • Mark Pogson1,2 • Jo U. Smith1 • Pete Smith1

Received: 9 June 2015/Accepted: 10 March 2016

© The Author(s) 2016. This article is published with open access at

Abstract The Welsh Government is committed to reduce greenhouse gas (GHG) emissions from agricultural systems and combat the effects of future climate change. In this study, the ECOSSE model was applied spatially to estimate GHG and soil organic carbon (SOC) fluxes from three major land uses (grass, arable and forest) in Wales. The aims of the simulations were: (1) to estimate the annual net GHG balance for Wales; (2) to investigate the efficiency of the reduced nitrogen (N) fertilizer goal of the sustainable land management scheme (Glastir), through which the Welsh Government offers financial support to farmers and land managers on GHG flux reduction; and (3) to investigate the effects of future climate change on the emissions of GHG and plant net primary production (NPP). Three climate scenarios were studied: baseline (1961-1990) and low and high emission climate scenarios (2015-2050). Results reveal that grassland and cropland are the major nitrous oxide (N2O) emitters and consequently emit more GHG to the atmosphere than forests. The overall average simulated annual net GHG balance for Wales under baseline climate (1961-1990) is equivalent to 0.2 t CO2e ha-1 y-1 which gives an estimate of total annual net flux for Wales of 0.34 Mt CO2e y-1. Reducing N fertilizer by 20 and 40 % could reduce annual net GHG fluxes by 7 and 25 %, respectively. If the current N fertilizer application

Editor: Will Steffen.

& Mohamed Abdalla

1 Institute of Biological and Environmental Sciences, School of Biological Sciences, University of Aberdeen, 23 St. Machar Drive, Aberdeen AB24 3UU, UK

2 Engineering, Sports and Sciences Academic Group, University of Bolton, Deane Road, Bolton BL3 5AB, UK

rate continues, predicted climate change by the year 2050 would not significantly affect GHG emissions or NPP from soils in Wales.

Keywords ECOSSE • Soil greenhouse gas balance • Net primary productivity • Climate change • Wales • Nitrogen fertilizer


Land use is an important factor for carbon (C) and nitrogen (N) dynamics of ecosystems and can have a great effect on greenhouse gas (GHG) emissions from soils (Forster et al. 2007). In the UK, agriculture represents the second largest source of GHG emissions accounting for 9 % of the UK's total emissions (Defra 2013) and emits 79 % of the total anthropogenic emission of nitrous oxide (N2O) (Thomas et al. 2011). This is due to field management practices such as application of synthetic N fertilizer (Bouwman et al. 2002), crop type and cover crop (Bell et al. 2012; Abdalla et al. 2014) and manure management and grazing (Forster et al. 2007) which influence nutrient inputs and hydrolog-ical and physical conditions of the soil. Forests and woodlands compose a great stock of C in trees, vegetation and in soils by removing a substantial amount of C from the atmosphere. They are a key component in developing climate change mitigation strategies for GHG emissions (Morison et al. 2012).

Nitrous oxide is a potent GHG that contributes about 6 % to the anthropogenic greenhouse effect (IPCC 2013). Emissions of N2O tend to occur in short-lived bursts following the application of N fertilizers (Leahy et al. 2004; Skiba et al. 2012).The availability of soil mineral N has a direct effect on N2O production by providing N for

Published online: 07 April 2016

1 Springer

both nitrification and denitrification (Baggs and Blum 2004). On a mass basis, N2O has a global warming potential of 298 times that of CO2, over a 100-year timescale (IPCC 2013). Methane is produced when organic materials decompose under anaerobic condition in soils (Regina et al. 2006) with high emissions from his-tosols areas (Mosier et al. 1998). However, generally, cropland soils are often CH4 sinks (Abdalla et al. 2014). Soils are also a source of CO2 emission which is associated with disturbance and land use transitions (Janzen 2004) and decomposition of litter roots and soil organic matter (SOM) (Bernhardt et al. 2006).

Application of well-tested process models provides a robust way to estimate GHG emissions from soils to evaluate potential mitigation options and allow the investigation of a variety of climate change-land use scenarios (Giltrap et al. 2010). ECOSSE is an example of such a process-based model (Smith et al. 2010a). ECOSSE was developed to simulate C and N cycling and GHG production in mineral, organo-mineral and organic soils, using concepts originally developed for mineral soils in the two parent models, RothC (Coleman and Jenkinson 1996) and SUNDIAL (Smith and Glendining 1996). Building on these established models, ECOSSE uses a pool approach, describing SOM as pools of inert organic matter, humus, biomass, resistant plant material (RPM) and decomposable plant material (DPM) (Smith et al. 2010a, b).

Climate change is expected to increase surface air temperature and evaporation leading to higher levels of atmospheric water vapour. As a result, the frequency and extent of rainfall is likely to be highly variable (Kattenberg et al. 1995). Such changes in temperature (Fiscus et al. 1997) and precipitation (Mearns 2003) would be expected to influence mineralization and denitrification, and thereby GHG production in the soil, but could also increase plant productivity (Anwar et al. 2007).

The Welsh Government is committed to reducing GHG emissions from agriculture, protecting the environment and combating the effects of future climate change. To achieve these objectives, the Glastir programme is in force, in which farmers are financially supported to adopt a range of on-farm measures to protect soil C, reduce GHG emissions, improve water quality and enhance biodiversity. The main aims of this simulation study were: (1) to estimate the annual net GHG balance for Wales; (2) to investigate the efficiency of the reduced nitrogen (N) fertilizer goal of the sustainable land management scheme (Glastir), through which the Welsh Government offers financial support to farmers and land managers on GHG flux reduction; and (3) to investigate the effects of future climate change on the emissions of GHG and plant net primary production (NPP).

Materials and Methods ECOSSE model

In this study, we applied the latest version of the ECOSSE (Estimation of Carbon in Organic Soils-Sequestration and Emissions; v. 5.0.1) model to estimate GHG and SOC fluxes for Wales. The ECOSSE model includes all of the major processes of C and N turnover in the soil using well-established equations driven by readily available input variables (Smith et al. 2010a). ECOSSE can be used to carry out site-specific simulations with detailed input data (e.g. Bell et al. 2012; Dondini et al. 2015), or spatial simulations using the limited data typically available at larger scales (e.g. Smith et al. 2010b). Data describing initial SOC, soil water, plant inputs, nutrient applications and timing of management operations are used to run the model.

The water module in ECOSSE is based on SUNDIAL (Smith and Glendining 1996), where water passes through soil layers as 'piston flow'. Precipitation fills the uppermost soil layer with water until it reaches field capacity. Any remaining precipitation then fills the next layer to field capacity. This process is repeated until no precipitation remains or the bottom of the profile is reached. Water remaining after filling all layers to field capacity is partitioned between drainage (water leaving the soil profile), and excess, which fills layers to saturation from the bottom of the profile upwards. The ECOSSE model uses the observed depth of the water table (where available), the available water at saturation and weather data to calculate the restriction to drainage (i.e. the partitioning between drainage and excess), that is required to achieve the observed water table depth. Addition or loss of C and N from different vegetation types is estimated using the C and N fractions in different parts of the plant, and harvest index for crops. Potential evapotranspiration is calculated using the Thornthwaite equation (1948). Total SOC and the proportions of the total C that is inert are added as inputs. The amount of inert organic matter has been estimated in these calculations using an equation derived by Falloon et al. (1998). The ECOSSE model then estimates the amount of organic matter input from plant material by adjusting plant inputs until the simulated SOC at steady state matches the measured values (Wong et al. 2013). Plant material is divided into resistant and decomposable material, based on vegetation-specific ratios (as used in the RothC model; Coleman and Jenkinson 1996).

