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Developing an inventory of N2O emissions from British soils
Atmospheric Environment 36 (2002) 987–998
Developing an inventory of N2O emissions from British soils M. Sozanskaa,b,1,*, U. Skibaa, S. Metcalfeb b
a Centre for Ecology and Hydrology, Bush Estate, Penicuik EH26 0QB, UK Department of Geography, The University of Edinburgh, Drummond Street, Edinburgh, Scotland EH8 9XP, UK
Received 4 January 2001; received in revised form 17 August 2001; accepted 24 August 2001
Abstract A spatial inventory of N2O emissions from agricultural and non-agricultural soils in Great Britain was prepared using a simple regression model within a GIS framework. The regression model was based on published N2O data from soils of temperate climates. It describes emissions as a function of N input (N), water filled pore space (WFPS), soil temperature (Ts ) and land use (A): ln ðN2 OÞ ðkgN ha1 y1 Þ ¼ 2:7 þ 0:60ln N ðkgN ha1 y1 Þ þ 0:61ln WFPS ð%Þþ 0:035Ts ð1CÞ 0:99A: The regression model predicted the highest fluxes of 6–21 kg N ha1 y1 from grazed grasslands. On tilled land, predicted N2O emissions did not exceed 6 kg N ha1 y1, while fluxes below 0.1 kg N ha1 y1 were estimated for semi-natural land. N2O emissions from soils in spring and summer were a factor of 2–3 higher than in the remaining part of the year. Total N2O emissions for Great Britain were estimated at 127 kt N2O-N y1. Distribution maps of annual and seasonal N2O emissions outlined the areas with the largest fluxes as those of intensive livestock farming in wet western regions of Britain. The annual emissions predicted by this study were much higher for agricultural soils than those suggested by the IPCC emission factor of 1.25% (0.25–2.25%) of N input, which range between 3 and 9 kg N ha1 y1 for grasslands and 2 and 3 kg N ha1 y1 for tillage crops and predict a total emission from agricultural sources of 56 kt N2O-N y1. Main uncertainty of the linear regression model is caused by scaling from published short-term N2O emission data to annual averages. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Regression model; Geographical Information System (GIS); Nitrogen; Soil moisture; Temperature
1. Introduction In recent years, research has been focused on the development of accurate emission estimates for nitrous oxide (N2O), a long-lived greenhouse gas and precursor of stratospheric NOx (IPCC, 1997). Soils are the major source of N2O and are estimated to contribute 57% of the total global annual emission (IPCC, 1997). In soil, N2O is produced mainly by the biological processes of nitrification and denitrification. Nitrification is an *Corresponding author. Department of Geography, The University of Edinburgh, Drummond Street, Edinburgh, Scotland EH8 9XP, UK. E-mail address: [email protected] (M. Sozanska). 1 Current address: Department of Plant and Soil Science, Aberdeen University, Cruickshank Building, St. Machar Drive, Aberdeen AB24 3UU, UK.
aerobic process of NH+ 4 biological oxidation to NO2 and NO , and denitrification an anaerobic process in 3 which NO 3 or NO2 are reduced to gaseous products of N, including N2O (Bremner et al., 1981). The main variables controlling N2O production and emission are substrate availability (nitrogen), soil temperature and soil water content, and in the case of denitrification, labile organic carbon (Skiba and Smith, 2000). The Kyoto Protocol requires countries to provide national inventories for the main greenhouse gases, including N2O. The standard methodology for estimating N2O emissions from soil was presented by IPCC (2000). IPCC recommends estimating N2O emissions as a fraction of the N input to soil. This method was based on a simple regression model established by Bouwman (1996) who suggests one emission factor for all agricultural soils, regardless of variations in soil management and climate.
1352-2310/02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved. PII: S 1 3 5 2 - 2 3 1 0 ( 0 1 ) 0 0 4 4 1 - 1
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Variations in soil and climate conditions are considered by process-based models, which describe dynamic processes of nitrification and denitrification. Those models are mainly applied at field scales. A few studies compare the performance of selected process-based models in different field locations (De Willigen, 1991; Baggs, 1997; Frolking et al., 1998). While models like the Denitrification–Decomposition (DNDC) model, the CENTURY model, CASA or ExpertN predict comparable rates of nitrogen cycling in the soils, but gaseous N losses vary among the models and need further parameterisation (Frolking et al., 1998). Detailed input data of site-specific soil parameters improve their performance, but their limited availability may restrict wider application of process-based models. At larger scales, however, physical models have been used in few studies. The DNDC model was successfully applied to predict N2O emissions from agricultural land in the USA (Li et al., 1994). Muller . et al. (1997) coupled a mechanistic model describing denitrification and nitrification processes in grasslands with a Geographical Information Systems (GIS) framework that estimated the distribution of N2O emission trends for an area of 10 km2 near Canterbury, New Zealand. The aim of this study was to provide a model of soil N2O emissions for Great Britain at the national scale that requires very few input data, but which is still able to account for land use and climatic variations. We used multiple regression analysis to describe soil N2O emissions from published field measurements in temperate climates. The regression model was then coupled with a GIS framework to estimate spatial distribution of N2O emissions from soils for Great Britain.
2. Data sources and methods 2.1. Nitrous oxide Results of field measurements of N2O emissions from temperate climates were compiled from the literature published between 1980 and 1997. The compiled data included information on the factors controlling the emissions: N fertiliser and atmospheric inputs, temperature (soil or air), soil moisture, organic C content, soil textural class, land use, frequency and time scale of measurements and the site location. Annual averages of soil temperature and moisture were estimated on the basis of published measurements, or directly derived from the publications (Sozanska, 1999). When the required information on atmospheric N input and soil temperature was not available it was derived from other published sources. Details of this data set can be obtained from Sozanska (1999).
2.2. Land use data Information on the extent of crops and grasslands, and livestock distribution according to different types, was obtained in raster format at 5 km grid resolution from the 1988 Agricultural Census (http://datalib.ed.ac.uk/EUDL/agriculture/griddata.html). The Centre for Ecology and Hydrology (CEH) Land Cover Map (Fuller et al., 1994) provided data of semi-natural vegetation classes at 1 km grid resolution. For the purpose of the multivariate regression analysis, land cover was reclassified into: managed grasslands, tilled land and semi-natural land, which were assigned with factors of 1, 2 and 3, respectively. 2.3. Total N input data The total N input (N) was calculated as the sum of inorganic and organic N fertiliser (including livestock excreta) applied to agricultural soils and the wet and dry atmospheric N deposition to both agricultural and seminatural land. Data on inorganic N fertiliser input to agricultural crops were obtained from published fertiliser guidelines (FMA, 1998; MAFF, 1988; Dyson, 1992). Mean inorganic N fertiliser input was estimated for each 5 km2 as a function of recommended N inputs and aerial-weighted crops. For grasslands, the varying fertiliser rates recommended for different stocking densities and type of grassland, grazed or cut, were taken into account. Livestock numbers obtained from the Agricultural Census (AC) were converted to stocking units. One stocking unit is equivalent to one dairy cow and produced 80 kg N y1 (Chadwick, 1995). Mean organic N input for each 5 km grid was calculated with data of livestock numbers and published nutrient content of organic manures (Dyson, 1992). It was assumed that all N from animal waste was distributed evenly onto the grassland soils within each 5 km2. N input estimated with fertiliser recommendations was evaluated with a survey of farmers in the Tyne–Clyde area of North Britain (Sozanska, 1999). Atmospheric N deposition data for 1992–94 were obtained for 20 km2 grids based on results from the UK’s monitoring networks (RGAR, 1997). Increased rainfall and wet deposition in areas of high altitude (>300 m a.s.l.) were taken into account. 2.4. Soil moisture and temperature The Climate LINK model (Barrow et al., 1993) provided information on air temperature and precipitation for each 10 km grid. Soil moisture, as described by water filled pore space (WFPS), was modelled using SPACTeach (Simmonds et al., 1995) based on rainfall intensity, evaporation rate, soil drainage, crop type and extent of plant cover. Soil
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drainage data were obtained from the Hydrology of Soil Types (HOST) classification (Boorman et al., 1995). The eleven soil response HOST classes were assigned into three classes of free, moderate and poor drainage. Based on the Climate LINK precipitation data, Great Britain was divided into five rainfall zones. Adjustments for the four seasons were made in respect to extent of plant cover and rainfall intensity. Default evaporation rates suggested by the SPACTeach model were used (Simmonds et al., 1995). Application of SPACTeach to estimate WFPS was presented in detail by Sozanska (1999). SpacTeach was designed for mineral agricultural soils and cannot be used to calculate soil moisture for peat. For peat, WFPS values were obtained from direct measurements at Great Dun Fell in NW England (MacDonald, 1997). It was assumed that these values were applicable to all peats in Great Britain. Mean WFPS of mineral and organic soils and their aerial proportions were used to map distributions of soil WFPS at a 5 km resolution in ArcInfo framework. Soil temperature (Ts ) at a depth of 30 cm was derived from monthly long-term air temperature data provided by the Climate LINK model. The heat flux theory, developed by Monteith and Unsworth (1990), was applied to calculate soil temperatures. Seasonal changes in soil temperature were predicted for each 5 km grid in an ArcInfo environment using the function of harmonic oscillation of temperature in soils developed by Campbell (1977). This method required spatial data of mean volumetric soil moisture content for each season (calculated with SPACTeach) and average values of thermal diffusivity (Monteith and Unsworth, 1990). The uncertainty of the modelled data was evaluated by soil temperature measurements from 40 selected meteorological stations. These were provided by the British Atmospheric Data Centre. 2.5. Statistical analyses Field measurement data of N2O emissions, N input, soil temperature, soil moisture, soil C content and land use type were analysed with multivariate statistical methods, which describe complex relationships between several variables. The aim was to establish important factors controlling the emissions and to define empirical function of their relationship that could be later used to predict N2O emissions from British soils. Factor analysis was applied to the compiled N2O data in order to identify the groups of inter-related factors controlling N2O emissions (Shaw and Wheeler, 1994). Multivariate regression analysis was used then to define the relationships between N2O emissions and the established controlling factors. Snedecor and Cochran (1978) and Webster (1997) present number of requirements that have to be met by the analysed data to ensure the
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appropriate application of linear regression method. In view of those requirements, prior to the regression analysis, non-normally distributed data were normalised by log transformation. The data were also tested for correlation between independent variables by Pearson’s correlation test and independence of all data points with Durbin–Watson test (Shaw and Wheeler, 1994). 2.6. Spatial GIS model The spatial distribution of soil N2O emissions was estimated within a framework of ArcInfo GRID at a scale of 5 km2. The scale, at which the model was applied, was determined by the resolution of the land cover data sets. The Agricultural Census data were available at the 5 km2 scale the CEH land cover data had a resolution of 1 km2. The spatial resolution of the model was adjusted to the resolution of the coarser data set according to the GIS rule that the amount of details in output results is the same or lower than that provided by input data (Johnston, 1998). Nitrous oxide emissions were estimated for four seasons: winter (December– February), spring (March–May), summer (June–August) and autumn (September–November). The seasonal time-step was determined by the details of climatic data (monthly values) and timings of N fertiliser inputs (accuracy within 3–4 weeks).
