Crop growth, soil water and nitrogen balance simulation on three experimental field plots using the Opus model—A case study

Crop growth, soil water and nitrogen balance simulation on three experimental field plots using the Opus model—A case study

Ecological Modelling 190 (2006) 116–132 Crop growth, soil water and nitrogen balance simulation on three experimental field plots using the Opus mode...

412KB Sizes 4 Downloads 122 Views

Ecological Modelling 190 (2006) 116–132

Crop growth, soil water and nitrogen balance simulation on three experimental field plots using the Opus model—A case study Martin Wegehenkel ∗ , Wilfried Mirschel Centre of Agricultural Landscape Research, Institute of Landscape Systems Analysis, Eberswalder Strasse 84, D-15374 M¨uncheberg, Germany Received 23 October 2003; received in revised form 21 December 2004; accepted 28 February 2005 Available online 13 June 2005

Abstract Over a period of 5 years, the agro-ecosystem model Opus was used to simulate soil water and nitrogen balance as well as crop growth for three experimental field plots. At these plots, different agricultural management practices were applied. The data set obtained from these plots consists of automatically recorded time series of daily volumetric soil water contents measured by TRIME-probes as well as daily pressure heads measured by tensiometer. Aboveground total biomass, yield, nitrogen-uptake by crops as well as nitrate contents in the soil were measured at 6–10 sample times per year. The objective of this study was an evaluation of the accuracy of Opus regarding the simulation of crop growth, soil water and nitrogen balance. The simulations of soil water contents and pressure heads correspond with the commonly measured trends in soil depths shallower than 60 cm. In depths deeper than 60 cm, some differences between measured and simulated soil water contents as well as pressure heads could be observed. Nitrate contents in the root zone and the aboveground total biomass were simulated satisfactorily. In contrast to that, simulated and observed yields show greater discrepancies. This indicates the need of a site specific calibration of crop growth parameters within the Opus model. © 2005 Elsevier B.V. All rights reserved. Keywords: Agro-ecosystem; Modelling; Nitrogen cycle; Crop growth; Soil water balance

1. Introduction The quantification and prediction of the potential effects of agricultural management practices on crop growth, soil water and nitrogen balance – especially ∗ Corresponding author. Tel.: +49 33432 82275; fax: +49 33432 82334. E-mail address: [email protected] (M. Wegehenkel).

0304-3800/$ – see front matter © 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolmodel.2005.02.020

nitrogen leaching, which can affect the quality of surface and shallow groundwater – is an essential task in any agro-ecological research. Within this research, simulation models were developed and used as tools to predict the potential effects of agricultural management practices (e.g. Addiscott and Whitmore, 1987; de Willigen, 1991; Neeteson, 1990; Rodda et al., 1995). Among such simulation models are, e.g. SwaP (Van Dam et al., 1997), Leachm-N (Wagenet and Hutson,

M. Wegehenkel, W. Mirschel / Ecological Modelling 190 (2006) 116–132

1990), Ceres (Godwin et al., 1989), SoilN (Bergstr¨om et al., 1991), Opus (Smith, 1992a), Wave (Vanclooster et al., 1995) and Daisy (Svendsen et al., 1995). Such models have to be validated by comparing simulated model outputs with those measured in the field. Validation studies with different models have been carried out at different locations (e.g. Bonilla et al., 1999; Smith, 1995; de Willigen, 1991; Diekkr¨uger et al., 1995; Porter et al., 1993). This study is a summary of a 5 year long application of the Opus model on three experimental field plots to simulate soil water and nitrogen balance in the root zone as well as crop growth. The objective of the study was an evaluation of the accuracy of Opus regarding the simulation of crop growth, soil water and nitrogen balance dynamics under the conditions of the north-east German lowlands, which are characterized by mean annual precipitation rates of about 500 mm year−1 and sandy soils with high percolation rates as well as low soil water storage.

2. Material and methods 2.1. Field data Three field plots were established for a long-term monitoring of soil water balance, crop growth and nitrogen balance in the root zone at the experimental field of the Centre for Agricultural Landscape Research (ZALF), M¨uncheberg, Germany. Between 1993 and 1997, a crop rotation consisting of sugar beet → winter wheat → winter barley → winter rye → sugar beet was established on every plot, each of which was of the size 107 m × 28 m. The data set consisted of: • Daily weather data such as precipitation, global radiation, maximum and minimum air temperature, wind speed, relative air humidity as well as soil temperatures taken from an automatically recording weather station located at the southern part of the experimental field. • Soil data including texture, bulk density and soil hydraulic properties. • Continuous measurements of daily values of soil water pressure heads and volumetric soil water contents using automatically recording tensiometers

117

Fig. 1. Sensor installation scheme at the field plots (from Wenkel and Mirschel, 1995, modified).

and Time Domain Reflectometry with Intelligent MicroElements (TRIME)-probes. • Gravimetric determination of soil moisture as well as analysis of nitrate contents in the root zone on the basis of 6–10 sample dates per year. • Determination of aboveground total biomass, yield and crop nitrogen content on the basis of 6–10 sample dates within the growing seasons. The soil type on the experimental field is a Eutric Cambisol according to the FAO-classification. A description of the soil profiles at the three field plots can be found in Table 1 (Schindler, 1980). For the measurement of depth-averaged soil water contents for the 0–30, 30–60, 60–90, 90–120, and 120–150 cm compartments, TRIME-probes were installed vertically in depths of 15, 45, 75, 105, and 135 cm at each field plot (Fig. 1). This installation scheme also enables the estimation of the total soil water mass down to 150 cm depth including the amount of soil water storage available for soil evaporation and crop transpiration. However, this installation scheme can cause effects such as possible underestimations concerning the soil water dynamics due to the fact, that the heads of the probes with a diameter of 63 mm can react as small barriers, and therefore, delay percolation. Tensiometers were installed at soil depths of 30, 60, 90, 120, 150, 200 and 300 cm (Fig. 1). At each depth, one TRIME-probe and one tensiometer were installed. During frost periods, tensiometers in the depths of 30 and 60 cm were removed. The data set covers the time

M. Wegehenkel, W. Mirschel / Ecological Modelling 190 (2006) 116–132

118

Table 1 Soil properties, saturated hydraulic conductivity Ks , Brooks–Corey parameters hb , λ, α, saturated and residual soil water contents θ s and θ r , descriptions of the layers according to AG Boden (1994) Horizon

Depth from to (cm)

Bulk Org. C density (%) (g cm−3 )

Ks (mm d−1 )

hb (mm)

λ

α

θ s (mm3 mm−3 )

θ r (mm3 mm−3 )

