Model-based coefficient method for calculation of N leaching from agricultural fields applied to small catchments and the effects of leaching reducing measures

Journal of Hydrology 304 (2005) 343–354 www.elsevier.com/locate/jhydrol

Model-based coefficient method for calculation of N leaching from agricultural fields applied to small catchments and the effects of leaching reducing measures K. Kyllmar*, K. Ma˚rtensson, H. Johnsson Division of Water Quality Management, Department of Soil Sciences, Swedish University of Agricultural Sciences. P.O. Box 7072, SE-750 07 Uppsala, Sweden Received 30 November 2003; revised 1 May 2004; accepted 1 July 2004

Abstract A method to calculate N leaching from arable fields using model-calculated N leaching coefficients (NLCs) was developed. Using the process-based modelling system SOILNDB, leaching of N was simulated for four leaching regions in southern Sweden with 20-year climate series and a large number of randomised crop sequences based on regional agricultural statistics. To obtain N leaching coefficients, mean values of annual N leaching were calculated for each combination of main crop, following crop and fertilisation regime for each leaching region and soil type. The field-NLC method developed could be useful for following up water quality goals in e.g. small monitoring catchments, since it allows normal leaching from actual crop rotations and fertilisation to be determined regardless of the weather. The method was tested using field data from nine small intensively monitored agricultural catchments. The agreement between calculated field N leaching and measured N transport in catchment stream outlets, 19–47 and 8–38 kg haK1 yrK1, respectively, was satisfactory in most catchments when contributions from land uses other than arable land and uncertainties in groundwater flows were considered. The possibility of calculating effects of crop combinations (crop and following crop) is of considerable value since changes in crop rotation constitute a large potential for reducing N leaching. When the effect of a number of potential measures to reduce N leaching (i.e. applying manure in spring instead of autumn; postponing ploughing-in of ley and green fallow in autumn; undersowing a catch crop in cereals and oilseeds; and increasing the area of catch crops by substituting winter cereals and winter oilseeds with corresponding spring crops) was calculated for the arable fields in the catchments using field-NLCs, N leaching was reduced by between 34 and 54% for the separate catchments when the best possible effect on the entire potential area was assumed. q 2004 Elsevier B.V. All rights reserved. Keywords: Process-based model; Coefficient method; Nitrogen leaching; Arable field; Catchment; SOILNDB

* Corresponding author. Tel.: C46 18 672597; fax: C46 18 673430. E-mail address: [email protected] (K. Kyllmar). 0022-1694/$ - see front matter q 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.jhydrol.2004.07.038

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1. Introduction Diffuse N pollution from arable land to surface waters and groundwater is a serious problem in many regions. In Sweden, where agricultural land occupies only 7.6% of the available area, the diffuse N pollution from agricultural land contributed 40% of the land-based net load to the sea between 1985 and 1999 (Brandt and Ejhed, 2002). A national goal is that anthropogenic waterborne N pollution reaching the sea should decrease by at least 30% between 1995 and 2010 (Swedish EPA, 2003). With the implementation of the European Water Framework Directive (WFD) (EC, 2000), water quality goals will also be set up for regional water administration areas, which will require characterisation of catchments and evaluation of the effects of action plans aimed at reducing diffuse pollution to surface waters and groundwater. Models are important tools for evaluating the impact of diffuse N pollution from arable land on water quality and for finding the most effective measures to reduce N losses. Combined with data from intensively monitored small agricultural catchments, further knowledge of the relationship between actual crop management and water quality could be acquired. Models used for determination of N leaching from arable land can be classified into statistical regression models, such as those described by Andersen et al. (2001) and Simmelsgaard and Djurhuus (1998), and process-based models, such as ANIMO (Berghuijs-van Dijk et al., 1985), SOILN (Johnsson et al., 1987) and DAISY (Hansen et al., 1990). Statistical models normally use simple generalised input data for agricultural land and are easy to apply. However, the effects of countermeasures, especially those concerning changed crop management and changed crop rotations, are difficult to calculate. The more process-based models can simulate a large variety of measures but require detailed input data and are therefore more timeconsuming to apply. Management-orientated tools have been developed to reduce and simplify input data requirements in the use of process-based models. SOILNDB (Johnsson et al., 2002) is a management-orientated modelling system for quantification of N leaching from arable land, based on the mechanistic research-orientated water flow and the N leaching models SOIL and

