ECOMOD_2003

Ecological Modelling 166 (2003) 3–18

Decomposition of plant residues of different quality in soil—DAISY model calibration and simulation based on experimental data Torsten Müller∗ , Jakob Magid, Lars Stoumann Jensen, Niels Erik Nielsen Department of Agricultural Sciences, Plant Nutrition and Soil Fertility Lab., The Royal Veterinary and Agricultural University, Frederiksberg, Denmark Received 22 January 2002; received in revised form 6 January 2003; accepted 17 February 2003

Abstract A parameter setup for the DAISY model calibrated on data measured in the field was evaluated on data obtained from a lab-incubation experiment with residues of leguminous green manure plants, blue grass, rape straw and barley straw. The aim of this study was to test and further develop the principles and parameters for the turnover and the initial characterisation of these plant materials in the DAISY model.

The field-calibrated parameter set led to considerable problems when applied to the lab-incubation experiment. For mineral N, soil microbial biomass N and added organic matter, none of the model simulations was fully satisfactory. The only exception was the treatment without addition of plant material.

As a consequence, the parameters controlling the turnover of added organic matter and soil microbial biomass have been modified. Further conceptual changes have been suggested.

It was not possible to simulate the initial decay and N release from added organic matter (AOM) by simply subdividing it into a water-insoluble part (AOM1) and a water-soluble part (AOM2). However, the C/N ratio and the cellulose content of the added plant residues may be useful indicators for the partitioning of plant materials into a slowly decomposable (AOM1) and a rapidly decomposable (AOM2) part. The general concept of two AOM-pools with predefined constant turnover rates and C/N ratios is questioned for plant residues with very different properties.

After addition of easily decomposable green plant materials, death and maintenance respiration rates of modelled soil microbial biomass pools had to be reduced considerably in order to fit simulated mineral N to measured values. This is in contrast to the assumption of two SMB-pools with different but constant properties.

After these modifications it was possible to achieve reliable simulations of mineral N, cumulative soil respiration and added organic matter.

However, a major problem remained after recalibration. It was not possible to simulate SMB-N satisfactorily in the treatments with red clover, white clover or white melilot. It is concluded that the DAISY model does not fully reflect the flow of N through SMB after addition of easily decomposable leguminous plant materials and the following turnover into soil microbial residual N (SMR-N). The introduction of a separate SMR-pool is proposed. © 2003 Elsevier Science B.V. All rights reserved.

Keywords: DAISY; Plant residues; Particulate organic matter; Soil microbial biomass; Nitrogen mineralisation

∗ Corresponding author. Present address: Department of Soil Biology and Plant Nutrition, University of Kassel (GhK), FB11, Nordbahnhofstraße 1a, Witzenhausen D-37213, Germany. Fax: +49-5542-98-1596.

E-mail address: tmuller@wiz.uni-kassel.de (T. Müller).

0304-3800/$ – see front matter © 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0304-3800(03)00114-5

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T. Müller et al. / Ecological Modelling 166 (2003) 3–18

1. Introduction Recently, an increasing number of Danish stockless farms have converted to organic farming practice without re-establishing livestock (Mueller and Thorup-Kristensen, 2001). Due to the lack of animal manure on such farms, recycling and spatial relocation of nutrients is limited in these organic cropping systems. The introduction of a 1-year clover grass fallow within a 4-year crop rotation as green manure is a classical strategy to solve at least some of the problems related to stockless farming. The development of new crop rotations, including an extended use of catch crops and legume based green manure may be another possibility, avoiding the economic loss during the fallow year (Mueller and Thorup-Kristensen, 2001).

Modelling of the N and C turnover in the soil–plant– atmosphere system may be used as one of the tools to develop new organic crop rotations (Thorup-Kristensen et al., 1997). However, it is crucial that the turnover of organic matter is described and parameterised appropriately in models used in this context, if the strategies and crop rotations developed are to be valid in practice.

Based on a field experiment with rape straw, Mueller et al. (1997) recalibrated the DAISY model parameters for turnover of added organic matter. They subdivided the added organic matter (AOM) pool by simply allocating water-insoluble C and N of the added plant material to a recalcitrant pool (AOM1), and water-soluble C and N to an easily decomposable pool (AOM2).

However, no final calibration of the soil C and N turnover parameters used in the DAISY model has been done for the addition of fresh green plant material such as catch crops and leguminous green manure plants. Mueller et al. (1998b) showed that it may be inappropriate to apply their recalibrated parameter set and the modelling approach developed for post harvest residues (rape or cereal straw) to materials containing high amounts of metabolic components (chopped grass and maize).

The aim of our study was to evaluate and further develop the parameter setup and the modelling approach proposed by Mueller et al. (1997) with respect to the turnover of leguminous green manure and catch crop residues in the soil, and to the corresponding N-cycling.

An important factor for the development of new crop rotations in stockless organic farming systems is the expected N mineralisation and immobilisation after incorporation of the potential leguminous green manure plant materials. Therefore, special emphasis was attached to the simulation of mineral N. Kirchmann (1988) found considerable difference in N content between aboveground and below ground legume materials. Therefore, it was decided to use experimental data obtained from a lab-experiment with both above ground and below ground leguminous plant materials of different quality.

