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Ecological Modelling 111 (1998) 1 – 15

Soil C and N turnover after incorporation of chopped maize, barley straw and blue grass in the field: Evaluation of the DAISY soil – organic-matter submodel T. Mueller *, J. Magid, L.S. Jensen, H. Svendsen, N.E. Nielsen The Royal Veterinary and Agricultural Uni6ersity, Department of Agricultural Sciences, Plant Nutrition and Soil Fertility Laboratory, Thor6aldsens6ej 40, DK-1871 Frederiksberg C (Copenhagen), Denmark Received 1 July 1997; accepted 23 April 1998

Abstract The DAISY soil–organic-matter submodel was evaluated against independent data from a 1 year field study with incorporation (0–15 cm) of chopped barley straw, blue grass and maize into a sandy loam soil. Investigated parameters were soil respiration, soil mineral N, soil microbial biomass-C and N (SMB-C and N) dynamics and the predicted decomposition of the added organic matter (AOM) measured as light particulate organic matter (LPOM \ 100 mm, r B1.4 g cm − 3). Significant differences between values predicted from the model and measured values of soil respiration, soil mineral N, and soil microbial biomass-C and N were observed in all treatments. However, the model predictions for the unamended soil and for the soil receiving barley straw were better than those for the two other treatments. Discrepancies, in the blue grass and in the maize treatment, led to suggestions for model improvements. A distinct short-term pulse of SMB growth observed immediately after incorporation of the plant materials was not predicted fully. However, the difference between the measured and the predicted SMB pools did not induce a complementary difference for the mineral N pool. Soil microbial residues (SMR), temporarily protected against recycling via the microbial turnover and mineralisation, are discussed as a possible sink for the N from SMB. The predicted dynamics of the less labile AOM1-pool (initialised as water inextractable AOM) was correlated with the measured amounts of LPOM from the added plant materials. A slight overestimation of measured LPOM by AOM1 in the initial period after incorporation of AOM was followed by a slight underestimation later on. This trend might be attributed to the assumed constant C/N-ratio and turnover rate during the simulated decay of AOM1, contrasting reality in which LPOM is changing, e.g. C/N-ratio and lignin content. The simple initial partition of AOM into a water extractable part (AOM2) and a water inextractable part (AOM1), both parameterised with predetermined

* Corresponding author. Tel.: +45 35283499; fax: + 45 35283460; e-mail: tm@kvl.dk 0304-3800/98/$ - see front matter © 1998 Elsevier Science B.V. All rights reserved. PII S0304-3800(98)00094-5


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turnover rates and utilisation efficiencies, calls for re-evaluation. Suggestions are made to include the concept of SMR and the changing composition of LPOM into the DAISY model. © 1998 Elsevier Science B.V. All rights reserved. Keywords: DAISY; Particulate organic matter; Soil microbial biomass; Soil microbial residual products; Soil respiration

1. Introduction DAISY is a deterministic model that simulates the C and N-fluxes in the soil-plant-atmosphere system (Hansen et al., 1990, 1991). In its original form, DAISY’s ‘soil – organic-matter-submodel’ was calibrated with data from the Rothamsted long term field experiments (Jenkinson et al., 1987; Hansen et al., 1990) and with data from short-term laboratory incubation experiments (Lind et al., 1990). At that time, methodological restrictions made it impossible to measure the key-pools of short-term soil – organic-matter turnover (e.g. soil microbial biomass, remaining added organic matter) under field conditions. Due to methodological progress, Mueller et al. (1997) estimated the turnover rate coefficients of some of the soil–organic-matter pools with a fast turnover (i.e. soil microbial biomass and added organic matter pools) and the efficiencies of the microbial substrate utilisation by fitting the model to detailed data on a number of soil pools and fluxes from a 1 year field study with incorporation of rape straw (Jensen et al., 1997; Magid et al., 1997a) and evaluated the resulting parameter set using literature data. However, the mentioned authors did not test the new parameter set with independent field data. The objective of the present study was to evaluate the DAISY model using the new parameter set against an independent set of experimental data (Mueller et al., 1997).