The ECOSSE model can simulate the soil profile up a depth of three metres, the soil being divided into 5-cm layers to facilitate the accurate simulation of processes to depth. During the decomposition process, material is exchanged between the SOM pools according to first-order rate

equations, characterized by a specific rate constant for each pool. The rate constant of each pool is modified depending on the temperature, water content, plant cover and pH of the soil. The N content of the soil follows the decomposition of the SOM, with a stable C/N ratio defined for each SOM pool at a given pH, and N being either mineralized or immobilized to maintain that ratio. Nitrogen is released from decomposing SOM as ammonium (NH4?) and nitrified to nitrate (NO3-). Carbon and N may be lost from the soil by leaching of NO3-, dissolved organic C, and dissolved organic N, denitrification to nitric oxide (NO) and N2O, volatilization of NH4? or off-take of NH4? and NO3- in the plant. Carbon and N may be returned to the soil by plant inputs, inorganic fertilizers, atmospheric deposition or organic amendments (e.g. manure, crop residues). Figure 1 shows the structure of C (a) and N (b) components in ECOSSE model and emissions of the different gases, i.e. CO2, CH4, N2O, NH3 and N2. More details about the ECOSSE approach are found in Smith et al. (2010a).

Spatial simulations

Application of the ECOSSE model for spatial simulation of GHG and SOC fluxes was carried out for the whole of

Wales on a 1 km2 soil grid basis. Grid simulations represent the five dominant soil types in each grid cell to capture soil heterogeneity at the sub-grid cell level. Each grid cell value in the model output represents the area-weighted mean of the simulations carried out for each soil type in the grid cell. Land cover was obtained from the Land Cover Map (LCM2007; Morton et al. 2011) simulating three main land uses (arable, grassland and forest). The land use is not linked to the data from the crop yields as such; rather, it is used to calculate the spatial average of emissions per unit area in each grid cell. Rotational grassland is included in arable land use, as the grass ley phase forms part of an arable crop rotation (Richards et al.; unpublished).

As is common practice for soils models (Coleman and Jenkinson 1996; Smith et al. 2010a), ECOSSE was initialized before running each simulation, based on the assumption that the SOC in the soil column was in steady state under the initial land use at the start of the simulation. The equilibrium assumption is used to initialize many biogeochemical models and is used to ensure reasonable distribution of soil C among the various pools in the model. Alternative methods have been proposed (e.g. Hashimoto et al. 2011), but when compared with different methods, the equilibrium run has been shown to be the most robust

Fig. 1 Structure of C (a) and N (b) components in ECOSSE model showing emissions of the different gases, i.e. CO2, CH4, N2O, NH3 and N2. BIO biomass, HUM humus, RPM resistant plant material, DPM decomposable plant material, IOM inert organic matter, NPP net primary production, DON dissolve organic N and LU land use. Adapted from Smith et al. (2010a)

(e.g. Senapati et al. 2013). So whist many soils are unlikely to be in equilibrium in reality, the equilibrium assumption provides the most robust initialization procedure, and post-initialization outputs from the model are relatively insensitive to the validity of the assumption (e.g. Senapati et al. 2013). Physical disturbance of SOM during cultivation is simulated by homogenizing the vertical distribution of the SOM pools down to the cultivation depth. For all land uses, the changes in GHG and SOC fluxes are calculated for the top metre of the soil profile. Only the top metre is considered, because this is the depth to which soil parameters are provided by the soil database. Results of GHG and SOC emissions were all reported in terms of CO2-equivalent values (CO2e) using the IPCC 100-year global warming potentials (GWPs) (IPCC 2001). The recent IPCC Fifth Assessment Report (2013) provided updated GWPs, but for consistency and compliance with IPCC National GHG Inventories, we have used the IPCC 2001 GWP values, where N2O has a GWP of 296 and CH4 has a GWP of 23. The net GHG balance represents the combined impact of changes in N2O, CH4 and CO2 from SOC change (all expressed as CO2e) and calculated as the sum of N2O and CH4 fluxes, minus the change in SOC. A positive net GHG balance is harmful (i.e. emissions to the atmosphere), and a negative net GHG balance is beneficial (i.e. removals from the atmosphere), discounting all other factors.

In ECOSSE, emissions of N due to denitrification and partial nitrification are simulated. The emissions are then partitioned into N2, N2O and NO according to the water and nitrate content of the soil. From the fully nitrified N, only 2 % is lost as gas, with 40 % of this gas as NO and 60 % as N2O. From the partially nitrified N, 2 % is assumed to be lost as gas at field capacity with a linear decrease in this loss as water content declines. The amount of N2O release due to nitrification can be calculated by Eq. 1 below:

Uf X + («gas X(1 - UNOX N„ (1)

where Nn,N2O is the amount of N2O during nitrification (kg N ha-1), Wc is the amount of water held above the permanent wilting point in a soil layer (mm layer-1), Wf is the amount of water held between field capacity and the permanent wilting point (mm layer-1), Uf 0.2 is the proportion of N2O produced due to partial nitrification at field capacity, ngas = 0.02 is the proportion of full nitrification lost as gas, nNo = 0.01 is the proportion of the full nitrification gaseous lost as NO and Nn is the amount of N nitrified (kg N ha-1).

The amount N lost by denitrification is partitioned into N2 and N2O. The calculation of N2O gas lost is given in Eq. 2 below:

Nd,N2O = (1 - (Pw X PnoJ) X Nd (2)

where Nd,N2O is the amount of N2O during denitrification (kg N ha-1), Nd is the amount of N denitrified (kg N ha-1) and pw and pNO3 are the proportions of denitrification into N according to water and nitrate contents of the soil, respectively. Further details can be found in Smith et al. (2010a).

ECOSSE simulates CH4 emissions using a process-based but simple approach, as the difference between CH4 production and CH4 oxidation, the oxidation process adding to emissions of carbon dioxide. The production of CH4 is then given by Eq. 3 below:

CH4 = (1 - a - b) (3)

where a is the proportion of decomposing materials partitioned to biomass, and b is the proportion partitioned to humus. Further details can be found in Smith et al. (2010a).