2. Materials and methods 2.1. Simulation model DAISY is a deterministic model that simulates water-, energy-, C- and N-fluxes in a one-dimensional soil–plant–atmosphere system (Hansen et al., 1990; Hansen et al., 1991). In our study, we applied the DAISY soil-organic-matter module in combination with the soil mineral N module to a lab-experiment.

Three discrete soil organic pools (added organic matter (AOM), soil microbial biomass (SMB) and native nonliving soil organic matter (SOM), soil mineral N and soil respiration (CO2 ) are simulated by the soil-organic-matter submodel (Fig. 1). The organic pools (AOM, SMB, SOM) are each considered to be a continuum with a certain range of turnover rates. In the original development of the model, it was found that these continuums could be simulated satisfactorily if each pool is subdivided into two subpools, one with a slower turnover (i.e. SOM1) and one with a faster turnover (i.e. SOM2). Furthermore, it was assumed that the turnover of the pools follows first order kinetics (see Eq. (A.1) in Appendix A). The latter is consistent with the view that the rate-limiting step in the turnover is the rate at which a given pool dissolves into the soil solution (Nielsen et al., 1988). ∗ (day−1 )) under stanTurnover rate coefficients (kX ◦ dard conditions (10 C, −10 kPa, 0% clay) are defined for each carbon pool. For SMB1 and SMB2, the death rate coefficient and maintenance respiration rate coefficient have to be defined separately. In order to determine actual rate coefficients (kX ), the rate

T. Müller et al. / Ecological Modelling 166 (2003) 3–18

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Fig. 1. C- and N-fluxes between the various pools and subpools of organic matter, mineral N and evolved CO2 in the DAISY submodel for soil organic matter (Hansen et al., 1990, 1991). AOM: added organic matter, SMB: soil microbial biomass, SOM: native dead soil organic matter. fX : partition coefficient. ∗ ) are mulcoefficients under standard conditions (kX tiplied by modifiers that are functions of the actual soil temperature and of the actual soil water potential.

Additionally, modifiers depending on the soil clay content are added for the pools SOM1, SOM2 and SMB1 (see Eq. (A.2) in Appendix A).

Partitioning of C-fluxes is defined by partitioning coefficients (fX in Fig. 1 and fSMBi in Eq. (A.3) of Appendix A). Carbon fluxes into the microbial biomass are multiplied by substrate utilisation efficiencies (EX ; see Eq. (A.3) in the appendix). EX defines the fraction of the substrate C coming from pool X (SMB1, SMB2, SOM1, SOM2 . . . ), which can be used for microbial growth. The remaining substrate C is respired as CO2 .

After every time step (t), the N pools (NX ) are calculated from the actual amount of C in the pools multiplied by the reciprocal value of a fixed C/N ratio for each pool (see Eq. (A.4) in the appendix). Net

N-mineralisation or N-immobilisation ( Nmin / t) is then derived from the N-balance.

If immobilisation occurs during growth of SMB1 and SMB2, this growth may be limited by the lack of mineral N in the soil. NH4 + is immobilised in preference to NO3 − if present. Total CO2 -evolution does not take into account gas diffusion through the soil matrix. The time step of the soil-organic-matter submodel in this investigation is 1 day. A more detailed description of the soil-organic-matter submodel is given by Mueller et al. (1997). In this paper, a parameter set (Mueller et al., 1997; Table 1) recalibrated on data from a field experiment with incorporation of rape straw was applied.

For the comparisons with the measured values, total simulated soil microbial biomass N (Nmic ) was calculated as the sum of SMB1-N and SMB2-N. A more detailed description of the DAISY model including nitrification and denitrification is given by

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Original parameter set

−1

Death rate SMB1 (day ) SMB2 (day−1 ) Maintenance respiration rate SMB1 (day−1 ) SMB2 (day−1 ) Decomposition rate AOM1 (day−1 ) AOM2 (day−1 ) AOM-C in AOM1 (%) AOM2 (%) C/N ratio of AOM1 AOM2 Substrate utilisation efficiency AOM1 AOM2

Rape straw (Mueller et al., 1997)