3 5 8 6 7 8

1.45 1.50 1.48 – – –

0.45 0.26 0.10 0.0 0.0 0.0

936 1632 312 1632 1632 1632

−47 −45 −35 −45 −45 −45

0.503 0.580 0.534 0.580 0.580 0.580

3.51 3.74 3.60 3.74 3.74 3.74

0.385 0.319 0.302 0.319 0.310 0.310

0.0218 0.0244 0.0623 0.0244 0.0244 0.0244

5 5 12 10 5 5

10 5 8 10 5 5

1.45 1.51 1.48 – – –

0.45 0.26 0.10 0.0 0.0 0.0

936 1632 312 312 1632 1632

−47 −45 −35 −35 −45 −45

0.503 0.580 0.534 0.534 0.580 0.580

3.51 3.74 3.60 3.60 3.74 3.74

0.385 0.319 0.302 0.302 0.319 0.319

0.0218 0.0244 0.0623 0.0623 0.0244 0.0244

6 5 13 11

9 5 6 9

1.45 1.51 1.48 –

0.45 0.26 0.10 0.0

936 1632 312 312

−47 −45 −35 −35

0.503 0.580 0.534 0.534

3.51 3.74 3.60 3.60

0.385 0.319 0.302 0.302

0.0208 0.0244 0.0623 0.0623

Sand (%)

Clay (%)

Plot no. 1 Ap 0–30 Ael 30–60 Bt 60–90 C1 90–120 C2 120–150 C3 150–225

90 90 80 90 90 90

7 5 12 4 3 2

Plot no. 2 Ap 0–30 Ael 30–90 Bt1 90–130 Bt2 130–170 C1 170–180 C2 180–225

85 90 80 80 90 90

Plot no. 3 Ap 0–30 Ael 30–100 Bt1 100–110 Bt2 110–225

85 90 81 80

Silt (%)

period from 1 January 1993 to 31 December 1997. The TRIME-probes in the soil compartments 90–120 and 120–150 cm were installed later in 1995. At 6–10 (sample) dates per year, soil samples were taken from the layers 0–30, 30–60 and 60–90 cm at each plot using an auger for the purpose of gravimetric soil moisture determination. This data were also used to calibrate the TRIME-system. The accuracy of the calibrated TRIME-system was at ±2.5 vol% (Wegehenkel, 1998). A detailed overview of the soil hydrological measurement techniques is given in Wegehenkel (1998, 2005). Additionally at some sampling days, 12–14 spatially distributed individual soil samples from each layer were sampled randomly from each plot, and for each sample, the soil water content was determined. From these soil water contents, mean values and standard deviations for the corresponding soil layers were calculated. The standard deviations as one potential measure for the spatial variability of the soil water contents in the field plots were between 0.4 and 2.5 vol%. In a study of Dekker et al. (1999), which deals with the spatial variability of soil water contents in depths from

0 to 35 cm on two fields with sandy soil located also in north-east Germany, the corresponding standard deviations of soil water contents were between 1.8 and 5.5 vol%. Each field plot was treated with a separate management practice. However, seeding and harvest dates were identical at all field plots. At plot no. 1, an intensive agriculture was applied using chemical plant protection and inorganic fertilization on a high level. The second management practice at plot no. 2, an organic agriculture, only used organic fertilizer and non-chemical plant protection. At plot no. 3, an extensive agriculture was applied using a mixture of both, inorganic and organic fertilizers as well as chemical plant protection on a low level (Table 2). An additional description of the investigations carried out at the three field plots is given in Wenkel and Mirschel (1995). The main objective of these investigations was the analysis of the effects of different management practices on water and matter balance as well as assembling a database for rigorous tests of agro-ecosystem models (Wenkel and Mirschel, 1995). This database was used in our study.

M. Wegehenkel, W. Mirschel / Ecological Modelling 190 (2006) 116–132

119

Table 2 Selected management data of the field plots (ANL: ammonium-nitrate–lime; LAU: liquid ammonium urea) Crop type

Seeding date

Harvest date

Fertilizer type

Amount (kg ha−1 )

Date of application

Plot no. 1 Sugar beet

26.4.93

6.10.93

LAU Inorganic fertilizer

80 N 30 P, 160 K, 50 Mg

01.05.93 12.10.93

Winter wheat

15.10.93

29.7.94

LAU LAU ANL Inorganic fertilizer

50 N 40 N 50 N 120 K

06.04.94 04.05.94 27.05.94 12.09.94

Winter barley

26.9.94

21.7.95

LAU LAU LAU LAU Inorganic fertilizer

35 N 40 N 30 N 40 N 30 P, 160 K, 50 Mg

20.03.95 11.05.95 17.05.95 30.05.95 11.09.95

Winter rye

2.10.95

21.8.96

LAU LAU LAU ANL

40 N 30 N 30 N 60 N

19.04.96 17.05.96 05.06.96 09.09.96

Sugar beet

3.4.97

23.9.97

Inorganic fertilizer LAU ANL

45 P, 171 K, 118 Mg 70 N 60 N

06.03.97 10.04.97 02.06.97

26.4.93

6.10.93

Farmyard manure Farmyard manure

33000 11100

02.09.92 14.10.93

Winter wheat

15.10.93

29.7.94

Liquid manure Liquid manure

30 N 30 N

27.04.94 09.05.94

Winter barley

26.9.94

21.7.95

Liquid manure

37 N

09.03.95

Winter rye

2.10.95

21.8.96

Liquid manure Farmyard manure

64 N 11100

29.04.96 04.09.96

Sugar beet

3.4.97

23.9.97

Inorganic fertilizer

45 P, 149 K, 30 Mg

06.03.97

26.4.93

6.10.93

Farmyard manure LAU

33000 80 N

02.09.92 01.05.93

Winter wheat

15.10.93

29.7.94

Farmyard manure LAU LAU Inorganic fertilizer

11000 40 N 50 N 120 K

14.10.93 06.04.94 04.05.94 12.09.94

Winter barley

26.9.94

21.7.95

LAU LAU Inorganic fertilizer

35 N 60 N 30 P, 160 K, 50 Mg

20.03.95 11.05.95 11.09.95

Winter rye

2.10.95

21.8.96

LAU LAU

30 N 45 N

19.04.96 17.05.96

Sugar beet

3.4.97

23.9.97

Farmyard manure ANL Inorganic fertilizer LAU ANL

33000 40 N 45 P, 171 K, 118 Mg 70 N 40 N

04.09.96 09.09.96 06.03.97 10.04.97 02.06.97

Plot no. 2 Sugar beet

Plot no. 3 Sugar beet

M. Wegehenkel, W. Mirschel / Ecological Modelling 190 (2006) 116–132

120

2.2. Model Opus Opus is a deterministic agro-ecosystem model with the following basic components: evapotranspiration, infiltration, surface runoff and erosion, unsaturated flow in the soil profile, crop growth, nitrogen, carbon, phosphorus and pesticides (Smith, 1992a). Similar models are, e.g. SwaP (Van Dam et al., 1997), CropSyst (Stoeckle et al., 2003), animo (Groenendijk and Kroes, 1997), Coupmodel (Jansson and Karlberg, 2004), Wave (Vanclooster et al., 1995) and Daisy (Svendsen et al., 1995). All these deterministic, research-oriented models describe the processes with a high degree of accuracy and thus require a large number of input data. At present, these models are state-of-theart within research oriented agro-ecosystem modelling (e.g. Shaffer et al., 2001). When developing Opus, the design goals were: • to create a model suitable to use without calibration; • to maintain a balance regarding the complexity of different model components and process detail; • to create a model that is easy to operate since it should be useful for both, research and management applications (Smith, 1992a,b). Therefore, we decided to select Opus for the application in our study. More detailed information about Opus can be obtained from Smith (1992a,b). 2.2.1. Soil water balance Up to six soil horizons can be represented by the model and the total soil profile is subdivided in up to 20 numerical layers. Evapotranspiration (Et in mm d−1 ) is calculated as follows: Et =