5. Results and discussion 5.1. A linear regression model to calculate soil N2O emissions The N2O data from the field studies represent a variety of land use and soil types from 59 studies in the temperate climates of the North–West Europe and North America (Sozanska, 1999). The regression model was based on N2O emission measurements made over a wide range of time periods and frequencies. Six per cent of the studies were shorter than 1 month, 46% lasted 1–2 months and 40% 1 yr and longer. Only 3% of all studies were long-term (>1 yr) and of high frequency (weekly measurements). Twenty-three per cent measured weekly N2O emissions for at least one season, and 11% were short-term (maximum 1 month) with at least daily frequency (hourly measurements for very short studies). All mean emission rates reported, even by short-term studies, were scaled up to annual rates (kg N2ON ha1 y1). Short-term studies characterise the event driven pulse in emission, i.e. after fertilisation, manure application, animal excretions, freeze-thaw cycles and ploughing (Skiba and Smith, 2000). Lower ‘background’ emissions between such events are measured less frequently. It is therefore expected that this approach lead to overestimation of mean annual N2O emissions.
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Future field and modelling studies should address high spatial and temporal uncertainties of scaling up N2O emissions. In the compiled N2O emission data set, intensively managed agricultural land was very well documented (72%), but semi-natural land contributed only a small part of the data set. Eleven studies (6%) investigated effects of organic fertilisers’ inputs to grasslands and arable land on N2O emissions. The most widely represented soil texture classes were: sandy loam (17%), silt loam (13.7%), clay (9.4%), sand (10%) and loam (8.7%). Heathlands and moorlands that play an important part in the British landscape, particularly in Scotland (41% of the land cover), were represented only by a single study of a fen environment in The Netherlands (Martikainen et al., 1993). It can therefore be expected that our model will be more accurate for agricultural land and its application to semi-natural environments will be limited. The compiled data showed great variability in N2O emissions for different land classes (Fig. 1). While N2O emissions were relatively low from semi-natural land (0.1–0.7 kg N2O-N ha1 y1), great differences were observed among the agricultural soils. The highest N2O emissions occurred on fallow soils and tillage crops, where on average 5.8 kg N2O-N ha1 y1 were emitted. The mean estimated N2O emissions from grasslands, vegetables and cereals were 3.6, 2.6 and 2.1 kg N2O-N ha1 y1, respectively. The greatest variability described by standard deviation (S.D.=27.7 kg
N2O-N ha1 y1) was observed for grasslands, where the emissions depended on the intensity of livestock management, the quantity and types of applied fertilisers (Fig. 1). Factor analysis outlined three important factor groupings with Eigenvalue ðlÞ >1 (Table 1). Ln (WFPS) and ln (soil C) had high loadings on factor 1 (>0.5). This factor grouping represents the production processes leading to N2O emissions that were influenced by decomposition processes (Granli and Bockman, 1994; Velthof et al., 1996). Factor 1 explained 37% of data variability (Table 1). Factor 2 was highly influenced by soil temperature. The third factor was influenced by the ln (N input); soil temperature had a negative factor loading. Only factors 2 and 3 were important for N2O emissions as implied by ln (N2O) loadings of 0.61 and 0.47 (Table 1). Multivariate regression analysis was carried out for the dependent factor of ln (N2O) and independent factors of ln (N input), ln (WFPS), ln (soil C) and Ts : Additionally, an independent factor (n) of random numbers was introduced to the analysis to test for the possibility of another existing factor controlling N2O emissions at the regional scale. The analysis showed that the random variable (n) was not significant ( p > 0:1). This finding does not totally exclude the possibility of other variables, as the analysis was limited by the availability of factors measured in the field. The results of the multivariate regression analysis indicated that the natural logarithm of N input was the
100 90
N2O emissions (kg N ha -1 y-1)
80 70 60 50 40 30 20 10 0 0
100
200
300
400
500
600
700
N fertiliser input (kg N ha-1)
Fig. 1. The relationship between N2O emissions and N input from different land uses in Europe and North America. The land uses were: cereals (E); maize and rape (m); vegetables (’); grassland (J); fallow (n); shrub (}) and forest (&).
