• Crop types
• Soil types
• Agricultural practises Þ 177 unique records on Ø Manure
Ø Mineral fertiliser Ø harvest
GIS
• Agricultural ”block”-field-polygons
• Soil polygons
• Sub-catchment-polygons
• Groundwater Grid-map
Flow separation
- N-leaching to groundwater - Direct runoff / ”fast-flow”
- Polygon to grid conversion Hydrograph analyses
SoilN-light
N-leaching from
root zone
WEKU
groundwater on the other, and spatially redistrib-ute this information according to the input requirements of the groundwater retention model (Fig. 6.4).
How crucial the investigation of subsurface retention was became particularly clear in connection with the discussion that arose after the publication of the evaluation effects of the measures taken within the framework of the second stage of the “Action Plan against Nutrient Pollution of the Aquatic Environment”. One of the main questions was to which extent and after how many years a reduction of agricultural nitrogen inputs can be expected to cause a reduction of riverine nitrogen loads. This question can only be answered if a long-term scenario analysis is performed, explicitly also accounting for ground-water residence time. The leaching model applied within the framework of this part of the thesis, SoilN-light, was developed at the University of Linköping in Sweden, based on the SOILN model, which is a process-oriented numerical approach simulating major C- and N-flows in agricultural and forest soils and plants (Johnson et al., 1987).
SoilN-light was derived through the identification of the most important processes and parameters for long-term nitrogen leaching based on statistical methods. The basic idea is that all nitrogen inputs, i.e. those from manure, inorganic fertilizers and atmospheric deposition either cause a change in storage pools, or leave the soil system through crop harvesting, denitrification or nitrogen leaching:
storage tion
dentrifica +
harvest + Leaching
= N c atmospheri +
N fertilizer inorganic
+ N Manure
∆ +
Given that all inputs to the soil system as well as harvest are known, the simulation of N-leaching requires estimation of the total amount of nitrogen as being either denitrified or contributing to changes in the nitrogen storage pool. This amount can be computed by linear regression:
} {
{ } { } Harvest Manure
storage + ation Denitrific
9 1 2
1
3
1
⋅ +
⋅
⋅ +
⋅ +
⋅
=
∆
å å
å
= =
= =
= =
c I
b I
ac
I as
k
k crop i j
j cropclass j
i
i soil j
(6.2) where the parameters a (additive intercepts) and b depend on soil types (3 soil types), crop class (2 crop classes: ley or other crop types) and crop type (9 crop types), respectively. Finally, leaching can be estimated as follows:
Leaching = Max (0, Manure-N + N from inorganic fertilizers + atmospheric Ndeposition Harvest -asoil, cropclass – bcrop • Harvest – c • Manure) (6.3) Equation (6.3) reveals the necessary input information required by the SoilN-light model for each area investigated, which is i) the amount of Nitrogen-N added to the soil system from manure, inorganic fertilizers or atmospheric deposition, ii) the amount of Nitrogen-N removed from the soil system through crop harvest for up to 3 different soil types and up to 9 different crop classes. The nitrogen surplus at the soil surface needed to run SoilN-light was estimated by computing the input with fertilizers, atmospheric deposition and biological nitrogen fixation, and then subtracting (6.1)
the removal of nitrogen with the harvest. The atmospheric deposition was assumed to be constant (20 kg N ha-1yr-1) in the study area, and the biological nitrogen fixation was estimated by using literature data for different crops (Kristensen and Olesen, 1998). Input information on manure and inorganic fertilizers as well as on harvest was based on data from both reference catchments located near the Gjern river basin and the CHR and GLR agricultural databases available at the nation scale (Fig. 6.5).
Each farm applying for subsidies is registered in national databases. Information on livestock and resulting P and N inputs to the environment is provided at farm level. All farmers may own fields at various locations inside and outside the catchment of interest, and it is not possible to differentiate exactly the degree of livestock intensity between the fields belonging to the same farm. This is different for crop types that are differentiable at the scale of block fields. It is, however, not possible to say where crops are grown within each block. All blocks are available as GIS data, represented by polygons, thus allowing analytical and overlay operations as well.