Soil data

The soil data were derived from the national soil map of Wales and are the product of 60 years of soil survey as described by Falloon et al. (1998). This database provides soil data to a depth of 1 m at a resolution of 1 km, for the dominant soil types under each of the three land use types (arable, grass, forest) in each grid cell. Data for organic C content, bulk density, sand, silt and clay faction from the database were used to drive ECOSSE. Data on the waterholding capacities of soils are not included in the database so these were estimated using pedotransfer functions. For non-peat soils, the British Soil Survey pedotransfer functions were used (Hutson and Cass 1987), which performed well in evaluations (Givi et al. 2004). The soil database also provides the percentage cover of each soil type in each grid cell. The model is run for each dominant soil type in each grid cell and the output area-weighted by the percentage cover in each grid cell to calculate the mean soil responses.

Climate data

Spatial ECOSSE simulations require monthly precipitation and air temperature to drive the soil water model and to determine temperature and moisture rate modifiers for the soil processes. The meteorological data were taken from the UKCP09 Spatially Coherent Projections (Murphy et al. 2009). UKCP09 provides, for high and low emissions scenarios, average monthly temperature and precipitation for Wales on a 25-km UKCP09 rotated pole grid for overlapping 30-year periods centred upon decades ranging from the 2020s to the 2080s. The data were re-projected to the British National Grid for compatibility with other data used in this application of ECOSSE.

To investigate the effects of climate change on GHG and SOC fluxes, two climate scenarios (high and low emission scenarios) for a 35-year period running from 2015 to 2050 were used and compared to the baseline climate (1961-1990). The mean monthly precipitation is relatively unaffected by future climate; however, the mean monthly temperature increases fairly uniformly by around 2 °C. The UKCP09 low and high emission climate scenarios correspond to the B1 and A1F1 emission scenarios of the Forster et al. (2007), respectively.

Yield data

The ECOSSE model can use yield data for each land use type to adjust the plant inputs calculated at steady state (Smith et al. 2010a). Yield data for the different arable crops were obtained from EUROSTAT (2014), whilst biomass data for other land uses were estimated using the Miami model (Lieth 1975). Miami is an empirical NPP model that estimates annual NPP from mean annual temperature and precipitation. Yield estimates for grass and forest are obtained using NPP estimates from Miami, which are then linearly rescaled according to observed peak yields (Living Countryside, 2013) to reflect differences in grass and forest productivity. The model equations are:

NPP = min(NPPT; NPPP) (4)

with NPPt = 3000 (1 + exp(1.315 - 0.119 T))-1 (5)

NPPp = 3000 (1 - exp(-0.000664 - 0.119 P)) (6)

where T is mean annual temperature (C) and P is total annual precipitation (mm). The Miami estimate of NPP was calculated for each decade in each grid cell using the same meteorological data as used in ECOSSE and was used to modify the steady state plant inputs to the soil using the ratio of calculated NPP to the NPP at steady state.

Fertilizer application

Nitrogen fertilizer was applied in the form of inorganic fertilizer (ammonium nitrate) which is the main form of N fertilizer uses in the UK (British Survey of Fertilizer Practices 2013) and at a rate equal to the annual crop N demand. Crop N demand is a function of plant yield and the C/N ratio of the plant. Full fertilizer application rate (100 %) meets 100 % of the annual crop N demand, whilst 80 and 60 % fertilizer application rate meets only 80 and 60 % of the annual crop N demand, respectively. Arable and grasslands are assumed to be fertilized, whist forest is assumed to remain unfertilized.


Estimated present greenhouse gas fluxes in Wales

Figure 2 shows the predicted mean annual net GHG fluxes under baseline climate (1961-1990) for Wales. Fluxes of GHGs were variable, depending on the land use investigated. These variations in GHG fluxes resulted in variations in the amount of net GHG balance between the different land uses as shown in Table 1 (positive means GHG balance is detrimental and negative means GHG balance is beneficial). The highest emitting land uses are grass and arable, with an effective net GHG balance of 0.405 and 0.191 t CO2e ha-1 y-1, respectively, accounting for the available land cover. The net fluxes from the forest land use of 0.033 t CO2e ha-1 y-1 are relatively small compared with that from the grass and arable land uses. For all land uses, N2O fluxes were the highest contributor to the net GHG balance, especially for the grass and arable land uses, where N fertilizer was applied. However, fluxes of N2O from the forest land use were low and contributed less to the net GHG balance (Table 1). For all land uses, fluxes of CH4 were very low and represent a small sink for atmospheric C. The overall annual average uptake of CH4 is 0.008 t CO2e ha-1 y-1 (Table 1). Likewise, the fluxes of SOC were a minor sink with an overall average C uptake of 0.013 t CO2e ha- y- (Table 1). The overall average net GHG balance, from the three investigated land uses across Wales, combining all gas fluxes is equivalent to 0.2 t CO2e ha- y- . Considering the Welsh land use area of 1,857,690 ha (Office for National Statistics 2005), where agriculture (grass and arable) accounts for 75 % and forestry accounts for 13 %, the calculated total annual GHG fluxes from the three investigated land uses in Wales under the baseline climate (1961-1990) suggest a total flux of 0.34 Mt CO2e y-1.

Effects of the Glastir measure of reducing nitrogen fertilizer application rates on GHG and SOC fluxes in Wales

Application of lower N fertilization rates compared with the baseline resulted in lower N2O fluxes and thereby lower net GHG fluxes from soils (Table 2). Here, N2O decreased from 0.230 t CO2e ha-1 y-1 (at baseline) to 0.197 t CO2e ha-1 y-1 (at 80 % crop N demand) and 0.172 t CO2e ha-1 y-1 (at 60 crop N demand). Figure 3 shows the predicted changes in annual net GHG fluxes from the grass and arable land uses at baseline (100 % crop N demand) compared to the 40 %reduced fertilizer application rate scenario (60 % crop N demand) for Wales.

The CH4 and SOC fluxes were not affected by reducing N fertilizer application rate (Table 2). The amounts of CH4

Fig. 2 Simulated mean annual net GHG fluxes (effective t CO2- which are rescaled by the fraction of the land cover within each grid

e ha-1 y-1) from Welsh fertilized grassland (a), fertilized arable land cell, thus giving the effective emissions per hectare across each grid (b) and forest (c), at baseline climate (1961-1990). Results are for cell. Light grey areas show zero flux; dark grey areas show zero land

emissions per hectare of the specified land cover in each grid cell, cover

and SOC fluxes, at all fertilization scenarios, represented small sinks of 0.008 and 0.013 t CO2e ha-1 y-1, respectively (Table 2). Reducing applied N fertilizer by 20 % reduced annual N2O fluxes from 0.44 to 0.37 t CO2e ha-1 (-15 %) and from 0.20 to 0.17 t CO2e ha-1 (-17 %) for the grass and arable lands, respectively (Table 3). Reducing applied N fertilizer by 40 % resulted in reducing annual N2O fluxes from 0.44 to 0.32 t CO2e ha-1 (-25 %) for the grassland and from 0.20 to 0.14 t CO2e ha-1 (-32 %) for the arable land (Table 3). The overall annual N2O fluxes, from all land uses, reduced from 0.23 to 0.20 (-15 %) and 0.17 (-25 %) t CO2e ha-1 for 20 % and 40 % N fertilizer reductions, respectively. Consequently, the annual net GHG balance reduced from 0.209 to 0.202 (for 20 % reduction) and 0.177 (for 40 % N reduction) t CO2e ha-1 y-1 (Table 2). This is equivalent to annual reductions in C

Table 1 ECOSSE-estimated mean annual GHG (N2O and CH4), SOC fluxes and net GHG balance (effective t CO2e ha-1 y-1; i.e. fluxes rescaled according to available land cover) at baseline climate 1961-1990, for Wales

Ecosystem n2o CH4 SOC Net GHG balance

Grassland 0.441 -0.014 -0.022 0.405

Arable land 0.200 -0.002 -0.007 0.191

Forest 0.050 -0.007 -0.010 0.033

Average 0.230 -0.008 -0.013 0.209

loss of 7 and 25 t CO2e ha-1 for the 20 and 40 % N fertilizer reductions, respectively, compared to the baseline (application of 100 % crop N demand).

