Mapping of nitrogen risk areas

Agriculture, Ecosystems and Environment 195 (2014) 149–160

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Agriculture, Ecosystems and Environment journal homepage: www.elsevier.com/locate/agee

Mapping of nitrogen risk areas Gitte Blicher-Mathiesen a, *, Hans Estrup Andersen a , Jacob Carstensen b , Christen Duus Børgesen d, Berit Hasler c, Jørgen Windolf a a

Aarhus University, Department of Bioscience, Vejlsøvej 25, DK-8600 Silkeborg, Denmark Aarhus University, Department of Bioscience, Frederiksborgsvej 399, DK-4000 Roskilde, Denmark c Aarhus University, Department of Environmental Science, Frederiksborgsvej 399, DK-4000 Roskilde, Denmark d Aarhus University, Department of Agroecology, Blichers Allé 20, DK-8830 Tjele, Denmark b

A R T I C L E I N F O

A B S T R A C T

Article history: Received 25 November 2013 Received in revised form 23 May 2014 Accepted 4 June 2014 Available online 24 June 2014

Loading of nitrogen (N) from diffuse sources to Danish marine waters was reduced by 41% in the period 1990–2012 by a suite of general measures. However, further reductions in N loading are required if the ambitious quality goals of the EU Water Framework Directive are to be achieved. Measures will be more effective if they are implemented in N loss hot spots or risk areas. Additionally, the highly variable N reduction in groundwater and surface waters needs to be taken into account as this strongly influences the resulting effect of mitigation measures. The objectives of this study were to develop and apply an N risk tool to the entire agricultural land area in Denmark. The purpose of the tool is to identify high risk areas, i.e. areas which contribute disproportionately much to diffuse N losses to the marine recipient, and to suggest cost-effective measures to reduce losses from risk areas. In the N risk mapping part of the tool, we combined a modelled root zone N leaching with a catchment-specific N reduction factor which in combination determines the N load to the marine recipient. N leaching was calculated using detailed information of agricultural management from national databases as well as datasets of percolation and soil parameters as input parameters to an empirical N leaching model. The developed N risk tool showed that the Danish agricultural area is distributed almost evenly between N retention classes (N retention being divided into classes of <40%; 40–60%; 60–80% and >80% N retention). Hot spots for marine N loading are, however, mainly located in catchments with N retention less than 80%. Hot spots for N leaching from the root zone are mainly located in the western part of Jylland, while hot spots for marine N loading are found in all regions and especially in small catchments close to the coast. In a case study for the catchment of the Danish Odense Fjord estuary, we demonstrated how the tool facilitates cost-effective implementation of measures and how measures can be combined to reach a given reduction target for an estuary. ã 2014 Elsevier B.V. All rights reserved.

Keywords: Denmark Cost efficiency Marine N load Mitigation measures Nitrogen management

1. Introduction Large anthropogenic nutrient inputs to aquatic ecosystems have long been recognised as the primary cause of eutrophication in European waters (EEA, 2007). Eutrophication reduces the water quality and alters the ecological structure and function of aquatic ecosystems (Dodds and Welch, 2000). To remedy this, the European Union implemented the Water Framework Directive (2000/60/EC) in 2000 with the aim to maintain or restore groundwater and all surface waters to a state close to a pristine or near-pristine state. Denmark is

* Corresponding author. Tel.: +45 87158769. E-mail addresses: [email protected] (G. Blicher-Mathiesen), [email protected] (H.E. Andersen), [email protected] (J. Carstensen), [email protected] (C.D. Børgesen), [email protected] (B. Hasler), [email protected] (J. Windolf). http://dx.doi.org/10.1016/j.agee.2014.06.004 0167-8809/ ã 2014 Elsevier B.V. All rights reserved.

traditionally a farming country; thus, about 63% of the entire country is used for intensive farming. Agricultural activities are the main pollutant source of Danish groundwater, surface waters and seawaters. The total load of nitrogen (N) from point and diffuse sources to surface and coastal waters has decreased by almost 50% since 1990 (95% confidence interval = 39%, 58%) (Windolf et al., 2012, 2013). Point sources are now increasingly controlled via the establishment of N removal at wastewater treatment plants (WWTPs). Danish agriculture has in a range of national action plans that has been requested to implement a number of measures to decrease diffuse N losses (Fig. 1). The action plans include requirements regarding manure storage capacity, rules for timing and application of manure, and limits for the maximum amount of N which may be applied to different crops (Kronvang et al., 2008). Common for these measures are that they are general, i.e. they have been applied uniformly everywhere irrespective of, for instance,

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AP I (1987)

APSA (1991)

AP III (2004)

AP II (1998)

GV (2009)

Nitrogen (1000 tonnes)

500

Mineral fertiliser Applied manure

400

Field N balance Diffuse N load to marine waters N leaching

300 200 100 0 1990

1995

2000

2005

2010

Fig. 1. National N leaching from the root zone, field N balance, consumption of mineral N fertilisers and applied N in manure for the Danish agricultural area and N loading of marine waters from diffuse sources (Grant et al., 2009; Windolf et al., 2012). Action Plan for the aquatic environment I–III (API-III), Action plan for a sustainable agriculture (APSA) and action plan for green growth (GV).