Once all input data had been derived for the SoilN-light, the model was run to simulate leaching in the root zone. Mean N-leaching from agricultural areas was 66 kg N ha-1 year-1 from clay/organic soils and 62 kg N ha-1 year-1 from areas dominated by sandy soils. The negligible difference between the N-leaching from the two different soil types
and the circumstance that N-leaching is highest from clay soils can be explained by the specific distribution of crop types in the catchment.
The overall N-leaching level is about the same as that previously reported for other research catchments located in similar Danish landscapes (Kronvang et al., 1995 (cf. Chapter 11)). These values had to be supplemented by estimates for non-agricultural areas and blocks with missing information. Finally, estimates were available for all polygons of the river basin and had to be converted to GRID-data using a bilinear interpola-tion with a mesh size that is much more detailed than the mesh size of the WEKU model, i.e. 10 m instead of 100 m. This detailed GRID-map has to be aggregated (resampled) in such a way that each single grid cell, which covers different polygon blocks and leaching properties, respectively, represents mean conditions of the contributing blocks. Since SoilN-light does not provide any information on the amount of percolate reaching the groundwater, runoff had to be separated into a fast-flow component and a component represent-ing percolation to groundwater. The resultrepresent-ing values for the ratio between percolation or base-flow to total base-flow were subsequently multiplied with the leaching values available for every 100 m grid cell to calculate leaching to groundwater. The model subsequently used to estimate nitrogen retention in groundwater (Wendland et al., 2001) is an extension of the WEKU groundwater residence time model (Wendland, 1992; Kunkel and
R egis nr.
Fertilised area
F allow-area
LU cattle
N cattle
P cattle
LU pigs
N pigs
P pigs
LU total
N_total P_total L U_ha N_ha P_ha 2 32.8 2.9 . . . 15.92 1269.56 318.38 15.919 1269.55 318.38 0.485 38.706 9.707 32 121.9 9.4 119.52 11952.00 1860.13 . . . 119.520 11952.00 1860.13 0.980 98.04815.259 53 30.9 3.5 . . . 77.75 6528.19 1879.75 77.748 6528.19 1879.75 2.516211.268 60.833
54 77 9 . . . 48.51 4135.59 1234.67 48.510 4135.59 1234.67 0.630 53.70916.035
58 29.3 4 . . . . . . . . . . . .
71 71.8 7.1 . . . 98.01 7816.30 1960.20 98.010 7816.30 1960.20 1.365108.862 27.301 74 54.7 4.7 75.05 7505.00 1171.02 . . . 75.050 7505.00 1171.02 1.372137.203 21.408 114 101.71 8.1 . . . 131.87 10516.47 2637.36 132.326 10585.17 2652.02 1.301104.072 26.074
Regis-no.
Block field
Block no
Area-type (code)
Area type (text)
Crop-type (kode)
C rop-type (text)
Crop area (ha) 2 601131 75 20 corn 3 spring wheat 3.2 2 602132 18 20 corn 4 winter wheat 25.5
2 602132 18 20 corn 8 corn 0.3
2 602132 18 65 other 69 grass 0.9
2 602132 56 20 corn 4 winter wheat 11
2 602132 56 20 corn 8 C orn 0.3
2 602132 56 65 other 69 grass 0.9
2 602132 80 20 corn 3 spring wheat 1.4 2 602133 34 65 other 97 X -m as-trees 2.1
2 602133 57 20 corn 8 corn 0.4
Block-field Block-no. x-coord x-coord netto-area ekslusiv area
446242 79 212996.56 348450.24 434232 0
448262 58 232851.28 346218.08 481568 0
450154 36 124553.92 346592.88 139995 0
451153 69 123831.24 345263.4 544621 0
451156 25 126555.56 345623.84 154651 0
451156 89 126885.96 344991.12 79349 0
451157 54 127297.72 345314.32 330248 0
451157 84 127281 344984.6 29790 0
451157 89 127914.04 345034.56 312699 0
451158 86 128547.52 345016.08 62653 0
451249 81 219127.44 343131.04 25894 0
451264 77 234673.72 342956.8 409196 20953
To specify Livestock-units (<1,5 / ha, <1,5 / ha) Manure use (N and P)
Management type (cattle/pig-dominated or mixed)
To specify Crop type information To distribute manure and mineral fertiliser
Geographicallynot explicitGeographically explicit
Figure 6.5 Basic information on agricultural practises and land cover stored in the GLR and CHR databases.