(1 + cw )∆(Ri (1 − ξ)/58.3) ∆ + 0.68

(1)

where cw is a coefficient expressing effects of wind and humidity, ∆ the slope of curve for saturation vapour pressure at mean air temperature in mbar K−1 , Ri the incoming solar radiation in langley d−1 and ξ is the albedo of the field surface. Runoff modelling depends on the degree of accuracy of available rainfall data. An infiltration-based method is used for pluviographic data. When only daily data values are available, runoff is estimated on the basis of a modified curve number method of the Soil Conser-

vation Service. The model simulates one-dimensional movement of soil water using the Richards’ equation:   ∂ K(h) ∂h ∂h ∂z + K(h) (2) = + qe Cc (h) ∂t ∂z where Cc (h) is the slope of the curve ∂θ/∂h, h the soil water pressure head in mm, t the time, z the depth in mm, K(h) the hydraulic conductivity and qe is a sink due to the removal of water by plants and/or losses of water by evaporation from soil surface layers, both in mm d−1 . A modification of the Brooks–Corey functions (Brooks and Corey, 1964; Smith, 1992a) describes the soil water retention and hydraulic characteristics:   α −λ/α h θ − θr = 1+ (3) θs − θ r hb   θ − θr (2+3λ)/λ K = Ks (4) θs − θ r where θ is the actual volumetric soil water content in mm3 mm−3 , θ r and θ s the residual and saturated soil water contents in mm3 mm−3 , hb the an air-entry parameter in mm, λ the pore-size distribution index, α the curvature coefficient affecting the shape of the θ(h) curve near hb , K the unsaturated soil hydraulic conductivity and Ks is saturated hydraulic conductivity, both in mm d−1 . 2.2.2. Nitrogen balance The dynamics of the soil microbial system and the carbon, nitrogen and phosphorus cycles in the soil are simulated using the approaches of the organic residue decomposition model Century of Parton et al. (1987). Nitrogen and phosphorus from any source are part of a carbon-based organic matter system, which includes three pools of carbon material with different turnover rates. Plant residue and organic carbon pools are characterized by C:N ratios. Decomposition is accompanied by mineralization or immobilization according to the relative C:N ratios of the source material and the receiving organic carbon pool. The model is applied to three activity zones, i.e. the surface litter zone, the active upper soil zone (upper 20 cm) and the lower soil zone below 20 cm, all three being treated separately. Processes of nitrification and denitrification are simulated similar to the soil water movement and linked to

M. Wegehenkel, W. Mirschel / Ecological Modelling 190 (2006) 116–132

local soil water and soil nitrogen contents as well as soil temperatures. The following processes are accounted to determine changes in the mineral nitrogen content inside each layer: • incorporation of surface vegetal litter; • additions of structural and metabolic nitrogen to active, passive, and slow soil organic pools; • atmospheric contributions; • biological nitrogen fixation; • amount of applied fertilizer; • losses from leaching, volatilization and erosion; • extraction by crops; • immobilization of residue nitrogen. To simulate the transport of partially soluble and partially adsorbed chemicals in the unsaturated zone, Opus considers two kinds of solute adsorption processes: • instantaneous equilibrium, described by a linear adsorption isotherm; • first order kinetic reaction, the rate of transfer between adsorbed and solution phases is a linear function of the current phase ratio compared to the instantaneous equilibrium. Nitrate movement is simulated with a linear equilibrium isotherm model with the same arrangement of numerical layers in the soil profile as used for water movement. The following equation is used for the mass balance (Smith, 1992a):   ∂C (Vw + Kd Ms ) = Ci Qi − C o Qo ∂t

(5)

where C is the nitrate content in the soil solution in kg l−1 , Vw the volume of water held in the corresponding layer in litre, Kd the adsorption constant in l kg−1 , Ms the mass of soil in the numerical layer under consideration in kg ha−1 , Q the flow rate in l d−1 , and the subscripts i and o are used to represent the conditions upon entry into and exit from the numerical layer (Smith, 1992a). 2.2.3. Crop growth Crop growth is simulated with a simple mechanistic model which relates daily dry matter production to leaf area index LAI and solar radiation. Daily dry matter production is limited by temperature, root zone water

121

content and available nutrients: dM = ce Ri fg (DM)ft S

(6)

in which dM is the daily rate of biomass growth and DM presents the total amount of accumulated biomass, both in kg dry matter ha−1 , ce is a plant specific biomass conversion factor in kg dry matter ha−1 langley−1 , Ri the daily global radiation in langley (cal (cm2 d)−1 ), fg a growth limit coefficient which goes to zero as the plant nears its physiological size limit, fL the photosynthetic area factor which is calculated from LAI, which is a function of DM, fT a senescence coefficient which goes to zero as the plant ontogenesis reaches maturity in thermal time (◦ C days) and S is a stress factor representing the most critical of independently calculated stresses from water, temperature and nutrients. Immediately after the emergence of the crop, daily plant material production is allocated among root, stem and leaf as well as fruit material. Physiological crop stages are not described, but water and nitrogen-uptake by the plant stand, leaf area and yield are simulated between the time of emergence and maturity (Smith, 1992a, 1995). 2.2.4. Modelling procedures In our study, the simulation period of Opus lasted from 1 January 1993 to 31 December 1997 on all field plots. The soil profile of each field plot was discretized by Opus in 17 numerical layers with variable thickness. This discretization is an internal procedure within Opus and is determined by the maximum rooting depth of the crops defined by the model user (Smith, 1992a,b; Table 3). The upper system boundary is represented by the evapotranspiration and the daily net precipitation rate (precipitation rate minus interception rate). The lower system boundary was the condition of free drainage. To enable a comparison with the TRIME-measurements, the simulated volumetric soil water contents of the numerical layers were integrated for the corresponding soil layers in 0–30, 30–60, 60–90, 90–120, and 120–150 cm depth. The soil hydraulic data for each field plot in Table 1 were calculated from the existing functions h(θ) and k(h) according to van Genuchten (1980), which result from former field investigations at the plots (Schindler, 1980). For the simulation runs, the initial soil water contents of all layers were set equally to the field capac-

M. Wegehenkel, W. Mirschel / Ecological Modelling 190 (2006) 116–132

122

Table 3 Parameters used in the crop growth model within Opus Crop

Max. LAI

Maturity SSDM (◦ C days)

Potential dry matter, PDM (kg ha−1 )

Potential yield, PY (kg ha−1 )

Max. root depth (mm)

Max. soil cover

Sugar beet Winter wheat Winter rye Winter barley

4.0 5.0 4.3 5.0

5500 3500 2000 1500

16500 15000 16800 16800

8429 7000 6000 6000

1500 1100 1100 1100

0.79 0.99 0.99 0.99

Crop

Max. plant height (m)