geology and the natural nitrate reducing capacity in the subsoil and irrespective of the vulnerability of the receiving water body to eutrophication. The action plans have led to a 41% decrease in N loading from diffuse sources to marine waters (95% confidence interval = 36%, 50%) for the period 1990–2012 (Windolf et al., 2012, 2013). Today, the loading from diffuse sources accounts for more than 90% of the total land-based N loading of the aquatic environment (Kronvang et al., 2009). However, further N loading reductions are required if the ambitious quality goals of the Water Framework Directive are to be achieved (Økonomi- og Erhversministeriet, 2009). These reductions must come through diminished loading from diffuse sources as further reductions of point source loading would be very costly (Miljøministeriet, 2010). Given the complexity of the rural landscape, this is a difficult task because diffuse pollution varies considerably due to the complex interactions between soil, climate, topography, hydrology, land use and management (Heathwaite et al., 2005). Mitigation measures will be more effective if implemented in N loss hot spots or risk areas. Additionally, the highly variable N attenuation (retention) in aquifers and surface water should be taken into account as this strongly influences the overall effect of mitigation measures on the resulting N loading of freshwater and marine ecosystems. An overview of the previous work on agricultural N risk assessment is given in Buczko and Kuchenbuch (2010). Modelling of N loading to aquatic systems and calculation of effects of mitigation measures by dynamic models has been undertaken for a few individual Danish catchments (Styczen and Storm, 1993a,b). More simple models have ranked the level of N leaching as a function of soil, climate, management and off-site impacts as shallow vs. confined aquifers, tile drained areas, lakes, and buffer strips (Shaffer and Delgado, 2002; Williams and Kissel, 1991; Pierce et al., 1991). Common for these studies is that the N loss is addressed as the leaching out of the root zone thus ignoring the very variable N attenuation in groundwater and surface waters. Bechmann et al. (2009) describe an integrated assessment tool for both N and phosphorus (P) and found that critical source areas differ for N and P, implying that use of different mitigation methods will often be necessary in order to reduce losses of both P and N in a catchment. In Margette et al. (2007) N leaching via soil and groundwater and N loss by overland flow was set up for agricultural systems with grassland in Ireland. Management input in this model is based on a coarse level of agricultural management

data. Identification of N risk areas by direct measurement will be too expensive to implement on larger scales. Physically based, dynamic models have huge demands for input data and calibration data and are often difficult and time consuming to operate. Thus for work on large scale, there is a need for a more simplified, transparent approach which includes N retention in groundwater and surface waters. The objective of this study was to develop a transparent and simple-to-use tool for mitigation of diffuse N loading to marine areas, and subsequently to apply this tool to the entire Danish agricultural area. The purpose of the tool is two-fold: (i) to identify risk catchments and areas, i.e. areas which contribute disproportionately much to diffuse N losses to the marine recipient, and (ii) to suggest cost-effective measures to reduce losses from risk areas. In the tool we have combined a detailed calculation of root zone N leaching at field block scale (average field block size = 8 ha) with estimates of nitrate reduction in groundwater and surface waters at catchment scale. For each field block (in total more than 330,000 field blocks in Denmark), we have assessed the potential of seven different measures to mitigate the N loss and calculated the effects and costs of the measures. As a demonstration of the tool, we showed for the 1000 km2 Danish River Odense catchment draining into an estuary how the tool can be used to fulfil a set N reduction target by suggesting cost-effective measures to be applied to identify risk areas within the catchment.

2. Methods In this study a tool to map risk of N loss and to plan mitigation methods were developed. In the N risk mapping part of the tool, we combined a calculated root zone N leaching with a catchmentspecific N reduction factor which in combination determines the N load to the marine recipient. This was done for all the field blocks in Denmark. A field block is a collection of fields (from one to ten fields) defined by the natural borders in the landscape. The field block level is the smallest areal unit with nationally available agricultural management information. In the N mitigation planning part of the tool we included seven mitigation measures. For each of these measures we calculated the areal potential, the effect on root zone N leaching, and the resulting reduction of the N load to the marine recipient and the cost of implementing the measure. A potential user of the tool will decide which measure(s)

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Fig. 2. Visualization of N loading of marine areas from agricultural areas divided into three steps: (i) N leaching from the root zone of agricultural fields, (ii) transport from the root zone to the catchment outlet through groundwater and surface waters, and (iii) transport through surface waters from the catchment outlet to the sea.

to apply to the selected field blocks, for instance the most effective measure or the most cost-effective measure, and will automatically get access to the pre-calculated areal potentials, effects and costs. In the demonstration exercise on the River Odense catchment the cumulated effect and costs of selected measures are calculated. N loading of marine areas from agricultural areas can be divided into three steps: (i) N leaching from the root zone of agricultural fields, (ii) transport from the root zone to the catchment outlet through groundwater and surface waters; during this transport part of the nitrate may be reduced to atmospheric N, and (iii) transport through surface waters from the catchment outlet to the sea; during this transport part of the nitrate may be reduced to atmospheric N (Fig. 2). The main inputs for the calculation of root zone N leaching and calculation of N reduction are listed in Table 1. 2.1. N leaching from the root zone N leaching from the root zone is used to calculate both the hot spots for leaching from the root zone and the effect of three out of