SOILN, a parameter database and algorithms for parameter estimations. The underlying SOIL and SOILN models have been used in many applications, mainly at field scale (see e.g. review by Hoffmann, 1999). With SOILNDB, the time-consuming process of parameterisation, administrating model runs and presenting model results is reduced, allowing a large number of calculations for various agro-environmental conditions to be made efficiently. The SOILNDB modelling system was tested on field plot scale by Larsson and Johnsson (2003) for a 14-year period and it was concluded that SOILNDB could satisfactorily describe both the year-to-year dynamics and the large variation in N leaching between the 10 plots with different management practices (e.g. amount of applied fertiliser and manure, cultivation of catch crop, time of tillage). Recently, SOILNDB has also been applied to a small agricultural monitoring catchment with 317 fields (Kyllmar et al., submitted). Nitrogen leaching from each field in the catchment was simulated with a daily time step for a 5-year period. The agreement between area-weighted leaching rates and measured N discharge in the stream outlet adjusted for influences from other land uses was satisfactory. At a considerably larger scale, SOILNDB has been used to calculate standard N leaching coefficients (NLCs) for arable land using official agricultural statistics as input data (Johnsson and Ma˚rtensson, 2002). These NLCs were defined as the accumulated leaching during a year with normal weather and normal yields for a specific crop under specific site conditions, i.e. the effects of weather variations between different years were eliminated. Using this approach, N leaching from agricultural land was calculated for the whole of Sweden. The aims of the study presented in this paper were (i) to further develop the standard N leaching calculation method into a user-friendly coefficient method for high-resolution N leaching calculations at e.g. field level, including calculations of field N leaching coefficients (field-NLCs) for the most southern part of Sweden; (ii) to test the field-NLCs for N leaching calculations for nine small (177–1460 ha) intensively monitored agricultural catchments using agricultural field data and measurements in stream outlets in these catchments; (iii) to use the field-NLCs for analysing the effects of a

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number of possible measures to reduce N leaching from the arable fields in the selected catchments.

2. Materials and methods 2.1. Field N leaching coefficients The method for calculation of field N leaching coefficients (field-NLCs) described here is a further development of the calculation method for obtaining standard N leaching coefficients (NLCs) for agricultural land in Sweden (Johnsson and Ma˚rtensson, 2002). Sweden was divided into 22 leaching regions by Johnsson and Ma˚rtensson (2002) on the basis of the 18 production areas used for presentation of official agricultural statistics. In the present project, fieldNLCs were produced for four of these leaching regions located in the most southern part of Sweden (Table 1). The SOILNDB modelling system Ver. 1.0 (Johnsson et al., 2002; Larsson et al., 2002) was used for simulation of N leaching. SOILNDB links input data and data from a parameter database to automatic parameterisation procedures for the underlying water and heat model SOIL (Jansson and Halldin, 1979) and the nitrogen model SOILN (Johnsson et al., 1987), and administrates the model runs and presentation of model results. The simulation of N leaching with SOILNDB was carried out using climate series with 20 years of daily values for the four leaching regions in combination with crop management data (e.g. crop areas, standard yields, amounts of fertiliser and manure applied) based on official agricultural statistics for the year 1999 for these regions. Using a randomising procedure, crop sequences (i.e. crop rotations) were produced for each region, where the occurrence of each crop in the crop sequence was proportional to its areal representation in the actual region. When randomising the crop sequences, limitations in crop rotations were taken into consideration. To achieve a sufficient number of simulated outcomes for calculations of leaching coefficients with acceptable confidence intervals, 60,000 yearlong crop sequences were produced for each region. The crop sequences were combined with the climate data by repeating the 20-year climate periods 3000 times so that each crop occurred at different places in

