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e c o l o g i c a l m o d e l l i n g 2 0 5 ( 2 0 0 7 ) 209–220

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A model simulation analysis of soil nitrate concentrations—Does soil organic matter pool structure or catch crop growth parameters matter most? Anders Pedersen a,b,∗ , Bjørn M. Petersen c , Jørgen Eriksen c , Søren Hansen a , Lars S. Jensen a a

University of Copenhagen, Faculty of Life Sciences, Department of Agricultural Sciences, Thorvaldsensvej 40, DK-1871 Frederiksberg C, Denmark b University of Aarhus, Faculty of Agricultural Sciences, Department of Horticulture, Research Centre Aarslev, DK-5792 Aarslev, Denmark c University of Aarhus, Faculty of Agricultural Sciences, Department of Agroecology, Research Centre Foulum, P.O. Box 50, DK-8830 Tjele, Denmark

a r t i c l e

i n f o

a b s t r a c t

Article history:

Three different soil organic matter (SOM) submodels were tested within the framework of

Received 28 October 2005

the soil–plant–atmosphere model Daisy. The three submodels were: the original Daisy SOM

Received in revised form

module (OLD) with relatively non-dynamic humus pools, a recalibrated Daisy SOM module

13 February 2007

(STD) with the same pool structure as the original, but with a more rapid turnover of the

Accepted 20 February 2007

active humus pool, and a newly developed SOM model structure (CNSIM), with inclusion

Published on line 6 April 2007

of a soil microbial residuals pool of relatively rapid turnover, and a relatively recalcitrant added organic matter pool, producing a larger and more sustained residual N mineralisa-

Keywords:

tion. Furthermore, two different parameterisations of the catch crop submodules, differing

Daisy

in grass growth and N assimilation dynamics, were tested and the relative influence of SOM

Soil organic matter

module or catch crop growth module on the simulated variables assessed. The simulations

Modelling

were carried out with data from a field experiment with four mixed cropping systems and

Catch crop modelling

compared to measured results of crop production, N uptake and soil nitrate concentration. The cropping sequence was 3 years of grassland (cut or grazed) followed by 3 years of spring cereals with ryegrass as a catch crop and two levels of fertiliser application. Independently of the SOM module, plant production and nitrogen uptake for cereals were simulated well. The dynamics of the added organic matter (AOM) and SOM of the two Daisy submodules were nearly identical, whereas the CNSIM submodule built much more nitrogen into the AOM pools, especially during the pasture years. During the period with spring barley, the CNSIM module simulated similar amounts of AOM as the other modules. In general, the simulated nitrate concentrations at 100 cm depth were higher than the measured values, but the changed dynamics in the CNSIM simulations resulted in even higher overestimation of the nitrate concentration than the two other modules. The choice of catch crop submodule had a considerable effect on nitrate concentration and therefore the potential for nitrate leaching, possibly overshading more futile differences produced by the different SOM submodules.

∗ Corresponding author at: University of Copenhagen, Faculty of Life Sciences, Department of Agricultural Sciences, Thorvaldsensvej 40, DK-1871 Frederiksberg C., Denmark. Tel.: +45 35283494. E-mail address: anders@life.ku.dk (A. Pedersen). 0304-3800/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolmodel.2007.02.016


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The simulations show the importance of applying appropriate intercrop submodels when the model is used for simulating rotations with intercropping of grass-clover or undersown catch crops. © 2007 Elsevier B.V. All rights reserved.

1.