seven measures: set aside, afforestation and conversion to bioenergy crops (Table 2). N leaching at field level was calculated on an annual basis by the statistical model N-LES (nitrogen leaching estimator) (Simmelsgaard and Djurhuus, 1998; Andersen et al., 1999) and subsequently aggregated to field block level. The N-LES model is regularly updated. The latest version is based on 1467 measurements of yearly nitrate leaching; of these 1058 observations were from research programmes, while the remaining 409 observations were from 32 soil stations which were located on ordinary farms and monitored for the period 1990– 2007 within the framework of the Agricultural Catchment Monitoring Program (Kristensen et al., 2008). The model yields an annual leaching value and includes parameters such as application of inorganic and organic fertilisers, grazing, N fixation, and annual percolation from the root zone, major soil type, soil organic matter content, the main and previous crop as well as possible catch or winter crops grown during autumn and winter following the main crop (Table 1). The degree to which the N leaching model could explain the variation in the observation data reflected the number of observations: for cereal crops with many observations, the N leaching model predicted correctly the average

Table 1 Input to the N leaching model and to the N retention model. Input N leaching Input of nitrogen Modelled percolation Soil parameters

N retention within catchment Monitored catchments Non-monitored catchments empirical model for the fraction of aerobic/anoxic water flux above and beneath the redox cline

N retention in surface waters Lakes

Streams N retention

Output

Fertiliser, manure, N fixation, deposition Annual N leaching from the root zone Percolation Clay in top soil, soil organic matter and C/N for top soil

Difference between modelled N leaching and measured N loading at the gauging station Precipitation, temperature, depth to the redox cline, modelled water flux above and below the redox cline

N reduction between bottom of the root zone and the stream gauge % N reduction, calibrated to monitored catchments using clay content in the top soil

Monitored lakes: data on water residence time in monitored lakes Un-monitored lakes: an assumed annual reduction of 400 kg N ha
1 lake sediment 2% of the N transport in streams N retention within catchments and during transport in surface water to the coast

N reduction in lakes

N reduction in streams N reduction from the root zone to the coast of marine waters

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Table 2 The decrease in root zone N leaching calculated for seven mitigation measures and the associated costs for two soil types: loamy and sandy. Measure

Reduced root zone N leaching (kg N ha
1 year
1)

Set aside Afforestation Conversion to bioenergy crops

Present N leaching – 10 Present N leaching – 12 Present N leaching – 23

Loamy soil

Catch crops holdings <0.8 LU/ha 16 Catch crops holdings >0.8 LU/ha 28 (crop-specific N Reduced fertiliser application standard  0.1)  0.25 20 Conversion to organic dairy production Wetlands 100 kg N ha
1

Costs Reference (s ha
1 year
1) Sandy soil

34 46 (crop-specific N standard  0.1)  0.35 40

measured N leaching for both winter and spring cereals followed by bare soil or with under sown grass. For fodder crops with a lower number of observations, a small bias was found in model predictions. Especially grass is difficult to predict due to high deviation in the measured data. The highest bias was found for maize grown for fodder with only 26 observations of measured leaching. For the observation data set as a whole (1467 observations), the N leaching model was able to correctly predict the mean leaching of 52 kg N ha
1 year
1 (Kristensen et al., 2008). However, this version of the model has not been validated using independent data and cross-validation was only performed for a former version of the model (Larsen and Kristensen, 2007). The cross-validation was undertaken by excluding a subset of the measured data followed by re-estimation of the parameters in the leaching function. This revealed that the parameters in the leaching function were relatively stable and that the uncertainty between estimated and measured data on a single prediction ranges between 20 and 40% but decreases to 10–30% if the estimation is done on an average of several fields or years. Data on crop cover, fertiliser use and livestocks are available from the national databases maintained by Danish authorities. Information on crops grown at field level is supplied by farmers when they apply for EU single payment subsidies. Data on consumption of fertilisers and manure N are collected annually by Danish authorities for cross-compliance control of Danish farmers. In the Danish system, each farm has to meet a farm-specific N quota. For each farm, we combine information on crops grown at field level with livestock data and the total consumption of fertiliser and manure at farm level to distribute fertiliser and manure to each field. In this procedure, we use data on fertilisation of individual crops in normal agricultural practices recorded in the Agricultural Catchment Monitoring Programme (Blicher-Mathiesen et al., 2012). Average annual percolation from the root zone is calculated using the deterministic soil–water–plant–atmosphere model DAISY (Børgesen et al., 2004) with climate data for 1990– 2006 available in a national 10 km grid (Cappelen and KernHansen, 2008). The soil map is a raster-based map with a grid size of 250 m (Greve et al., 2007). Each grid describes a three-layer soil profile: A horizon (0–30 cm), B horizon (30–70 cm) and C horizon (70–120 cm). In total, there are 48 unique soil profiles. In the present version of the tool, root zone N leaching is calculated from the agricultural data for 2005. Subsequently, we calculated an average based on the yearly calculations in order to level out interannual climatic variations. 2.2. Nitrate reduction in aquifers and surface waters Part of the nitrate leached from the root zone is reduced to atmospheric nitrogen during its transport through the catchment. In