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the climate periods and before and after different crops in the sequences. The N leaching calculations were repeated for each soil type in the leaching regions. Mean values of annual N leaching (representing the period 1 July of the crop year to 30 June the following year) were calculated for all combinations of main crop, following crop and fertilisation regime. In this way leaching coefficients normalised for weather variations for each leaching region and soil type were acquired. A total of 2690 coefficients were produced and stored in a database. Confidence intervals were calculated based on the annual leaching values for the different combinations. The field-NLCs differ from the NLCs for national load calculations in that they include 35 crop combinations (instead of only 12 crops) and three fertilisation regimes (instead of only two). 2.2. Field-NLCs: data and parameterisation The annual mean water discharge was estimated for each leaching region using a discharge map of Sweden (Brandt et al., 1994) and was used as a target value for the simulated average discharge (Table 1). Daily values of climatic data, i.e. precipitation, air temperature, humidity, wind speed and cloudiness, were obtained from representative meteorological stations in the leaching regions. Precipitation was adjusted to achieve a simulated root zone discharge at the same level as the target discharge for the region and free drainage was assumed to occur at a depth of 1.5 m. Simulations were carried out for all soil types (FAO textural classes) occurring in the regions and the soil organic matter content was set to 4.3% for all soils. Data concerning crop types for 1999 (Table 4) were obtained from official agricultural statistics Table 1 Leaching regions and annual mean values of water discharge (target), adjusted precipitation and temperature Leaching region

Discharge (mm)

Precipitation (mm)

Temperature (8C)

1a 1b 2a 2b

290 450 310 190

750 900 800 650

8 8 8 7

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(Statistics Sweden, 2000a). In the randomised crop sequences, 12 crops were included. Ley was assumed to be undersown in the preceding crop and to occur during three successive years. Minor crops (i.e. crops with less than 1% areal coverage) were not included in the simulations. To reduce uncertainty in the coefficients produced, area-weighted means were calculated for a number of crop groups by merging crops with similar properties, resulting in a total of seven crop groups (Table 2). In the same way, the crops in the following year were merged to five crop groups. This resulted in a total of 35 (7!5) crop group combinations. Catch crops were not explicitly included in the simulations; instead we assumed that undersown ley in the main crop could represent a catch crop sown in the main crop. Spring-sown crops followed by ley had approximately 50% lower leaching than the average for other following crops, which is in accordance with results obtained in field plot experiments with catch crops on loamy sand soils in south-western Sweden (Aronsson, 2000). In the simulations, three fertilisation regimes were used for each crop (except for green fallow, which was unfertilised): fertiliser applied in spring; manure applied in autumn with a supplementary application of fertiliser in spring; and manure applied in spring with a supplementary application of fertiliser in spring. For the coefficients produced, these fertilisation regimes could be chosen for both the main crop and following crop, which resulted in a total of nine combinations of fertilisation regimes for each of the Table 2 Crop groups for actual year and following year Crop group Actual year Winter cereals Winter oilseeds Spring cereals and spring oilseeds Potatoes Sugar beet Ley Green fallow in crop rotation Following year Winter cereals Winter oilseeds Spring sown crops Ley Green fallow in crop rotation