Introduction

Models of soil organic matter (SOM) turnover have often been developed in order to predict crop fertiliser demand or to analyse the environmental impacts of different agricultural management systems. Dynamic simulation models can be used to predict the mineralisation of nitrogen (N) in added organic matter (AOM), e.g. from animal manure or incorporated crop residues, and the timing of N availability to the succeeding crops. In this way, such models may be used to aid in avoiding excessive fertilisation and hence to minimise nitrate leaching. A large number of models for SOM turnover in arable and grassland soils have been developed over the past decades, many of which share the approks imate same structure and features (McGill, 1996). Much of the work on these models has focused on the appropriateness of the basic pool structure of SOM, pioneered by the now classical five-compartment model of Jenkinson and Rayner (1977). Often carbon (C) and N are divided into three different types of organic matter, AOM, soil microbial biomass (SMB) and native SOM. The Daisy submodel for simulating SOM turnover, described in Hansen et al. (1991) and Abrahamsen and Hansen (2000), also utilises this general structure, and in Daisy each of these organic matter fractions has been divided into two sub-pools one with a faster and the other with a slower turnover rate. In some models, part of the organic matter may be allocated to a biologically inert pool (‘SOM3’ in Daisy). Many SOM models are integrated with models of soil water movement and crop production. Initialisation and parameterisation of the SOM module controls mineralisation of N. The dynamics of mineral N are further controlled by both crop uptake and leaching with percolating water, and therefore the appropriateness of the crop modules and their parameterisation may be as important for an adequate simulation of nitrate leaching as SOM module structure and parameter¨ isation. Diekkruger et al. (1995) concluded that models with similar levels of detail in all sub-modules often performed better than models with a very advanced description of, e.g., SOM dynamics but a poor description of crop growth. It was also found that although the Daisy model performed very well with respect to soil water dynamics, temperature and crop production, among all the models compared, the simulation of SOM dynamics and hence soil N mineralisation was in-adequate. Recently, a comprehensive SOM pool structure analysis and parameter calibration was carried out on a very large compiled dataset comprising both short-term laboretory studies and long-term field experiments (Petersen et al., 2005a,b). Berntsen et al. (2005, 2006a,b) have evaluated this new SOM module in the agro-ecosystem model FASSET on a number of independent datasets, and demonstrated that the model simulates crop dry matter production and crop N accumulation reasonably well, but that the simulation of soil nitrate concentration

dynamics in some cases appear appropriate, in other situations differs markedly from measured values. However, the performance of this newly developed SOM sub-module within Daisy has not yet been tested. The objective of this work was therefore to analyse the consequences of applying different SOM and crop modules (differing in pool structure or parameterisation) in the soil–plant–atmosphere model Daisy on the simulated crop production, soil N dynamics and soil nitrate concentrations of a pasture and cereal cropping sequence. We addressed the following two hypotheses: (i) simulations of crop production, N assimilation and soil nitrate concentration following a large organic matter input will be more appropriate when applying a more dynamic SOM module and (ii) simulations of the temporal pattern of soil nitrate concentrations will be more affected by choice of crop module parameterisation than choice of SOM module. We used an extensive 6-year field experiment for comparing simulated and measured data, the same dataset which was also used by Berntsen et al. (2005). The experiment comprised 3 years with perennial ryegrass and grass-clover pastures under different management regimes and subsequently cropped to spring cereals (including catch crops) with different application levels of cattle slurry for 3 years.

2.

Materials and methods

2.1.

Field experiment

The field experiments and sampling are described in detail by Eriksen and Mogensen (2001), Eriksen (2001) and Søegaard et al. (2001). Briefly, the experiment was designed to study the residual effects of different grassland systems on plant production and nitrate leaching in 3 years following ploughing of the sward in a crop rotation where good management practices were adopted regarding time of ploughing and catch crop use. The experiment was located at the Research Centre Foulum, Denmark (9◦ 34 E, 56◦ 29 N) with an average annual rainfall of 704 mm and ran from spring 1993 to spring 2000. The soil is a Typic Hapludult with 9.4% clay, 28.9% silt, 56.5% sand and 3.6% C in the plough layer. In the 3 years prior to the experiment, the site had been cropped with cereals. From 1993 to 1997, four different pasture management strategies comprised the main treatments. Grass leys were either pure ryegrass (Lolium perenne L.) or a mixture of ryegrass and clover (Trifolium repens L.), both established in 1993 in spring barley (Hordeum vulgare L.). From 1994 the grass leys were either cut four times each year (subsequently called ryegrass-cut or grass-clover-cut, respectively) or grazed with dairy cows receiving 140 g N cow−1 day−1 in supplements for 150 days each year in the grazing period (subsequently called ryegrass-grazed or grass-clover-grazed, respectively). Both ryegrass treatments received 300 kg mineral fertiliser