Loamy

Sand

810 830 270

505 520 –

80 80 40

50 50 40

90

90

625

490

Waagepetersen (1992) Gundersen et al. (2004); Hansen (2004) Jørgensen and Mortensen (2000); Mortensen et al. (1998) Hansen (2004) Hansen (2004) Petersen and Djurhuus (2007) Dalgaard et al. (1998); Nielsen and Kristensen (2005) Miljøministeriet (2010)

surface waters, denitrification in anaerobic zones is the most important process of nitrate removal, whereas in groundwater both microbial denitrification and denitrification by pyrite oxidation occur. A significant amount, up to 80% (Andersen et al., 2001), of the nitrate leached can be denitrified during transport through the aquifers or riparian zones. For streams, Danish measurements report a range of 5–700 kg N ha
1 of river channel year
1 (Kronvang et al., 2004). N depletion in monitored Danish freshwater lakes is, on average, 400  245 kg N ha
1 lake area year
1, the rates being related to the N loading and water retention time (Kronvang et al., 2004; Windolf et al., 1996). Therefore, high nitrate reduction within a catchment is a function of hydrological pathways of N (Vagstad et al., 2004) and the proportion of the nitrate transported through nitrate reduction zones of riparian areas, in aquifers or through lakes with different water retention time. Approximately half of the Danish land area lies within catchments equipped with stream water gauging stations where runoff and riverine N concentrations are regularly measured (Kronvang et al., 1993, 2008). For these monitored catchments, we calculate N reduction as the difference between N leaving the root zone as leaching and the measured N transport in the stream. The N reduction for these measured catchments includes both the N reduction in aquifers and riparian areas as well as the N reduction in the freshwater system within the catchment. N leaching from the root zone at catchment scale is composed of leaching from agricultural land, calculated as described above, and leaching from non-agricultural land such as forest, heather etc., estimated in experimental research studies (Blicher-Mathiesen et al., 2006). Nitrate leaching from the root zone was calculated for the year 1989/90, which was taken as representative of nitrate leaching for the previous 12–15 years, a period with a fairly constant level of net N input in Danish agriculture (Kyllingsbæk and Hansen, 2007). For the riverine N transport, we used data from the Danish Stream Monitoring Programme for the period 1989/90–1996/97 (Kronvang et al., 1993, 2008). The hydrological year 1994/95 was the first year in which riverine N transport decreased in response to lower application of fertilisers and a lower net nitrogen input (Windolf et al., 2012) (Fig. 1). For the monitored catchments, we used the N transport for the period 1989/90–1993/94, reflecting the N leaching from the root zone for the period with a high and relatively stable net N input. For some catchments, we observed a delay in the reduction of the N transport and for these we calculated the mean N transport for the 4-year period with the highest N transport. The N leaching and N transport and runoff to marine ecosystems reveal a substantial inter-annual variation (Kronvang et al., 2008), and we therefore calculated both nitrate leaching and riverine N transport with a range in climatic conditions. Thus, root

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zone leaching is as a mean of the modelled leaching for each year during the period 1989/90–1999/2000. The mean N transport for a 4-year period was corrected for discharge during the same period such that the N transport reflected the average runoff measured in each catchment during the period. A further N reduction in surface waters may occur during transport in streams and lakes downstream of the gauging station. This additional nitrate reduction in surface waters is estimated as a fixed 2% reduction of the riverine N transport and a fixed reduction of 400 kg N ha
1 lake sediment during the through-lake transport process (Kronvang et al., 2004). In non-monitored catchments, the groundwater nitrate reduction was estimated by a statistical model developed on simulated water fluxes from a dynamic numeric groundwater model (Ernstsen et al., 2010; Henriksen et al., 2007). The N reduction was estimated as the fraction of aerobic/anoxic water flux above and the anaerobic water flux beneath the redox cline in 1 km2 grids. The depth to the redox cline was defined from the changes in the sediment colour of 12,000 national drillings or, for grids with no measured drillings, from geological descriptions (Ernstsen et al., 2010). The estimated N reduction was calibrated to the N reduction calculated for 56 large (>50 km2) monitored catchments with a fit of r2 = 0.45 (Ernstsen et al., 2006). Prior to the calibration, the measured N loading to the stream was corrected for the nitrate reduction occurring in surface waters within the measured catchment upstream of the gauging station, the corrections for N reductions applied being, respectively, 2% of the N transport for streams and 400 kg N ha
1 for lakes.

2.3. Measures used to mitigate diffuse N losses In the N risk tool, seven different measures to reduce diffuse N losses are included. These measures include set aside, afforestation, conversion to bioenergy crops, catch crops, conversion of conventional to organic dairy production, reduction in fertiliser application and wetlands. The measures catch crops, conversion to organic dairy production and reduced fertilisation can be applied simultaneously to the same field. In contrast, the measures set aside, conversion to bioenergy crops and afforestation can only be applied one at a time to the same field. The N risk tool keeps track of which measures are applied to which fields. The effect of measures is generally based on measurements from research projects in Denmark and presented as average values. For example, the estimate for average leaching from forest grown on former agricultural land is calculated as the average value of 16 observations with a huge variation that mainly was related to the amount of water through-fall, N deposition and the C/N ratio in the forest floor (Gundersen et al., 2009). Given the gradient in N deposition and precipitation in Denmark with low values in the eastern part and high values in the western part of the country, the effect of afforestation may be smaller than estimated in areas with low deposition and precipitation and higher where these drivers are high. For other measures, such as crops grown for bioenergy production, the experimental basis is even smaller and comprises measurements on only three sites with sandy soil (Jørgensen and Mortensen, 2000). The knowledge base on effects of this measure can thus not justify a subdivision into soil types. Set aside and afforestation both involve a significant change in plant cover. Following set aside or afforestation, a former agricultural field will have a low N leaching estimated to be, respectively, 10 and 12 kg N ha
1 year
1 (Table 2) (Waagepetersen, 1992; Gundersen et al., 2004). Crops for bioenergy, for instance willow and poplar, are only relevant on sandy soils as they are not economically profitable on loamy soils (Schou et al., 2007). N leaching after conversion to