Rape straw

1.85E−04 0.01 0.0018 0.01 0.012 0.05 96.0 4.0 92 19 0.13 0.69

1.85E−04 0.01 0.0018 0.01 0.012 0.05 96.3 3.7 166 5.6 0.40 0.69

Modified parameter set Barley straw

1.85E−04 0.01 0.0018 0.01 0.0144 0.05 93.7 6.3 110 12 0.40 0.69

Blue grass

1.85E−04 0.006 0.0018 0.006 0.0204 0.15 87.7 12.3 64 3.8 0.40 0.69

Red clover

White clover

White melilot

Fine

Coarse

Fine

Coarse

Fine

Coarse

1.85E−04 0.002 0.0018 0.002 0.0204 0.14 65.9 34.1 34 4.8 0.40 0.69

1.85E−04 0.002 0.0018 0.002 0.0264 0.35 78.1 21.9 29 4.6 0.40 0.55

1.85E−04 0.001 0.0018 0.001 0.0144 0.4 71.8 28.2 18.4 4.4 0.40 0.69

1.85E−04 0.009 0.0018 0.009 0.0264 0.3 75.0 25.0 26.1 4.1 0.40 0.55

1.85E−04 0.004 0.0018 0.004 0.0204 0.4 58.5 41.5 26.4 5.2 0.40 0.69

1.85E−04 0.004 0.0018 0.004 0.036 0.25 67.5 32.5 54.9 4.9 0.20 0.35

Original parameter set field calibrated for rape straw by Mueller et al. (1997) and the parameter sets for the different plant materials after modification. SMB: soil microbial biomass, AOM: added organic matter.

T. Müller et al. / Ecological Modelling 166 (2003) 3–18

Table 1 Parameter sets of the model simulations

T. Müller et al. / Ecological Modelling 166 (2003) 3–18

Hansen et al. (1991). A full version of the DAISYmodel including complete technical documentation is available on the internet (http://www.dina.kvl.dk/ ∼daisy).

2.2. Experimental setup Intact soil samples (0–20 cm) containing residues of leguminous green manure plants were taken in spring 1996 from a field experiment located at the Agricultural Research Station Årslev (10◦ 27 E, 55◦ 18 N), field 111 at the isle of Funen, Denmark. The soil was a sandy loam (1.9% Ct , Ct /Nt = 11.6, 14.3% clay, pH 7.0). The soil was deep-frozen for storage.

After defrosting, the soil samples were air-dried.

Plant materials were extracted from the field moist soil by manual collection of visible plant residues, mostly roots, using a pair of tweezers. In contrast to root washing, this extraction procedure retains water-soluble components in the plant residues, which are assumed to be easily decomposable (Boyer and Groffman, 1996). The dry plant material was subdivided into a fine and a course fraction using a 4 mm sieve.

The leguminous green manure plants were red clover (Trifolium pratense L.), white clover (Trifolium repens L.), and white melilot (Melilotus alba M.). In addition to the legumes, three dried plant materials from other field experiments (Jensen et al., 1997; Mueller et al., 1998b) were used but not further subdivided: chopped rape straw (Brassica napus L.) and chopped barley straw (Hordeum vulgare cv. hexastichon), representing typical post harvest residues with a wide C/N ratio, and chopped blue grass (Poa pratensis), representing a catch crop. These plant residues were manually cut into pieces <2 cm. Powder-milled plant residues were analysed for total C and N (Europa 20-20 mass-spectrometer), water-soluble N (Mueller et al., 1998a), and cellulose and lignin content (Van Soest, 1963). Table 2 shows the properties of all plant materials.

Soil (0–20 cm) from the above described field experiment was collected in December 1996 prior to the following incubation experiment. The soil was sieved (<3 mm), homogenised, re-wetted to 50% WHC and pre-incubated for 3 weeks at room temperature. The plant materials were then mixed into the soil. The concentration of all plant materials (0.4%) in the

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soil corresponded to the mean amount of the leguminous plant materials originally extracted from the soil samples. The soil was incubated in sealed glass jars for 52 days at 9 ◦ C (approximate annual mean temperature of the original location). In addition to the soil samples, each jar contained a beaker with demineralised water in order to avoid desiccation of the soil and a beaker with NaOH solution to trap respired CO2 . The jars were opened regularly in order to take soil samples (see below) and to replace NaOH solution.

The experiment was carried out with three replicates. The jars were placed in a climate chamber in a randomised design. For logistic reasons, the experiments were set up in two separate groups, one for the leguminous plants (group 1) and one for the non-leguminous plants (group 2). Each group had its own nil-treatment without addition of plant material.

The second group was postponed by 8 days compared to the first group. Because of a technical problem, the temperature increased up to a daily mean temperature of up to 15 ◦ C for a period of 3 days. This happened during days 4–6 for group 1 and days 12–14 for group 2.

On five or six different dates, all soil samples were analysed for soil microbial biomass N (SMB-N) by chloroform fumigation extraction (Brookes et al., 1985; Joergensen and Mueller, 1996).

Mineral N (Nmin = ammonium and nitrate) was measured in the non-fumigated 0.5 m K2 SO4 extracts by standard colorimetric methods using flow-injection analysis (Keeney and Nelson, 1982).

Additionally, coarse particulate organic matter > 400 ␮m (CPOM) was extracted from the rape straw, barley straw and blue grass treatments at every sampling date using the decanting and size separation procedure described by Magid and Kjærgaard (2001).

C and N in the CPOM fraction were measured with a mass spectrometer (type 20-20) coupled to an ANCA-SL sample preparation module (both Europa Scientific Ltd., Crewe, UK). Reported data of the CPOM-fractions were calculated as the differences of the treatments with addition of plant materials and the nil-treatment without incorporation of plant material. This fraction can be assumed to represent the particulate part of the added plant material remaining in the soil during decomposition (Magid et al., 1997).