35 crop combinations. Amounts of N applied in actual fertilisation regimes were set according to the 1999 fertilisation survey (Statistics Sweden, 2000b). Fertiliser was applied once in spring; for winter cereals, winter oilseeds and ley at the beginning of the N uptake period, and for spring crops in connection with sowing. In autumn, manure was applied at sowing for winter cereals and winter oilseeds, and at dates according to the fertilisation statistics for other crops. In spring, manure was applied at the same times as fertiliser. Applied manure was divided into net amounts of inorganic N (after gaseous losses) and organic N. We assumed that ploughing occurred in midOctober for spring-sown crops according to general practice, and one week before sowing for winter-sown crops. Ploughing of green fallow before spring-sown crops was carried out in late October and before winter-sown crops in late July according to regional regulations. Dates for sowing and harvest were in general set to the beginning and end of the growing period for each crop, respectively. Standard yields for 1999 (Table 4) (Statistics Sweden, 1999) were used as mean targets for the simulated yields. In individual years, simulated yields were allowed to be lower or up to 10% higher than the mean target yield. For one of the measures to reduce N leaching, additional simulations were required. In crops receiving manure in spring, the supplementary application of fertiliser was adjusted because the sum of inorganic N in applied manure and fertiliser was higher than that in the fertilisation regime with fertiliser only. Since this measure influences other years (with other crops) in the crop sequences, a new complete set of fieldNLCs were calculated for use in all fields when applying this measure. 2.3. Monitoring catchments Within the Swedish Monitoring Programme for Agriculture, a number of small catchments with a high percentage of arable land are being investigated concerning the impact of agriculture activities on surface and groundwater bodies (Carlsson et al., 2002). Nine of these catchments, situated in four leaching regions in southern Sweden, were used in this model application (Fig. 1 and Table 3). The arable land in the selected catchments is to a large extent tile-

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Fig. 1. Leaching regions and monitoring catchments in southern Sweden.

drained. As a consequence of climate and soil type, the type of farming varied between the catchments. Two catchments were dominated by crop production and one was characterised by animal husbandry and ley production (Table 4). The other six represented a mixture of these two types of farming. The average crop distribution for catchments in leaching region 1aC1b was similar to the general crop distribution in the region, whereas the average crop distribution for catchments in leaching region 2aC2b included more sugar beet and less ley than the region. Concerning application of manure, catchments in leaching region 1aC1b received less manure than the region, whereas the opposite occurred in catchments in leaching region 2aC2b.

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Water samples were taken manually in the catchment stream outlets every second week and the water was analysed for total N (Swedish Standards Institute, 1976, 1998). Water discharge at the stream outlets was recorded continuously with water level gauges at well-defined control sections in the streams (e.g. V-notches). The mass transport of total N in the streams was calculated by interpolating the measured concentrations to daily values and then multiplying them by daily water discharge values. The daily transport values were then accumulated to annual values. The annual mean concentrations were obtained by dividing total transport by total water discharges. Confidence intervals were calculated based on annual N transport. The contribution of N from arable land to the total stream outflow was estimated by a source apportionment technique (Carlsson et al., 2002) whereby the estimates of the average contribution from point sources and other land uses such as forest, farmyards and wasteland was subtracted from the long-term average load of N. The remaining load was assumed to be the net load from arable land (i.e. a load in which the retention between the fields and the stream outlet is included). The average specific net loss (kg haK1 yrK1) was then calculated by dividing the net load by the area of arable land. Crop rotations, cultivation practices, animal husbandry and point sources (e.g. scattered households) were surveyed in all catchments. In this project, crop and field management data (Table 4) were used for the year 1996 and for the following year except for two catchments. For catchment 4, data for 1997 and 1998 were used, while for catchment 1, data were only

Table 3 Catchment characteristics for nine monitoring catchments in southern Sweden in 1996 No.

Leaching region

Area (ha)

Arable land (%)

Dominating soil types

1 2 3 4 5 6 7 8 9

1b 1b 1a 1a 1a 2a 2a 2b 2b

650 1460 791 867 902 683 1228 177 750

93 92 79 95 95 90 67 80 34

Sandy loam, loam Sandy loam Sandy loam, clay Clay loam Sandy loam, loam Sandy loam, loam Sandy loam Loamy sand, sandy loam Sandy loam

348 Table 4 Monitored field data compared to regional statistics for crop distribution (%), application of manure on arable land (%) and application of manure in autumn on arable land (%). Fertilisation and yields for spring barley and winter wheat (kg N haK1 yrK1). Data in catchments (1–9) in 1996 and for leaching regions (LR) in 1996 and in 1999 (Statistics Sweden, 1997, 2000a,b).