Effects of climate change on GHG and SOC fluxes and net primary production for Wales

The ECOSSE model was applied to assess the effects of climate change on GHG and SOC fluxes and NPP in Wales. Two future climate scenarios (low and high; 2015-2050) were compared with the baseline climate (1961-1990). Under climate change, N2O and SOC fluxes, for all land uses and both climate scenarios, were increased, whilst CH4 fluxes were decreased (Table 4). However, N2O dominated the flux change. The CH4 fluxes remain a small C sink, whilst SOC fluxes became a small C source. The annual net GHG fluxes increased from 0.209 t CO2e ha-1 y-1 at the baseline climate scenario, to 0.215 and 0.229 t CO2e ha-1 y-1 at the low and high emission climate scenarios, respectively (Table 5). Figure 4 shows the predicted changes in the net annual GHG flux (t CO2e ha- y- ) for the different land uses between the baseline and high emission climate change scenario. The NPP values under the low and high warming climate scenarios were increased by 8 % and 10 % compared to that at baseline, respectively (Table 5). The difference between the two climate scenarios is, however, small (about ± 2 %).

Table 2 ECOSSE estimated changes in net annual GHG (N2O and CH4), SOC fluxes and net GHG balance (effective t CO2e/ha) due to reduced N fertilization rate in Wales

Scenario N2O CH4 SOC The net GHG balance % change in net GHG

Baseline 0.230 -0.008 -0.013 0.209 -

20 % fertilizer N reduction 0.197 -0.008 -0.013 0.202 7

40 % fertilizer N reduction 0.172 -0.008 -0.013 0.177 25

Ecosystem Baseline N2O 20 % fertilizer N reduction 40 % fertilizer N reduction % change

-20% -40%

Fig. 3 Simulated change in annual GHG flux (effective t CO2e ha-1 y-1) from Welsh fertilized grassland (a) and fertilized arable land (b) between the baseline and 60 % N fertilizer application rates. Light grey areas show zero change; dark grey areas show zero land cover

Table 3 ECOSSE-estimated mean annual N2O (t CO2e ha-1) at baseline and two reduced N fertilization rates (- 20 and -40 %) at baseline climate, 1961-1990

Grassland 0.44 Arable land 0.20 Forest 0.05

0.37 0.17 n/a

Table 4 ECOSSE-simulated mean annual N2O, CH4 and SOC fluxes and net GHG balance (effective t CO2e ha-1 y-1) at baseline climate and the low and high climate scenarios to 2050, for Wales

Gas flux Baseline Low climate scenario High climate scenario

N2O 0.230 0.238 0.243

CH4 -0.008 -0.010 -0.011

SOC -0.013 0.013 0.003

Net GHG balance 0.209 0.241 0.235

Table 5 ECOSSE-simulated mean annual net GHG fluxes (t CO2-e ha-1) and plant NPP (kt ha-1) at baseline climate and the low and high climate scenarios and percentage change by 2050, for Wales

Climate scenario GHG (t CO2e ha-1) % change NPP (kt ha- % 1) change

Baseline 0.209 n/a 230 n/a

Low scenario 0.215 +2 250 +08

High scenario 0.229 +3 254 +10


Estimated GHG and SOC fluxes at baseline climate

In this study, the ECOSSE model was used to estimate GHG and SOC fluxes from three main land uses (grass, arable and forest) in Wales. The overall annual net GHG fluxes of 0.2 t CO2e ha- y- , at baseline climate, shows

0.32 -15 % -25

0.14 -17 % -32

n/a n/a n/a

that Wales has a positive net GWP. The calculated total annual net GHG fluxes from the three investigated land uses across Wales are estimated at an equivalent GWP of 0.34 Mt CO2e y-1. ECOSSE was previously validated and tested for Europe (Bell et al. 2012) and Ireland (Khalil et al. 2013) and showed its credibility to predict GHG emissions. The model responded appropriately to changes in air temperature, timing of precipitation events, land use and system management, which have strong impacts on GHG and SOC fluxes. For all land uses, N2O was the main contributor to the net GHG emissions from Welsh soils, whilst CH4 and SOC fluxes were minor sinks. Grasslands and croplands are much larger emitters of N2O compared to forest due to the addition of N fertilizer. Freibauer and Kaltschmitt (2003) reported that the fluxes of N to the atmosphere and to ground water by leaching (Hack-ten Broeke et al. 1999) are greater from the intensively managed grasslands than from croplands due to higher nitrate input. Lee et al. (2006) and Abdalla et al. (2014) observed negative CH4 fluxes from cropland; however, fluxes from organic soils, which are typically poorly drained in their natural state (not included in this study), could be high (Levy et al. 2012). However, the estimate of an annual net GHG balance of 0.34 Mt CO2e y-1 assumes stability of existing land uses, and any recent historic land use change is a source of uncertainty in the result, e.g. a recent net change from permanent grass/forest to arable would lead to higher fluxes, whereas a net change from arable to forest/grass would lead to a lower net GHG balance (e.g. Guo and Gifford 2002).

Fig. 4 Simulated change in annual GHG flux (effective t CO2e ha 1 y from Welsh fertilized grassland (a), fertilized arable (b) and forest (c) between baseline and high climate change scenario. Light grey areas show zero change; dark grey areas show zero land cover

Welsh forests have net annual GHG fluxes of 0.033 t CO2e ha-1 y -1 which is low compared with the grass and arable land uses. For many temperate forests, high rates of C uptake have been reported (Aubinet et al. 2001; Berbigier et al. 2001). Land use plays an important role as one of the prime driving forces behind changes in the Earth's climatic system and thereby GHG emissions (Forster et al. 2007). Hayashi et al. (2015) reported that soil-plant interactions strongly affect GHG emissions, in which functions of plant roots influence biogeochemical factors (e.g. availability of oxygen, labile organic C and inorganic N). The N fertilizer application rate estimated by ECOSSE, calculated from the crop N demand, is equivalent to 137 kg N ha- . Compared with the measured average field N fertilizer application rate for Wales in the period 1974-2012, of 121 kg N ha-1 (British Survey of Fertilizer Practices 2013), the ECOSSE estimation is a little higher, but reasonable. This is especially promising considering that the field N fertilizer application rate in Wales has fallen in recent years, and hence the average for the modelled period is likely to be higher than the quoted value (British Survey of Fertilizer Practices 2013). We assumed that arable and grassland were fertilized at a rate required to meet the annual plant N demand, and for simplicity assumed that this was applied as inorganic N rather than manure. According to IPCC default emission factors, the emissions of N2O from inorganic fertilizer and manures are identical per unit of applied N, so assuming that N was applied in inorganic form would not be expected to greatly affect estimates of N2O emissions. Further, the absence of manure does not affect modelled CO2 emissions because the C added though manure would be subsumed into the adjusted plant C inputs required to meet the assumption of equilibrium SOC (Smith et al. 2010a).