153

bioenergy crops is estimated to be approximately 23 kg N ha
1 year
1 (Mortensen et al., 1998; Jørgensen and Mortensen, 2000). The N uptake in catch crops prevents to a large degree N leaching during the runoff season. On loamy soils, the average decrease in N leaching is estimated to 16 and 28 kg N ha
1 year
1 for holdings with, respectively, less and more than 0.8 livestock unit ha
1. On sandy soils, the corresponding values are 34 and 46 kg N ha
1 year
1, respectively (Hansen, 2004). The maximum allowed livestock density (LU ha
1) for organic farms is 1.4 LU ha
1, which is lower than for conventional farms (1.7 LU ha
1) and for cattle farms (2.3 LU ha
1). In Denmark, the latter farm type has been granted a derogation from the nitrates directive. The effect on N leaching by conversion from conventional, intensive dairy production to organic production is estimated to be 20 and 40 kg N ha
1 year
1 for loamy and sandy soils, respectively (Dalgaard et al., 1998; Nielsen and Kristensen, 2005). All Danish crops have a standard for maximum allowable N fertiliser application. The standard is based on the economically optimal N application. With the introduction of Action Plan II in 1998 (Iversen et al., 1998), the application standard was reduced by 10%. In the present tool, we included a further 10% reduction as a mitigation measure. The annual effect on root zone N leaching of reduced input of N fertiliser was estimated from experimental data to 0.35 kg N ha
1 on sandy and 0.25 kg N ha
1 on loamy soils per 1 kg reduced N input (Petersen and Djurhuus, 2007). Re-establishment of natural groundwater hydrology in wetlands is a step towards achieving optimal conditions for denitrification due to high carbon and low oxygen levels. The N removal of re-established wetlands has been monitored in 10 Danish wetlands and the average annual reduction was estimated to 259 kg N ha
1 (Hoffmann and Baattrup-Pedersen, 2007). In the present N risk tool, we have applied an annual effect of wetlands of 100 kg N ha
1; this measure was included in Action Plan III and involved nature rehabilitation as well as targeting areas with high denitrification in riparian areas. The potential area of application of each of the seven mitigation measures was identified in GIS for each field block. The potential area where catch crops can be used is that with spring-sown crops. However, only half of the potato area is included as some potatoes are harvested too late for catch crops to be established in autumn. The effect on N leaching from the root zone is shown for the seven mitigation measures in Table 2. For each measure, the resulting effect on the marine recipient is calculated by multiplying the N leaching with the catchment-specific N retention. 2.4. Costs of applying mitigation measures The costs are calculated as part of the planning of the Danish programme of measures to fulfil the Water Framework Directive, and the costs are estimated for all the measures described above. For set aside, afforestation and conversion to bioenergy crops or to organic dairy production, the total economic cost includes the lost income from production forgone as well as costs of planting and growing trees and bioenergy material. Income from subsidies to the converted fields, income from selling the agricultural land, income from timber, bioenergy crops and organic production reduce the costs and are also included. The cost of growing catch crops embraces the cost of sowing as well as the lost profits resulting from lost yields, both because the catch crop requires space and because of the change from crops sown in the autumn to spring-sown crops. The cost of reduced fertiliser application is related to a lower crop yield, where the yield loss effect varies between crops and soil fertility. For all the measures, the differences in soil fertility between clay and sand are included in the calculations of the economic cost.