Min. grow days

Base temperature, Tbase (◦ C)

Optimum temperature, Topt (◦ C)

Sowing-emergence duration (◦ C days)

Biomass conversion factor, ce (kg (ha langley)−1 )

Sugar beet Winter wheat Winter rye Winter barley

0.3 1.0 1.1 1.1

81 39 43 44

2 2 2 2

15 15 15 15

5 170 197 170

25 40 47 40

ity. The initial nitrate contents in the root zone were estimated from field data (Wenkel and Mirschel, 1995). Within the data set for the crop growth model in Table 3, the most sensitive parameters are base and optimum temperatures Tbase and Topt which control the temperature stress for the crop, the biomass conversion factor ce , the potential (no stress) amount of dry matter at maturity (PDM) and potential yield (PY) as well as the thermal time up to maturity (SSDM) in ◦ C days (Smith, 1995). LAI is assumed to be a sine function of relative aboveground dry matter, i.e. it increases most rapidly when the plant is young and reaches a maximum target value Max.LAI (Table 3). In our study, these parameters were obtained from Smith (1992b). Tbase , Topt , SSDM and Max.LAI were modified for European conditions according to Boons-Prins et al. (1993). The effects of the different plant protection procedures applied at the three field plots on crop growth were neglected in the study. No optimization or calibration procedures were carried out for parameter sets used for the model runs. The fit between simulated and observed model outputs was analyzed using the modelling efficiency index IA according to Willmott (1982) and the root mean squared error, RMSE, and is stated as follows: n 2 i=1 (θsim − θobs ) IA = 1− n 2 i=1 [|θsim −θobs−mean |+|θobs −θobs−mean |] (7)  RMSE =

n

i=1 (θsim

n

− θobs )2

(8)

where θ sim and θ obs are the simulated and measured values and θ obs−mean represent the measured mean value. IA is in a range between 0 and 1. The closer it is to 1 the better is the fit between observed and simulated values. Both IA and RMSE are common tools to validate simulation models (e.g. Diekkr¨uger et al., 1995; Eitzinger et al., 2004).

3. Results 3.1. Soil water balance As an example, the time courses of the simulated soil water contents and pressure heads for plot nos. 1 and 2 are presented in Figs. 2–5. At both plots, simulated soil water contents in depths shallower than 60 cm mostly run similar with those measured by gravimetry and TRIME (Figs. 2 and 4). Only in the summer period of 1996, measured soil water contents in the compartment 30–60 cm indicate no root water uptake due to transpiration in contrast to the simulated ones at all field plots (Figs. 2 and 4). At plot no. 1, simulated soil water contents in the compartment 60–90 cm show a higher dynamic between low and high soil moisture in comparison with the measured ones (Fig. 2). The simulated soil water contents in 90–120 and 120–150 cm depth show lower values and lower dynamics than the observed ones (Fig. 2). At plot no. 2, most of the measured soil water contents in the compartments 60–90 and 90–120 cm were higher than the simulated ones (Fig. 4).

M. Wegehenkel, W. Mirschel / Ecological Modelling 190 (2006) 116–132

Fig. 2. Daily precipitation rates (Prc) in mm d−1 and simulated and observed volumetric soil water contents (vol%) in the compartments 0–30, 30–60, 60–90, 90–120, 120–150 cm (Swc30, Swc60, Swc90, Swc120, Swc150), plot no. 1.

123

Fig. 3. Daily precipitation rates (Prc) in mm d−1 and simulated and observed pressure heads (hPa) in the depths 30, 60, 90, 120, 150 cm (Prh30, Prh60, Prh90, Prh120, Prh150), plot no. 1.

3.2. Nitrogen balance In the winter periods, with soil water contents mainly near or above field capacity (Figs. 2 and 4), most of the simulated and measured soil water pressure heads are in the same order (Figs. 3 and 5). In contrast to the simulated pressure heads in the summer periods, observed pressure heads indicate only a low root water uptake, especially in the soil depths of 60 and 90 cm (Figs. 3 and 5). The annual rates of precipitation and simulated runoff ranged within 421–655 and 0–11 mm year−1 , respectively (Table 4). The long-term average of the annual precipitation rate is at 529 mm year−1 (Krumbiegel and Schwinge, 1991). The simulated annual rates of evapotranspiration and percolation were within 405 and 473 mm year−1 as well as 0 and 271 mm year−1 (Table 4).

At all field plots, the largest part of simulated nitrate contents in the compartment 0–30 cm are in the same order as the measured contents (Figs. 6 and 7). In 1996, however, simulated nitrate contents in the compartments 30–60 and 60–90 cm were significantly lower than the measured ones (Figs. 6 and 7). One year later, in 1997, calculated nitrate contents increase in all soil compartments of plot no. 1. In the same year, only low nitrate contents were simulated by Opus at plot no. 2 (Figs. 6 and 7). Between 1993 and 1995, simulated crop nitrogenuptake rates are overestimated at plot no. 1; in 1996 and 1997, however, they were simulated quite well in comparison with the measured ones (Fig. 6). At plot no. 2, the nitrogen-uptake was overestimated by Opus during the whole simulation period (Fig. 7). The highest

124

M. Wegehenkel, W. Mirschel / Ecological Modelling 190 (2006) 116–132

Fig. 4. Daily precipitation rates (Prc) in mm d−1 and simulated and observed volumetric soil water contents (vol%) in the compartments 0–30, 30–60, 60–90, 90–120, 120–150 cm (Swc30, Swc60, Swc90, Swc120, Swc150), plot no. 2.

nitrogen-uptake with 241 kg ha−1 (winter barley, 1995)

was calculated at plot no. 1, the lowest with 103 kg ha−1 (winter rye, 1996) at plot no. 2 (Figs. 6 and 7; Table 4). The daily rates of simulated nitrogen (N)-net mineralization (nitrification and mineralization) were up to 8 kg (ha d)−1 . In 1993 and 1997, simulated daily rates of N-net mineralization at plot no. 1 were significantly higher in comparison with those at plot no. 2 (Figs. 6 and 7). At plot no. 1, simulated daily nitrogen leaching rates were up to 5.8 kg (ha d)−1 . At plot no. 2, daily leaching rates were up to 1.2 kg (ha d)−1 (Figs. 6 and 7). At plot nos. 1 and 3, the calculated nitrogen balance components were in the same order (Table 4). In comparison with plot nos. 1 and 3, a lower annual nitrogen supply by fertilizer application corresponds with lower annual rates of N-net mineralization,

Fig. 5. Daily precipitation rates (Prc) in mm d−1 and simulated and observed pressure heads (hPa) in the depths 30, 60, 90, 120, 150 cm (Prh30, Prh60, Prh90, Prh120, Prh150), plot no. 2.

nitrogen leaching and nitrogen-uptake at plot no. 2 (Table 4). 3.3. Crop growth At all three field plots, the accumulation of the aboveground total biomass is simulated sufficiently in comparison with the observed data (Fig. 8). In the year 1993, Opus underestimated the aboveground total biomass at plot nos. 2 and 3, as it did in the year 1997 at all field plots (Fig. 8). Opus overestimated the yields at plot no. 1 in the years 1993 and 1994 and underestimated the yields in the following years (Table 5). The yield calculated by Opus at plot no. 2 in 1993 is too high, whereas, in the following years, simulated yields are significantly too low in comparison with the observed ones. At plot no. 3, Opus generally underestimated the yields (Table 5).