Higher net GHG fluxes were observed in coastal areas (Figs. 2) where rainfall was higher and, consequently, soil moisture was high. Both soil moisture and soil N availability are required for high N2O fluxes. Similar results in field-level studies have been demonstrated in maize (McSwiney and Robertson 2005) and in forest and grassland systems (Maljanen et al. 2002; Abdalla et al. 2010). Soil moisture stimulates denitrification by temporarily lowering oxygen diffusion into the soil (Dobbie and Smith 2001) in addition to increasing the solubility of organic C and NO3- (Bowden and Bormann 1986). The strong relationship between N2O fluxes, and the interaction between soil moisture and soil NO3-, suggest that a high rainfall in winter and early spring, together with soil properties such as drainage characteristics, is important in the regulation of N2O flux from soils. Fluxes of GHG were also increased with increasing air temperature. Here, microbial soil processes such as decomposition, N mineralization, nitrification and nutrient uptake are dependent on temperature (Shaver et al. 2000; Shaw and Harte 2001), and consequently, so are GHG emissions (Abdalla et al. 2009).

Effects of the Glastir measure of reducing nitrogen fertilizer rates on GHG and SOC fluxes for Wales

ECOSSE was applied to assess the efficiency of the Glastir measure of reducing N fertilizer application rate to reduce GHG and SOC fluxes. There are no databases that define application of N fertilizer spatially. ECOSSE therefore estimates the N fertilizer application rate depending on the crop N demand. Under the reduced N fertilizer scenarios, more N is needed by the crop to achieve the full yield. This additional amount of N required by the crop comes from the soil and could lead to more SOC loss to the atmosphere. However, this effect is not accounted for by our model

approach which accounts only for the direct effects of reduced N on the flux of N2O, likely to be the largest impact on net GHG balance due to the high GWP of N2O.

Heavy utilization of synthetic N fertilizers on grass and arable lands typically results in high N2O fluxes from soils. However, reducing N fertilizer application rate by 20 and 40 % from the baseline resulted in an overall reduction in net GHG fluxes of 7 and 25 %, thereby lowering climate forcing. Nitrous oxide has a high GWP, thus reducing its emissions would result in beneficial change to net GHG balance (Forster et al. 2007). Availability of mineral N has a direct influence on N2O production from soils by providing N for both nitrification and denitrification (Baggs and Blum 2004). Reduced N fertilizer inputs lead to slow denitrification rates and a lower proportion of denitrified N emitted as N2O. Nitrous oxide fluxes from soils occur in short-lived bursts following the application of N fertilizers (Leahy et al. 2004; Skiba et al. 2012). The spatial variability in N2O fluxes is high (Van den Heuvel et al. 2008) and controlled by interacting abiotic and biotic factors, such as plants, micro-organisms, precipitation and nutrients. Ganesan et al. (2015) reported that N2O emission seasonal cycles in the UK are due to seasonality in fertilizer application and in environmental drivers such as temperature and rainfall. These factors may vary on an annual basis with a significant effect on the magnitude of the N2O flux. The flux is also expected to vary on a temporal basis depending on the dominant controlling factor (Mummey et al. 1997). In this study, less reduction in GHG fluxes was observed in coastal areas than in inland areas, due to higher precipitation in coastal areas. Higher precipitation leads to a higher soil water content that in turn leads to a higher denitrification rate. However, although the proportion of denitrified N emitted as N2O decreases, the net result is an increase in N2O emissions as soil water content increases. No change was predicted in CH4 or SOC fluxes due to reducing N fertilizer. This study reveals that the Glastir measure for reducing N fertilizer is an efficient way to reduce N2O flux and, consequently, GHG emissions from agriculture. However, to further reduce N2O emissions from agriculture, we recommend matching the supply of mineral N to its spatial and temporal needs by crops and pastures, increasing N use efficiency and using slow-release N or split fertilizer application methods (Abdalla et al. 2010).

Effects of climate change on GHG and SOC fluxes and net primary production for Wales

The effects of climate change on GHG and SOC fluxes and NPP for Wales by the year 2050 were investigated using two climate scenarios, low and high, that correspond to the B1 and A1F1 scenarios of the Forster et al. (2007),

respectively. The fluxes of GHG and NPP were not significantly affected by climate change. Under climate change, soil N increases due to increasing mineralization with changing temperature and precipitation (Wennman and Katterer 2006; Abdalla et al. 2010). Soil mineral N and N mineralization are the main sources of N2O production (Bouwman 1990). Soil characteristics and environmental conditions affect this mineralization (Schoenau and Campbell 1996). The fluxes of N2O have a threshold response to N, and the amount of N lost to the atmosphere depends on the amount of N taken up by plants (McSwiney and Robertson 2005). Changes in precipitation (Mearns 2003), temperature (Fiscus et al. 1997) and atmospheric CO2 concentrations could also have positive effects on the productivity of plants (Anwar et al. 2007). Many factors are responsible for CO2 effects. (1) High CO2 concentrations directly influence soil C availability by activating photosynthesis and decreasing photorespiration (Akita and Moss 1973). (2) High CO2 concentrations decrease stom-atal conductance (Morison and Gifford 1984) which decreases the transpiration rate per unit leaf area. Low transpiration rates increase the leaf temperature and thereby further increase photosynthesis (Acock 1990). An increase in photosynthesis combined with a decrease in transpiration leads to an increase in the water use efficiency. (3) Higher CO2 concentrations decrease the crop N concentration (Hocking and Meyer 1991). However, the increase in NPP predicted in this study is small (8-10 %). Nevertheless, the slight increase in N2O fluxes under climate change, in this study, was likely due to high temperature as precipitation shows little change. Temperature increase soil mineralization and denitrification and consequently N2O emissions (Abdalla et al. 2010). The SOC fluxes were changed from sinks at baseline climate to small sources under future climate scenarios, with a small difference between the two scenarios (±2 %). The future increased plant photosynthesis due to high CO2 concentration increases plant growth, belowground C input and substrate, leading to greater root and microbial activities and respiration (Zak et al. 2000). Previous studies indicate that prediction of soil C fluxes in response to climate change should consider changes in biotic factors, e.g. plant growth and substrate supply, and abiotic factors, e.g. temperature and moisture (Wang et al. 2007; Xia et al. 2009). Temperature is one of the main driving factors affecting C flux from soils (Jabro et al. 2008). The increase in plant growth and aboveground biomass produces more litter fall and may in the short term lead to higher C loss through soil respiration (Zak et al. 2000; Deng et al. 2010), but also to longer-term SOC accumulation. Both soil organic matter decomposition and microbial response to other perturbations, such as fertilization, temperature and rainfall, can increase (Wennman and Katterer 2006).