e c o l o g i c a l m o d e l l i n g 2 0 5 ( 2 0 0 7 ) 209–220

N ha−1 year−1 . Each treatment was applied in two replicate plots. In spring 1997 the pasture swards were ploughed and spring barley was sown with an undersown ryegrass catch crop. In 1998 spring wheat (Triticum aestivum L.) was sown and in 1999 spring barley, again with undersown ryegrass catch crops in both. The cereals were harvested at maturity in 1997 and 1998, whereas in 1999 spring barley was harvested green on 20 July for silage. Two different fertilisation strategies were subsequently applied to each of the four pasture treatments, where the cereals received either 0 or 230 kg total N ha−1 in cattle slurry (approximately 55% NH4 -N). Nitrate concentration of the percolate leaving the rooting zone was determined during the last three experimental years by sampling of soil solution with ceramic suction cups installed at 1 m depth. Aboveground plant biomass production and N uptake was estimated in the pastures from the increase over a week. Cuts were made at a height of 3.5 cm in six replicates (0.5 m2 area) from a fenced part of each plot. Cereal and catch crop biomass production and N uptake were estimated from a number of plant cuts at the soil surface of four replicates (0.25 m2 area). The plant cuts were separated into cereal and ryegrass catch crop material.

2.2.

The Daisy model

The Daisy model can be characterised as a dynamic, deterministic and semi-mechanistic agro-ecosystem model (Hansen et al., 1991; Abrahamsen and Hansen, 2000). The model includes a hydrological model, a crop model, a mineral N model and a SOM model. The hydrological model simulates evapotranspiration, soil water transport (using Richard’s equation) and soil temperature. The N model simulates transformation and

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transport (using the convection–dispersion equation) of N. The SOM model simulates immobilisation and mineralisation of N, coupled to C transformations. Daisy has performed well in ¨ several model comparisons (de Willigen, 1991; Diekkruger et al., 1995; Smith et al., 1997). The soil column was set up in the model with measured properties of the experimental soil (Eriksen, 2001). Hydraulic conductivity was measured in 15 plots with nine replicates and soil water retention from one plot with nine replicates. Weather data were taken from a meteorological station situated at the adjacent Research Centre Foulum, Denmark, and recorded precipitation was corrected according to Allerup et al. (1998). Wet and dry atmospheric N deposition was set according to Ellerman et al. (2002). Simulations were carried out for the period 1991–2000, and the 3 years with cereals prior to the start of the experiment in 1994 with first year pasture ley were considered as a warm-up period to allow equilibration of model pools. The SOM and soil N turnover was modelled with three alternative submodels: (a) the original SOM module developed and calibrated when the first version of Daisy was released, subsequently called Daisy-OLD (Hansen et al., 1990, 1991); (b) the same basic SOM module structure as in (a), but with recalibration of SOM turnover rates and partitioning coefficients, subsequently called Daisy-STD (Abrahamsen and Hansen, 2000; Bruun and Jensen, 2002; Bruun et al., 2003); (c) a new SOM module structure and extensive calibration of all significant parameters developed by Petersen et al. (2005a), subsequently called Daisy-CNSIM. Fig. 1 shows the SOM model structures. For all three alternative submodels, initial soil C and N contents were set according to measured data and initialisations of the different pools are given in Table 1. With the Daisy-OLD SOM module, the original default parameters for turnover rates and partitioning of C and N set

Fig. 1 – The soil organic matter model of Daisy-OLD, Daisy-STD and Daisy-CNSIM. (A) Model structure for Daisy-OLD and Daisy-STD, which consists of added organic matter (AOM), soil microbial biomass (SMB) and native soil organic matter (SOM). Each of these pools is divided into a pool of relatively fast (2) and a pool of relatively slow (1) turnover. (SOM3) is an inert pool. (B) Model structure for Daisy-CNSIM, which consists of AOM and SMB, each divided into a pool of fast (2) and a pool of slow (1) turnover, and three soil organic matter pools, two active (native organic matter (NOM) and soil microbial residuals (SMR)), and one inert organic matter pool (IOM).