M. Wegehenkel, W. Mirschel / Ecological Modelling 190 (2006) 116–132

125

Table 4 Annual rates of precipitation (Prc) and amount of nitrogen fertilizer (N-fertilizer) as well as the simulated components of water and nitrogen balance, runoff (Roff), actual evapotranspiration (Etr), percolation (Perc.), N-net mineralization, nitrogen leaching (N-leaching) and nitrogenuptake by crops (N-uptake) Prc (mm year−1 )

Roff (mm year−1 )

Etr (mm year−1 )

Perc. (mm year−1 )

N-fertilizer (kg (ha year)−1 )

N-net mineralization (kg (ha year)−1 )

N-leaching (kg (ha year)−1 )

N-uptake by crops (kg (ha year)−1 )

Plot no. 1 1993 594 1994 655 1995 483 1996 421 1997 440

11 0 3 6 0

430 413 424 424 419

154 261 120 3 24

80 140 145 160 130

196 51 75 73 288

15 84 3 0 1

147 220 241 175 144

Plot no. 2 1993 594 1994 655 1995 483 1996 421 1997 440

11 0 3 6 0

469 409 422 409 406

118 256 124 0 42

66 60 40 130 0

112 80 119 41 110

5 32 7 0 1

156 132 142 154 103

Plot no. 3 1993 594 1994 655 1995 483 1996 421 1997 440

11 0 3 6 0

473 405 415 405 405

109 271 119 5 47

146 90 95 323 110

166 64 74 102 264

8 69 16 0 9

160 166 196 158 144

Table 5 Comparison of simulated and measured yields Crop type and year of harvest

Yield (kg ha−1 ) Plot no. 1

Sugar beet, 1993 Winter wheat, 1994 Winter barley, 1995 Winter rye, 1996 Sugar beet, 1997

Plot no. 2

Plot no. 3

Measured

Calculated

Measured

Calculated

Measured

Calculated

8455 4516 5562 6812 11639

9960 4773 3412 1863 9187

9467 3245 3020 2450 11762

12015 891 1823 1734 4638

15427 4797 5681 5135 12419

12293 2645 1978 1829 9060

4. Discussion At all field plots, the simulation accuracy regarding the soil water contents shows two different patterns. In the soil compartments with 0–30 and 30–60 cm depth, IA and RMSE ranged within 0.58 and 0.75 and from 3.6 up to 4.6 vol%, respectively (Table 6). In the deeper compartments, IA was within 0.10 and 0.57 whilst RMSE ranged from 1.6 up to 10.4 vol% (Table 6). A similar trend could be observed regarding the pressure heads. In the depth of 30 cm at plots nos. 1 and 3, IA ranged within 0.58 and 0.68 and RMSE from 105 up to 152 hPa. At the plots in depths deeper than 30 cm,

IA and RMSE ranged within 0.10 and 0.41 and from 28 up to 185 hPa, respectively. At plot no. 2, however, the best IA = 0.68 and the best RMSE = 16 hPa could be observed in the depth of 150 cm (Table 6). At an international workshop on the validity of 18 different agro-ecosystem models held at Brunswick, Germany, in 1993, the simulation quality was evaluated using also RMSE obtained from simulated and observed soil water contents as well as pressure heads. At this workshop, RMSE was within 2.0 and 12.6 vol% regarding the soil water contents and within 100 and 555 hPa with respect to the pressure heads (Diekkr¨uger et al., 1995). In the study of Jacques et al. (2002), a

126

M. Wegehenkel, W. Mirschel / Ecological Modelling 190 (2006) 116–132

Fig. 6. Observed and simulated nitrate contents in the soil compartments 0–30, 30–60, and 60–90 cm (NO3 30, NO3 60, NO3 90), nitrogen-uptake by crops (N-uptake), net mineralization (Net min.) and nitrate leaching (NO3 -L), all in kg (ha d)−1 , plot no. 1.

model for the calculation of field-scale water flow was validated, using also the TDR-method and tensiometer measurements. Here, the RMSE for simulated and measured soil water contents showed a range of 3.8 and 12.5 vol%, whilst the RMSE for calculated and measured pressure heads was within 31 and 371 hPa (Jacques et al., 2002). In a similar study dealing with the validation of different pesticide leaching models, IA and RMSE obtained from the comparison of soil water contents measured with TDR with those simulated with the pesticide leaching models ranged within 0.65 and 0.91 and from 1.0 up to 2.3 vol% (Vanclooster and Boesten, 2000). In the validation study of Heidmann et al. (2000) with the agro-ecoystem model Soiln using also TDR-measurements, RMSE obtained from simulated and observed soil water contents ranged between 1 and 3.5 vol%. In a similar study with the models Ceres and Swap, the corresponding IA for simulated

Fig. 7. Observed and simulated nitrate contents in the soil compartments 0–30, 30–60, and 60–90 cm (NO3 30, NO3 60, NO3 90), nitrogen-uptake by crops (N-uptake), net mineralization (Net min.) and nitrate leaching (NO3 -L), all in kg (ha d)−1 , plot no. 2.

and observed soil water contents was in a range from 0.58 up to 0.93, whilst RMSE was between 0.7 and 6.8 vol% (Eitzinger et al., 2004). At all field plots, simulated soil water contents and pressure heads in soil depths deeper than 30 cm were, in comparison with the observed ones, distinctly lower in the summer periods, especially in the years 1996 and 1997 (Figs. 2–5). This indicates higher root water uptake rates simulated by Opus in comparison with the actual uptake rates. One likely reason might be differences between simulated and actual rooting depths. For all crops, Opus calculated rooting depths up to 100 cm. If actual rooting depths shallower than 60 cm were indicated by the measured pressure heads and soil water contents, they would lead to an increased water stress for the crops, especially in dry years such as 1996 with the lowest annual precipitation rate being

M. Wegehenkel, W. Mirschel / Ecological Modelling 190 (2006) 116–132

127

Fig. 8. Comparison of simulated and observed above ground total biomass (ABM in kg ha−1 ) at all three field plots (Sb: sugar beet, Ww: winter wheat, Wb: winter barley, Wr: winter rye).