2. Simulation model, experimental set-up and statistical evaluation

atmosphere system (Hansen et al., 1990, 1991). The soil profile is stratified into soil layers represented by user-defined node-points. DAISY integrates physico-chemical processes with biological processes and includes submodels for: (a) soil water movement (Richard’s Equation) including solute movement (numeric solution of the convection-dispersion equation); (b) soil temperature (conduction and convection); (c) soil–organicmatter dynamics; (d) soil mineral N (nitrification and denitrification); (e) crop growth; and (f) system management. The model in its present form is adapted to the moist temperate climate of NorthWestern Europe (Fig. 1).

2.2. The soil–organic-matter submodel 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 having a certain range of turnover rates. In the original development of the model, it was found that those continuums can 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 is assumed that the turnover of the pools follows first order kinetics. The latter is in agreement 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). The turnover of each subpool is described by:

2.1. General model description

dX = kX CX dt

DAISY is a deterministic model that simulates C and N-fluxes in a one dimensional soil-plant-

where dX/dt is the turnover rate of pool X (kg C m − 3 d − 1), kX is the turnover rate coefficient for

(1)


T. Mueller et al. / Ecological Modelling 111 (1998) 1–15

pool X (d − 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.). Turnover rate coefficients (k*X (d − 1)) under standard conditions (10°C, −10 kPa, 0% clay) are defined for each carbon pool. For SMB1 and SMB2, the death rate coefficient and the maintenance respiration rate coefficient have to be defined separately. In order to determine actual rate coefficients (kX ), the rate coefficients under are multiplied by standard conditions (k *) X 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 (Hansen et al., 1991).

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Partitioning of C fluxes is defined by the partitioning coefficients ( fX ). Carbon fluxes into the microbial biomass are multiplied by substrate utilisation efficiencies (ESMB, ESOM1, ESOM2, EAOM1, EAOM2). Here, EX defines the fraction of the substrate C coming from pool X (SMB1, SMB2, SOM1, SOM2…), that 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 using a fixed C/N ratio for each pool: NXt = CXt

NX CX

(2)

where NXt is the soil N content in pool X at time

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.


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t (kg N m − 3), CXt is the soil C content in pool X at time t (kg C m − 3) and NX /CX is the reciprocal C/N-ratio of pool X, assumed to be constant over the whole simulation period. Net N-mineralisation or N-immobilisation (dNmin/dt) 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 has been calculated simply by summation of CO2-evolution in the individual soil layers for every time step. The time step of the soil – organic-matter submodel is 1 h. A more detailed description of the soil – organic-matter submodel is given by Mueller et al. (1997). The other submodels of DAISY are described elsewhere (Hansen et al., 1990, 1991).

2.3. Field experiment On 24 August 1994, 6 t ha − 1 chopped barley straw, fresh chopped blue grass or fresh chopped maize plants (0 and 6 t ha − 1) were incorporated to a depth of 0–15 cm into a sandy loam soil (Højbakkega˚rd, field 6) using a rotovator. Previously, the soil had been in fallow for 1 year. Control plots did not receive any plant material but were tilled like the treated plots. In the following year, the soil was kept bare using herbicides when necessary. After incorporation (day 0), soil mineral N, soil surface CO2-flux, SMB-C and N, and light particulate soil – organic-matter (LPOM \100 mm, r B 1.4 g cm − 3) were measured with a decreasing frequency over a period of 1 year. Soil surface CO2-flux was measured using a chamber method with passive trapping of CO2 in alkali over periods of 24 h (Jensen et al., 1996). The content of the SMB was measured by chloroform fumigation extraction (Brookes et al., 1985; Vance et al., 1987) using a fEC-factor of 2.22 (Wu et al., 1990) and a fEN-factor of 1.85 (Brookes et al., 1985; Joergensen and Mueller, 1996). The LPOM (\100 mm, r B 1.4 g cm − 3) was separated from the soil by the size-density fractionation method using Na-polytungstate solution (r =1.4 g cm − 3) as a density reagent (Magid et

Table 1 Specification of SMB-initialization, SMB-turnover rate, AOM turnover rate and substrate utilisation efficiencies in the DAISY simulation scenarios (Mueller et al., 1997) %Ct in SMB1 SMB2 C/N SMB1 SMB2 Mean C/N SMB

1.89 0.15 6.7 6.7 6.7

Death rate coefficient SMB1 (d−1) SMB2 (d−1) Maintenance respiration rate coefficient SMB1 (d−1) SMB2 (d−1)