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Table 1 – Initialisation of soil organic matter pools for the different SOM module versions in Daisy 0.0–0.25 m

0.25–2.0 m

% of C

C/N ratio

% of C

C/N ratio

Daisy-OLD AOM1 AOM2 SMB1 SMB2 SOM1 SOM2 SOM3a

3.50 0.87 0.73 0.05 75.88 18.96 0.0

40 40 6.7 6.7 15.44 15.44 –

3.50 0.87 0.73 0.05 75.88 18.96 0

40 40 6.7 6.7 15.44 15.44 –

Daisy-STD AOM1 AOM2 SMB1 SMB2 SOM1 SOM2 SOM3a

0.30 0.08 1.32 0.34 68.57 29.39 0.0

90 12.4 6.7 6.7 16.0 16.0 –

0.03 0.002 0.01 0.01 0.41 0.43 99.10

90 12.4 6.7 6.7 16.0 16.0 16.0

40 – 6.6 9.33 6 15.64/10.0 15.64

2.20 0 0.37 0.03 0.40 27.0 70.00

40 – 6.6 9.33 6.0 15.67/10.0 15.67

2.3.

Daisy-CNSIM AOM1 4.40 AOM2 0 SMB1 0.73 SMB2 0.05 SMR 0.79 53.50 NOMb 40.50 IOMa

Soil organic matter distribution is divided into plough layer (0–25 cm) and below plough layer (25–200 cm). a b

Compared to Daisy-OLD and STD, a special CNSIM module for the influence of soil clay content on organic matter transformations (Petersen et al., 2005a) was incorporated into Daisy.

IOM or SOM3: this pool is inert. NOM pool initialised with measured C/N ratio, in simulation income organic matter had C/N ratio 10.0.

by Hansen et al. (1991) was used. Initialisation of the pools was based on a default distribution for arable soils following Hansen et al. (1991). This SOM module has relatively nondynamic humus pools, due to the parameterisation. For the Daisy-STD SOM module, the recalibrated parameter values for initialisation, turnover and partitioning of SMB Mueller et al. (2003), and the parameter values for SOM and rhizodeposition given by Bruun and Jensen (2002) and Bruun et al. (2003) were used. This SOM module has the same pool structure as the original (OLD), but with a much more rapid turnover of the active humus pool (SOM2, Fig. 1A and Table 2). SOM and SMB pools were initialised by use of a routine which partitions C and N based on equilibrium in SOM pools with simulated C inputs. Simulated C inputs for the present simulations were based on a simulation of 3 years of cereals, 3 years of grass-clover and 3 years of cereals with ryegrass catch crop. For the Daisy-CNSIM module, parameterisation is described in detail by Petersen et al. (2005a,b), and the setup is described in Berntsen et al. (2005). The pools were initialised with steady-state values, obtained by the addition of crop residues at a level maintaining the content of soil C at the same level for an infinite length of time (Petersen et al., 2005a). This SOM model includes a soil microbial residuals pool (SMR) of relatively rapid turnover, and a relatively recalcitrant AOM pool, producing a larger and more sustained residual N mineralisation.

Crop and fertiliser parameterisation

In the simulations, the default Daisy crop modules from Abrahamsen and Hansen (2000) were generally used. However, grass-clover pasture was simulated using intercropping of two modules specially developed for clover and ryegrass (S.G. Olesen, unpublished Master’s thesis, The Royal Veterinary and Agricultural University, Denmark, 2002). Parameterisation of the clover development stage after cutting was set to 0.3, which gave simulated N fixation comparable to that estimated for the present field study. Default parameterisations of decomposition of the different crop residues were used for Daisy-OLD and Daisy-STD (Hansen et al., 1991), whereas for Daisy-CNSIM decomposition parameters were set according to Petersen et al. (2005a). A spring barley crop module was used to simulate the three experimental years with cereals (also in 1998 where spring wheat was grown). For simulating the catch crop undersown in all cereals, we used three alternative setups: (i) ryegrass developed for intercropping in Daisy (Intercrop Grass), (ii) monoculture ryegrass crop module (Default Grass) and (iii) no catch crop at all (No Catch Crop). The maximum root penetration depth for both ryegrass catch crop modules was set to 120 cm. The slurry applied as fertiliser in the years with cereal crops contained 10% dry matter, 200 mg C g−1 DM, 46 mg total N g−1 DM, of which 33 mg was ammonium N g−1 DM, the remainder bound as organic N. Pool distribution for slurry organic matter is shown in Table 2. The volatilisation of ammonia upon field application was assumed to be 10%.