However, contradicting findings about the effects of rainfall and soil moisture are reported in the literature with increased (Jabro et al. 2008) or unaffected (Ding et al. 2007) C fluxes.

In this study, CH4 fluxes were low and not significantly affected by climate change. Future overall net GHG balance from Welsh soil will have a positive net GWP, as with baseline climate. Both changes in SOC and plant C inputs (i.e. plant growth) are due to changes in climate, mainly arising through changes in temperature and soil moisture (Smith et al. 2007). This suggests that climate change has little effect on GHG flux and NPP to 2050, and Welsh soils will continue to be a net source of GHG emissions by the year 2050.


In this study, the ECOSSE SOM model was used to estimate the GHG flux of soils in Wales under different climates and fertilizer levels. The GHG fluxes were significantly different between the three investigated land uses. In terms of GWP, the overall average annual net GHG balance at baseline climate (1961-1990) is equivalent to 0.2 t CO2e ha-1 y-1, which makes a total of 0.34 Mt CO2e y-1 for the whole of Wales. The Glastir measure of reducing N fertilizer by 20 and 40 % is effective and could reduce annual net GHG fluxes by 7 and 25 %, respectively, although there is some uncertainty in the resultant effect on yield and soil fertility. If the current N fertilizer application rate continues, climate change up to the year 2050 would not significantly affect net GHG balance or NPP from Welsh soils. The difference in results between the two climate scenarios is small (about ±2 %).

Acknowledgments This work was supported by a Grant from the Welsh Government (Glastir Monitoring and Evaluation Project— GMEP).

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Abdalla M, Wattenbach M, Smith P, Ambus P, Jones M, Williams M (2009) Application of the DNDC model to predict emissions of N2O from Irish agriculture. Geoderma 151:327-337. doi:10. 1016/j.geoderma.2009.04.021 Abdalla M, Jones M, Williams M (2010) Simulation of N2O fluxes from Irish arable soils: effect of climate change and

management. Biol Fert Soils 46:247-260. doi:10.1007/s00374-009-0424-5

Abdalla M, Hastings A, Helmy M, Prescher A, Osbourne B, Lanigan G, Forristal D, Killi D, Maratha P, Williams M, Rueangritsarakul K, Smith P, Nolan P, Jones MB (2014) Assessing the combined use of reduced tillage and cover crops for mitigating greenhouse gas emissions from arable ecosystem. Geoderma 223:9-20. doi:10.1016/j.geoderma.2014.01.030 Acock B (1990) Effects of carbon dioxide on photosynthesis, plant growth, and other processes. In: Impact of carbon dioxide, trace gases, and climate change on global agriculture. American Society of Agronomy, Madison WI ASA especial pub. No. 53 Akita S, Moss DN (1973) Photosynthetic responses to CO2 and light by maize and wheat leaves adjusted for constant stomatal apertures. Crop Sci 13:234-237. doi:10.2135/cropsci1973. 0011183X001300020025x Anwar MR, O'Leary G, McNeil D, Hossain H, Nelson R (2007) Climate change impact on rain fed wheat in south-eastern Australia. Field Crops Res 104:139-147. doi:10.1016/j.fcr.2007. 03.020

Aubinet M, Chermanne B, Vandenhaute M, Longdoz B, Yernaux M, Laitat E (2001) Long term carbon dioxide exchange above a mixed forest in the Belgian Ardennes. Agric For Meteo 108:293-315. doi:10.1016/S0168-1923(01)00244-1 Baggs EM, Blum H (2004) CH4 oxidation and emissions of CH4 and N2O from Lolium perenne swards under elevated atmospheric CO2. Soil Biol Biochem 36:713-723. doi:10.1016/j.soilbio.2004. 01.008

Bell MJ, Jones E, Smith J, Smith P, Yeluripati J, Augustin J, Juszczak R, Olejnik J, Sommer M (2012) Simulation of soil nitrogen, nitrous oxide emissions and mitigation scenarios at 3 European cropland sites using the ECOSSE model. Nutr Cycl Agroecosyst 92:161-181. doi:10.1007/s10705-011-9479-4 Berbigier P, Bonnefond JM, Mellmann P (2001) CO2 and water vapour fluxes for 2 years above Euroflux forest site. Agric For Meteo 108:183-197. doi:10.1016/s0168-1923(01)00240-4 Bernhardt ES, Barber JJ, Pippen JS, Taneva L, Andrews JA, Schlesinger WH (2006) Long-term effects of Free Air CO2 enrichment (FACE) on soil respiration. Biogeochem 77:91-116. doi:10.1007/s10533-005-1062-0 Bouwman AF (1990) Exchange of greenhouse gas between terrestrial ecosystems and atmosphere. In: Bouwman AF (ed) Soil and the greenhouse effects. Wiley, Chichester, pp 61-127 Bouwman AF, Boumans LJM, Batjes NH (2002) N2O and NO emissions from fertilized fields. Summary of available measurement data. Glob Biogeochem Cycl 16:1080. doi:10.1029/ 2001GB00181

Bowden WB, Bormann FH (1986) Transport and loss of nitrous oxide in soil water after forest clear cutting. Science 233:867-869. doi:10.1126/science.233.4766.867 British Survey of Fertilizer Practices (2013) Fertilizer use on farm crops for crop year 2012. uploads/system/uploads/attachment_data/file/192605/fertiliser use-report2012-25apr13.pdf. Accessed 26 June 2015 Coleman K, Jenkinson DS (1996) ROTHC-26.3-A model for the turnover of carbon in soil. In: Evaluation of soil organic matter models using existing long-term datasets, NATO ASI Series I, vol 38, Springer, Heidelberg, pp 237-246 Defra (2013) Department for Environment, Food and Rural Affairs. Protecting our Water, Soil and Air. A Code of Good Agricultural Practice for farmers, growers and land managers. ISBN 978 0 11 243284 5. Accessed 26 June 2015 Deng Q, Zhou G, Liu J, Liu S, Duan H, Zhang D (2010) Responses of soil respiration to elevated carbon dioxide and nitrogen addition in young subtropical forest ecosystems in China. Biogeoscience 7:315-328. doi:10.5194/bg-7-315-2010

Ding W, Cai Y, Cai Z, Yagi K, Zheng X (2007) Soil respiration under maize crops: effects of water, temperature, and nitrogen fertilization. Soil Sci Soc A J 71:944-951. doi:10.2136/ sssaj2006.0160