1.85 · 10−4 0.01

Turnover rate coefficient AOM1 (d−1) AOM2 (d−1) SOM1 (d−1) SOM2 (d−1)

0.012 0.05 2.7 · 10−6 1.4 · 10−4

Substrate utilisation efficiency SOM1 SOM2 SMB AOM1 AOM2

0.40 0.50 0.60 0.13 0.69

0.0018 0.01

al., 1997a; Mueller et al., 1998). The field experiment was laid out as a block design with two replicates. The field experiment has been described in detail elsewhere (Mueller et al., 1998) (Table 1).

2.4. Model initialization Mueller et al. (1997) parameterised the DAISY model for a field experiment with incorporation of 0 and 8 t ha − 1 rape straw at the same site in the previous year (August 1993 to August 1994). This parameterisation (Table 1) was used to simulate C and N dynamics in the above described field experiment from August 1994 to August 1995 (Mueller et al., 1998). The simulations were initiated 1 August 1993 in order to include the effect of the previous year of fallow. The driving variables (global radiation (W m − 2), precipitation and air temperature 2 m over ground) were measured at a meteorological station placed next to the experimental fields at Højbakkega˚rd. The model input data on soil texture, water release characteristics, Corg content and Corg/Nt-ratio of the various soil layers were measured (Mueller et


T. Mueller et al. / Ecological Modelling 111 (1998) 1–15

al., 1998). The initial content of mineral N was estimated from simulations of the previous year with bare soil. Simulations were carried out with incorporation of 0 and 6 t ha − 1 barley straw, blue grass or maize, and with a soil rotavation to 0 – 15 cm depth in all treatments (Table 2). The measured C and N contents of the incorporated plant materials (total, water extractable and water inextractable) from Mueller et al. (1998) were used to obtain initial conditions of the AOM-pools (Table 2). AOM1 and AOM2 were assumed to be the water inextractable and water extractable part of AOM, respectively. The values for rape straw given in Table 2 were used by Mueller et al. (1997) for the parameterisation of the DAISY set-up. As shown in Table 2, the incorporated plant materials represented substrates with a widely differing quality. The lignin/ N-ratios which were 27, 10.4, 2.5 and 3.2 in the rape straw, barley straw, blue grass and maize material, respectively (Jensen et al., 1997; Mueller et al., 1998).

2.5. Statistical e6aluation Addiscott and Whitmore (1987) concluded that one method alone to quantify the discrepancy between model simulations and measured data can be misleading. Therefore, a statistical analysis of the residuals (the differences between the ob-

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served and the predicted values) was performed in four different ways. As a quantification of the residuals we calculated the mean difference between model simulated and measured data (Ma ): Ma =

1 N %

m ¯ − pi

N i=1 i

(3)

where m ¯ i 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 (Addiscott and Whitmore, 1987, modified). 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 RMSE, the better is the agreement between model predicted and measured values. RMSE=



n

1 N % (p − m ¯ i )2 N i=1 i

0.5

·

100 m ¯

(4)

In order to detect significant differences between the measured and the model predicted time courses as a whole, an analysis of variance was done 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). N

ni

RSS= % % (mij − pi )2

(5)

i=1 j=1 N

n

SSE= % % ((mij − pi )− (m ¯ i − pi ))2

(6)

i = 1j = 1

Table 2 Specification of the C contents and the C/N-ratios of AOM, and of the partition into AOM1 (water insoluble) and AOM2 (water soluble) in the DAISY simulations

AOM-C (kg ha−1) AOM-N (kg ha−1) % of AOM-C in AOM1 % of AOM-C in AOM2 C/N of AOM C/N of AOM1 C/N of AOM2

Rape strawa

Barley strawb

Blue grassb

Maizeb

3969 50 96 4 80 92 19

2712 38 94 6 72 110 12

2682 122 88 12 22 25 12

2700 85 77 23 32 37 23

All values are based on experimentally determined data. a Used by Mueller et al. (1997) for the DAISY setup. b Used for the present DAISY simulations.