3.

Results

The simulations used the same main crop modules and hydrological model, but different SOM modules. The differences in simulated crop production, N uptake and nitrate leaching are thus the result of differences in SOM pool dynamics and the resulting feedback is obtained in terms of altered crop production and crop residue returns. For the Daisy-STD SOM module, different catch crop modules were applied to indicate the relative importance for nitrate leaching of SOM modelling compared to modelled catch crop dynamics.

3.1. Pasture biomass production and N uptake (spring 1994–spring 1997) Simulations of the cut management systems showed a nitrogen uptake of about 70–90% of the experimentally estimated values, whereas the grazed management showed a higher N uptake of 100–140% of the experimentally estimated values (Table 3). The three different SOM modules had few consistent effects on crop simulations. However Daisy CNSIM showed a lower crop N uptake, in particular at high estimated production levels.


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Table 2 – Slurry parameters Daisy-CNSIM

fC C/N Decay rate (day−1 ) fSMB1 fSMB2 fNOM or fSOM2

Daisy-OLD and Daisy-STD

AOM1

AOM2

NOM

AOM1

AOM2

0.45 10.8 0.0152 0.312 0.312 0

0.35 444 0.28 0.688 0.688 0

0.2 10

0.72 100 0.0048 0.5 0 0

0.18 0.39 0.048 0.5 1 0

0 0 1

SOM2 0.1 11 1 0 0 1

Pool distribution for stored slurry applied to cereals in the last 3 years of the experiment. For Daisy-CNSIM simulations parameters were taken from Petersen et al. (2005a). Daisy-STD and Daisy-OLD are standard values.

Table 3 – Plant nitrogen uptake in simulations for ryegrass and grass-clover Year

Pasture management: cut Experimentally Simulated estimateda Daisy-OLD

Pasture management: grazed

Simulated Daisy-STD

Simulated Daisy-CNSIM

Experimentally Simulated estimatedb Daisy-OLD

Simulated Daisy-STD

Simulated Daisy-CNSIM

Ryegrass 1994 1995 1996

287 287 287

257 209 230

256 215 238

258 195 235

293 254 316

373 350 369

370 359 379

352 305 334

Grass-clover 1994 1995 1996

288 288 288

219 245 266

217 237 261

221 227 260

249 347 335

256 400 425

255 404 430

247 390 397

All values in kg N ha−1 . a b

Data taken from Eriksen (2001). Data taken from Søegaard et al. (2001).

For both management systems, the simulated clover above-ground dry matter was between 60 and 70% of the total above-ground dry matter, independent of SOM module (data not shown), which corresponds well with observed values of about 50–80%. Clover N fixation in this period was simulated almost identically in all simulations by all SOM modules (Table 4), but was lower than the experimentally estimated values, except for the first and last year in the grazed treatment.

after ploughing the ryegrass grass-clover swards, respectively, were in reasonable agreement with the observed values. With the Daisy-OLD SOM module, biomass production and in particular N uptake was underestimated in all 3 years for the treatment without slurry application. For the treatments with slurry application, all three SOM modules produced a reasonable fit to observed data.

3.3. 2000)

3.2. Cereal production and N uptake (spring 1997–harvest 1999) The simulated temporal pattern and magnitude of cereal and catch crop dry matter production (Fig. 2) and N uptake (Fig. 3)

Soil nitrate concentration (spring 1997–spring

Except the first year, simulated nitrate concentrations at 1 m depth were generally at a higher level than the observed values with any of the three different SOM modules (Fig. 4). Simu-

Table 4 – Clover nitrogen-fixation in grass-clover rotations Pasture management: cut

1994 1995 1996

Pasture management: grazed

Experimentally estimateda

Simulated Daisy-OLD

Simulated Daisy-STD

Simulated Daisy-CNSIM

Experimentally estimateda,b

Simulated Daisy-OLD

Simulated Daisy-STD

Simulated Daisy-CNSIM

300 300 300

209 278 302

205 258 289

205 236 289

232 408 258

255 350 317

242 345 315

270 393 366

Estimated values account for fixed N in clover roots and aboveground plant parts, as well as transfer of fixed N to ryegrass. Simulated values account for clover fixation for the whole crop in this year. All values in kg N ha−1 . a b

Data taken from Eriksen (2001). Data taken from Søegaard et al. (2001).