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Table 2 Properties of the investigated plant materials Rape

Total C (%afdm) Total N (%afdm) Total C/N NWS C (%Ct) NWS N (%Ct) NWS C/N Lignin (%afdm) Cellulose (%afdm)

47.5 0.59 80 96.3 83.6 92 14.1 48.1

Barley

47.1 0.66 72 93.7 60.9 110 6.8 43.0

Blue grass

47.6 2.17 22 87.7 78.3 25 5.4 32.0

Red clover

White clover

White melilot

Fine

Coarse

Fine

Coarse

Fine

Coarse

46.6 4.19 11 83.5 69.8 13 9.7 18.3

46.6 3.47 13 79.2 49.7 21 6.0 26.5

46.5 4.82 10 79.0 60.3 13 9.9 18.4

47.6 4.29 11 75.0 39.6 21 6.5 20.5

41.3 4.19 10 65.0 46.1 14 7.7 18.0

45.7 3.60 13 67.5 32.8 26 5.4 22.2

afdm: ash free dry matter, NWS: non-water soluble (particulate) organic matter.

Soil respiration (CO2 -flux) was measured by passive trapping of CO2 in NaOH-solution. Remaining NaOH was determined by titration with HCl (Isermeyer, 1952).

2.3. Statistics and parameter calibration Addiscott and Whitmore (1987) concluded that it may be misleading to use one method only to quantify the discrepancy between model simulations and measured data. Therefore, a statistical analysis of the residuals (the differences between the observed and the predicted values) was performed in three different ways.

As a comparable relative measurement of the residuals, we calculated the root mean square error (RMSE) as recommended by Loague and Green (1991). The lower the RMSE, the better is the agreement between model predicted and measured values.

N 0.5 1 100 RMSE = (pi − m ¯ i )2 · (1) N m ¯ i=1

m ¯ is the mean of the replicate measurements at the ith sampling date, pi its corresponding simulation value and N is the number of these pairs.

The modelling efficiency (EF) was calculated

N N 2− 2 ( m ¯ − m) ¯ (p − m ¯ ) i i i i=1 i=1 EF = (2) N ¯ i − m) ¯ 2 i=1 (m where m ¯ is the average of all m ¯ i . The maximum value of EF is 1, which indicates that observed and model predicted values are identical. If EF is less than zero,

the model predicted values are worse than simply using the observed mean of the measured data (Loague and Green, 1991).

In order to partition the sum of squares of the deviations between simulation and measurement into components due to lack of fit of the model and to pure error, an analysis of variance was carried out as described by Whitmore (1991). The sum of squares of the lack of fit (LOFIT) was obtained by subtraction of the error (SSE) from the residual sum of squares (RSS).

RSS =

N N

(mij − pi )2

(3)

((mij − pi ) − (m ¯ i − pi ))2

(4)

i=1 j=1

SSE =

N N

i=1 j=1

LOFIT = RSS − SSE =

N

ni (m ¯ i − p i )2

(5)

i=1

Abbreviations were used as above. n is the number of replicate measurements at one single sampling date and mij is the measurement of the jth replicate at the ith sampling date. Whitmore (1991) pointed out, that LOFIT should be minimised in order to optimise a model setup.

Parameter calibration has been done in a stepwise iterative process by trial and error. The single steps are described in the next chapter. It was the aim of each iteration step to minimise (RMSE, LOFIT) or optimise (EF) the statistical parameters of model performance described above.

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3. Results and discussion 3.1. Model simulations with original setup Mueller et al. (1997) recalibrated the original parameter setup for the added organic matter turnover in the DAISY model on a field experiment with rape straw. Mueller et al. (1997, 1998b) subdivided AOM by simply allocating water-insoluble C and N of the added plant material to AOM1 and water-soluble C and N to AOM2. In the model simulation, measured CPOM (in these studies measured as light particulate organic matter) was then considered to be AOM1 in the model simulations.

This approach together with the recalibrated parameter set (Mueller et al. (1997), Table 1) was applied to the experimental data described above. Measured quality parameters (Table 2) of the different plant materials were used to initiate the AOM-pools (C and N) as a water-soluble part (AOM2) and a water-insoluble part (AOM1). Total soil Corg , C/N, Nmin and SMB-N were initiated according to measured values.

Figs. 2â&#x20AC;&#x201C;6 compare the model output for different C and N pools and fluxes with the measured data (dots).

Table 3 shows the statistical evaluation. Except for the nil-treatment without addition of any plant material (Fig. 2), none of the model simulations were fully satisfactory. General differences in trend and amount between model simulated and measured values could be observed for Nmin in all simulations and for SMB-N in the simulations of the three legume treatments. This was also indicated by negative values for EF and relatively high values for the other statistical parameters (Table 3). A similar increase of simulated mineral N above the measured values during the later stage of the simulation was also observed by De Neergaard et al.