T. Mueller et al. / Ecological Modelling 111 (1998) 1–15

6 N

LOFIT= RSS −SSE = % ni (m ¯ i −pi )2

(7)

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. FLOFIT was calculated as follows: FLOFIT = MSLOFIT/MSE =

LOFIT SSE / DFLOFIT DFSSE (8)

3.2. Predicted soil respiration and measured soil surface CO2 -flux Immediately after incorporation of the plant materials and rotavation, soil respiration predicted by the model was significantly lower than the measured soil surface CO2-flux (Figs. 2 and 3). Later on, the significant differences disappear. RMSE was lowest in the barley straw and in the blue grass treatment (Table 3).

3.3. Soil mineral N

The degrees of freedom (DF) were calculated N

DFLOFIT =N and DFSSE =% (ni −1)

(9)

i

The statistical evaluation was achieved by comparing FLOFIT with tabulated F-values. Afterwards, single model predicted points were compared with the corresponding measured values using a multiple t-test.

'

LSD=t(DFSSE,a)

MSE

1 + (n − 1) 1 · (n −1)

(10)

1 and (n− 1) are the degrees of freedom of the simulated values and of the measured values, respectively (Whitmore, personal communication). Due to methodological problems (see below), the first measurement of SMB-C and N was not included in the statistical calculations for the treatments with addition of plant materials.

3. Results

3.1. First sampling date Due to problems caused by the sampling technique, the transport and the storage of the soil samples, SMB-measurements from the first sampling date (day 0) are not reliable and have to be interpreted with caution as has already been discussed by Mueller et al. (1998). Nevertheless, we decided to present the complete data set in the figures (Figs. 2 and 3, Table 3).

RMSEs for soil mineral N were very similar (31–35%) in all of the four treatments (Table 3). However, the values of Ma in Table 3 suggests, that the soil mineral N was best predicted after incorporation of barley straw (Figs. 2 and 3). The simulations of the unamended treatment showed a general tendency to underestimate mineral N during periods of heavy N mineralisation (Fig. 2). In the barley straw and in the maize treatment, the significant over estimations of mineral N by the DAISY model in the beginning of the simulated period seem to be the result of a very small time delay only (Fig. 3). In the blue grass treatment DAISY underestimated the initial pulse of mineral N but tended to overestimate mineral N during the following months (Fig. 2). At the last sampling date, soil mineral N was significantly underestimated by the model in three of the four treatments.

3.4. Soil microbial biomass Except the first sampling date, SMB-C and N were simulated reasonably well in the unamended treatment (Fig. 2). This was also indicated by the lowest RMSE for both pools. During the first 2–4 weeks after incorporation of the plant materials, SMB-N was significantly underestimated by the model (Figs. 2 and 3). The maximum difference between the predicted and the mean measured values was − 25 to − 60 kg SMB-N ha − 1 and − 325 to − 450 kg SMB-C ha − 1. After this initial period, measured and simulated values agreed well in all treatments. At the last sampling date, SMB-N was significantly overestimated by the model in the blue grass treatment.


T. Mueller et al. / Ecological Modelling 111 (1998) 1–15

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Fig. 2. Time courses of model predicted (lines) and mean measured values (symbols) of soil respiration, mineral N, soil microbial biomass C (SMB-C) and soil microbial biomass N (SMB-N). The figures show the treatments with 0 and 6 t ha − 1 incorporation of chopped blue grass. Full squares for measured values indicate significant differences (a =0.05) to the corresponding predicted values. Due to methodological problems (see above), the first measurement of SMB-C and N (full triangles) was not included in the statistical calculations for the treatments with addition of plant materials.


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Fig. 3. Time courses of model predicted (lines) and mean measured values (symbols) of soil respiration, mineral N, soil microbial biomass C (SMB-C) and soil microbial biomass N (SMB-N). The figures show the treatments with 6 t ha − 1 incorporation of chopped barley straw and maize plants. Full squares for measured values indicate significant differences (a =0.05) to the corresponding predicted values. Due to methodological problems (see text), the first measurement of SMB-C and N (full triangles) was not included in the statistical calculations for the treatments with addition of plant materials.