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Fig. 2 – Simulated and observed cereal dry matter production for the 3 years after ploughing in the ryegrass and grass-clover management system. Left part, dry matter production for no application of slurry. Right part, dry matter production for supply of 230 kg total N ha−1 in slurry. Rotations simulated with three different SOM modules, using the Intercrop Grass crop module as catch crop. Bar for measured data is standard error.

lated concentrations were highest in the winter period and decreased over the summer period, whereas the observed data for the treatments without slurry were low with no peaks for all 3 years. In the treatments with slurry application, the annual fluctuations in the simulated values resembled the observed pattern, although at a considerably higher level. Daisy-CNSIM produced the highest simulated concentration values and highest RMSE in most treatments and years, greatly exceeding the values estimated from measured con-

centrations, whereas Daisy-OLD and Daisy-STD produced slightly lower and more similar concentrations and similar RMSE values. In the grass-clover cut (with or without slurry) and the ryegrass grazed (with slurry) treatments, the simulated and experimentally estimated leaching values corresponded reasonably well (Fig. 4). RMSE values were higher for grazed simulations, compared with cut simulations, due to addition of animal manure in the preceding three simulations years, which result in higher AOM and SOM


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Fig. 3 – Simulated and observed cereal N uptake for 3 years after ploughing in ryegrass or grass-clover. Left part, N uptake for no application of slurry. Right part, N uptake for application of 230 kg N ha−1 in slurry. Rotations simulated with three different SOM modules using the Intercrop Grass crop module as catch crop. Bar for measured data is standard error.

pools and mineralisation of N in the three succeeding years (Fig. 6). When the Daisy-STD SOM module was used with the No Catch Crop, the Intercropping Grass or the Default Grass (monoculture) modules for catch crop simulation, distinctly different nitrate concentration patterns at 1 m depth were simulated (Fig. 5). Simulated nitrate concentration was very low with the Default Grass module, indicating that the N uptake of the catch crop was too efficient and the small

peaks during the years did not fit well with observed winter peaks. The Default Grass module had the lowest RMSE, but the temporal variation pattern is resembled best with the intercropping grass module, and quite poorly with the Default Grass module. Simulations without a catch crop and with the intercropping ryegrass module followed quite similar patterns. Naturally the highest values were simulated with no use of catch crop, and also the highest RMSE value, which showed a pattern of peaks during winter time fairly well resembling


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Fig. 4 – Soil nitrate-N concentration at 1 m depth for the 3 years after ploughing in ryegrass or grass-clover. Lines represent simulations with three different SOM modules, all with Intercrop Grass module as catch crop in the rotations. RMSE for simulations are mentioned in same order as left corner.

the observed values, although at a higher level. However, the differences between the No Catch Crop and the Intercropping Grass modules were often surprisingly small, indicating that the Intercropping Grass module does not simulate N uptake appropriately, predicting too low an uptake. RMSE values for Daisy-CNSIM SOM module and use of Default Grass module were similar with RMSE values for Daisy-STD SOM module (data not shown).

3.4.

Simulation of soil organic matter pool dynamics

Differences in simulated crop N uptake and nitrate leaching losses between the different SOM modules mainly derived from the differences in soil C and N pool dynamics. In Fig. 6, the temporal patterns of the AOM and SOM pools in the model are shown for two of the treatments. The simulation for the grass-clover cut treatment represents the simulation with the


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Fig. 5 – Soil nitrate-N concentration at 1 m depth for the 3 years after ploughing in ryegrass or grass-clover. Lines represent simulations with Daisy-STD SOM module and two different grass modules, Intercrop Grass module and Default Grass module, as catch crop plus a third simulation with No Catch Crop. RMSE for simulations are mentioned in same order as left corner.

least dynamics and the grass-clover grazed treatment represents the simulation with the largest pool dynamics. The other treatments were intermediate to these two. AOM pools for the Daisy-CNSIM module were at a higher level than in Daisy-OLD and Daisy-STD, and furthermore built in more N in the pasture period. In the last 3 years with cereal production, the AOM pool decreased faster for Daisy-CNSIM than for the other two modules. Adding slurry had nearly the same effect in all three SOM modules, but the Daisy-CNSIM AOM

pools declined more slowly compared to the others. SOM pools for all three modules in the simulation of the grassclover cut treatment were nearly in steady state in all years, whereas in the grass-clover grazed treatment, SOM pools accumulated significant amounts of N during the pasture years, releasing part of this after ploughing in the grass-clover pasture in 1997. Again, Daisy-CNSIM displayed more dynamics than Daisy-STD, and Daisy-OLD displayed very little dynamics.