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2.5. Case study An important aspect of the Water Framework Directive is the introduction of River Basin Districts. They are managed according to River Basin Management Plans, accompanied by a programme of measures (PoM), which should provide a clear indication of the way the objectives set for the river basin are to be reached within the required timescale. We demonstrate for the Danish Odense River basin draining into an estuary how the N risk tool can be used to compose a cost-effective PoM based on an identification of N risk areas. Firstly, we calculate the N loading reduction required to achieve a good ecological status of the estuary. Secondly, by applying the mitigation measures of the tool in a stepwise fashion and calculating the effects of these, we point to required actions to be taken by farmers in the catchment to reach the reduction target. In the case study, we assume that all further N loading reductions are to be achieved for diffuse inputs. The 1046 km2 Odense Fjord catchment has an annual mean precipitation of 825 mm. Mean runoff from the catchment is 305 mm year
1. Land use is dominated by agriculture (68%) followed by urban areas (16%), forests (10%) and natural vegetation (6%). Livestock density for agricultural land was 0.7 livestock units ha
1 in 2005, and the consumption of fertiliser and manure was 77 and 78 kg N ha
1, respectively. Average N leaching from the root zone was 53 kg N ha
1 in 2005 (data from the N risk mapping tool). The average total N export from the catchment to the estuary was 1774 t N year
1 in 2001–2005 (Miljøcenter Odense, 2010) of which 66% was diffuse losses from agriculture, 11% was from WWTPs and scattered dwellings not connected to a WWTP, and 23% derived from natural background (Miljøcenter Odense, 2007). If measures already adopted in Action Plan III are fully implemented in agriculture, the N export is estimated to decrease from 1774 to 1632 t N year
1 (Miljøcenter Odense, 2010). The estuary occupies 60 km2 and has a mean depth of 2.25 m and a hydraulic residence time of 7 days during winter, it being longer in summer when the freshwater input is lower (Miljøcenter Odense, 2007). The estuary can be divided into an inner part, which is strongly influenced by the freshwater input (salinity 5–18m) and is fully mixed, and an outer part, which is more saline (>18m) and stratified (Miljøcenter Odense, 2007). The depth distribution of eelgrass (Zostéra marina L.) has been used as the only indicator for ecological status of coastal waters in the Danish implementation of the Water Framework Directive due to the strong correlation with eutrophication. Currently, the ecological status of the estuary is characterised as “poor” due to several years of excessive external nutrient loading (Miljøcenter Odense, 2010). Eelgrass reacts negatively to high turbidity, which in the estuary is caused primarily by excessive production of phytoplankton and algae. Eelgrass is currently limited to depths shallower than 3 m. Eelgrass should be present at depths up to 4.2 m in order to achieve good ecological status in the Odense Fjord estuary (Miljøcenter Odense, 2010). Eelgrass in Danish estuaries has an average depth distribution corresponding to approximately 75% of the Secchi depth (KrauseJensen et al., 2011), and a target for eelgrass of 4.2 m will therefore approximately correspond to a target for Secchi depth of 5.6 m. In order to couple the environmental target for water transparency in Odense Fjord with nutrient management in the catchment, quantitative links between the target, nutrient input and management measures had to be established. For Odense Fjord, this was achieved by means of two empirical relationships estimated from the extensive monitoring data collected over the last 30–40 years (Fig. 4). Water transparency, measured as Secchi depth (ZSD), is related to the attenuating components of the water, including water itself. Particulate matter scatters and absorbs light, whereas dissolved organic matter absorbs only light (Carstensen

et al., 2013), but these different fractions are not routinely measured in the monitoring programme. In coastal areas such as Odense Fjord, particulate matter is closely connected with primary production, either as viable phytoplankton cells or detritus originating from algae production. Moreover, primary production during summer in Odense Fjord is, as in most Danish estuaries, limited by nitrogen (Conley et al., 2000). Therefore, total nitrogen (TN) was used as an aggregate proxy for all the light-attenuating substances in the water in addition to a constant background attenuation of the water (kwater). Using the Beer–Lambert law, the light is reduced with depth down through the water column as I ¼ I0 expð
ðkwater þ kTN  TNÞ  ZÞ

(1)

where I is the light irradiance at depth Z, I0 is the irradiance at the surface and kTN is a parameter describing the light attenuation by TN. A relationship between Secchi depth (ZSD) and TN can be established under the general assumption that ZSD represents 10% of the surface irradiation. Z SD ¼


lnð0:1Þ ðkwater þ kTN  TNÞ

(2)

where kwater and kTN are the light-attenuating coefficients for water and TN. Annual means of TN and summer means of Secchi depth (May–October) for each monitored station in the Odense Fjord estuary were used for estimating the relationship (Fig. 5A). Given this general relationship, a target Secchi depth of 5.6 m would correspond to a target TN level of 336 mg l
1 in the outer part of the estuary. Nutrient levels differed pronouncedly between the 10 stations in Odense Fjord. Therefore, station-specific relationships between total N loading and the resulting N concentration in the estuary water had to be established by the approach described in Carstensen and Henriksen (2009). Use of an estimated linear regression model from station-specific slopes and a common intercept provided a station-specific slope clearly reflecting the nutrient-enrichment gradient from the head (station 6910003) to the mouth (station 6940622) of the estuary (Fig. 5B). Assuming that the 5 stations in the outer part of Odense Fjord (stations 6900007, 6900017, 6900019, 6900803 and 6900804) represent this waterbody equally, these 5 station-specific regressions translated the target for TN of 336 mg l
1 into an average TN input of 782 t N. The N reduction target for the catchment is calculated as the difference between the maximum allowable input of 782 t N and the projected baseline export from the catchment of 1632 t N. Thus, the reduction target is 850 t N, requiring implementation of additional measures for N loading reductions.