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Table 3 Mean difference between measured and predicted values (Ma ), root mean square error (RMSE) and F-values derived from the analysis of variance for the investigated pool in the different treatments Plant material

Pool

Barley

CO2 Mineral N SMB-C SMB-N DLPOM-C/AOM1-C CO2 Mineral N SMB-C SMB-N DLPOM-C/AOM1-C CO2 Mineral N SMB-C SMB-N DLPOM-C/AOM1-C CO2 Mineral N SMB-C SMB-N

Blue grass

Maize

None

Ma 0.3 4.8 92 14 277 0.3 8.9 97 24 366 0.5 6.5 91 22 340 0.5 6.7 80.7 11.1

kg kg kg kg kg kg kg kg kg kg kg kg kg kg kg kg kg kg kg

ha−1 ha−1 ha−1 ha−1 ha−1 ha−1 ha−1 ha−1 ha−1 ha−1 ha−1 ha−1 ha−1 ha−1 ha−1 ha−1 ha−1 ha−1 ha−1

d−1

d−1

d−1

d−1

RMSE

F-value

34a,b 33 22b 18b 22a,b 36 35 25 29 41 50 31a,b 20 28 46 94 34 20a 32a

10.4*** 34.5*** 7.9* 8.2** 0.6n.s. 4.0* 6.9 10.4** 99.3*** 2.8n.s. 16.9*** 6.9* 30.1*** 22.5*** 0.5n.s. 81.6*** 127.7*** 6.8* 7.9**

Due to methodological problems (see above), the first measurement of SMB-C and SMB-N was not included in the statistical calculations for the treatments with addition of plant materials. a Lowest RMSE for this pool (all four treatments). b Lowest RMSE for this pool (treatments with addition of plant material only). n.s. not significant. *, **, *** significant difference between model predicted and measured time courses (a =0.05, 0.01, 0.001 resp.).

3.5. Light particulate soil– organic-matter and added-organic-matter In a field experiment, Magid et al. (1997a) calculated the light particulate organic matter derived from crop residues (DLPOM) as the difference between LPOM in treatments with addition of rape straw and LPOM in an unamended control soil. They identified DLPOM as the remaining particulate part of the decomposing added rape straw in the soil. Mueller et al. (1997) initiated AOM1 in their DAISY scenarios as the water inextractable (=particulate) part of the added rape straw and compared the model predicted AOM1-C with the measured DLPOM-C. We used the same approach. Fig. 4 compares the model predicted AOM1-C and the measured DLPOM-C of our experiments. In the beginning of the simulation, AOM1 was in the same order of magnitude as the measured DLPOM (Fig. 4).

After this initial phase, predicted AOM1 tended to be higher than the measured DLPOM (Fig. 4). In contrast, in the later stage of decomposition of all added plant materials predicted AOM1 tended to be lower than the measured DLPOM. RMSE indicates that AOM1-C and measured DLPOM-C agreed best in the barley treatment (Table 3).

4. Discussion

4.1. General assessment of the model The F-values in Table 3 indicate that significant differences between model predicted and measured values could be found for all treatments and for nearly all parameters. Only the comparison of measured DLPOM-C with model predicted AOM1-C did not show any significant difference. Hence, the model was not able to completely


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simulate reality in any of the four treatments from a statistical point of view. In the following we will use RMSE and Ma in Table 3 and the results shown in Figs. 2–4 to differentiate this general picture.

For four of the five investigated parameters, the barley straw treatment yielded the lowest RMSE of all plant material treatments (Table 3). For mineral nitrogen, RMSE was slightly lower in the

Fig. 4. Time courses of model predicted water insoluble crop residues (DAOM1-C (lines)) and mean measured light particulate organic matter (DLPOM-C\100 mm, r B 1.4 g cm − 3 (symbols)). The figures show the differences (D) between incorporation of 6 and 0 t ha − 1 chopped blue grass, barley straw and maize plants. None of the differences between measured and model predicted were significant (a= 0.05).