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M. Sozanska et al. / Atmospheric Environment 36 (2002) 987–998 Table 1 Results of the factor analysis applied to the N2O data set, from which the regression equation was developed Variable
Factor 1
Factor 2
ln (N2O) ln N ln WFPS Soil temperature ln C Land use Eigenvalue % variance
Factor loadings 0.096 0.611 0.286 0.351 0.863 0.297 0.042 0.793 0.872 0.198 0.787 0.449 2.218 1.454 0.370 0.242
Factor 3
Factor 4
0.471 0.709 0.130 0.542 0.105 0.198 1.084 0.181
0.629 0.532 0.167 0.113 0.144 0.066 0.744 0.124
most significant single predictor of N2O emissions accounting for 21% of the data variability (Eq. (1), Table 2). This agrees with Bouwman’s model, which explained 80% of N2O emission variability with N input (Bouwman, 1996). This lower coefficient of determination in our model was caused by the much larger selection of field studies (59 compared to 20 by Bouwman, 1996). Smith et al. (1998) also found a poor correlation between the controlling parameters and N2O emissions and observed that the controlling parameters were limiting at different times. Land use was the second most important single ‘predictor’ of transformed N2O emissions that accounted for 10.7% of ln (N2O) variability (Eq. (2), Table 2). The opposite signs of N input and land use loadings on factor 3 (Table 1) suggest that the role of vegetation cover in the production of organic material and the uptake of available soil N may have a limiting effect on the influence of N input. Caution is advised towards using land use as a single predictor of N2O emissions, as this data set only distinguished between the three main land-class groups. Relationships between ln (N2O) and ln (WFPS) or Ts (Eqs. (3) and (4), Table 2) were rather weak (standard
error=1.9), for the variable Ts this was despite its high loading on factor 2 (Table 1). This is caused by a counter-active effect of factor 3 with an opposite sign of soil temperature loading and is probably due to limited measurements of soil climatic conditions. The relationship between ln (N2O) and ln C was not significant (r2 ¼ 0:2 %; F ¼ 0:37; p > 0:5), C content was therefore excluded from further multivariate regression analysis. The combination of ln (WFPS) and ln (N input) improved the significance of the regression (r2 ¼ 28%; Eq. (5), Table 2) and reflects the observation of many studies that soil moisture and nitrogen are important controllers of soil N2O emission (Davidson, 1991; Smith et al., 1998). Inclusion of Ts into the equation only moderately improved the significance (r2 ¼ 31%; Eq. (6)). The weakness of soil temperature as a predictor is not surprising. The relationship between changes in diurnal and seasonal soil temperature and N2O emission are easily suppressed when soil WFPS and inorganic N supply become suboptimal (Dobbie et al., 1999; Skiba et al., 1998a, b). Land use was a strong single controlling factor of N2O emissions, its introduction to the above group of variables resulted in an improvement of the coefficient of determination (r2 ) to 40% (Eq. (7), Table 2). This empirical relationship (Eq. (7)) was used to estimate N2O emissions from British soils. 5.2. The spatial distribution of soil N2O emissions in Great Britain The regression model was applied to the spatial data in ArcInfo GRID (Fig. 2) and soil N2O emissions were calculated for every 5 km2 in Great Britain (Fig. 3). Mean annual N2O emissions were predicted at 3.9 kg N2O-N ha1 y1, ranging from 0.1 to 20.4 kg N2ON ha1 y1 (S.D.=4.5). The range of N2O emissions corresponded with the predictions made by Conen et al. (2000) for agricultural soils in Scotland (o1 to >20 kg N2O-N ha1 y1). The highest N2O emissions of 6–21 kg N2O-N ha1 y1 were predicted in the areas with
Table 2 Results of the multivariate linear regression analysis of the N2O experimental dataa Model definition
n
r2
p
Equation
ln ðN2 OÞ ¼ 1:7 þ 0:64ln N ln ðN2 OÞ ¼ 2:9 0:99A ln ðN2 OÞ ¼ 1:8 þ 0:75ln WFPS ln ðN2 OÞ ¼ 0:6 þ 0:04Ts ln ðN2 OÞ ¼ 1:6 þ 0:09ln C ln ðN2 OÞ ¼ 4:9 þ 0:71ln Nþ0:75ln WFPS ln ðN2 OÞ ¼ 6:0 þ 0:75ln Nþ0:83ln WFPS þ 0:035Ts ln ðN2 OÞ ¼ 2:7 þ 0:60ln Nþ0:61ln WFPS þ 0:035Ts 0:99A
296 299 133 185 159 130 107 107
21.9 10.7 3.9 1.7 0.2 28.3 31.1 39.8
0.0 0.0 0.024 0.078 0.543 0.0 0.0 0.0
(1) (2) (3) (4) (5) (6) (7)
a Where N2O emissions are expressed in kg N ha1 y1, NFnitrogen input (kg N ha1 y1), WFPSFwater filled pore space (%), Ts Fsoil temperature (1C), AFland use type.
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Data Base Management System Monthly mean precipitation
Monthly mean air temperature
Recommendations of good fertiliser practice
Atmospheric N deposition (ITE model) Rain zones
LCM/ AC Vegetation cover (A)
Seasonal air temperature
Heat flux
Arc/Info GRID
SPACTeach Soil temperature (Ts)
N input to soils (N)
Soil moisture (WFPS)
Empirical models of N 2O emissions from soils
Fig. 2. The structure of the Arc/Info grid emission model used to provide the spatial inventory of soil N2O emissions for every 5 km2 grid in Great Britain.
dominant intensively managed grasslands along the fringes of the uplands in southern Scotland, southwest England and Wales (Fig. 3). In the east of Britain, where tilled land predominates, maximum N2O emissions of 6 kg N2O-N ha1 y1 were predicted. Semi-natural land contributed little to the annual N2O emissions (o1 kg N2O-N ha1 y1). Atmospheric N deposition contributed 16% of all N2O emissions from British soils (21 kt N y1), but on semi-natural land it provided the main source of N input. In the western uplands, where high precipitation and topography enhance atmospheric N deposition (RGAR, 1997), up to 2 kg N ha1 y1 of N2O were emitted. For the majority of Great Britain predicted N2O emissions were within 2 kg N2O-N ha1 y1 of the emissions estimated using the IPCC methodology (Bouwman, 1996). The areas with the greatest differences were intensively managed grasslands and seminatural land. In the lowlands of southwest England and Wales, where grazed grasslands with high livestock
densities dominate, the regression model predictions exceeded IPCC estimates by up to 8 kg N2O-N ha1 y1. This was mainly caused by accounting for variations in soil moisture content in the regression model presented here. The effect of seasonal variations in rainfall and temperature on N2O emissions was estimated using the regression model. Nitrous oxide emissions for spring and summer were a factor of 2 higher than for autumn and winter, but the broad spatial pattern was maintained for all seasons (Fig. 4). The effect of seasonal climatic variability was suppressed by the non-dynamic parameterFland use typeFand the N2O ‘hot spots’ were maintained at all seasons. 5.3. Model sensitivity It is a recognised problem that evaluating models defined for regional scales with field measurements at a much smaller scale is not necessarily possible (Davidson
M. Sozanska et al. / Atmospheric Environment 36 (2002) 987–998
Fig. 3. A spatial inventory of soil N2O emissions in Great Britain, calculated from the regression Eq. (7) (Table 2).
and Kingerlee, 1997), as simplified, national scale models cannot reproduce the high variability of site specific measurements. Currently, large-scale N2O measurements are not available. The limitations of the regression model were therefore tested by varying the mean annual values of the controlling factors according to the scenarios presented in Table 3. Rates of N2O emission increased in a linear fashion for all the parameters tested (Fig. 5). When N input was increased from 85 to 510 kg N ha1 y1 (50–300% of the average annual N input rate, Table 3), and WFPS and soil temperature were fixed at the average annual rate for Great Britain, the model increased N2O emission rates from 2.5 to 7.5 kg N2O-N ha1 y1 (Fig. 5a). The uncertainty in these N2O predictions depends on the magnitude of the uncertainty in the N applied and
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deposited to the different land classes. For arable land, N input rates are likely to range from 120 to 220 kg N ha1 y1 and the regression model would predict N2O emissions to vary by 1 kg N ha1 y1. On intensively managed grasslands, N input rates are more difficult to predict and can be as large as 510 kg N ha1 y1. Uncertainties in the actual N input by fertilisation and animal excretion would therefore suggest a maximum variation of the N2O emission by 3.7 kg N ha1 y1. The model uncertainty under average climatic conditions is likely to range from 25% to 50%. This uncertainty could be reduced by a more accurate quantification of N input to agricultural soils. The regression model predicts a linear increase in N2O emissions, proportional to the changes in soil moisture from very dry to very wet soils (Fig. 5b). This is not entirely correct, many experiments have shown a parabolic relationship between N2O emission and WFPS, with maximum emissions at 60% (Davidson, 1991) or 80–85% (Dobbie et al., 1999; Veltkamp et al., 1998). The current version of our regression model will therefore overestimate emissions in soils that are too dry for N2O production (WFPS o40%) and in near-saturated soils, where all produced N2O is further reduced to N2. However, unlike the variations in N input, the climatic variables play a much less important role in the regression model and are expected to cause less uncertainty in the predicted N2O emissions from agricultural areas. On seminatural land, where N inputs and N2O emissions are much smaller, changes and uncertainty in soil moisture and soil temperature can affect N2O emissions considerably. A reduction in soil moisture by the value of the maximum error estimated for the SPACTeach model (22.4%) caused a decrease in mean N2O emissions from all British soils by 0.8 kg N2O-N ha1 y1. The model predicts a linear increase in N2O emission with increasing soil temperature from 81C to 131C. A gradual increase in soil temperature by 1–51C caused only a very small increase in N2O emissions (Fig. 5c). The response of the model suggests that the maximum increase expected with a change of soil temperature by 51C would be 0.7 kg N2O-N ha1 y1. On the other hand, there might be no change in N2O emissions as soil temperature and soil moisture can act as counteracting controlling factors, e.g. in an extreme case of soil temperature increasing by 51C and soil moisture decreasing from 55% to 25% WFPS. 5.4. Total N2O emissions from British soils The regression model estimated total N2O emissions from soils in Great Britain at 127 kt N2O-N y1 (Table 4), agriculture contributed 84% of this emission. Fertiliser-induced emissions of N2O from arable and grassland accounted for 56 kt N2O-N y1. Nitrous oxide emissions from animal excreta and slurry accounted for
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Fig. 4. Seasonal variations of soil N2O emissions in Great Britain.