3. Results 3.1. Hot spot areas of N leaching and load to the sea N retention in aquifers and surface waters varies from less than 40% of the N leaching in a catchment to more than 80% of the N leaching in other areas (Fig. 3A). In general, N reduction is higher in Jylland than on the islands of Fyn and Sjælland. For catchments with several large lakes, the N reduction is above 80% as in, for example, the eastern part of Jutland and in the northern part of Zealand. Low N reduction is observed in small coast-near catchments. High N leaching is found on farms with intensive livestock production, and such farms are mainly located in the northern and western parts of the country and in areas dominated by sandy soils with high precipitation. Lower N leaching is found on arable and pig farms, mainly located in the eastern part of Denmark in areas dominated by loamy soils and lower

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155

Fig. 3. National maps for Denmark (a) N retention between the root zone and the marine recipient estimated as a catchment-average fraction (%) of root zone N leaching; (b) root zone N leaching calculated at field block level; (c) N loading of the marine recipient from each field block.

precipitation (Fig. 3B). The N risk tool shows that the Danish agricultural area is distributed almost evenly between N retention classes (N retention being divided into classes of <40%; 40–60%; 60–80% and >80% N retention) (Table 3). Hot spots for marine N loading are, however, mainly located in catchments with N retention less than 80% (Fig. 3C, Table 3). Hot spots for N leaching from the root zone are mainly located in the western part of Jylland,

while hot spots for marine N loading are found in all regions and especially in small catchments close to the coast. 3.2. Case study For the sub-catchments of the Odense Fjord catchment, N removal (i.e. nitrate reduction in aquifers and surface waters)

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Fig. 4. Location of the 10 monitoring stations in Odense Fjord (inner and outer parts represented by 5 stations each) and 1 station for the outer boundary of the estuary. Odense Fjord is characterised by a deeper channel with broad shallow flanks (see colour-coded bathymetry).

varies between less than 40% and up to 70–80% of the root zone N leaching (Fig. 6A). The effect of measures to reduce diffuse N loading will also be stronger for this catchment, the lower is the natural N removal. Fig. 6B illustrates the N loading of the estuary from individual field blocks found by combining calculated root zone N leaching with sub-catchment specific N removal. High risk field blocks, here defined as field blocks with loading above the 90th percentile, are primarily found in sub-catchments with relatively low N removal. In the application of measures, our first priority was to affect the size of the agricultural area and production as little as possible. Catch crops, conversion of conventional dairy farming to organic

8

6,000 A)

6900007 6900017 6900019 6900803 6900804

B)

7

5,000 Total nitrogen (µg l-1)

Secchi depth (m)

dairy farming and the establishment of wetlands are all considered as measures having relatively little impact on the present production (Jacobsen et al., 2009). Catch crops can be grown without affecting the preceding harvest and may even have a positive effect on the following crop. In organic dairy farming, the ban on commercial fertilisers can be counteracted by inclusion of N fixating crops (e.g. clover) in the crop rotation. Wetlands are typically re-established in low-lying naturally water-logged areas where cultivation requires artificial drainage. As a first step (M I), we utilised the full potential area for catch crops, conversion to organic dairy production and wetlands. These measures affected 26% of the agricultural area and yielded a reduction of 476 t N year
1 of the load to Odense estuary (Fig. 7). As these three measures did not suffice to meet the reduction target, in the next step (M II) we applied crops for bioenergy production in 10% of the area with the highest N loading and then added the full potential of the measures used in M I to the remaining fields. These measures produced a reduction of 563 t N year
1 of the N loading to Odense estuary (Fig. 7). Since a change in crop production in 10% of the high risk area did not suffice to reach the target, in the third step (M III) we identified and included 20% of the field blocks with the highest N loading. We selected set aside as the most efficient measure to be applied to the 10% of the risk areas having the highest load and applied bioenergy crops to the 10% of the risk areas with an N loading between the 80th and the 90th percentile. Additionally, we introduced reduced fertilisation as a measure, exploiting its full potential, and used the remaining areas for application of M I measures: catch crops, crops for bioenergy and wetlands. The effect of these measures combined was a reduction in the total loading to the estuary of 795 t N year
1 (Fig. 7). The final combination of measures necessary to reach the reduction target was found by extending the area with bioenergy crops to field blocks with N loading between the 70th and the 90th percentile, setting aside the 10% of the field blocks with the highest N loading and utilising the full remaining potential of the measures catch crops, conversion to organic dairy farming, re-establishment of wetlands and reduced fertilisation. The combined effect of these measures is a reduction in the loading to the estuary of 855 t N year
1. This package of measures will have a high impact on agricultural production in the catchment with an annual cost amounting to 13 million s. The average cost per hectare agricultural land (66,273 ha agricultural land in the Odense Fjord catchment) is 195 s. The cost efficiency of measures varies between 4 and 22 s kg N
1 ha
1, the cheapest measures being wetlands and conversion to bioenergy crops and the most expensive reduced fertilisation (Fig. 7).

6 5 4 3 2

4,000

6910003 6910008 6910010 6910606 6910705

3,000 2,000 1,000

1

6940622

R2=0.9076

0 100

0 1,000 Total nitrogen (µg l-1)

10,000

0

1,000

2,000

3,000

4,000

Total nitrogen input (tonnes N yr-1)

Fig. 5. Relationships linking water transparency with TN by non-linear regression (A) and TN with nitrogen inputs by linear regressions with station-specific slopes and a common intercept (B). In (A) annual means from the shallow stations 6910008, 6900803 and 6910705) (marked by circles) were not used in the estimation as Secchi depths were mostly identical with bottom depth.