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Table 4 Residuals of the difference between treated and untreated ((predicted treated−predicted untreated)−(measured treated−measured untreated)) for soil mineral N (DNmin) and SMB-N (DSMB-N) Sampling

Barley straw (kg N ha−1 15 cm−1)

Blue grass (kg N ha−1 15 cm−1)

Maize (kg N ha−1 15 cm−1)

Day

Date

DNmin

DSMB-N

DNmin

DSMB-N

DNmin

DSMB-N

6 13 28 56

30/08/94 06/09/94 21/09/94 19/10/94

23 n.d. 2 1

−20 −10 −11 7

−4 n.d. 10 18

−51 −39 1 7

15 n.d. −1 −4

−59 −37 6 18

First sampling date excluded due to methodological problems (Mueller et al., 1998). n.d., not determined.

maize treatment than in the barley straw treatment but Ma still indicates the smallest absolute residuals in the barley straw treatment. We can conclude that the model was able to simulate an incorporation of barley straw better than an incorporation of blue grass or maize. This may be due to the quality of the barley straw. Table 2 shows that the properties of the barley straw were most similar to the properties of the rape straw used for the parameterisation of the model set-up by Mueller et al. (1997).

activity. This hypothesis was supported by an elevated soil surface CO2-flux in the unamended treatment. Reicosky and Lindstrom (1993) found tillage induced increases in the soil surface CO2flux which was dependent upon the soil tillage equipment they used. However, the present version of DAISY does not include any soil tillage induced stimulation of organic matter turnover or microbial activity.

4.2. Predicted soil respiration and measured soil surface CO2 -flux

The model predicted SMB was significantly smaller than the measured SMB in the first weeks of the study (Figs. 2 and 3). Obviously, the simulated rate of added organic matter (AOM2) turnover into the SMB2-pool (zymogeneous SMB-pool with a fast turnover) was not large enough to predict the observed initial microbial growth. Possible reasons are (a) an inadequate partitioning of AOM-C and N into AOM1 (slow turnover) and AOM2 (fast turnover); (b) the turnover rate coefficient of AOM2 (k*AOM2 ) was too small; (c) the substrate utilisation efficiency of AOM2 was too small; (d) the order of decomposition kinetic is not the assumed first order kinetic in the initial phase (Whitmore, 1996). Furthermore, it has to be taken into account that (e) the added plant materials were not sterile and could have introduced a substantial amount of SMB into the soil. The first explanation is supported by the lower values of measured DLPOM compared to model predicted AOM1 (Fig. 4) during the first few months. However, our data does not allow us

Significant differences between predicted soil respiration and measured soil surface CO2-flux may be due to methodological bias related to the CO2-flux measurement (Jensen et al., 1996; Mueller et al., 1998). Furthermore, total CO2-evolution was simulated simply by summation of CO2-evolution in the individual soil layers per day. The latter does not take into account slow gaseous diffusion at high water contents and dissolution of gaseous CO2 in the aquaeous phase as considered by S& imu˚nek and Suarez (1993) for example. This may be a second reason for disagreement between model predicted soil respiration and measured soil surface CO2-flux. Furthermore, the low simulated soil respiration immediately following the incorporation of the plant material (Figs. 2 and 3) could be due to the stimulating effect of soil tillage on the microbial

4.3. Soil microbial biomass and mineral N


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to decide whether we can reject the other explanations (Table 4). The classical concept, upon which the DAISY soil – organic-matter turnover is based, suggest that N immobilised in a growing SMB, e.g. after substrate addition, will be remineralised if SMB decreases again (Hansen et al., 1990). Based on this assumption, the residuals (the differences between the observed and the predicted values) during the increase and decrease of SMB-N induced by the addition of the different plant materials (Figs. 2 and 3) should be reflected by residuals of N remineralisation in Figs. 2 and 3. Since we have to distinguish between the residuals already visible in the unamended treatment and the residuals of the changes induced by the addition of the different plant materials, we made the calculations summarised in Table 4. Table 4 shows the residuals of the differences between treated and untreated ((predicted treated− predicted untreated) − (measured treated-−measured untreated)) for soil mineral N (DNmin) and SMB-N (DSMB-N). Due to methodological problems (Mueller et al., 1998), the first sampling date was excluded from the table. The residuals of DSMB-N increased from August to October 1994 in all treatments. However, only in the barley straw treatment was this increase reflected by a decrease in the residuals of DNmin in the same order of magnitude. In the blue grass treatment, additional N appeared, indicated by a considerable increase over time (from day 6 to 56) of the residuals of DNmin instead of the expected decrease (Table 4). Obviously, N released from the measured SMB was not mineralised immediately. This N may have remained as soil microbial residues (SMR: residues of dead microorganisms, empty fungal hyphae and microbial exudates) which were temporarily resistant to recycling via microbial metabolism and subsequent mineralisation. Mueller et al. (1998) calculated N-balances for periods with low N-leaching and supposed that SMR could have been a hidden sink for disappearing N in the barley treatment. Our data suggest that the formation of SMR has to be considered as a sink in the turnover of blue grass and maize material as well. Since the quality of the three substrates (barley straw, blue grass,

maize) was very different (Table 2 and Mueller et al., 1998), the development of different microbial communities may have occurred (Mueller et al., 1998). This results in SMR-pools having very different properties. A more detailed discussion of the concept of SMR from the experimental point of view was made by Mueller et al. (1998).