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M. Sozanska et al. / Atmospheric Environment 36 (2002) 987–998 Table 3 Scenarios applied to test the regression model N inputa
Soil moistureb
Soil temperatureb
50% of N (lower confidence limit of N applied to arable crops) 125% of N (upper confidence limit of N applied to arable crops) 145% of N (maximum N input rate to potatoes)
WFPS22.4%c (mean soil moisture error from comparison with the field capacity) WFPS30% (the above plus loss of moisture due to climate change) WFPS40% (as above)
Ts +1 (within the error limits of soil temperature) Ts +2 (as above)
155% of N (maximum N input rate to oilseed rape)
WFPS+10% (soil moisture error plus increase in precipitation due to climate change)
Ts +3 (error limits of predicted soil temperature) Ts +4 (the above plus climate warming)
266% of N (lower confidence limit of N to grasslands) 300% of N (upper limit of the above)
Ts +5 (as above)
a
Fractions were defined on the basis of a Survey of Farmers described by Sozanska (1999). General trends expected due to climate changes (Hulme and Jenkins, 1998). c Uncertainty of the SPACTeach model in predicting soil moisture for Great Britain as described above.
8
N input
-1
-1
Mean N2O emissions (kg N ha y )
b
soil moisture
soil temperature
6
4
2
0 0
(a)
200
400
Mean N input (kg N ha
600 -1
)
20
(b)
40
60
80
Mean WFPS (%)
100
8
(c)
9
10
11
12
13
14
o
Mean soil temperature ( C)
Fig. 5. Sensitivity of the regression Eq. (7) (Table 2) to changes in N input (a) WFPS (b) and soil temperature (c).
50 kt N2O-N y1 and N deposition accounted for 21 kt N2O-N y1 (Table 4). These annual emission rates would be equivalent to emission factors (percentage of Ninput lost as N2O) of 2.9% for arable land and grassland fertilised with inorganic N, 2.7% for animal waste and 5.7% for semi-natural soils. The predictions from the regression model are therefore larger than if they would have been predicted with the IPCC methodology (IPCC, 2000) and in comparison with other GB inventories (Chadwick et al., 1999; Salway et al., 1999). For agricultural soils fertilised with inorganic N, IPCC (2000) suggest an emission factor of 1.25%. Background emission is 1 kg N ha1 y1. Emission factor for untreated animal waste and several types of
animal storage systems is 2%. For atmospheric N deposition, IPCC (2000) suggest an emission factor of 1%. According to this approach, N2O emissions for GB would be calculated at 24.3 kt N2O-N y1 (inorganic fertiliser), 27.4 kt N2O-N y1 (animal waste) and 3.7 kt N2O-N y1 (atmospheric N deposition). The largest differences between the IPCC method and the regression model were observed in the atmospheric-N-deposition source of N2O. This is perhaps not surprising, as only 28% of all N2O emission studies investigated background emissions and the effects of atmospheric N inputs. Other field studies in Scotland and North England estimated average emission factor from atmospheric N deposition at 0.76%, with the range from
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Table 4 Soil N2O emissions calculated for the main sources in Great Britain Source
Total N input (kt N y1)
Total annual N2O emission (kt N y1)
regression model for groups of soils, climates and land use types. The main achievement of the regression model presented here is the spatial and temporal disaggregation of N2O emissions from soils in Great Britain.
6. Conclusions
Agricultural land Arable crops Tillage crops Vegetables Grazed grass Mown grass Organic N Totals for agricultural soils
667 98 45 592 502 1838 3742
17 3 1 18 17 50 106
Semi-natural land Atmospheric N deposition Totals for all soils
371 4113
21 127
0.2% to 15% (Skiba et al., 1998a, b). This corresponded with the emission factor used by the IPCC (2000). Field studies and IPCC methodology suggest that the regression model greatly overestimates N2O emissions from atmospheric N deposition. Further work should redefine the relationship between the emissions and their controlling factors in semi-natural environments once more field data are available. The differences between the estimates of N2O emissions from agricultural soils and input from animal waste with the regression model and the IPCC could have been a result of scaling up from short-term measurements to annual means. On the other hand, the larger estimates of the regression model are probably the additional effect of land use, soil moisture and temperature used to predict soil N2O emissions, in that respect the new estimates might be correct. Recent direct aircraft-based N2O measurements suggest that IPCC methodology underestimates total annual N2O emissions from British soils. The aircraft method calculated total annual N2O emissions from all sources at 160 kt N2O-N y1 (Fowler et al., 1999). Assuming that, as for the global emission (IPCC, 1997), 57% of the total N2O emissions in Great Britain is derived from soils, then the aircraft-based measurements suggest 90 kt N2O-N y1 are emitted from soils. This is higher than 61 kt N2ON y1 estimated by Salway et al. (1999), but lower than 127 kt N2O-N y1 calculated with the regression model. In principal, the regression model approach should provide a better way of predicting large-scale N2O emissions, by including their main controlling variables. This study shows, however, that not enough N2O emission data are available from long-term highfrequency field studies to derive an accurate regression model. This approach could be improved by varying
The regression model of N2O emissions from British soils was established through multivariate regression analysis of field measurement data from temperate climates. The model outlined four controlling factors of N2O emissions: land use type, N inputs to soils from agriculture and atmospheric deposition, soil moisture and temperature. Variations in N input to the soils and the wetness of the soil were the main drivers of the regression model, whereas the sensitivity to variations in soil temperature was low. The model offers a first attempt to develop a simple method for producing a spatial inventory of N2O emissions from soils for every 5 km2 grid in Great Britain. The spatial aspect of this approach helped to outline the areas of the highest soil N2O emissions. They were associated with intensive livestock farming and caused by high organic and inorganic N fertiliser inputs to grasslands. Those N2O-emission ‘hot spots’ were located in southern Scotland, southwest England and Wales, where soil moisture is high throughout the year, which increased N2O emissions further. The total emissions calculated for agricultural soils, animal waste and N deposition for Great Britain were much larger than those previously estimated. Further developments of the regression model are necessary to reduce the uncertainty of the current N2O emission estimate. Future studies should investigate the uncertainty of scaling up. Our main uncertainty in this paper was caused by creating an annual emission inventory based on mainly short-term N2O measurements. More long-term data from field measurements in semi-natural environments are needed to redefine the regression model. The newly defined model will need evaluation with N2O measurement data at the regional scale once they are available.
Acknowledgements We would like to acknowledge NERC for providing a Ph.D. CASE studentship for M. Sozanska (Reference: GT4/95/85). Special thanks go to all the people who contributed data to this project. They are Ronnie Milne and Ron Smith of CEH (Edinburgh), Alison Bayley of Data Library (the University of Edinburgh), David Morris of CEH (Wallingford) and the British Atmospheric Data Centre. We would also like to acknowledge Chris Place and Mike Mineter for their help with the software solutions in this project.
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N2O emissions from soils at three temperate agricultural sites: simulations of year-round measurements by four models. Nutrient Cycling in Agroecosystems 52, 77–105. Fuller, R.M., Groom, G.B., Jones, A.R., 1994. The Land Cover Map of Great Britain: an automated classification of Landsat Thematic Mapper data. Photogrametric Engineering and Remote Sensing 60, 553–562. Granli, T., Bockman, O.C., 1994. Nitrous oxide from agriculture. Norwegian Journal of Agricultural Sciences 12, 128. Hulme, M., Jenkins, G.J., 1998. Climate change scenarios for the UK: scientific report. UKCIP Technical Report No. 1, Climatic Research Unit, Norwich, 80pp. IPCC, 1997. Greenhouse Gas Inventory Workbook (revised 1996 guidelines for national gas inventories), Vol 2. IPCC, Bracknell. IPCC, 2000. Good practice guidance and uncertainty management in national greenhouse gas inventories, Institute for Global Environmetnal Strategies, Japan. Johnston, C.A., 1998. Geographic Information Systems in Ecology, 1st Edition. Blackwell Science, Edinburgh. Li, C., Frolking, S.E., Harris, R.C., Terry, R.E., 1994. Modelling nitrous oxide emissions from agriculture: a Florida case study. Chemosphere. 28 (7), 1401–1415. MacDonald, J.A., 1997. Methane oxidation in temperate and tropical soils. Doctoral Thesis, The University of Edinburgh. MAFF, 1988. Fertiliser Recommendations for Agricultural and Horticultural Crops, 5th Edition. Reference Book 209. HMSO, London, 194pp. Martikainen, P.J., Nykanen, H., Crill, P., Silvola, J., 1993. Effect of a lowered water table on nitrous oxide fluxes from northern peatlands. Nature 366, 51–53. Monteith, J.L., Unsworth, M.H., 1990. Principles of Environmental Physics, 2nd Edition. Edward Arnold, London, 291pp. Muller, . C., Sherlock, R.R., Cameron, K.C., Barringer, J.R.F., 1997. Applications of a mechanistic model to calculate nitrous oxide emissions at a national scale. In: Jarvis, S.C., Pain, B.F. (Eds.), Gaseous Nitrogen Emissions from Grasslands. CAB International, Wallingford, pp. 339–351. RGAR, 1997. Acid deposition in the United Kingdom 1992– 1994. Fourth Report of the Review Group on Acid Rain, AEA Technology plc, Abingdon, UK. Salway, A.G., Dore, C., Watterson, J., Murrells, T., 1999. Greenhouse gas inventories for England, Scotland, Wales and Northern Ireland: 1990 and 1995. A scoping study. Report AEAT-6196 Issue 1, AEA Technology, Culham, Oxon, UK. Shaw, G., Wheeler, D., 1994. Statistical Techniques in Geographical Analysis, 2nd Edition. David Fulton Publishers, London, 359pp. Simmonds, L., Schofield, J., Mullins, C., 1995. SPACTeach a computer assisted learning module exploring water movement in the soil-plant-atmosphere continuum. Software Manual, University of Aberdeen, UK, 49pp. Skiba, U., Smith, K.A., 2000. The control of nitrous oxide emissions from agricultural and natural soils. Chemosphere 2, 379–386. Skiba, U., Sheppard, L.J., MacDonald, L.J., Fowler, D., 1998a. Some key environmental variables controlling nitrous oxide
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emissions from agricultural and semi-natural soils in Scotland. Atmospheric Environment 32, 3311–3320. Skiba, U., Sheppard, L.J., Pitcairn, C.E.R., Leith, I., Crossley, A., van Dijk, S., Kennedy, V.H., Fowler, D., 1998b. Soil nitrous oxide and nitric oxide emissions as indicators of elevated atmospheric N deposition rates in seminatural ecosystems. Environmental Pollution 102, 457–461. Smith, K.A., McTaggart, I.P., Dobbie, K.E., Conen, F., 1998. Emissions of N2O from Scottish agricultural soils, as a function of fertilizer N. Nutrient Cycling in Agroecosystems 52, 123–130. Snedecor, G.W., Cochran, W.G., 1978. Statistical Methods. The Iowa State University Press, Iowa.