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Fig. 6 – AOM and SOM pools to 1 m depth for the whole period of the simulations grass-clover-cut (0 kg N ha−1 year−1 ) and grass-clover-grazed (230 kg N ha−1 year−1 ). Lines represent simulations with three different SOM modules all with grass modules for Intercrop Grass as catch crop in rotations. AOM pools are AOM1 and AOM2. SOM pools are SOM1 and SOM2 for Daisy-OLD and also include SOM3 for Daisy-STD and NOM, SMR and IOM for Daisy-CNSIM.

4.

Discussion

It is a major challenge to construct and calibrate intercropping modules, i.e. for ryegrass and clover intercropped in pastures or spring barley and undersown ryegrass catch crops. Pasture contributes significantly to the build-up of soil organic matter pools from aboveground and belowground crop residues and rhizodeposits, which in turn influences the residual effect when the ryegrass or grass-clover sward is incorporated through ploughing. For this reason, our major emphasis was to obtain simulations of pasture dry matter and N production that were in reasonable agreement with observed values, with less emphasis on the other properties mentioned above. This was also done because the experimental data available could not support a thorough calibration and validation of the ability of the model to simulate intercropping of ryegrass and clover. However simulated clover N fixation rates (Table 4) and proportion of clover indicated that the pasture module of the model performed reasonably well. For the best starting point to comply with measured nitrate concentration, the modules in the model were parameterised to reach observed plant production values in the 3 years of grass or grass-clover pastures. In the Daisy-CNSIM module, larger AOM pools were built up in the grazed treatments (Fig. 6) and these were transformed into a large rise in the NOM pool in the subsequent cereal period, resulting in an increased N release from the conversion between sub-pools after pasture. Often, simulated nitrate concentration values were higher than those measured in soil solution extracted in the field by

suction cups. In simulations, all nitrate is assumed to be in the soil solution and to follow the percolation of water, but in reality nitrate may to some degree be retained by immobile water in small pores inside aggregates. If macropore flow occurs in the field, to some extent bypassing the suction cups, a lower concentration may be measured. Djurhuus and Jacobsen (1995) showed that KCl-extracted samples had a higher level of nitrate than samples taken from suction cups at high level of nitrate in a sandy loam when measured in the soil layer 20–30 cm, but the values were more equal in 70–90 cm. This indicates that a sandy loam, as in the current study, may contain a fraction of nitrate, which will not necessarily be measured by suction cups. However, suction cup nitrate concentrations will reflect the temporal pattern of the treatments, but may not reflect the absolute level of soil nitrate at the measuring depth, although it is unlikely that differences will be as large as those observed to simulations. Measured nitrate concentrations at 1 m depth did not exceed the EU Drinking Water Directive upper limit of 11.3 mg Nitrate-N l−1 (50 mg Nitrate-N l−1 ), but for the simulations of the grazed treatments with slurry application, nitrate concentrations in winter time exceeded this level, especially for the Daisy-CNSIM simulations, whereas Daisy-OLD and Daisy-STD only exceeded this level in the first year after ploughing the grazed grass clover sward. The SOM modules determine the mineralisation of N from subpools, whereas crop modules determine the uptake of N and water. The soil nitrate concentration at 1 m depth, which this study focused on, is determined by a combined effect of the above modules and the soil water model. The two grass