4.4. Parameters controlling the turno6er of added-organic-matter As mentioned above, the model was able to simulate an incorporation of barley straw better than an incorporation of blue grass or maize. This may be explained by the properties of the barley straw which were most similar to the properties of the rape straw used for the parameterisation of the model set-up by Mueller et al. (1997). However, if we assume that the main differences between plant materials are described with the principles of the initial subdividing of AOM (AOM1=water inextractable, AOM2= water extractable), the addition of different organic materials does not require the adjustment of the two turnover rate coefficients and/or the substrate utilisation efficiencies. As mentioned above, the simulated incorporation of the fast decomposing added organic matter (AOM2) into the SMB2pool was not large enough to predict the observed initial microbial growth. In order to increase the AOM-2 pool, a part of the water inextractable added organic matter must be considered as fast decomposing in addition to the water extractable added organic matter. Results of Melillio et al. (1982) and Tian et al. (1992) suggest that the turnover rates of various types of litter in the soil are correlated with the lignin/N ratio of the materials. Based on a calculated regression between the lignin/N ratio and the turnover rate of plant materials given in these two papers, we used the lignin/N ratios of our plant materials (Mueller et al., 1998) to modify the turnover rate coefficients of the AOM1 pools relative to those of the rape straw (Mueller et al., 1997). The resulting model predictions (data not shown) overestimated N mineralization and soil respiration, and underestimated the remaining AOM1 compared with DLPOM. This may indi-


T. Mueller et al. / Ecological Modelling 111 (1998) 1–15

cate that the lignin/N is not the critical determinant of the short and medium-term decompositon or that this ratio has to be complemented with other parameters. However, the regression derived from data of Melillio et al. (1982) and Tian et al. (1992) was based on a relatively small data set (11 points only). Hence, further studies may improve our preliminary findings.

4.5. Light particulate soil-organic-matter and added-organic-matter

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CO2 in the aquaeous phase, as considered by S& imu˚nek and Suarez (1993), should be included into the DAISY model. Only a few papers allow for the quantification of the effect of soil tillage on parameters of soil microbial activity, such as soil surface CO2-flux, in the short term (Reicosky and Lindstrom, 1993). In order to include and to parameterize this effect in models of soil–organic-matter turnover detailed field studies are necessary.

5.2. The concept of soil microbial residues (SMR) The simulated AOM1-pool corresponded quite well with the DLPOM or tended to over estimate DLPOM in the early stage of AOM decomposition (Fig. 4). However, in the later stage of AOM decomposition model the predicted AOM1 tended to underestimate DLPOM slightly. This general pattern is in agreement with findings from Mueller et al. (1997) and Magid et al. (1997b) who found that simulated AOM1 agreed well with DLPOM in the first months after rape straw incorporation but underestimated DLPOM in the later stage (\1 year). Mueller et al. (1997) and Magid et al. (1997a) pointed out that LPOM changed its quality during decomposition, indicated by a decreasing C/N ratio, an increasing lignin content and a decreasing cellulose content. Changes in the C/N-ratio and in recalcitrant components, as lignin, will alter the turnover rate of the material. This contradicts the assumptions of the DAISY model which assumes constant C/Nratios and constant turnover rate coefficients (k*AOM1 ). Hence a satisfactory prediction of DLPOM by AOM1 can not be expected at the later stage of decomposition.