Sozanska, 1999. Distribution and amounts of nitric and nitrous oxide emissions from British soils. Doctoral Thesis, The University of Edinburgh. Velthof, G.L., Koops, J.G., Duyzer, J.H., Oenema, O., 1996. Prediction of nitrous oxide fluxes from managed grassland on peat soil using a simple empirical model. Netherlands Journal of Agricultural Science 44, 339–356. Veltkamp, E., Keller, M., Nunez, M., 1998. Effects of pasture management on N2O and NO emissions from soils in the humid tropics in Costa Rica. Global Biogeochemical Cycles 12, 71–79. Webster, R., 1997. Regression and functional relations. European Journal of Soil Science 48, 557–566.
Developing an inventory of N2O emissions from British soils M. Sozanskaa,b,1,*, U. Skibaa, S. Metcalfeb b
a Centre for Ecology and Hydrology, Bush Estate, Penicuik EH26 0QB, UK Department of Geography, The University of Edinburgh, Drummond Street, Edinburgh, Scotland EH8 9XP, UK
Received 4 January 2001; received in revised form 17 August 2001; accepted 24 August 2001
Abstract A spatial inventory of N2O emissions from agricultural and non-agricultural soils in Great Britain was prepared using a simple regression model within a GIS framework. The regression model was based on published N2O data from soils of temperate climates. It describes emissions as a function of N input (N), water filled pore space (WFPS), soil temperature (Ts ) and land use (A): ln ðN2 OÞ ðkgN ha1 y1 Þ ¼ 2:7 þ 0:60ln N ðkgN ha1 y1 Þ þ 0:61ln WFPS ð%Þþ 0:035Ts ð1CÞ 0:99A: The regression model predicted the highest fluxes of 6–21 kg N ha1 y1 from grazed grasslands. On tilled land, predicted N2O emissions did not exceed 6 kg N ha1 y1, while fluxes below 0.1 kg N ha1 y1 were estimated for semi-natural land. N2O emissions from soils in spring and summer were a factor of 2–3 higher than in the remaining part of the year. Total N2O emissions for Great Britain were estimated at 127 kt N2O-N y1. Distribution maps of annual and seasonal N2O emissions outlined the areas with the largest fluxes as those of intensive livestock farming in wet western regions of Britain. The annual emissions predicted by this study were much higher for agricultural soils than those suggested by the IPCC emission factor of 1.25% (0.25–2.25%) of N input, which range between 3 and 9 kg N ha1 y1 for grasslands and 2 and 3 kg N ha1 y1 for tillage crops and predict a total emission from agricultural sources of 56 kt N2O-N y1. Main uncertainty of the linear regression model is caused by scaling from published short-term N2O emission data to annual averages. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Regression model; Geographical Information System (GIS); Nitrogen; Soil moisture; Temperature
1. Introduction In recent years, research has been focused on the development of accurate emission estimates for nitrous oxide (N2O), a long-lived greenhouse gas and precursor of stratospheric NOx (IPCC, 1997). Soils are the major source of N2O and are estimated to contribute 57% of the total global annual emission (IPCC, 1997). In soil, N2O is produced mainly by the biological processes of nitrification and denitrification. Nitrification is an *Corresponding author. Department of Geography, The University of Edinburgh, Drummond Street, Edinburgh, Scotland EH8 9XP, UK. E-mail address: [email protected] (M. Sozanska). 1 Current address: Department of Plant and Soil Science, Aberdeen University, Cruickshank Building, St. Machar Drive, Aberdeen AB24 3UU, UK.
aerobic process of NH+ 4 biological oxidation to NO2 and NO , and denitrification an anaerobic process in 3 which NO 3 or NO2 are reduced to gaseous products of N, including N2O (Bremner et al., 1981). The main variables controlling N2O production and emission are substrate availability (nitrogen), soil temperature and soil water content, and in the case of denitrification, labile organic carbon (Skiba and Smith, 2000). The Kyoto Protocol requires countries to provide national inventories for the main greenhouse gases, including N2O. The standard methodology for estimating N2O emissions from soil was presented by IPCC (2000). IPCC recommends estimating N2O emissions as a fraction of the N input to soil. This method was based on a simple regression model established by Bouwman (1996) who suggests one emission factor for all agricultural soils, regardless of variations in soil management and climate.
1352-2310/02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved. PII: S 1 3 5 2 - 2 3 1 0 ( 0 1 ) 0 0 4 4 1 - 1
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Variations in soil and climate conditions are considered by process-based models, which describe dynamic processes of nitrification and denitrification. Those models are mainly applied at field scales. A few studies compare the performance of selected process-based models in different field locations (De Willigen, 1991; Baggs, 1997; Frolking et al., 1998). While models like the Denitrification–Decomposition (DNDC) model, the CENTURY model, CASA or ExpertN predict comparable rates of nitrogen cycling in the soils, but gaseous N losses vary among the models and need further parameterisation (Frolking et al., 1998). Detailed input data of site-specific soil parameters improve their performance, but their limited availability may restrict wider application of process-based models. At larger scales, however, physical models have been used in few studies. The DNDC model was successfully applied to predict N2O emissions from agricultural land in the USA (Li et al., 1994). Muller . et al. (1997) coupled a mechanistic model describing denitrification and nitrification processes in grasslands with a Geographical Information Systems (GIS) framework that estimated the distribution of N2O emission trends for an area of 10 km2 near Canterbury, New Zealand. The aim of this study was to provide a model of soil N2O emissions for Great Britain at the national scale that requires very few input data, but which is still able to account for land use and climatic variations. We used multiple regression analysis to describe soil N2O emissions from published field measurements in temperate climates. The regression model was then coupled with a GIS framework to estimate spatial distribution of N2O emissions from soils for Great Britain.
2. Data sources and methods 2.1. Nitrous oxide Results of field measurements of N2O emissions from temperate climates were compiled from the literature published between 1980 and 1997. The compiled data included information on the factors controlling the emissions: N fertiliser and atmospheric inputs, temperature (soil or air), soil moisture, organic C content, soil textural class, land use, frequency and time scale of measurements and the site location. Annual averages of soil temperature and moisture were estimated on the basis of published measurements, or directly derived from the publications (Sozanska, 1999). When the required information on atmospheric N input and soil temperature was not available it was derived from other published sources. Details of this data set can be obtained from Sozanska (1999).