Winter cereals

Manure (% of area) Spring cereals, oilseeds

Potatoes

Sugar beet

Ley

Leaching regions 1aC1b Catchments 20 0 1a 2 23 5 3 26 4 4b 25 16 5 39 9 Mean 27 7 LR 1996 26 4 LR 1999 22 3

30 30 36 34 36 33 30 33

1 10 1 0 0 2 2 2

4 5 1 20 9 8 12 12

34 18 17 2 2 15 13 12

Leaching regions 2aC2b Catchments 6 24 17 7 13 8 8 21 0 9 8 0 Mean 16 6 LR 1996 19 2 LR 1999 17 2

28 29 19 17 23 23 26

0 0 13 0 3 4 5

5 19 39 0 16 5 6

12 18 4 53 22 35 28

a b c d

Winter oilseeds

Cropping data for 1995. Cropping data for 1997. Refer to 1995. Standard yield.

Green fallow

Spring barley (kg N haK1 yrK1)

Winter wheat (kg N haK1 yrK1)

Total

Autumn

Fertilisation

Yield

Fertilisation

Yield

6 6 4 2 3 4 7 7

25 40 30 1 15 22 30c 27

7 9 16 1 14 9 13c 9

102 101 100 103 110 103 96c 102

99 89 89 87 112 95 98 88d

111 131 160 168 177 149 150c 164

119 120 133 138 166 135 148 145d

9 11 5 15 10 7 8

50 40 50 50 48 45c 39

30 12 17 10 17 15c 10

92 90 72 94 87 90c 85

88 82 89 85 86 75 74d

104 143 111 147 126 133c 142

97 111 117 127 113 118 128d

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Crops (% of area)

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available for 1995. Crop distribution and field management in 1996 were in this case assumed to be the same as in 1995 and since information about autumn-sown crops in the following year was included in data for 1995, possible crop combinations could be constructed. In each catchment, data were compiled for each field and year concerning crop, following crop and fertilisation regimes as a basis for application of coefficients. Amounts of N in applied manure in the catchments were calculated using standard values of N content in manure (Statistics Sweden, 1998) and the application rates obtained in the surveys. Fields not covered by the surveys were assumed to have the same cropping and field management practices as fields included in the surveys. Soil texture in topsoil and subsoil on arable land was analysed only for catchment 5. For the remaining catchments, soil texture was estimated from soil survey maps published by the Geological Survey of Sweden. Since the soil texture in these maps is classified according to the Swedish classification system (Ekstro¨m, 1953), they were translated to international classes (FAO, 1990). 2.4. Application of coefficients in monitoring catchments For each field a leaching coefficient was applied and the mean leaching rate for each catchment was calculated as an area-weighted mean for all fields. The area of fields in the catchments not covered by the surveys (varying between 0 and 27% of the arable land) was assumed to have the same leaching as the weighted mean value for the surveyed fields. Minor crops not included in the coefficient database were represented by coefficients for crops with similar cropping and N uptake properties. The effects of a number of measures to reduce N leaching on the basis of the cropping in the catchments during 1996 (1995 and 1997 for two catchments) were calculated by changing coefficients for all fields where the measures were implemented. The measures were chosen to be feasible in existing production, i.e. crop distribution, amounts of manure applied and total yields, and production was also assumed to be unchanged. We assumed that the measures could be implemented in all possible fields

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with the best possible effect. Detailed technical information concerning the method and the application to catchments can be found in Kyllmar et al. (2002).