Dobbie KE, Smith KA (2001) The effects of temperature, water filled pore space and land use on N2O emissions from imperfectly drained gleysol. Eur J Soil Sci 52:667-673. doi:10.1046/j.1365-2389.2001.00395.x Dondini M, Jones EO, Richards M, Pogson M, Rowe RL, Keith AM, Perks MP, McNamara NP, Smith JU, Smith P (2015) Evaluation of the ECOSSE model for simulating soil carbon under short rotation forestry energy crops in Britain. Glob Chan Biol Bioeng 7(3):527-540. doi:10.1111/gcbb.12154 EUROSTAT (2014) Areas harvested, yields, production by NUTS 2

regions. Accessed 14 July 2014 Falloon P, Smith P, Coleman K, Marshall S (1998) Estimating the size of the inert organic matter pool from total soil organic carbon content for use in the Rothamsted Carbon Model. Soil Biol Biochem 30:1207-1211. doi:10.1016/S0038-0717(97)00256-3

Fiscus EL, Reid CD, Miller JE, Heagle AS (1997) Elevated CO2 reduces O3 flux and O3-induced yield losses in soybeans: possible implications for elevated CO2 studies. J Exp Bot 48:307-313. doi:10.1093/jxb/48.2.307 Forster P, Ramaswamy V, Artaxo P, Berntsen T, Betts R, Fahey DW, Haywood J, Lean J, Lowe DC, Myhre G, Nganga J, Prinn R, Raga G, Schulz M, Van Dorland R, (2007) Changes in atmospheric constituents and in radiative forcing. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge Freibauer A, Kaltschmitt M (2003) Regionalized inventory of biogenic greenhouse gas emissions from European agriculture. Eur J Agron 19:135-160. doi:10.1016/S1161-0301(02)00020-5 Ganesan AL, Manning AJ, Grant A, Young D, Oram DE, Sturges WT, Moncrieff JB, O'Doherty S (2015) Quantifying methane and nitrous oxide emissions from the UK using a dense monitoring network. Atmos Chem Phys Discuss 15:857-886. doi:10.5194/acp-15-6393-2015 Giltrap DL, Li C, Saggar S (2010) DNDC: a process-based model of greenhouse gas fluxes from agricultural soils. Agric Ecosys Environ 136:292-300. doi:10.1016/j.agee.2009.06.014 Givi J, Prasher SO, Patel RM (2004) Evaluation of pedotransfer functions in predicting the soil water contents at field capacity and wilting point. Agric Water Manag 70:83-96. doi:10.1016/j. agwat.2004.06.009 Guo LB, Gifford RM (2002) Soil carbon stocks and land use change: a meta-analysis. Glob Chan Biol 8:345-360. doi:10.1046/j.1354-1013.2002.00486.x Hack-ten Broeke MJD, Schut AGT, Bouma J (1999) Effects on nitrate leaching and yield potential of implementing newly development sustainable land use systems for dairy farming on sandy soils in the Netherlands. Geoderma 91:217-235. doi:10.1016/S0016-7061(99)00015-4

Hashimoto S, Wattenbach M, Smith P (2011) A new scheme for initializing process-based ecosystem models by scaling soil carbon pools. Ecol Mod 222:3598-3602. doi:10.1016/j.ecolmo del.2011.08.011

Hayashi K, Tokida K, Kajiura M, Yanai M, Yano M (2015) Cropland soil-plant systems control production and consumption of methane and nitrous oxide and their emissions to the atmosphere. Soil Sci Plant Nutr 61:2-33. doi:10.1080/00380768.2014.994469

Hocking PJ, Meyer CP (1991) Carbon dioxide enrichment decreases critical nitrate and nitrogen concentrations in wheat. J Plant Nutr 14:571-584. doi:10.1080/01904169109364225 Hutson JL, Cass A (1987) A retentivity function for use in soil water simulation models. J Soil Sci 38:105-113. doi:10.1111/j.1365-2389.1987.tb02128.x IPCC (2001) Good practice guidance and uncertainty management in national greenhouse gas inventories. Intergovernmental Panel on Climate Change (IPCC), Institute for Global Environmental Strategies, Tokyo, Japan IPCC (2013) Summary for policymakers. In: Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge Jabro JD, Sainju U, Stevens WB, Evans RG (2008) Carbon dioxide flux as affected by tillage and irrigation in soil converted from perennial forages to annual crops. J Environ Manag 88:1478-1484. doi:10.1016/j.jenvman.2007.07.012 Janzen HH (2004) Carbon cycling in earth systems-a soil science perspective. Agric Ecosyst Environ 104:399-417. doi:10.1016/j. agee.2004.01.040 Kattenberg AF, Giorgi H, Grassl GA, Meehl JFB, Mitchell RJ, Stouffer T, Tokioka AJ, Weaver Wigley ML (1995) Climate models—projections of future climate, in climate change 1995. In: Houghton JT, Filho LGM, Callander BA, Harris N, Kattenberg A, Maskell K (eds) The science of climate change. Cambridge University Press, Cambridge, pp 285-357 Khalil MI, Richards M, Osborne B, Williams M, Miiller C (2013) Simulation and validation of greenhouse gas emissions and SOC stock changes in arable land using the ECOSSE model. Atmos Environ 81:616-624. doi:10.1016/j.atmosenv.2013.09.038 Leahy P, Kiely G, Scanlon TM (2004) Managed grasslands: a greenhouse gas sink or source? Geophys Res Lett 31:L20507. doi:10.1029/2004GL021161 Lee J, Six J, King AP, Van Kessel C, Rolston DE (2006) Tillage and field scale controls on greenhouse gas emissions. J Environ Qual 35:714-725. doi:10.2134/jeq2005.0337 Levy PE, Burden A, Cooper MDA, Dinsmore KJ, Drewer J, Evans C, Fowler D, Gaiawyn J, Gray A, Jones SK, Jones T, McNamara NP, Mills R, Ostle N, Sheppard LJ, Skiba U, Sowerby A, Ward SE, Zielinski P (2012) Methane emissions from soils: synthesis and analysis of a large UK data set. Glob Chan Biol 18:1657-1669. doi:10.1111/j.1365-2486.2011.02616.x Lieth H (1975) Modeling the primary productivity of the world. In: Primary Productivity of the Biosphere (eds) Ecological studies, vol 14. Springer, Berlin, pp 237-263. doi:10.1007/978-3-642-80913-2_12

Maljanen M, Martikainen PJ, Aaltonen H (2002) Short term variation in fluxes of carbon dioxide, nitrous oxide and methane in cultivated and forested organic boreal soils. Soil Biol Biochem 34:577-584. doi:10.1016/S0038-0717(01)00213-9 McSwiney CP, Robertson GP (2005) Non-linear response of N2O flux to incremental fertilizer addition in a continuous maize (Zea mays L.) cropping system. Glob Chan Biol 11:1712-1719. doi: 10.1111/j.1365-2486.2005.01040.x Mearns LO (2003) Issues in the impacts of climate variability and change on agriculture. Clim Chan 60:1-6. doi:10.1023/A: 1026077531798

Morison JIL, Gifford RM (1984) Plant growth and water use with limited water supply in high CO2 concentrations. 1. Leaf area, water use and transpiration. Func Plant Biol 11:361-374. doi:10. 1071/PP9840361