2.2. Land use data Information on the extent of crops and grasslands, and livestock distribution according to different types, was obtained in raster format at 5 km grid resolution from the 1988 Agricultural Census (http://datalib.ed.ac.uk/EUDL/agriculture/griddata.html). The Centre for Ecology and Hydrology (CEH) Land Cover Map (Fuller et al., 1994) provided data of semi-natural vegetation classes at 1 km grid resolution. For the purpose of the multivariate regression analysis, land cover was reclassified into: managed grasslands, tilled land and semi-natural land, which were assigned with factors of 1, 2 and 3, respectively. 2.3. Total N input data The total N input (N) was calculated as the sum of inorganic and organic N fertiliser (including livestock excreta) applied to agricultural soils and the wet and dry atmospheric N deposition to both agricultural and seminatural land. Data on inorganic N fertiliser input to agricultural crops were obtained from published fertiliser guidelines (FMA, 1998; MAFF, 1988; Dyson, 1992). Mean inorganic N fertiliser input was estimated for each 5 km2 as a function of recommended N inputs and aerial-weighted crops. For grasslands, the varying fertiliser rates recommended for different stocking densities and type of grassland, grazed or cut, were taken into account. Livestock numbers obtained from the Agricultural Census (AC) were converted to stocking units. One stocking unit is equivalent to one dairy cow and produced 80 kg N y1 (Chadwick, 1995). Mean organic N input for each 5 km grid was calculated with data of livestock numbers and published nutrient content of organic manures (Dyson, 1992). It was assumed that all N from animal waste was distributed evenly onto the grassland soils within each 5 km2. N input estimated with fertiliser recommendations was evaluated with a survey of farmers in the Tyne–Clyde area of North Britain (Sozanska, 1999). Atmospheric N deposition data for 1992–94 were obtained for 20 km2 grids based on results from the UK’s monitoring networks (RGAR, 1997). Increased rainfall and wet deposition in areas of high altitude (>300 m a.s.l.) were taken into account. 2.4. Soil moisture and temperature The Climate LINK model (Barrow et al., 1993) provided information on air temperature and precipitation for each 10 km grid. Soil moisture, as described by water filled pore space (WFPS), was modelled using SPACTeach (Simmonds et al., 1995) based on rainfall intensity, evaporation rate, soil drainage, crop type and extent of plant cover. Soil
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drainage data were obtained from the Hydrology of Soil Types (HOST) classification (Boorman et al., 1995). The eleven soil response HOST classes were assigned into three classes of free, moderate and poor drainage. Based on the Climate LINK precipitation data, Great Britain was divided into five rainfall zones. Adjustments for the four seasons were made in respect to extent of plant cover and rainfall intensity. Default evaporation rates suggested by the SPACTeach model were used (Simmonds et al., 1995). Application of SPACTeach to estimate WFPS was presented in detail by Sozanska (1999). SpacTeach was designed for mineral agricultural soils and cannot be used to calculate soil moisture for peat. For peat, WFPS values were obtained from direct measurements at Great Dun Fell in NW England (MacDonald, 1997). It was assumed that these values were applicable to all peats in Great Britain. Mean WFPS of mineral and organic soils and their aerial proportions were used to map distributions of soil WFPS at a 5 km resolution in ArcInfo framework. Soil temperature (Ts ) at a depth of 30 cm was derived from monthly long-term air temperature data provided by the Climate LINK model. The heat flux theory, developed by Monteith and Unsworth (1990), was applied to calculate soil temperatures. Seasonal changes in soil temperature were predicted for each 5 km grid in an ArcInfo environment using the function of harmonic oscillation of temperature in soils developed by Campbell (1977). This method required spatial data of mean volumetric soil moisture content for each season (calculated with SPACTeach) and average values of thermal diffusivity (Monteith and Unsworth, 1990). The uncertainty of the modelled data was evaluated by soil temperature measurements from 40 selected meteorological stations. These were provided by the British Atmospheric Data Centre. 2.5. Statistical analyses Field measurement data of N2O emissions, N input, soil temperature, soil moisture, soil C content and land use type were analysed with multivariate statistical methods, which describe complex relationships between several variables. The aim was to establish important factors controlling the emissions and to define empirical function of their relationship that could be later used to predict N2O emissions from British soils. Factor analysis was applied to the compiled N2O data in order to identify the groups of inter-related factors controlling N2O emissions (Shaw and Wheeler, 1994). Multivariate regression analysis was used then to define the relationships between N2O emissions and the established controlling factors. Snedecor and Cochran (1978) and Webster (1997) present number of requirements that have to be met by the analysed data to ensure the
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appropriate application of linear regression method. In view of those requirements, prior to the regression analysis, non-normally distributed data were normalised by log transformation. The data were also tested for correlation between independent variables by Pearson’s correlation test and independence of all data points with Durbin–Watson test (Shaw and Wheeler, 1994). 2.6. Spatial GIS model The spatial distribution of soil N2O emissions was estimated within a framework of ArcInfo GRID at a scale of 5 km2. The scale, at which the model was applied, was determined by the resolution of the land cover data sets. The Agricultural Census data were available at the 5 km2 scale the CEH land cover data had a resolution of 1 km2. The spatial resolution of the model was adjusted to the resolution of the coarser data set according to the GIS rule that the amount of details in output results is the same or lower than that provided by input data (Johnston, 1998). Nitrous oxide emissions were estimated for four seasons: winter (December– February), spring (March–May), summer (June–August) and autumn (September–November). The seasonal time-step was determined by the details of climatic data (monthly values) and timings of N fertiliser inputs (accuracy within 3–4 weeks).
5. Results and discussion 5.1. A linear regression model to calculate soil N2O emissions The N2O data from the field studies represent a variety of land use and soil types from 59 studies in the temperate climates of the North–West Europe and North America (Sozanska, 1999). The regression model was based on N2O emission measurements made over a wide range of time periods and frequencies. Six per cent of the studies were shorter than 1 month, 46% lasted 1–2 months and 40% 1 yr and longer. Only 3% of all studies were long-term (>1 yr) and of high frequency (weekly measurements). Twenty-three per cent measured weekly N2O emissions for at least one season, and 11% were short-term (maximum 1 month) with at least daily frequency (hourly measurements for very short studies). All mean emission rates reported, even by short-term studies, were scaled up to annual rates (kg N2ON ha1 y1). Short-term studies characterise the event driven pulse in emission, i.e. after fertilisation, manure application, animal excretions, freeze-thaw cycles and ploughing (Skiba and Smith, 2000). Lower ‘background’ emissions between such events are measured less frequently. It is therefore expected that this approach lead to overestimation of mean annual N2O emissions.
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Future field and modelling studies should address high spatial and temporal uncertainties of scaling up N2O emissions. In the compiled N2O emission data set, intensively managed agricultural land was very well documented (72%), but semi-natural land contributed only a small part of the data set. Eleven studies (6%) investigated effects of organic fertilisers’ inputs to grasslands and arable land on N2O emissions. The most widely represented soil texture classes were: sandy loam (17%), silt loam (13.7%), clay (9.4%), sand (10%) and loam (8.7%). Heathlands and moorlands that play an important part in the British landscape, particularly in Scotland (41% of the land cover), were represented only by a single study of a fen environment in The Netherlands (Martikainen et al., 1993). It can therefore be expected that our model will be more accurate for agricultural land and its application to semi-natural environments will be limited. The compiled data showed great variability in N2O emissions for different land classes (Fig. 1). While N2O emissions were relatively low from semi-natural land (0.1–0.7 kg N2O-N ha1 y1), great differences were observed among the agricultural soils. The highest N2O emissions occurred on fallow soils and tillage crops, where on average 5.8 kg N2O-N ha1 y1 were emitted. The mean estimated N2O emissions from grasslands, vegetables and cereals were 3.6, 2.6 and 2.1 kg N2O-N ha1 y1, respectively. The greatest variability described by standard deviation (S.D.=27.7 kg
N2O-N ha1 y1) was observed for grasslands, where the emissions depended on the intensity of livestock management, the quantity and types of applied fertilisers (Fig. 1). Factor analysis outlined three important factor groupings with Eigenvalue ðlÞ >1 (Table 1). Ln (WFPS) and ln (soil C) had high loadings on factor 1 (>0.5). This factor grouping represents the production processes leading to N2O emissions that were influenced by decomposition processes (Granli and Bockman, 1994; Velthof et al., 1996). Factor 1 explained 37% of data variability (Table 1). Factor 2 was highly influenced by soil temperature. The third factor was influenced by the ln (N input); soil temperature had a negative factor loading. Only factors 2 and 3 were important for N2O emissions as implied by ln (N2O) loadings of 0.61 and 0.47 (Table 1). Multivariate regression analysis was carried out for the dependent factor of ln (N2O) and independent factors of ln (N input), ln (WFPS), ln (soil C) and Ts : Additionally, an independent factor (n) of random numbers was introduced to the analysis to test for the possibility of another existing factor controlling N2O emissions at the regional scale. The analysis showed that the random variable (n) was not significant ( p > 0:1). This finding does not totally exclude the possibility of other variables, as the analysis was limited by the availability of factors measured in the field. The results of the multivariate regression analysis indicated that the natural logarithm of N input was the
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N2O emissions (kg N ha -1 y-1)
80 70 60 50 40 30 20 10 0 0
100
200
300
400
500
600
700
N fertiliser input (kg N ha-1)
Fig. 1. The relationship between N2O emissions and N input from different land uses in Europe and North America. The land uses were: cereals (E); maize and rape (m); vegetables (’); grassland (J); fallow (n); shrub (}) and forest (&).