5. Suggestions for model improvement

5.1. CO2 -e6olution and soil tillage As discussed above, the simple summation of the simulated CO2-evolution in the individual soil layers for each day is not sufficient to predict the actual soil surface CO2-flux. Existing concepts, taking into account the slow gaseous diffusion at high water contents and dissolution of gaseous

DAISY describes the turnover of soil–organicmatter within at least six pools (if organic matter is added to the soil once) differing in C/N ratio and turnover rate coefficients. As shown in this study, the modelled total C, total SMB, AOM1, AOM2, mineral N and CO2 evolution have experimentally measurable counterparts in nature. Other pools in the model and especially the partitioning into the subpools (SMB1 and SMB2, SOM1 and SOM2) can only be estimated. Any improvement in the DAISY model should avoid introducing more pools without any experimentally measurable counterparts in nature. As mentioned above, SMR seem to play an important role in the turnover of organic matter in soils and have to be considered as a separate pool. Gregorich et al. (1991) estimated the formation of SMR after addition of [14C]glucose. However, glucose is water soluble and highly available to microbial metabolism. Hence, the validity of their results for field conditions is restricted. In laboratory experiments with labelled AOM in combination with the measurement of substrate consumption via DLPOM, it will be possible to calculate the production rates of SMR after addition of various substrates with high contents of particulate matter. Furthermore, Lemaıˆtre et al. (1995) compared methods to extract a pool of labile organic matter associated with the soil microbial biomass. They found that tetraborate buffer 0.5 N at pH 8 with a 30 min shaking time may be the best method for specifically isolating microbial metabolites. Some other models (e.g. CANDY and CENTURY; Franko et al., 1995; Partoon et al., 1988) combine SMB and SMR


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(and probably other labile soil – organic-matter) resulting in a ‘(biologically) active organic pool’ with very diffuse experimental and methodological boundaries. However, the methodological approaches discussed above suggest that we are not far from a separate experimentally determinable SMR-pool. SMR is not represented as a separate pool in the current version of the DAISY model. Material released from the SMB-pools is directly recycled into SMB2 or transferred to SOM2. In principle SMR can be seen as a kind of added-organic-matter. SMR in the soil can then can be modelled as two separate pools (SMR1 and SMR2) analogous to AOM (AOM1 and AOM2) and characterised by different turnover rates and C/N-ratios. The partition into the two subpools will then depend on the composition of the microbial community represented by a specific partition of SMB into SMB1 and SMB2. The turnover rate coefficients will be expected to be between those of SMB and SOM. Gregorich et al. (1991) investigated the influence of soil clay content on the formation of SMR. However, the extent to which other conditions in the microbial microhabitat influence the formation and the turnover of SMR has not yet been investigated.

rate coefficient. However, no concepts exist as to how this process should be controlled. Another possibility is the introduction of a ‘native’ pool of particulate organic matter (POM). This is in accordance with the observation that untreated soil has a relatively constant level of POM-C and N (Magid et al., 1997a). The C/N ratio of the POM pool must be lower and the turnover rate coefficient must be smaller than those of AOM1. If, as decomposition proceeds, an increasing proportion of AOM1 is transferred to POM the sum of both pools (AOM1+ POM) will show the typical pattern: a decreasing C/N ratio and a decreasing turnover rate. The sum (AOM1 + POM) will be measurable as total particulate soil–organic-matter in treated soil. The basal content and the C/N ratio of POM can be determined in untreated soil.

5.4. Further e6aluation In this study, we evaluated a parameter set-up of the DAISY submodel ‘soil–organic-matter’ against independent short-term field data from a bare soil. The effect of growing plants was not investigated in this study and any influence has yet to be demonstrated.

5.3. Initialisation and changing quality of the added-organic-matter pools Acknowledgements A simple initial partitioning of AOM into a water extractable part (AOM2) and a water inextractable part (AOM1), was not satisfactory for several reasons. However, we could not present a better approach based on measured data in this paper and simply altering the partitioning to obtain a better fit would not be valid since the objective was to use measured properties for parameterisation of added plant materials. Further investigations with added-organic-materials of different quality, partly under standardised labconditions, will be necessary in order to get a more detailed data set. There are two different ways of including the concept of changing quality of the decomposing particulate part of AOM into the DAISY model. One possibility is to simulate at least AOM1 with a decreasing C/N ratio and a decreasing turnover

We thank S. Hansen for cooperation on using the DAISY model. The present study was financed by the Danish Environmental Research Programme. T. Mueller was a collaborator via a research fellowship of the German Research Society (DFG).

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