(2001) for a lab-incubation experiment with perennial ryegrass and white clover. The simulations of the CPOM-C and -N pools and of CO2 -fluxes showed correct trends but for several of the simulations with big differences between absolute model simulated and measured values.

As already mentioned above, the quality of the rape straw used in this lab-experiment was very similar to the rape straw used by Mueller et al. (1997, 1998a) in the field experiment for the recalibration of the original DAISY parameter set. Mueller et al. (1998b) modelled the field turnover of the barley straw used in this

Fig. 2. Measured (symbols) and simulated (lines) mineral N (Nmin ), soil microbial biomass N (SMB-N) and cumulative soil respiration (CO2 -C) in the treatment without addition of plant material. Open and full symbols indicate experimental groups 1 and 2 respectively.

Bars show standard errors of measured values (n = 3).

experiment quite successfully. However, in the simulations done here for rape straw and barley straw, the DAISY model predicted a slight net N-mineralisation instead of the observed N-immobilisation together with a nearly constant SMB-N and an overestimated cumulative soil respiration. This indicates, that there is a considerable difference in the turnover of added plant materials between the field experiments mentioned above (Mueller et al., 1997, 1998a,b) and the lab-experiment reported in this study. The particle size of the added materials used in the field experiments (mechanically chopped) was very different from the particle size in this lab-experiment (finely cut). This

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Fig. 3. Measured (symbols) and simulated (lines) mineral N (Nmin ), soil microbial biomass N (SMB-N) and cumulative soil respiration (CO2 -C) during the decomposition of non-leguminous plant materials in experimental group 2 (rape straw, barley straw and blue grass).

Full lines indicate simulation scenarios with the original parameter setup calibrated for rape straw by Mueller et al. (1997). Dotted lines indicate simulation scenarios with the modified parameter setup. Bars show standard errors of measured values (n = 3).

is known to induce somewhat different dynamics in N turnover, especially with regard to N immobilisation (Jensen, 1994). Finer material may induce more immobilisation per unit carbon than a coarser version of the same material. Another explanation may be the distribution of the plant material in the soil, which was more homogeneous in the lab-experiments than it usually is in the field. This is also known to affect N immobilisation (Jingguo and Bakken, 1989).

3.2. Modification of the parameter setup In a second step, the parameter set was modified for each plant material separately (Table 1). It was the

aim of this modification to improve the simulation of mineral N. In a stepwise iterative process by trial and error, parameters of AOM, including C/N ratio of AOM1 and AOM2, substrate utilisation efficiency of AOM1, and the decomposition rates of AOM1 and AOM2 were modified (Table 1).

The time course of measured CPOM-N in the blue grass treatment indicates that this pool consists of two fractions: A fraction decomposing very fast within the first day after incorporation and a more recalcitrant fraction (Fig. 4). This is consistent with Magid et al.

(2001) who found that N from green manure plant material was mineralised very rapidly, independently of the C-mineralisation and even at low temperatures.

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Fig. 4. Measured coarse particulate organic matter C and N (symbols) and simulated AOM1-C and AOM1-N (lines) during the decomposition of rape straw, barley straw and blue grass. Full lines indicate simulation scenarios with the original parameter setup calibrated for rape straw by Mueller et al. (1997). Dotted lines indicate simulation scenarios with the modified parameter setup. Bars show standard errors of measured values (n = 3).

Consequently, it has to be assumed that the rapidly decomposing part of CPOM-N must be initiated as AOM2-N together with the water-soluble N. AOM1 of blue grass was initiated with a lower N-content, by increasing its C/N ratio and simultaneously decreasing the C/N ratio of the corresponding AOM2-pool. After this modification, AOM1-N in the blue grass treatment crossed the measured values of CPOM-N after the sharp initial decrease (Fig. 4, dotted lines). Simulation of the temporal dynamics in mineral N content in the blue grass treatment was greatly improved.

There are other models of organic matter turnover using solubility of organic matter pools as an indicator of high turnover rates (e.g. ANIMO; Groenendijk and Kroes, 1997). However, it could be shown here that particulate organic matter deriving from organic residues may be easily decomposable as well. In the CENTURY model, surface residues are partitioned into a metabolic pool and a structural pool (Parton et al., 1994; Parton, 1996). The latter contains the main part of the particulate organic matter (lignin and cellulose components). Lignin components are converted into a slowly decomposing pool, whereas the cellulose pool is transferred into a rapidly decomposing pool together with the metabolic pool.

Because of missing measurements of CPOM for the three legumes (red clover, white clover and white melilot), C/N ratios of the AOM1 pools of these plant materials could only be initiated with the measured C/N in the non-water soluble fraction and then increased by iteration until a better fit was obtained.

Total amounts of AOM-C and AOM-N remained unchanged.

De Neergaard et al. (2001) published an analogous incubation experiment with perennial ryegrass and white clover. For their DAISY simulations, they used the same parameter setup and the same approach for the subdivision of AOM. Their modifications of the parameter setup were very similar to the modifications described above. In order to improve the fitting of the model they had to increase the initial amount of AOM allocated to AOM2, to increase the utilisation efficiency of AOM1, to decrease the utilisation efficiency of AOM2 and to increase the decomposition rate of AOM2. In contrast to our investigation, they had to decrease the decomposition rate of AOM1.