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M. Sozanska et al. / Atmospheric Environment 36 (2002) 987–998 Table 1 Results of the factor analysis applied to the N2O data set, from which the regression equation was developed Variable
Factor 1
Factor 2
ln (N2O) ln N ln WFPS Soil temperature ln C Land use Eigenvalue % variance
Factor loadings 0.096 0.611 0.286 0.351 0.863 0.297 0.042 0.793 0.872 0.198 0.787 0.449 2.218 1.454 0.370 0.242
Factor 3
Factor 4
0.471 0.709 0.130 0.542 0.105 0.198 1.084 0.181
0.629 0.532 0.167 0.113 0.144 0.066 0.744 0.124
most significant single predictor of N2O emissions accounting for 21% of the data variability (Eq. (1), Table 2). This agrees with Bouwman’s model, which explained 80% of N2O emission variability with N input (Bouwman, 1996). This lower coefficient of determination in our model was caused by the much larger selection of field studies (59 compared to 20 by Bouwman, 1996). Smith et al. (1998) also found a poor correlation between the controlling parameters and N2O emissions and observed that the controlling parameters were limiting at different times. Land use was the second most important single ‘predictor’ of transformed N2O emissions that accounted for 10.7% of ln (N2O) variability (Eq. (2), Table 2). The opposite signs of N input and land use loadings on factor 3 (Table 1) suggest that the role of vegetation cover in the production of organic material and the uptake of available soil N may have a limiting effect on the influence of N input. Caution is advised towards using land use as a single predictor of N2O emissions, as this data set only distinguished between the three main land-class groups. Relationships between ln (N2O) and ln (WFPS) or Ts (Eqs. (3) and (4), Table 2) were rather weak (standard
error=1.9), for the variable Ts this was despite its high loading on factor 2 (Table 1). This is caused by a counter-active effect of factor 3 with an opposite sign of soil temperature loading and is probably due to limited measurements of soil climatic conditions. The relationship between ln (N2O) and ln C was not significant (r2 ¼ 0:2 %; F ¼ 0:37; p > 0:5), C content was therefore excluded from further multivariate regression analysis. The combination of ln (WFPS) and ln (N input) improved the significance of the regression (r2 ¼ 28%; Eq. (5), Table 2) and reflects the observation of many studies that soil moisture and nitrogen are important controllers of soil N2O emission (Davidson, 1991; Smith et al., 1998). Inclusion of Ts into the equation only moderately improved the significance (r2 ¼ 31%; Eq. (6)). The weakness of soil temperature as a predictor is not surprising. The relationship between changes in diurnal and seasonal soil temperature and N2O emission are easily suppressed when soil WFPS and inorganic N supply become suboptimal (Dobbie et al., 1999; Skiba et al., 1998a, b). Land use was a strong single controlling factor of N2O emissions, its introduction to the above group of variables resulted in an improvement of the coefficient of determination (r2 ) to 40% (Eq. (7), Table 2). This empirical relationship (Eq. (7)) was used to estimate N2O emissions from British soils. 5.2. The spatial distribution of soil N2O emissions in Great Britain The regression model was applied to the spatial data in ArcInfo GRID (Fig. 2) and soil N2O emissions were calculated for every 5 km2 in Great Britain (Fig. 3). Mean annual N2O emissions were predicted at 3.9 kg N2O-N ha1 y1, ranging from 0.1 to 20.4 kg N2ON ha1 y1 (S.D.=4.5). The range of N2O emissions corresponded with the predictions made by Conen et al. (2000) for agricultural soils in Scotland (o1 to >20 kg N2O-N ha1 y1). The highest N2O emissions of 6–21 kg N2O-N ha1 y1 were predicted in the areas with
Table 2 Results of the multivariate linear regression analysis of the N2O experimental dataa Model definition
n
r2
p
Equation
ln ðN2 OÞ ¼ 1:7 þ 0:64ln N ln ðN2 OÞ ¼ 2:9 0:99A ln ðN2 OÞ ¼ 1:8 þ 0:75ln WFPS ln ðN2 OÞ ¼ 0:6 þ 0:04Ts ln ðN2 OÞ ¼ 1:6 þ 0:09ln C ln ðN2 OÞ ¼ 4:9 þ 0:71ln Nþ0:75ln WFPS ln ðN2 OÞ ¼ 6:0 þ 0:75ln Nþ0:83ln WFPS þ 0:035Ts ln ðN2 OÞ ¼ 2:7 þ 0:60ln Nþ0:61ln WFPS þ 0:035Ts 0:99A
296 299 133 185 159 130 107 107
21.9 10.7 3.9 1.7 0.2 28.3 31.1 39.8
0.0 0.0 0.024 0.078 0.543 0.0 0.0 0.0
(1) (2) (3) (4) (5) (6) (7)
a Where N2O emissions are expressed in kg N ha1 y1, NFnitrogen input (kg N ha1 y1), WFPSFwater filled pore space (%), Ts Fsoil temperature (1C), AFland use type.
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Data Base Management System Monthly mean precipitation
Monthly mean air temperature
Recommendations of good fertiliser practice
Atmospheric N deposition (ITE model) Rain zones
LCM/ AC Vegetation cover (A)
Seasonal air temperature
Heat flux
Arc/Info GRID
SPACTeach Soil temperature (Ts)
N input to soils (N)
Soil moisture (WFPS)
Empirical models of N 2O emissions from soils
Fig. 2. The structure of the Arc/Info grid emission model used to provide the spatial inventory of soil N2O emissions for every 5 km2 grid in Great Britain.
dominant intensively managed grasslands along the fringes of the uplands in southern Scotland, southwest England and Wales (Fig. 3). In the east of Britain, where tilled land predominates, maximum N2O emissions of 6 kg N2O-N ha1 y1 were predicted. Semi-natural land contributed little to the annual N2O emissions (o1 kg N2O-N ha1 y1). Atmospheric N deposition contributed 16% of all N2O emissions from British soils (21 kt N y1), but on semi-natural land it provided the main source of N input. In the western uplands, where high precipitation and topography enhance atmospheric N deposition (RGAR, 1997), up to 2 kg N ha1 y1 of N2O were emitted. For the majority of Great Britain predicted N2O emissions were within 2 kg N2O-N ha1 y1 of the emissions estimated using the IPCC methodology (Bouwman, 1996). The areas with the greatest differences were intensively managed grasslands and seminatural land. In the lowlands of southwest England and Wales, where grazed grasslands with high livestock
densities dominate, the regression model predictions exceeded IPCC estimates by up to 8 kg N2O-N ha1 y1. This was mainly caused by accounting for variations in soil moisture content in the regression model presented here. The effect of seasonal variations in rainfall and temperature on N2O emissions was estimated using the regression model. Nitrous oxide emissions for spring and summer were a factor of 2 higher than for autumn and winter, but the broad spatial pattern was maintained for all seasons (Fig. 4). The effect of seasonal climatic variability was suppressed by the non-dynamic parameterFland use typeFand the N2O ‘hot spots’ were maintained at all seasons. 5.3. Model sensitivity It is a recognised problem that evaluating models defined for regional scales with field measurements at a much smaller scale is not necessarily possible (Davidson
M. Sozanska et al. / Atmospheric Environment 36 (2002) 987–998
Fig. 3. A spatial inventory of soil N2O emissions in Great Britain, calculated from the regression Eq. (7) (Table 2).