421 mm year−1 (Table 4). This would limit the accumulation of the actual aboveground total biomass and should lead to a distinct overestimation of the simulated aboveground total biomass in comparison with

the measured one. However, in 1996, this overestimation could not be observed (Fig. 8). Therefore, actual rooting depths shallower than 60 cm are not indicated by the comparison of simulated and measured above-

Table 6 IA and RMSE obtained from the comparisons of simulated with measured soil water contents with TRIME (SWC–IA, SWC–RMSE) as well as pressure heads (PRH–IA, PRH–RMSE) Compartment–TRIME (cm)

SWC–IA

Plot no. 1 0–30 30–60 60–90 90–120 120–150

0.73 0.58 0.52 0.57 0.51

Plot no. 2 0–30 30–60 60–90 90–120 120–150 Plot no. 3 0–30 30–60 60–90 90–120 120–150

SWC–RMSE (vol%)

Depth of tensiometer (cm)

PRH–IA

PRH–RMSE (hPa)

3.9 4.4 4.4 6.6 3.5

30 60 90 120 150

0.58 0.41 0.10 0.21 0.23

152 136 130 185 28

0.59 0.75 0.36 0.39 0.10

4.6 3.6 10.4 4.4 1.6

30 60 90 120 150

0.25 0.15 0.14 0.36 0.68

149 229 125 38 16

0.59 0.68 0.47 0.23 0.15

4.1 3.7 5.9 5.3 5.2

30 60 90 120 150

0.64 0.13 0.18 0.21 0.31

105 165 109 80 37

M. Wegehenkel, W. Mirschel / Ecological Modelling 190 (2006) 116–132

128

ground total biomass. Another reason may be errors in the TRIME and tensiometer measurements. The accuracy of the calibrated TRIME was at ±2.5 vol% (Wegehenkel, 1998, 2005) and the time courses of soil water contents measured by TRIME and gravimetry mostly run similar (Figs. 2 and 4). Moreover, tensiometer are precise instruments provided that they are properly operated. Errors in the parameterization of the functions θ(h) and k(h) can also lead to differences between measured and simulated model outputs, especially in the winter periods with mainly downward soil water fluxes. However, the observed differences between measured and calculated soil water contents as well as pressure heads are concentrated in summer periods with low soil water contents often reaching the wilting point and a high dynamic in the case of precipitation events (Figs. 2–5). This indicates, that parameterization errors in the θ(h) and k(h)-functions are also not the reason for the differences between simulated and observed soil water contents as well as pressure heads in the soil compartments deeper than 30 cm. Therefore, the most probable reason might be the impact of spatial heterogeneities of soil and plant properties such as soil water storage capacity, soil cover or rooting depth at the field plots. However, a clear reason for these differences between measured and simulated soil water contents and pressure heads was difficult to identify. Moreover, two patterns of simulation accuracy could be observed regarding the simulated nitrate contents in the root zone. At all field plots in the compartment 0–30 cm, IA and RMSE range from 0.62 to 0.91 and from 10 to 19 kg ha−1 (Table 7). In the compartments deeper than 30 cm, IA and RMSE were within 0.32 and 0.60 as well as 8 and 15 kg ha−1 (Table 7). At the international workshop on the validity of agroecosystem models in 1993, the RMSE obtained from simulated and observed nitrate contents in the root zone was within 5 and 42 kg ha−1 (Diekkr¨uger et al., 1995). In a study carried out by Asseng et al. (2000), the RMSE

between nitrate contents in the root zone simulated by the model Apsim and observed ones was at 9 kg ha−1 . In a similar study with the CropSyst model, the corresponding IA was at 0.23 (Bellocchi et al., 2002). The peaks in the time courses of the simulated nitrate contents in 0–30 cm depth correspond with the dates of the fertilizer applications (Figs. 6 and 7). The low simulated nitrate contents in the root zone of both field plots at the end of 1994, 1995, and 1996 are due to high nitrogen leaching rates in 1994 and low net mineralization rates in 1995 and 1996 (Figs. 6 and 7). Furthermore, most of the nitrogen supplied by fertilizer applications in 1995 and 1996 was used for the simulated nitrogenuptake of crops (Figs. 6 and 7). In 1997, the simulated increases in nitrate contents in the root zone at plot no. 1 are due to high N-net mineralization rates and low nitrogen leaching rates as well as fertilizer application (Fig. 6). At plot no. 2, where only organic fertilizer consisting of farmyard and liquid manure was applied, the comparison of measured and simulated nitrate contents, especially in the years 1994, 1996, and 1997, indicates an underestimation of the mineralization of the manure by Opus (Fig. 7). Since nitrogen-uptake by crops is simulated satisfactorily at all plots, the discrepancies between simulated and measured nitrate contents in the deeper compartments might be caused by errors in the N-net mineralization rates calculated by Opus. However, N-net mineralization rates are spatially and temporally variable making them difficult to measure or model (Jarvis et al., 1996). The accumulation of the aboveground total biomass calculated by Opus in comparison with the measurements resulted in an IA between 0.90 and 0.94 and a RMSE between 2708 and 2942 kg ha−1 (Table 7). In a study evaluating the performance of the Ceres model, the corresponding IA obtained from the comparison of simulated and observed biomass was within 0.87 and 0.96 (Bacsi and Zemankovics, 1995). In two other similar studies using the CropSyst model, IA and RMSE

Table 7 IA and RMSE (in kg ha−1 ) obtained from the comparisons of simulated with measured soil nitrate contents (IA-N, RMSE-N), nitrogen-uptake of crops (IA-Nupt, RMSE-Nupt), aboveground biomass (IA-crop, RMSE-crop) and crop yields (IA-yield, RMSE-yield) Plot IA-N no. 0–30 cm

RMSE-N 0–30 cm

IA-N 30–60 cm

RMSE-N 30–60 cm

IA-N 60–90 cm

RMSE-N 60–90 cm

IA-Nupt

RMSENupt

IA-crop

RMSEcrop

IA-yield

RMSEyield

1 2 3

19 13 10

0.55 0.32 0.52

14 8 15

0.60 0.34 0.45

12 11 12

0.91 0.74 0.90

39 46 35

0.93 0.90 0.94

2708 2942 2778

0.77 0.55 0.75

2499 3289 2898

0.86 0.62 0.91

M. Wegehenkel, W. Mirschel / Ecological Modelling 190 (2006) 116–132

129

Table 8 Nitrogen (Nstress) and water (Wstress) stresses at the field plots simulated with the Opus model Year

Nstress Plot no. 1

Wstress Plot no. 2

Plot no. 3

Plot no. 1

Plot no. 2

Plot no. 3

Sums of stress ratios 1993 1 1994 28 1995 13 1996 50 1997 0

7 52 44 57 54

0 41 31 54 0

6 24 16 23 34

11 17 29 17 20

13 20 12 22 36

Sum of stress days per year 1993 16 1994 45 1995 28 1996 67 1997 4

55 74 63 71 83

5 60 51 67 4

10 31 22 34 41

19 19 34 21 24

20 24 17 34 43

regarding aboveground biomass ranged within 0.86 and 0.996 and from 786 up to 1030 kg ha−1 (Bellocchi et al., 2002; Stoeckle et al., 2003). In the study with the Apsim model, RMSE obtained from simulated and observed aboveground biomass was at 1200 kg ha−1 (Asseng et al., 2000). IA and RMSE obtained from the comparison of observed yields with those simulated by Opus ranged within 0.55 and 0.77 and between 2499 and 3289 kg ha−1 (Table 7). In the two validation studies with the CropSyst model, IA and RMSE regarding yields ranged within 0.90 and 0.975 as well as 383 and 560 kg ha−1 (Bellocchi et al., 2002; Stoeckle et al., 2003). In the study with the Apsim model, RMSE was at 800 kg ha−1 (Asseng et al., 2000). In the year 1994, the yields calculated by Opus were at 891 kg ha−1 at plot no. 2 and 2645 kg ha−1 at plot no. 3 in comparison with the measured ones which were at 3245 and 4797 kg ha−1 (Table 5). However, in 1994, aboveground total biomass is simulated quite well at plot no. 2 and only slightly underestimated at plot no. 3 (Fig. 8). In Opus, the partitioning of total biomass to plant leaf, root and fruit is a function of the relative plant age (RDM) and the relative thermal time (Smith, 1992a). RDM is defined as the ratio between total accumulated biomass (DM) and the potential dry matter (PDM) whilst the relative thermal time is the ratio between the actual thermal time (SSD) and the thermal time at maturity (SSDM in Table 3). RDM is also influenced by nitrogen and water stress suffered by the crops. In Opus, nitrogen stress days and water stress days

Fig. 9. Simulated relative plant development (RDM), accumulation of total biomass (TBM) and yield at all three plots (Sb: sugar beet, Ww: winter wheat, Wb: winter barley, Wr: winter rye).