Wendland, 1997 (cf. Chapter 11), which considers denitrification as a first-order process. Essential input information provided as part of the River Gjern basin study is groundwater table maps, spatially distributed data, hydromechanical properties of the aquifers involved and spatially distributed inputs to the aquifer.
6.3.2 Estimating the effects of different agricul-tural practises
The second study on N-leaching (Chapter 12 of the thesis) was performed for the catchment of the Mariager Fjord, covering an area of approximately 572 km2. Mariager Fjord has been heavily affected by eutrophication in recent decades. Approxi-mately 75 % of the nitrogen loading, varying between 1100 and 1700 tons per year during the years 1984 and 1999, can be attributed to diffuse pollution from agricultural land. Contributions from point sources, currently amounting to about 10 % of the total loads, have decreased by 60 % from 1984. The national agricultural databases were used as in the River Gjern basin study to feed an empirical leaching model. Unlike SoilN-light, the N-LES approach of Simmelgaard et al. (2000) was not derived from the outputs of any other
simulation model. Moreover, in the Mariager Fjord study the empirical model was not applied directly to a specific field located within the catchment.
Rather, results from model runs performed in two reference catchments, both of which are also used as reference watersheds for the River Gjern basin study, were transferred to the Mariager Fjord catchment, based on similarities with respect to soil type, farm type, livestock density and crop type class (Fig. 6.6). The main focus of the study was on effects of different agricultural practises on N-leaching and subsequent eutrophication impacts on the Fjord. The study performed, for the period 1990-1997, aimed to compare the rates of N-leaching relative to current agricultural practises to values to be expected if farmers were to be forced to reduce the application of inorganic N-fertilizer in case fertilization should reach the maximum limit. Currently, in Denmark, the total application of N-fertilizer per farm is regulated by fixed maximum N-fertilization rates per crop type.
Changes resulting from the tightening were relatively small and never exceeded 7 kg ha-1 yr-1 in any of the 66 subcatchments of Mariager Fjord, which indicates that at present not many farmers fertilize “up to the limits”.
Detailed information agricultural practises from two intensively monitored
catchments (1997/98) Simulation of percolation
from rootzone in the Mariager Fjord catchment
• Four climatic zones
• Two soil types
• Nine crop types
• Seven years
Calculation of annual N-leaching under current
Climate and unchanged agricultural conditions
Derivation of leaching classes for 4 climatic
zones based on Agricultural practises
•• Soil types
• Crop types Information on agricultural
practises in the Mariager Fjord catchment (field level) from CHR, GLR, derivation
of classes
Extrapolation of N-leaching from each field located in the
Mariager Fjord catchment, basic scenario Estimation of leaching
from non-agricultural Areas in the Mariager Ford
catchment
N-leaching from each of the Mariager Ford sub-catchments,
basic scenario conditions
Calculation of annual N leaching for a scenario
-on agricultural practises
N-leaching from each field located in the Mariager Fjord
Catchment, changing conditions scenario N-leaching from each of the
Mariager Fjord sub-catchments, changing conditions scenario
scenario conditions Derivation of leaching
classes relative to the basic scenario Agricultural practises
•• Soil types
• Crop types
Figure 6.6 Flow chart of the modelling steps performed in the Mariager Fjord N-leaching study.
7. Establishment of a harmonized tool for calculating river discharge and nitrogen loads from unmonitored areas in Denmark
D.-I. Müller-Wohlfeil1, B. Kronvang1, S.E. Larsen1, N.B. Ovesen1 and F. Wendland2
1National Environmental Research Institute, Vejlsøvej 25, P.O. Box 314, DK-8600 Silkeborg
2Research Centre Jülich, Programme Group STE, D-52425 Jülich
Address: National Environmental Research Institute, Vejlsøvej 25, 8600 Silkeborg, Denmark