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module parameterisations displayed very different developments and nitrate dynamics at 1 m depth. The Intercrop Grass module simulated growth of the undersown catch crop which competed very little with spring barley and hence resulted in lower N-uptake than measured before harvest of the barley (Fig. 2), but then after harvest showed a very rapid growth and N uptake, especially in the first and third year. On the other hand, the simulated Default Grass module had DM levels equal to the spring barley and then after harvests a relatively slow growth and N uptake (data not shown). Developing a grass module, which is comparative with grass undersown in spring barley and has the right optimisation for N, water and light competition and grass grown as single culture without this competition, represents a delicate balance. This study shows both strategies for crop module optimisation and the user has to take into account the assumption they prefer. In the modelling study performed by Berntsen et al. (2005) on the same data with the FASSET model, including the CNSIM module, the simulations of catch crop dry matter production and N uptake were fairly close to the observed values. Although they achieved a somewhat closer correspondence between the measured and simulated trend in suction cup nitrate concentrations, they were also not able to simulate the temporal fluctuation of the different treatments very closely. However, in two other studies (Berntsen et al., 2006a,b) the FASSET model was able to predict the temporal variability of soil nitrate concentrations somewhat more closely. Altogether this may indicate that this particular dataset is relatively difficult to simulate, and that even with a better simulation of catch crop growth, the dynamics of N leaching in these pasture-cereal sequences may not be predicted adequately. Considering that the CNSIM model yielded good to acceptable descriptions of the multitude of field experiments and laboratory incubation datasets on which it was calibrated (Petersen et al., 2005a,b), it may seem surprising that this much more thoroughly tested and calibrated SOM module did not perform significantly better than the Daisy-OLD and Daisy-STD modules. An underlying assumption in the work of Petersen et al. (2005a,b) was that the model pools with rapid turnover (AOM, SMB, etc.) should be much more dynamic upon changes in organic inputs, and this is clearly also the case when looking at the dynamics of AOM and SOM pools in Fig. 6. However, the much more dynamic nature also means that these pools decline more rapidly in the cereal phase, leading to larger and more prolonged peaks in soil nitrate concentrations during the leaching season. But the SOM turnover in the present system is quite dynamic, which can be illustrated by the very large carry-over effects that were observed, up to over 200 kg N ha−1 in mineral fertiliser equivalents during the 3 years with cereals (Eriksen, 2001). The use of a very dynamic SOM model in a setup with generally too high nitrate concentrations will logically result in even higher nitrate concentrations and hence higher RMSE values.

5.

Conclusions

The application of the three SOM modules resulted in different model performance with respect to crop production. Particularly simulations without slurry addition showed that

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Daisy-CNSIM and Daisy-STD produced the highest spring barley dry matter production and N uptake, in line with the measured data and Daisy-Old the lowest, confirming our hypothesis (i) that the more dynamic SOM modules would produce a better prediction of crop production. In fields without slurry addition the measured soil nitrate concentrations were generally lower than simulated with any of the SOM modules. For fields with slurry application measured data and simulations showed the same seasonal pattern, but with higher simulated peaks than measured during the winter seasons indicating that the simulation of catch crop N uptake was not sufficient. For nitrate concentration, the most dynamic SOM module (CNSIM) therefore produced the largest deviation from measured nitrate concentrations, indicating a rejection of our hypothesis (i) with respect to soil nitrate. But this conclusion depends on the chosen grass module. Judged from a setup with the Default Grass module, having a high N uptake and hence low nitrate concentrations, the opposite conclusion would be obtained. This study therefore also confirmed the importance of correctly parameterising crop modules, for adequate simulation of soil nitrate concentrations subject to different cropping sequences. As demonstrated here, the simulation of catch crops in particular may have a completely overriding effect on simulated soil nitrate concentrations. The two kinds of catch crops simulated here demonstrated the extremes, from a leaching that was close to a situation without catch crops to very low soil nitrate concentration with a catch crop that took up virtually all available N, producing a completely different seasonal pattern. The correct answer will be somewhere in between, but for the current dataset the simulations of the temporal pattern of soil nitrate concentrations was more affected by choice of crop module parameterisation than by the choice of SOM module, confirming our hypothesis (ii). The present study work showed that future work should focus on improving and developing more specific crop modules for grass-clover mixtures and intercropping of cereals and catch crops, in order to respond to the increased interest in modelling nitrate dynamics in complex cropping systems such as that studied here.

Acknowledgement This work was funded by the Danish Research Centre for Organic Farming (DARCOF) under the programme: Interactions between N dynamics, crop production and biodiversity in organic crop rotations analysed by dynamic simulation models.

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