130

M. Wegehenkel, W. Mirschel / Ecological Modelling 190 (2006) 116–132

are defined as days, where the actual nitrogen-uptake (ANU) and the actual evapotranspiration (AET) rates are below the corresponding potential uptake (PNU) and potential evapotranspiration rates (PET). The sums of stress ratios are the sums of the ratios ANU:PNU and AET:PET. At plots nos. 2 and 3, the accumulation of biomass is much more limited by nitrogen-stress in comparison with plot no. 1 (Table 8). In comparison with plot no. 1, this leads to a lower RDM at both plots and causes a low partitioning of total biomass DM to the fruit or yield fraction especially in the years from 1994 up to 1997 (Fig. 9; Table 5). These effects are known from an earlier publication (Smith, 1995). In our study, IA and RMSE regarding the nitrogen-uptake by crops ranged within 0.90–0.93 and 35–39 kg ha−1 (Table 7). In a validation study of the model Daisy, IA obtained from simulated and observed nitrogen-uptake was at 0.97 (Hansen et al., 2001). In the study with the Apsim model, the corresponding RMSE was at 28.5 kg ha−1 (Asseng et al., 2000).

5. Summary and conclusions The application of the Opus simulation model in our study showed that the model adequately simulated the dynamics of soil water contents and pressure heads in soil depths shallower than 60 cm. Nitrate contents were simulated well in the soil compartment 0–30 cm. In deeper soil compartments, discrepancies between simulated and measured soil water contents, pressure heads and nitrate contents could be observed. Regarding the pressure heads and soil water contents, the most likely reason for that is the impact of the spatial variability of soil properties. Regarding the nitrate contents, the most likely reasons are, besides the impact of spatial soil heterogeneities, underestimations of N-net mineralization rates by Opus, especially in the case of organic fertilizer application at plot no. 2. Whilst the accumulation of aboveground total biomass and the nitrogen-uptake by crops were simulated quite well, the yields were mostly underestimated by Opus. Therefore, the parameters of the crop growth model in Opus have to be calibrated at the local scale to get an optimum precision of yield estimations. Furthermore, k(h) and θ(h)-functions obtained from measured soil water retention data of the three field

plots were used in our study. If such data are not available, the use of estimation procedures for k(h) and θ(h)-functions, such as pedotransfer functions, can lead to further errors in the model calculations (e.g. Wegehenkel, 2005). Therefore, one goal of the model developers – the use of Opus without calibration – is only partly fulfilled, if an overall simulation quality of Opus similar to all corresponding references cited in our study is required. The results of our study showed also that the use of different procedures for the statistical evaluation of the quality of the fit between simulated and observed model outputs can sometimes lead to contradictory results. When taking the compartment 120–150 cm at plot no. 2 as an example, the RMSE = 1.6 vol% indicates the best and the corresponding IA = 0.10 the lowest simulation quality (Table 6). In spite of correct temporal patterns, a general over- or underestimation trend of the model can, therefore, not be differentiated from those showing incorrect temporal patterns, if one only uses deviance measures such as RMSE (Fig. 4; Table 6). The modelling efficiency index IA directly compares simulations with observations and is, therefore, proposed as a more appropriate measure of the correspondence between simulated and observed time patterns of model outputs than RMSE (Mayer and Butler, 1993). Therefore, a combined use of IA and RMSE allows a more critical evaluation of the model performance than the isolated use of one of those procedures. In most of the references regarding the validation of agro-ecosystem models with field data cited in our study, only 1–3 year periods of model output observations were available (e.g. Asseng et al., 2000; Bacsi and Zemankovics, 1995; Bellocchi et al., 2002; Bonilla et al., 1999; Diekkr¨uger et al., 1995; Eitzinger et al., 2004; Heidmann et al., 2000; Vanclooster and Boesten, 2000). These validation periods are shorter than the 5 year period used in our study.

Acknowledgements The work was founded by the German Federal Ministry of Consumer Protection, Food and Agriculture (BMVEL) and the Ministry of Agriculture, Environmental Protection and Regional Planning (MLUR) of the Federal State of Brandenburg. Many thanks to Dipl. Ing. Michael B¨ahr for the supervision of the field mea-

M. Wegehenkel, W. Mirschel / Ecological Modelling 190 (2006) 116–132

surements and to Mrs. Sigrid Dittmar for the preparation of the experimental data.

References Addiscott, T.M., Whitmore, A.P., 1987. Computer simulation of changes in soil mineral nitrogen and crop nitrogen during autumn, winter and spring. J. Agricult. Sci. Camb. 108, 141–157. Asseng, S., van Keulen, H., Stol, W., 2000. Performance and application of the Apsim Nwheat model in the Netherlands. Eur. J. Agron. 12, 37–54. Bacsi, Z.S., Zemankovics, F., 1995. Validation, an objective or a tool? Results on a winter wheat simulation model. Ecol. Model. 81, 251–263. Bellocchi, G., Silvestri, N., Mazzoncini, M., Menini, S., 2002. Using the CropSyst Model in continuous rainfed maize (Zea mais L.) using alternative management options. Ital. J. Agron. 6 (1), 43–56. Bergstr¨om, L., Johnsson, H., Tortensson, J., 1991. Simulation of nitrogen dynamics using the SOILN model. Fertil. Res. 27, 181–188. Bonilla, C.A., Munoz, J.J., Vauclin, M., 1999. Opus simulation of water dynamics and nitrate transport in a field plot. Ecol. Model. 122, 69–80. Boden, A.G., 1994. Bodenkundliche Kartieranleitung. 3. Aufl. Schweizerbart, Stuttgart. Boons-Prins, E.R., de Koning G.H.J., van Diepen, C.A., Penning de Vries, F.W.T., 1993. Crop specific simulation parameters for yield forecasting across the European Community. Simulation Reports CABO-TT, No. 32, Wageningen, the Netherlands. Brooks, R., Corey, A., 1964. Hydraulic Properties of Porous Media. Hydrology Paper 3. Colorado State University, USA. de Willigen, P., 1991. Nitrogen turnover in the soil–crop-system; comparison of fourteen simulation models. Fertil. Res. 27, 141–149. Dekker, L.W., Ritsema, J.C., Wendroth, O., Jarvis, N., Oostindie, K., Pohl, W., Larsson, M., Gaudet, J.P., 1999. Moisture distributions and wetting rates of soils at experimental fields in the Netherlands, France, Sweden and Germany. J. Hydrol. 215, 4–22. Diekkr¨uger, B., S¨ondgerath, D., Kersebaum, K.C., McVoy, C.W., 1995. Validity of agroecosystem models—a comparison of results of different models applied to the same data set. Ecol. Model. 81, 3–29. Eitzinger, J., Trnka, M., H¨obsch, J., Zalud, Z., Dubrovsky, M., 2004. Comparison of Ceres, Wofost and Swap models in simulating soil water content during growing season under different soil conditions. Ecol. Model. 171, 223–246. Groenendijk, P., Kroes, J.G., 1997. Modelling the Nitrogen and Phosphorus Leaching to Groundwater Surface Water. Animo 3.5. Report 144. DLO, Winand Staring Centre, Wageningen. Godwin, D.C., Ritchie, J.T., Singh, U., Hunt, L., 1989. A Users Guide to Ceres–Wheat V2.10. International Fertilizer Development Center, Muscle Shoals, AL. Hansen, S., Thirup, C., Refsgaard, J.C., Jensen, L.S., 2001. Modeling nitrate leaching at different scales—application of the Daisy