3. Results Field-NLCs with the highest leaching rates were obtained for leaching region 1b, whereas the lowest rates were obtained for leaching region 2b (Fig. 2a). The main explanations are different agricultural management and climate differences, especially the water discharge levels, between the regions (Table 1). The N leaching rate also varied with soil type, the lowest leaching rates being obtained from clay soils and the highest from loamy sand soils (Fig. 2b). Application of manure also influenced N leaching. In each crop group, manure applied in autumn gave the highest N leaching rates, whereas the lowest rates were obtained when only fertiliser was applied (Fig. 2c). Depending on the combination of main crop and following crop, N leaching rates varied considerably (Fig. 3). Crops with long N uptake periods had the lowest leaching rates, i.e. ley and fallow not followed by winter-sown crops during the year in question, sugar beet, and to some extent winter cereals. When the main crops were followed by ley, leaching was lower compared to with other following crops. The confidence intervals were smallest for combinations of crops and fertilisation regimes that occurred often in the crop sequences. Field water discharge calculated with the coefficients was at the same level as measured discharge in stream outlets in six of nine catchments (Table 5). In catchments 1, 2 and 8, the calculated field water discharge was considerably higher than stream discharge. As a result of these water discharge discrepancies, field N leaching was larger than both the N transport measured in the stream outlet and the estimated net loss from agricultural land calculated through source apportionment. In catchment 5, field N leaching was larger than transport in the stream outlet as a consequence of concentration differences. In the five remaining catchments, field N leaching rates were at the same level (G10%) as the estimated net loss and somewhat higher, as expected, than measured total outflows in the stream outlets due to

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Fig. 2. N leaching coefficients (field-NLCs), including confidence intervals (95%), for spring cereals and spring oilseeds followed by spring sown-crops: (a) in different leaching regions on sandy loam and with only fertiliser applied; (b) in leaching region 1a on different soil types with only fertiliser applied; and (c) in leaching region 1a with different fertilisation regimes on sandy loam.

contributions from other land uses with lower area specific leaching. The calculated confidence intervals for the mean field N leaching from the catchments varied between 2 and 18% (Table 5). The measures to reduce N leaching gave different effects in the different catchments as a consequence of various crop rotations and crop management practices (Fig. 4). Application of manure in spring instead of in autumn, combined with adjustment of the fertiliser application in spring, resulted in a reduction of N leaching of between 4 and 16% for the individual catchments. The largest effect was obtained in catchments where many fields were receiving manure. Postponing ploughing-in of ley and green fallow by changing the following crops from winter crops to spring-sown crops reduced leaching by between 1 and 27% in the individual catchments. In this measure, following crops in other fields were changed to keep the initial crop distribution constant. Catch crops in spring cereals and spring oilseeds reduced leaching by 5–15%, and when catch crops were also incorporated into winter cereals and winter oilseeds, leaching decreased by between 15 and 21%. When all measures were combined in each catchment, leaching was reduced by between 24 and 37% for the individual catchments. This total reduction was not as large as the sum of the separate measures tested, solely because they cancel each other out to some extent. The decrease in N leaching was largest in catchments 1, 2, 6 and 7, with a reduction of approximately

Fig. 3. N leaching coefficients (field-NLCs), including confidence intervals (95%), for crop combinations with only fertiliser applied on sandy loam in leaching region 1a.

26 41 30 22 34 40 47 26 21 93 92 79 95 95 90 67 80 34 G17 G36 G16 G24 G16 G19 G22 G36 G18 25 38 25 22 33 37 32 22 8

CI (%) Mean (kg haK1 yrK1) Mean (mg lK1) Mean (mm)

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12 kg haK1 yrK1. In these catchments, initial leaching rates were relatively high (37–47 kg haK1 yrK1). When applying the additional, somewhat controversial, measure of increasing the possible area of catch crops by substituting winter cereals and winter oilseeds with corresponding spring crops, N leaching was reduced by 17–45%. To maintain the total production levels of these crops (spring cereals have a smaller yield per unit area than the corresponding winter crop), green fallow was partially substituted by spring cereals and spring oilseeds. When this measure was combined with the other measures, the total decrease in N leaching was calculated to between 34 and 54% for the individual catchments. For each measure applied, the confidence intervals slightly increased and in eight of nine catchments there was a significant difference between the initial leaching rate and the rate of the third and fourth measure (Fig. 4).