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Table 3 Distribution of the Danish agricultural area, N leaching from the root zone and the marine N load in classes of catchments N reduction. N-reduction (%)

Marine load (kg N ha
1)

Area (%)

N leaching (kg N ha
1)

<40 <40 <40 Sum 40–60 40–60 40–60 Sum 60–80 60–80 60–80 Sum >80 >80 Sum

<20 20–50 >50

4.7 16.6 1.8 23.1 5.9 16.8 0.5 23.2 18.3 11.3 0.03 29.6 23.4 0.6 24.0

23.2 47.2 81.5

<20 20–50 >50 <20 20–50 >50 <20 20–50

29.2 63.3 104.7 50.7 85.1 150.3 61.4 117.7

4. Discussion Measures implemented in the last 5 Danish action plans have in general been uniformly applied irrespective of the catchment N reduction. For example, all farms with more than 0.8 livestock units ha
1 have been required to grow catch crops on 14% of the areas with spring-sown crops. The calculation of hot spot areas of N loading to the sea in this study revealed that catch

(%) 1.9 13.7 2.6 18.2 3.0 18.6 0.9 22.5 16.2 16.8 0.1 33.0 25.1 1.2 26.2

Marin N load (kg N ha
1)

Area share of marine load (%)

15.3 31.9 57.6

3.3 24.6 4.9 32.8 3.9 24.6 1.2 29.8 11.7 14.5 0.1 26.2 10.5 0.6 11.1

14.3 31.5 55.9 13.8 27.6 54.8 9.7 22.5

crops will reduce the N loading more efficiently if applied to larger areas in catchments with low N reduction than if applied evenly on all farms. However, according to the Water Framework Directive it is the ecological status of the receiving water body which determines whether N load reductions in the contributing river basin are necessary. Thus, not all agricultural areas in catchments with low N reduction in aquifers and surface waters necessarily need implementation of further measures.

Fig. 6. Nitrate removal between the root zone and the estuary per sub-catchment. Removal calculated as percent of N leaching from the root zone (left). Nitrogen loading of the estuary from each field block. Loadings below and above the 90th percentile (= 36 kg N ha
1) are shown (right).

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Fig. 7. Four steps in mitigation measure planning: (A) area coverage of measures, (B) reduced nitrogen loading to the Odense estuary, (C) total cost of measures M I–IV, (D) cost efficiency of measures in M III.

The most cost-effective N load reduction is achieved by targeting N loss hot spot areas with mitigation measures. N loss hot spots are mainly found in catchments with low N reduction. It is therefore recommended to further analyse how to implement measures in those catchments. The N retention in aquifers and surface waters is mapped in this tool at relatively coarse catchment level. As N attenuation is also known to vary considerably within catchments depending on, for instance, soil type, depth to the groundwater table and artificial drainage, the true effect of mitigation measures on single fields may be different from those calculated. Consequently, the calculated effect of measures applied in large areas, such as catch crops, will be more certain than the effect of measures applied to only a few fields. The real conditions in individual fields may differ from the input to the N risk tool due to the various assumptions needed to create the input (see Section 2.1). Some examples: (i) distribution of fertiliser and manure between fields within a farm may be different from assumptions; (ii) N leaching may be lower than calculated due to unexpected high yield caused by unmapped high soil fertility/high mineralisation and vice versa; (iii) N leaching may be lower or higher than calculated due to local variations in denitrification differing from the catchment average value used in the tool. Therefore, the tool cannot be expected to give exact results for N leaching or N loading for individual fields. A similar uncertainty follows the estimates of mitigation measures (see Section 2.3).

Most N risk assessment tools described in the literature are restricted to estimate N loss by leaching through the soil profiles (Buczko and Kuchenbuch, 2010). However, many studies have shown that the fate of diffuse N load to surface waters is strongly influenced by the properties of the aquifers, both the geological settings, position of the redox zone and the transport mechanisms for the N in groundwater (de Ruijter et al., 2007). On the other hand, common groundwater vulnerability indices are more focused on the physical properties of the vadose zone and the aquifer and have minor focus on the N sources and the agricultural management (Leone et al., 2009). The strength of the Danish N risk tool described here is that both the N source through the agricultural management and the N reduction in aquifers and surface water at catchments level are well included in the N risk tool. Future versions of the N risk tool will benefit from the recent improvements in data processing that allow mapping at field rather than at field block scale. This will improve input data quality partly due to the higher spatial resolution and partly due to the fact that parts of the information supplied by farmers are given at field scale. Further improvements of the tool should aim at mapping retention with a higher spatial resolution. Mapping of tile drained or ditched areas will be one way forward as tile drains and ditches act as shortcuts for some of the net precipitation which then surpasses possible retention in ground water.

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5. Conclusion The N risk tool is a screening tool that identifies catchments and areas with high root zone N leaching as well as areas with high N loading of marine waters. The tool combines N risk mapping with measures to reduce N leaching and the costs of applying these measures, thus calculating the cost efficiency of the measures. The N risk tool is the first attempt in Denmark to map agricultural N leaching and diffuse N load in hot spots on a national scale and link this mapping to mitigation planning. The case study demonstrates how the tool facilitates cost-effective implementation of measures and how measures can be combined to reach a given reduction target for an estuary. As demonstrated for the Danish Odense Fjord estuary, reducing the catchment N export to acceptable levels is a formidable task. In order to solve this task, detailed knowledge is required on physiography, agricultural management and effects and costs of relevant measures. The N risk mapping tool combines all these features at sub-catchment level.

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