131

model. In: Shaffer, M.J., Liwang, M., Hansen, S. (Eds.), Modeling Carbon and Nitrogen Dynamics for Soil Management. Lewis Publishers, Boca Raton, pp. 511–549. Heidmann, T., Thomsen, A., Schelde, K., 2000. Modelling soil water dynamics in winter wheat using different estimates of canopy development. Ecol. Model. 129, 229–243. Jacques, D., Simunek, J., Timmerman, A., Feyen, J., 2002. Calibration of Richards’ and convection-dispersion equations to fieldscale water flow and solute transport under rainfall conditions. J. Hydrol. 259, 15–31. Jansson, P.E., Karlberg, L., 2004. Coupled Heat and Mass Transfer Model for Soil–Plant–Atmosphere Systems. Royal Institute of Technology, Dept. of Civil and Environmental Engineering, Stockholm, 435 pp ftp://www.lwr.kth.se/CoupModel/ CoupModel.pdf. Jarvis, S.C., Stockdale, E.A., Shepard, M.A., Powlson, D.S., 1996. Nitrogen mineralization in temperate agricultural soils. Ad. Agron. 57, 187–235. Krumbiegel, D., Schwinge, W., 1991. Witterung-Klima Mecklenburg-Vorpommern. Brandenburg, Berlin. DWD, Wetteramt Potsdam, 51. Mayer, D.G., Butler, D.G., 1993. Statistical validation. Ecol. Model. 68, 21–32. Neeteson, J.J., 1990. Development of nitrogen fertilizer recommendations for arable crops in the Netherlands. Fertil. Res. 26, 201–208. Parton, W., Schimel, D., Cole, C., Ojima, D., 1987. Analysis of factors controlling soil organic matter levels in Great Plains grasslands. Soil Sci. Soc. Am. J. 51, 1173–1179. Porter, J.P., Jamieson, P.D., Wilson, D.R., 1993. Comparison of the wheat simulation models Afrcwheat2, Ceres-Wheat and Swheat for non-limiting conditions of crop growth. Field Crops Res. 33, 131–157. Rodda, H., Scholefield, D., Webb, B., Walling, D., 1995. Management model for predicting nitrate leaching from grassland catchments in the United Kingdom. 1. Model development. Hydrol. Sci. J. 40 (4), 433–451. Schindler, U., 1980. Ein Schnellverfahren zur Messung der Wasserleitf¨ahigkeit im teilges¨attigten Boden an Stechzylinderproben. Arch. Acker, Pflanzenbau und Bodenkunde 24, 1–7. Shaffer, M.J., Liwang, M., Hansen, S. (Eds.), 2001. Modeling Carbon and Nitrogen Dynamics for Soil Management. Lewis Publishers, Boca Raton, p. 651. Smith, R., 1992a. Opus: An Integrated Simulation Model for Transport of Non-point-Source Pollutants at Field Scale, vol. I. USDA, Washington, DC, Documentation, USDA ARS-98. Smith, R., 1992b. Opus: An Integrated Simulation Model for Transport of Non-point-Source Pollutants at Field Scale, vol. II. USDA, Washington, DC, Users Manual, USDA ARS-98. Smith, R., 1995. Opus: Simulation of a wheat–sugar beet plot near Neuenkirchen, Germany. Ecol. Model. 81, 121–132. Stoeckle, C.O., Donatelli, M., Nelson, R., 2003. CropSyst, a cropping systems simulation model. Eur. J. Agron. 18, 289– 307. Svendsen, H., Hansen, S., Jensen, H., 1995. Simulation of crop production, water and nitrogen balances in two German agroecosystems using DAISY model. Ecol. Model., 197–212.

132

M. Wegehenkel, W. Mirschel / Ecological Modelling 190 (2006) 116–132

Van Dam, J.C., Huygen, J.C., Wesseling, J.G., Feddes, R.A., Kabat, P., vam Walsum, P.E.V., Groenendijk, P., van Diepen, C.A., 1997. Theory of SWAP-Version 2.0. Simulation of Water Flow, Solute Transport and Plant Growth in the Soil–Water–Atmosphere–Plant Environment. Report 71, Department Water Resources Wageningen University, Technical document 45, Alterra, Wageningen, the Netherlands, 167 pp. van Genuchten, M., 1980. A closed form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44, 892–898. Vanclooster, M., Viaene, P., Diels, J., Feyen, J., 1995. A deterministic evaluation analysis applied to an integrated soil-crop model. Ecol. Model. 81, 183–197. Vanclooster, M., Boesten, J.J.T.I., 2000. Application of pesticide simulation models to the Vredepeel data set I. Water, solute and heat transport. Agricult. Water Manag. 44, 105–117.

Wagenet, R., Hutson, J., 1990. Leach-M: A Process Based Model of Water and Solute Movement, Transformations, Plant Uptake, and Chemical Reactions in Unsaturated Zone, Version 2. Department of Agronomy, Cornell University, Ithaca, NY, USA. Wegehenkel, M., 1998. Zum Einsatz von TRIME-TDR zur Messung der Bodenfeuchte auf leichten Sandb¨oden. Zeitschrift f. Pflanzenern¨ahrung und Bodenkunde 161, 577–582. Wegehenkel, M., 2005. Validation of a soil water balance model using soil water content and pressure head data. Hydrol. Processes 19, 1139–1164. Wenkel, K.O., Mirschel, W. (Eds.), 1995. Agrar¨okosystemmodellierung – Grundlage f¨ur die Absch¨atzung von Auswirkungen m¨oglicher Landnutzungs – und Klima¨anderungen. ZALFBericht no. 24. Willmott, C.J., 1982. Some comments on the evaluation of model performance. Bull. Am. Meteorol. Soc. 64, 1309–1313.