10 5 12 7 9 17 8 13 8

Yearsa Net loss (N) arable land (kg haK1 yrK1) Arable land (%) Discharge N conc. Discharge

N transport

Measurements in catchment stream outlets Calculated field discharge

10 11 9 8 11 11 9 12 4

c

d

a

b

Studied years until 2001. Calculated field discharge based on cropping data for 1995. Calculated field discharge based on cropping data for 1997. Water samples taken upstream of a pond at catchment stream outlet.

264 338 280 282 297 350 375 180 220 G5 G7 G5 G3 G2 G18 G7 G3 G11 37 47 28 23 41 44 42 35 19 9 10 10 9 14 14 13 16 9 423 454 287 253 289 330 335 227 211 1 2b 3 4c 5d 6 7 8 9

Mean (mg lK1) Mean (mm) Mean (kg haK1 yrK1)

CI (%)

N conc.

4. Discussion

N leaching Catchments

Table 5 NLCs-calculated field discharge based on cropping data for 1996 and mean values of measurements in catchment stream outlets, both with 95% confidence intervals (CI).

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The actual crop distributions in the catchments during the year 1996 were assumed to be representative for most of the catchments and, consequently, also the calculated mean N leaching. However, in very small catchments, such as catchment 8, the crop distribution in a single year could be somewhat misleading due to the small number of fields. The discrepancies in the proportions of sugar beet and potatoes in some catchments compared to the surrounding regions are probably characteristic crop distributions for these catchments since production of these crops is locally constant. Since the crop distribution and the fertilisation regimes in the catchments were generally comparable with the regions, coefficients representing common combinations with low confidence intervals also covered the main proportion of the area in most of the catchments. In the catchments with the lowest calculated confidence intervals for mean N leaching from arable land (i.e. catchments 4, 5 and 8), cropping was then represented by common combinations of crop and fertilisation regimes. A large confidence interval for catchment 6 (18%) was the result of a relatively large area of winter oilseeds with applied manure, which had coefficients with large confidence intervals. In catchments with a different crop distribution from

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Fig. 4. Calculated mean N leaching from root zone, including confidence intervals (95%), in nine catchments in southern Sweden. Initial leaching and leaching after application of measures.

the surrounding regions, there is a risk that the coefficients are not fully representative. Larger calculated water discharge from the fields than measured in stream outlets in catchments 1, 2 and 8 could possibly be explained by losses of water to deeper groundwater. Almost all of the fields in the catchments are tile-drained but some of the drainage water from the soil profile probably bypasses the tiledrains and makes its way directly to deeper groundwater. If the catchments are also small and have the character of large tile-drained fields, which is the case for most of the catchments in this study (and especially catchments 1 and 8), it is reasonable to assume that some of this groundwater will have deep flow-paths resulting in an outflow to streams outside the catchment (i.e. downstream from the catchment stream outlet). The calculated field water discharge could then be correct. These kinds of local hydrological variations with both downward and upward seepage occurring within the same area are typical for the undulating agricultural landscape in Sweden. Another explanation for the deviation between calculated field water drainage and observations of water discharge in stream outlets is that the regional climate (basis for coefficients) does not represent the actual climate in some of the catchments. This would result in a difference between the real field water