In order to fit simulated Nmin satisfactorily in the treatments with blue grass, red clover, white clover and white melilot, death- and maintenance respiration rate coefficients of SMB2 had to be decreased (see

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Fig. 5. Measured (symbols) and simulated (lines) mineral N (Nmin ), soil microbial biomass N (SMB-N) and cumulative soil respiration (CO2 -C) during the decomposition of fine leguminous plant materials in experimental group 1 (red clover, white clover and white melilot).

Full lines indicate simulation scenarios with the original parameter setup calibrated for rape straw by Mueller et al. (1997). Dotted lines indicate simulation scenarios with the modified parameter setup. Bars show standard errors of measured values (n = 3).

Table 1). This is in contrast to the DAISY concept assuming that a zymogenous microflora would develop when applying easily degradable plant materials. This concept was originally adapted from the classical Rothamsted Model (Jenkinson et al., 1987). However, the classical Rothamsted model was originally developed and validated on long-term data not focussing on the short-term turnover of easily degradable organic matter. Changes of death- and maintenance respiration rate coefficients of SMB2 after the addition of blue grass, red clover, white clover and white melilot are furthermore in contrast to the concept of most of the other soil organic matter models assuming constant turnover rate coefficients of all pools throughout the whole simulation.

As a cumulative result of all modifications, high EF-values and low values for the other statistical parameters indicate much more reliable model simulations of Nmin (Figs. 3, 5 and 6, Table 3). Furthermore, the simulations of cumulative soil respiration and AOM1-C have been improved considerably (Figs. 3â&#x20AC;&#x201C;6, Table 3).

3.3. Soil microbial residues It was not possible to simulate SMB-N satisfactorily in the treatments with red clover, white clover and white melilot (Figs. 5 and 6, Table 3). In the early phase, the model underestimated SMB-N for most of the treatments. Later on, the model overestimated

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Fig. 6. Measured (symbols) and simulated (lines) mineral N (Nmin ), soil microbial biomass N (SMB-N) and cumulative soil respiration (CO2 -C) during the decomposition of coarse leguminous plant materials in experimental group 1 (red clover, white clover and white melilot). Full lines indicate simulation scenarios with the original parameter setup calibrated for rape straw by Mueller et al. (1997). Dotted lines indicate simulation scenarios with the modified parameter setup. Bars show standard errors of measured values (n = 3).

SMB-N except in the treatment with white melilot (coarse). The overestimation must be seen as a consequence of the strongly decreased SMB turnover rates (Table 1). Mueller et al. (1998b) explained shortcomings of the simulation of SMB-N (and Nmin ) with an insufficient model description of the turnover of N between SMB and soil microbial residues (SMR) such as cell exudates, exo-enzymes and microbial necromass.

This may also be an explanation here. In the beginning, N was temporarily incorporated into SMB and later on released again from this pool. As indicated by the satisfying model simulation of Nmin , the released N was not immediately mineralised. Consequently, N originating from SMB must have remained as SMR during this period. This explanation is supported by

measured data on K2 SO4 -extractable organic N in the unfumigated samples (Next , Fig. 7). Organic N released from SMB should at least partly be measurable as Next and this pool showed clear fluctuations in the leguminous plant treatments with an increasing tendency after the initial 7 days. However, in the other treatments (rape and barley straw, blue grass), Next showed much smaller fluctuations and a decreasing tendency between the last two sampling dates. We interpret these changes in Next measured in the leguminous plant treatments as a reflection of changes in K2 SO4 -extractable SMR-N. SMR originates completely from SMB. Therefore, the C/N ratio of SMR is expected to be close to the C/N ratio of SMB. Since SMR-N must be protected

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Table 3 Statistical evaluation of the model simulations with the original parameter set calibrated for rape straw by Mueller et al. (1997) and with the parameter sets for the different plant materials after modification Nmin Original