Morison J, Matthews R, Miller G, Perks M, Randle T, Vanguelova E, White M, Yamulki S (2012) Understanding the carbon and greenhouse gas balance of forests in Britain. Forestry Commission Research Report. Forestry Commission, Edinburgh. http:// Accessed 20 July 2014 Morton D, Rowland C, Wood C, Meek L, Marston C, Smith G, Wadsworth R, Simpson IC (2011) Final report for LCM2007— the new UK Land Cover Map. Countryside Survey Technical Report No. 11/07 NERC/Centre for Ecology & Hydrology (CEH Project Number: C03259). uk/outputs/land-cover-map-2007-final-report Mosier AR, Duxbury JM, Freney JR, Heinemeyer O, Minami K, Johnson DE (1998) Mitigating agricultural emissions of methane. Clim Chan 40:39-80. doi:10.1023/A:1005338731269 Mummey DL, Smith JL, Bolton H Jr (1997) Small-scale spatial and temporal variability of nitrous oxide flux from a shrub-steppe ecosystem. Soil Biol Biochem 29:1699-1706. doi:10.1016/ S0038-0717(97)00077-1 Murphy JM, Sexton DMH, Jenkins GJ, Booth B, Brown CC, Clark RT, Collins M, Harris GR, Kendon EJ, Betts RA, Brown SJ, Humphrey KA, McCarthy MP, McDonald RE, Stephens A, Wallace C, Warren R, Wilby R, Wood RA (2009) UK Climate projections science report: climate change projections. Met Office Hadley Centre, Exeter. http://ukclimateprojections.metof Office for National Statistics (2005) United Kingdom 2005. The Official Yearbook of the United Kingdom of Great Britain and Northern Ireland. London: The Stationery Office, p 279. ISBN 0-11-621738-3

Regina K, Pihlatie M, Esala M, Alakukku L (2006) Methane fluxes on boreal arable soils. Agric Ecosyst Environ 119:346-352. doi:10. 1016/j.agee.08.002 Schoenau JJ, Campbell CA (1996) Impact of crop residues on nutrient availability in conservation tillage systems. Can J Plant Sci 76:621-626. doi:10.4141/cjps96-111 Senapati N, Smith P, Wilson B, Yeluripati JB, Daniel H, Lockwood P, Ghosh S (2013) Projections of changes in grassland soil organic carbon under climate change are relatively insensitive to methods of model initialization. Eur J Soil Sci 64:229-238. doi:10.1111/ejss.12014 Shaver GR, Canadell J, Chapin FS III, Gurevitch J, Harte J, Henry G, Ineson P, Jonasson S, Melillo J, Pitelka L, Rustad L (2000) Global warming and terrestrial ecosystems: a conceptual framework for analysis. Bioscience 50:871-882. doi:10.1641/0006-3568(2000)050[0871:GWATEA]2.0.C0;2 Shaw MR, Harte J (2001) Control of litter decomposition in a subalpine meadow-sagebrush steppe ecotone under climate change. Ecol Appl 11:1206-1223. doi:10.2307/3061022 Skiba U, Jones SK, Dragosits U, Drewer J, Fowler D, Rees RM, Pappa VA, Cardenas L, Chadwick D, Yamulki S, Manning AJ (2012) UK emissions of the greenhouse gas nitrous oxide. Phil Trans R Soc B 367:1175-1185. doi:10.1098/rstb.2011.0356 Smith JU, Glendining MJ (1996) A decision support system for optimising the use of nitrogen in crop rotations. Rotations and cropping systems. Asp Appl Biol 47:103-110

Smith P, Chapman SJ, Scott WA, Black HIJ, Wattenbach M, Milne R, Campbell CD, Lilly A, Ostle N, Levy P, Lumsdon DG, Millard P, Towers W, Zaehle S, Smith JU (2007) Climate change cannot be entirely responsible for soil carbon loss observed in England and Wales, 1978-2003. Glob Chan Biol 13:2605-2609. doi:10. 1111/j.1365-2486.2007.01458.x Smith J, Gottschalk P, Bellarby J, Richards M, Nayak D, Coleman K, Hillier J, Wattenbach M, Aitkenhead M, Yeluripurti J, Farmer J, Smith P (2010a) Model to estimate carbon in organic soils-sequestration and emissions (ECOSSE) user-manual (pp 1-76). University of Aberdeen. uploads/soi450/ECOSSE%20User%20manual%20310810.pdf Smith JU, Gottschalk P, Bellarby J, Chapman S, Lilly A, Towers W, Bell J, Coleman K, Nayak DR, Richards MI, Hillier J, Flynn HC, Wattenbach M, Aitkenhead M, Yeluripurti JB, Farmer J, Milne R, Thomson A, Evans C, Whitmore AP, Falloon P, Smith P (2010b) Estimating changes in national soil carbon stocks using ECOSSE-a new model that includes upland organic soils. Part I. Model description and uncertainty in national scale simulations of Scotland. Clim Res 45:179-192. doi:10.3354/cr008 Thomas J, Thistlethwaite G, MacCarthy J, Pearson B, Murrells T, Pang Y, Passant N, Webb N, Conolly C, Cardenas L, Malcolm H, Thomson A (2011) Greenhouse gas inventories for England, Scotland, Wales and Northern Ireland: 1990-2009. Report to the Department for Energy and Climate Change, The Scottish Government, The Welsh Government and The Northern Ireland Department of Environment. Report number: AEAT/ENV/R/ 3222 Issue 1. ISBN: 978-0-9565155-5-1. Access 25 June 2015 Thornthwaite CW (1948) An approach toward a rational classification of climate. Geograp Rev 38:55-94. doi:10.1097/00010694-194807000-00007 Van den Heuvel RN, Hefting MM, Tan NCG, Jetten MSM, Verhoeven JTA (2008) Nitrous oxide hotspots at different spatial scales and governing factors for small scale hotspots. Sci Tot Environ 407:2325-2332. doi:10.1016/j.scitotenv.2008.11. 010

Wang W, Guo J, Oikawa AT (2007) Contribution of root to soil respiration and carbon balance in disturbed and undisturbed grassland communities, northeast China. J Biosci 32:375-384. doi:10.1007/s12038-007-0036-x Wennman P, Katterer T (2006) Effects of moisture and temperature on carbon and nitrogen mineralisation in mine tailing mixed with sewage sludge. J Environ Qual 5:1135-1141. doi:10.2134/ jeq2005.0142

Wong H, Hillier J, Clark DB, Smith JU, Smith P (2013) An explicit and computationally efficient method to initialise first-order-based soil organic matter models—The Geometric Series Solution (GSS). Ecol Mod 267:48-53. doi:10.1016/j.ecolmo del.2013.07.014

Xia J, Han Y, Zhang Z, Wan S (2009) Non-additive effect of day and night warming on soil respiration in a temperate steppe. Biogeosci Discuss 6:4385-4411 Zak DR, Pregitzer KS, King JS, Holmes WE (2000) Elevated atmospheric CO2, fine roots and the response of soil microorganisms: a review and hypothesis. New Phytol 147:201-222. doi:10.1046/j.1469-8137.2000.00687.x