and Kingerlee, 1997), as simplified, national scale models cannot reproduce the high variability of site specific measurements. Currently, large-scale N2O measurements are not available. The limitations of the regression model were therefore tested by varying the mean annual values of the controlling factors according to the scenarios presented in Table 3. Rates of N2O emission increased in a linear fashion for all the parameters tested (Fig. 5). When N input was increased from 85 to 510 kg N ha1 y1 (50–300% of the average annual N input rate, Table 3), and WFPS and soil temperature were fixed at the average annual rate for Great Britain, the model increased N2O emission rates from 2.5 to 7.5 kg N2O-N ha1 y1 (Fig. 5a). The uncertainty in these N2O predictions depends on the magnitude of the uncertainty in the N applied and
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deposited to the different land classes. For arable land, N input rates are likely to range from 120 to 220 kg N ha1 y1 and the regression model would predict N2O emissions to vary by 1 kg N ha1 y1. On intensively managed grasslands, N input rates are more difficult to predict and can be as large as 510 kg N ha1 y1. Uncertainties in the actual N input by fertilisation and animal excretion would therefore suggest a maximum variation of the N2O emission by 3.7 kg N ha1 y1. The model uncertainty under average climatic conditions is likely to range from 25% to 50%. This uncertainty could be reduced by a more accurate quantification of N input to agricultural soils. The regression model predicts a linear increase in N2O emissions, proportional to the changes in soil moisture from very dry to very wet soils (Fig. 5b). This is not entirely correct, many experiments have shown a parabolic relationship between N2O emission and WFPS, with maximum emissions at 60% (Davidson, 1991) or 80–85% (Dobbie et al., 1999; Veltkamp et al., 1998). The current version of our regression model will therefore overestimate emissions in soils that are too dry for N2O production (WFPS o40%) and in near-saturated soils, where all produced N2O is further reduced to N2. However, unlike the variations in N input, the climatic variables play a much less important role in the regression model and are expected to cause less uncertainty in the predicted N2O emissions from agricultural areas. On seminatural land, where N inputs and N2O emissions are much smaller, changes and uncertainty in soil moisture and soil temperature can affect N2O emissions considerably. A reduction in soil moisture by the value of the maximum error estimated for the SPACTeach model (22.4%) caused a decrease in mean N2O emissions from all British soils by 0.8 kg N2O-N ha1 y1. The model predicts a linear increase in N2O emission with increasing soil temperature from 81C to 131C. A gradual increase in soil temperature by 1–51C caused only a very small increase in N2O emissions (Fig. 5c). The response of the model suggests that the maximum increase expected with a change of soil temperature by 51C would be 0.7 kg N2O-N ha1 y1. On the other hand, there might be no change in N2O emissions as soil temperature and soil moisture can act as counteracting controlling factors, e.g. in an extreme case of soil temperature increasing by 51C and soil moisture decreasing from 55% to 25% WFPS. 5.4. Total N2O emissions from British soils The regression model estimated total N2O emissions from soils in Great Britain at 127 kt N2O-N y1 (Table 4), agriculture contributed 84% of this emission. Fertiliser-induced emissions of N2O from arable and grassland accounted for 56 kt N2O-N y1. Nitrous oxide emissions from animal excreta and slurry accounted for
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Fig. 4. Seasonal variations of soil N2O emissions in Great Britain.
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M. Sozanska et al. / Atmospheric Environment 36 (2002) 987–998 Table 3 Scenarios applied to test the regression model N inputa
Soil moistureb
Soil temperatureb
50% of N (lower confidence limit of N applied to arable crops) 125% of N (upper confidence limit of N applied to arable crops) 145% of N (maximum N input rate to potatoes)
WFPS22.4%c (mean soil moisture error from comparison with the field capacity) WFPS30% (the above plus loss of moisture due to climate change) WFPS40% (as above)
Ts +1 (within the error limits of soil temperature) Ts +2 (as above)
155% of N (maximum N input rate to oilseed rape)
WFPS+10% (soil moisture error plus increase in precipitation due to climate change)
Ts +3 (error limits of predicted soil temperature) Ts +4 (the above plus climate warming)
266% of N (lower confidence limit of N to grasslands) 300% of N (upper limit of the above)
Ts +5 (as above)
a
Fractions were defined on the basis of a Survey of Farmers described by Sozanska (1999). General trends expected due to climate changes (Hulme and Jenkins, 1998). c Uncertainty of the SPACTeach model in predicting soil moisture for Great Britain as described above.
8
N input
-1
-1
Mean N2O emissions (kg N ha y )
b
soil moisture
soil temperature
6
4
2
0 0
(a)
200
400
Mean N input (kg N ha
600 -1
)
20
(b)
40
60
80
Mean WFPS (%)
100
8
(c)
9
10
11
12
13
14
o
Mean soil temperature ( C)
Fig. 5. Sensitivity of the regression Eq. (7) (Table 2) to changes in N input (a) WFPS (b) and soil temperature (c).
50 kt N2O-N y1 and N deposition accounted for 21 kt N2O-N y1 (Table 4). These annual emission rates would be equivalent to emission factors (percentage of Ninput lost as N2O) of 2.9% for arable land and grassland fertilised with inorganic N, 2.7% for animal waste and 5.7% for semi-natural soils. The predictions from the regression model are therefore larger than if they would have been predicted with the IPCC methodology (IPCC, 2000) and in comparison with other GB inventories (Chadwick et al., 1999; Salway et al., 1999). For agricultural soils fertilised with inorganic N, IPCC (2000) suggest an emission factor of 1.25%. Background emission is 1 kg N ha1 y1. Emission factor for untreated animal waste and several types of
animal storage systems is 2%. For atmospheric N deposition, IPCC (2000) suggest an emission factor of 1%. According to this approach, N2O emissions for GB would be calculated at 24.3 kt N2O-N y1 (inorganic fertiliser), 27.4 kt N2O-N y1 (animal waste) and 3.7 kt N2O-N y1 (atmospheric N deposition). The largest differences between the IPCC method and the regression model were observed in the atmospheric-N-deposition source of N2O. This is perhaps not surprising, as only 28% of all N2O emission studies investigated background emissions and the effects of atmospheric N inputs. Other field studies in Scotland and North England estimated average emission factor from atmospheric N deposition at 0.76%, with the range from
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Table 4 Soil N2O emissions calculated for the main sources in Great Britain Source
Total N input (kt N y1)
Total annual N2O emission (kt N y1)
regression model for groups of soils, climates and land use types. The main achievement of the regression model presented here is the spatial and temporal disaggregation of N2O emissions from soils in Great Britain.
6. Conclusions
Agricultural land Arable crops Tillage crops Vegetables Grazed grass Mown grass Organic N Totals for agricultural soils
667 98 45 592 502 1838 3742
17 3 1 18 17 50 106
Semi-natural land Atmospheric N deposition Totals for all soils
371 4113
21 127
0.2% to 15% (Skiba et al., 1998a, b). This corresponded with the emission factor used by the IPCC (2000). Field studies and IPCC methodology suggest that the regression model greatly overestimates N2O emissions from atmospheric N deposition. Further work should redefine the relationship between the emissions and their controlling factors in semi-natural environments once more field data are available. The differences between the estimates of N2O emissions from agricultural soils and input from animal waste with the regression model and the IPCC could have been a result of scaling up from short-term measurements to annual means. On the other hand, the larger estimates of the regression model are probably the additional effect of land use, soil moisture and temperature used to predict soil N2O emissions, in that respect the new estimates might be correct. Recent direct aircraft-based N2O measurements suggest that IPCC methodology underestimates total annual N2O emissions from British soils. The aircraft method calculated total annual N2O emissions from all sources at 160 kt N2O-N y1 (Fowler et al., 1999). Assuming that, as for the global emission (IPCC, 1997), 57% of the total N2O emissions in Great Britain is derived from soils, then the aircraft-based measurements suggest 90 kt N2O-N y1 are emitted from soils. This is higher than 61 kt N2ON y1 estimated by Salway et al. (1999), but lower than 127 kt N2O-N y1 calculated with the regression model. In principal, the regression model approach should provide a better way of predicting large-scale N2O emissions, by including their main controlling variables. This study shows, however, that not enough N2O emission data are available from long-term highfrequency field studies to derive an accurate regression model. This approach could be improved by varying
The regression model of N2O emissions from British soils was established through multivariate regression analysis of field measurement data from temperate climates. The model outlined four controlling factors of N2O emissions: land use type, N inputs to soils from agriculture and atmospheric deposition, soil moisture and temperature. Variations in N input to the soils and the wetness of the soil were the main drivers of the regression model, whereas the sensitivity to variations in soil temperature was low. The model offers a first attempt to develop a simple method for producing a spatial inventory of N2O emissions from soils for every 5 km2 grid in Great Britain. The spatial aspect of this approach helped to outline the areas of the highest soil N2O emissions. They were associated with intensive livestock farming and caused by high organic and inorganic N fertiliser inputs to grasslands. Those N2O-emission ‘hot spots’ were located in southern Scotland, southwest England and Wales, where soil moisture is high throughout the year, which increased N2O emissions further. The total emissions calculated for agricultural soils, animal waste and N deposition for Great Britain were much larger than those previously estimated. Further developments of the regression model are necessary to reduce the uncertainty of the current N2O emission estimate. Future studies should investigate the uncertainty of scaling up. Our main uncertainty in this paper was caused by creating an annual emission inventory based on mainly short-term N2O measurements. More long-term data from field measurements in semi-natural environments are needed to redefine the regression model. The newly defined model will need evaluation with N2O measurement data at the regional scale once they are available.
Acknowledgements We would like to acknowledge NERC for providing a Ph.D. CASE studentship for M. Sozanska (Reference: GT4/95/85). Special thanks go to all the people who contributed data to this project. They are Ronnie Milne and Ron Smith of CEH (Edinburgh), Alison Bayley of Data Library (the University of Edinburgh), David Morris of CEH (Wallingford) and the British Atmospheric Data Centre. We would also like to acknowledge Chris Place and Mike Mineter for their help with the software solutions in this project.
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