SMB-N Modified

Original

CO2 -C Modified

CPOM-C

Original

Modified

Original

CPOM-N Modified

Original

Modified

Rape straw RMSE EF LOFIT

58.0 −0.76 77.2

16.3 0.86 6.1

26.1 0.20 131

23.6 0.34 107

42.0 0.75

6.9 0.99

5.4 0.89 3627

5.4 0.89 3627

59.1 −4.05 28.5

45.0 −1.92 16.5

Barley straw RMSE EF LOFIT

76.1 −1.00 116

16.8 0.90 5.6

22.0 0.19 145

15.9 0.57 139

21.9 0.92

9.1 0.99

6.5 0.90 14991

4.2 0.96 8203

20.9 −6.21 5.5

23.1 −7.81 6.7

Blue grass RMSE EF LOFIT

27.6 −2.87 55.4

8.8 0.61 5.6

27.7 −0.14 299

18.6 0.48 212

15.3 0.95

12.5 0.97

17.3 0.44 24516

11.7 0.74 11318

57.2 −0.75 227

55.7 −0.66 216

Red clover, fine RMSE 26.7 EF 0.69 LOFIT 252

14.9 0.71 43.7

31.1 −0.15 395

43.7 −10.33 752

142.4 −2.93

12.3 0.96

Red clover, coarse RMSE 38.8 EF −3.57 LOFIT 191

11.9 0.57 48.3

27.1 −0.53 345

36.9 −1.83 517

29.7 0.83

10.3 0.98

White clover, fine RMSE 36.4 EF −0.22 LOFIT 394

9.5 0.92 26.7

58.4 −0.62 2171

44.1 0.08 1239

36.3 0.69

13.1 0.96

White clover, coarse RMSE 41.0 EF −0.61 LOFIT 363

16.8 0.73 48.4

73.6 −1.06 2063

63.5 −0.53 1231

38.6 0.69

7.9 0.99

White melilot, fine RMSE 32.8 EF −0.04 LOFIT 229

9.9 0.90 20.9

50.4 −0.42 1313

35.3 0.30 645

25.3 0.84

15.9 0.94

White melilot, coarse RMSE 44.9 EF −0.41 LOFIT 274

11.1 0.91 49.7

65.7 −2.53 1350

62.2 −2.16 1084

60.2 0.21

9.4 0.98

nil: treatment without addition of plant material, Nmin : soil mineral N, SMB: soil microbial biomass, CO2 -C: soil respiration, CPOM: coarse particulate soil organic matter, RMSE: root mean square error (best fit if RMSE = 0), EF: modelling efficiency (best fit if EF = 1), LOFIT: lack of fit in the analysis of variance (best fit if LOFIT = 0).

from mineralisation in the short term, the turnover rate of SMR must, at least temporarily, be much lower than the turnover rate of SMB. In the DAISY model however, no SMR-pool exists, having a C/N ratio close to the C/N ratio of SMB but a much lower turnover rate.

In our modification, SMB2 simulated with a reduced turnover rate may be understood as a pool containing SMB-N and SMR-N accumulated after the turnover of easily available plant residue N. This may be an explanation for the differences between simulated and

T. Müller et al. / Ecological Modelling 166 (2003) 3–18

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3.4. Parameterisation of AOM

Fig. 7. K2 SO4 -extractable organic N (Next ) in the soil calculated as differences between the variants with incorporation of plant material and the variant without addition of plant material. Standard errors varied between less than 1 and 18 mg Next kg−1 around a mean value of 4.6 mg Next kg−1 . Bars show standard errors of measured values (n = 3).

measured SMB-N, and for the necessary strong decrease of the SMB2 death and maintenance respiration rate coefficients during the fitting process described above.

Other models have not yet considered a distinct SMR-pool. However, some models contain active organic matter pools including soil microbial residues (e.g. Verberne et al., 1990).

Gaseous N-losses via denitrification would be an alternative to N-fixation in the above postulated SMR-pool. Due to optimal water contents, the DAISY model did not predict denitrification in any of our experimental variants. However, denitrification was not measured in our lab-experiment and a minor uncertainty therefore remains on this point. Further experiments including measurements of denitrification are necessary to verify this.

During the second half of the experimental period, model simulated AOM1-N underestimated the measured values of CPOM-N (Fig. 4). The same effect was visible for the corresponding C-pools, but to a much smaller extent. Magid et al. (1997) and Mueller et al. (1997) pointed out that light particulate organic matter (LPOM) changed its quality during decomposition, as indicated by a decreasing C/N ratio, an increasing lignin content and a decreasing cellulose content. LPOM is comparable with CPOM measured in this investigation (Magid and Kjærgaard, 2001).

Changes in C/N ratio and in recalcitrant components, as lignin, will alter the decomposition rate. This contradicts the assumptions in the DAISY model, which assumes constant properties (C/N ratios and turnover ∗ rate coefficients, kAOM1 ) of each pool. Hence, an appropriate simulation of CPOM by AOM1 cannot be expected at the later stage of decomposition (Mueller et al., 1998b). Another explanation may be that N-rich SMB was accumulated on the surface of or within the CPOM-material over time, which would result in a significantly higher N-content not reflected by the simulations.

Constant turnover rate coefficients of simulated organic matter pools throughout the whole simulation period are a common feature of many models. However, there are differences concerning the C/N ratio of the modelled pools. In the DNC-Model (Li, 1996), the C/N ratio of soil microbial biomass, humads and humus are fixed, whereas the C/N ratio of litter is allowed to vary. Other models simulate C and N pools, allowing fluctuations of the C/N ratio within certain ranges (e.g. CENTURY (Parton et al., 1994; Parton, 1996)).

Only weak relationships (R2 < 0.5) could be detected between the measured quality parameters of added plant residues (C/N, lignin content, lignin/N, cellulose content) and the modified decomposition rates of the AOM1-pool shown in Table 1. Strong relationships were detected between log-transformed C/N ratio and log-transformed cellulose content of added plant residues and modified initial percent of AOM-C in AOM1 (Fig. 8). This is in contrast to Parton et al.