discharge in the catchment and the calculated field water discharge when using coefficients based on regional means. Precipitation measurements close to catchments 1 and 2 indicate a lower mean precipitation in these catchments compared to the regional mean. In a study where SOILNDB was used for dynamic N leaching calculations in catchment 2 (Kyllmar et al., submitted) it was indicated that groundwater flows were shallow and thus losses to deeper groundwater were small. This together with precipitation differences indicates that the regional climate was not fully representative for catchment 2. Knowing the differences in climate (mainly precipitation) between a catchment and the whole region affecting discharge levels would perhaps make it possible to adjust the region-based coefficients for this difference. However, such a discharge adjustment should be made taking into consideration the relationship between mean annual concentration and discharge rate. This is obvious in catchments 1 and 2, where the relative differences between estimated field N leaching and the N outflow measured in-stream were lower than the relative differences in the corresponding water flow rates. Considering all catchments and comparing calculated field N leaching and source-apportionmentbased net loss of N from arable land, we did not find

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any strong evidence that retention of N occurred when the water discharge discrepancies discussed above were taken into consideration. Further elaboration of assumed uncertainties in flows within the catchments might be achieved by coupling to groundwater or hydrological models where the spatially distributed water and N flows from the root zone constitute the input. Estimations of net N load on the recipient would thereby be facilitated. The measures that were applied to reduce N leaching in the catchments showed that the potential exists to decrease N leaching without drastically changing agricultural practices. Although the possible catch crop area is limited by the crop sequences, i.e. the following crop must be spring-sown, about 30% of the arable land in the catchments could be used for catch crop growing. During the period investigated, almost no catch crops were grown in the catchments. The catch crop used in the application was also assumed to have the best possible effect, which in reality could vary. Furthermore, it is not likely that all measures could be applied on all the possible area during a specific year. Consequently, the application should be seen as an example of potential reduction levels with the selected measures.

5. Conclusions † The agreement between calculated N leaching from the fields and measured N discharge in stream outlets was satisfactory for most catchments when contributions from land uses other than arable land were taken into consideration. In a few catchments, a deviation between calculated field N leaching and N outflow in stream outlets could be attributed to uncertainties in groundwater flows or to discrepancies in precipitation between catchment and region. The results indicate that regional agricultural statistics and regionally representative climate data series can be used for producing N leaching coefficients (field-NLCs) with sufficient accuracy for application on the small catchment scale. † The potential to calculate the effects of crop combinations (crop and following crop) is of considerable value since changes in crop rotation

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constitute a large potential for reducing N leaching. Combined with the opportunity to calculate the effect of changed time of manure application, leaching from a variety of crop management systems could be determined. When measures to reduce N leaching were applied to initial field management in the catchments, the N leaching rates were reduced by between 34 and 54% when the best possible effect on all the possible area was assumed. However, the leaching reduction was smaller than the sum of the effect of separate measures since some measures partially cancel each other out. † Water quality goals set up within the WFD could be followed-up using the field-NLCs method for regular impact calculations in e.g. small monitoring catchments (that would act as indicators for a larger area). Since the normal annual leaching from actual crop rotations and fertilisation regimes could be determined regardless of weather variations, possible water quality changes could be detected earlier than with direct measurements. In finding the most effective combinations of measures for fulfilment of the water quality goals, calculations could be made both on the monitoring catchment scale and on regional or national scale. † Using the field-NLCs for N leaching calculations is an easy way to take advantage of process-based N leaching simulations for a large variety of crop management situations. However, to be able to calculate even more crop management situations, such as N leaching as a consequence of the relationship between fertilisation and yield, calculations of additional coefficients are needed.

Acknowledgements This project was carried out within the Swedish Water Management Research Programme (VASTRA) funded by the Foundation for Strategic Environmental Research (MISTRA), which is gratefully acknowledged. Arne Joelsson, Kristian Wennberg and Lars Bengtsson from the County Administration Boards in Halland, Ska˚ne and Blekinge, respectively, also cooperated in the project and the fruitful discussions we had with them are especially acknowledged. The County Administration Boards also contributed

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funding. We also thank Arne Gustafson and Martin Larsson at the Department of Soil Sciences, Swedish University of Agricultural Sciences, for valuable discussions and comments on the manuscript.

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