(1994) and Parton (1996) who used the Lignin/N ratio to subdivide soil organic matter into structural and metabolic pools in the CENTURY model.

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3.5. Transfer to field investigations It has been shown above that there were considerable differences in the turnover rates and substrate utilisation efficiencies of added plant materials between the field experiments used for the original parameter calibration and the lab-experiment used for the recalibration in this study. This is mainly caused by differences in the particle size and other factors influencing the turnover of the added materials. Consequently, the transfer of the parameter set resulting from this study to field sites has to be done with caution.

However, suggested conceptional changes such as the introduction of a separate SMR-pool and changes in the subdivision of organic matter pools such as AOM into AOM1 and AOM2 may therefore be directly transferable to field conditions.

Fig. 8. Modified initial % of AOM-C in AOM1 as a function of log-transformed C/N ratio and cellulose content of added plant residue.

The turnover rate coefficients of AOM1 and AOM2 are different. Therefore, modifying the % of AOM-C in AOM1 also alters the turnover rate of total AOM, which is the balanced sum of the AOM1 and AOM2 turnover rates. This is consistent with other investigations in which the C/N ratios or N contents of plant residues were related to their turnover rates (Nicolardot et al., 2001; Tian et al., 1995; Trinsoutrot et al., 2000; Henriksen and Breland, 1999) or to the size of easily mineralisable N fractions (De Neve and Hofman, 1996). Lignin content of added plant residues had no influence on the initial % of AOM-C in AOM1.

In the original concept of the DAISY model, it was assumed that any added plant residue could be parameterised using a specific partition of AOM into AOM1 and AOM2. It was furthermore assumed that the turnover rate constants of AOM1 and AOM2 are fairly constant throughout a wide range of added organic materials. In the final parameterisation (Table 1), the turnover rate coefficients of AOM1 and AOM2 are markedly higher for grass and leguminous plant materials than the original turnover rates used for rape straw, in case of AOM2 up to a factor of 8. This indicates clearly, that the original concept has to be given up at least for green plant materials containing large amounts of highly available N.

4. Conclusions With the modified parameter sets, it was possible to achieve reliable simulations of net N-mineralisation after the incorporation of the different plant materials including leguminous green manure plants.

Considerable amounts of water-insoluble N in green leguminous plant materials were easily decomposable. For these plant materials, it is therefore not possible to subdivide AOM by simply allocating the water-insoluble part of the added plant material to AOM1 and the water-soluble part to AOM2, as has also been indicated by Henriksen and Breland, 1999. C/N ratio and cellulose content of added plant residues may be useful indicators for the partition of plant materials into a slowly decomposable (AOM1) and a rapidly decomposable (AOM2) part. AOM1 and AOM2 cannot be simulated with the same turnover rate constants for plant materials differing considerably in their properties. Furthermore, the assumption that the quality of AOM1 is constant during its decay is in contradiction to the observed changes in quality of the CPOM-pool during the later stage of decomposition. This assumption results in an overestimation of AOM1 by the DAISY model simulation.

The DAISY model does not fully reflect the incorporation of N into SMB after addition of easily decomposable plant material and the following turnover into SMR-N. Conceptual changes in the model should

T. Müller et al. / Ecological Modelling 166 (2003) 3–18

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consider an SMR-pool with a C/N ratio close to that of SMB but with a lower turnover rate coefficient.

It is questionable whether parameter sets developed under field conditions are transferable to the simulation of lab-experiments and vice versa. There seems to be a clear difference in the substrate utilisation efficiency of recalcitrant organic components with a relatively high C/N ratio between field and lab-experiments. In addition, the decomposition rate of added organic matter seems to be higher under lab-conditions than under field conditions.

The parameter setups achieved here need to be confirmed by model simulations done for an independent data set, including measurement of denitrification.

Furthermore, field investigations are necessary to verify the results found here under lab-conditions.

dCX,SMBi /dt is the carbon flux from pool X into SMB1 (i = 1) or SMB2 (i = 2) (kg C m−3 day−1 ). −dCX /dt is the negative turnover rate of pool X. fX,SMBi is the partitioning coefficient defining the portion of dCX /dt entering SMBi. EX is the substrate utilisation efficiency, defining the fraction of substrate C coming from pool X that can be used for microbial growth.

The remaining substrate C is respired as CO2 .

Acknowledgements

References

This study was financed by the Danish Agricultural Research Centre for Organic Farming under the NICLEOS project and by the Danish University Consortium on Sustainable Land Use and Natural Resource Management (DUCED-SLUSE).

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dCX /dt is the turnover rate of pool X (kg C m−3 day−1 ), ki is the turnover rate coefficient for pool X (day−1 ), CX is the concentration of carbon in pool X (kg C m−3 ) and X is an organic matter pool (SOM1, SOM2, SMB1, etc.). ∗ c T ψ kx = kX Fm Fm Fm

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