European Journal of Agronomy 16 (2002) 43–55

Decomposition of white clover (Trifolium repens) andryegrass (Lolium perenne) components: C and N dynamicssimulated with the DAISY soil organic matter submodel

Andreas de Neergaard *, Henrik Hauggaard-Nielsen, Lars Stoumann Jensen,Jakob Magid

Plant Nutrition and Soil Fertility Laboratory, Department of Agricultural Sciences,The Royal Veterinary and Agricultural Uni�ersity, Thor�aldsens�ej 40, DK-1871 Frederiksberg C, Denmark

Received 22 June 2000; received in revised form 14 May 2001; accepted 18 June 2001

Abstract

Using data from a decomposition study, we aimed to test the parameterisation of the soil organic matter moduleof the DAISY model, and link measurable plant litter fractions (lignin, water-soluble) with the model defined plantlitter pools. Shoot and root material from perennial ryegrass and white clover was incubated in a sandy loam soil at9 °C for 94 days. Accumulated CO2 evolution, soil mineral nitrogen (N) and soil microbial biomass-N were measuredduring the incubation. Marked differences in decomposition rates between above- and below-ground material as wellas between the two plant species were observed. The DAISY model was used to interpret the incubation results.Decomposition rates and utilisation efficiencies were modified, under the constraint that rates of specific pools wereindependent of the type of material, to obtain good agreement between observed and simulated values. Measurablequality parameters were evaluated against the sizes of pools in the model and measured fluxes. The size of the slowestdecomposing fraction of the DAISY model was proportional to the lignin content of the plant material, but twice aslarge. The easily decomposable fraction in the model was well correlated with the water-soluble fraction of the plantmaterial (r2=0.84). The size of this pool in the model was larger than the water-soluble fraction of the plant materialin three of the five plant materials. The initial carbon mineralisation was correlated with water-solubility of the plantmaterial and total mineralisation with the lignin:N ratio. Net N mineralisation was well correlated with the C:N ratioand the N content of the added material. At the end of the experiment, the mineral N content was overestimated bythe DAISY model for all treatments, except one. A soil microbial residual pool, consisting of undecomposedmicrobial tissue is suggested as a possible N-sink during the incubation. The study demonstrated a correlationbetween the model-defined pools and chemical plant fractions, but also that the pools in the model were larger thantheir measured counterparts. © 2002 Elsevier Science B.V. All rights reserved.

Keywords: DAISY; Litter quality; C and N mineralisation; Ryegrass; White clover

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* Corresponding author. Tel.: +45-3528-3484; fax: +45-3528-3460.E-mail address: adn@kvl.dk (A. de Neergaard).

1161-0301/02/$ - see front matter © 2002 Elsevier Science B.V. All rights reserved.

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A. de Neergaard et al. / Europ. J. Agronomy 16 (2002) 43–5544

1. Introduction

The importance of white clover–ryegrass (Tri-folium repens–Lolium perenne) leys in many tem-perate cropping systems is due to three valuablecharacteristics. First, clover–grass leys producehigh quality feed for livestock, either fresh or assilage. Dry matter yields may exceed 10 t ha−1

year−1 dry matter in unfertilised pastures where95% of the N is supplied by the N2–fixing clover(Høgh-Jensen and Schjoerring, 1994). Second,roots and stubble, containing 60–156 kg N ha−1

(Eriksen and Jensen, 2001; Hauggaard-Nielsen etal., 1998; Laidlaw et al., 1996), provide a N richresidue, which, when mineralised, may signifi-cantly reduce fertiliser requirements for the suc-ceeding crop. Finally, soil structure is improvedduring the pasture period (Tisdall and Oades,1979).

In order to fully predict the fertilising potentialof residues, insight into the decomposition charac-teristics is needed. Mineralisation of N and othernutrients must match the demands of the follow-ing crop in order to maximise yields and avoidsubsequent leaching. In this study, measurablequality parameters of the plant material werecompared with the course of decomposition inorder to find parameters determining litterquality.

The DAISY model (Hansen et al., 1990, 1991)was developed to simulate carbon (C) and Ndynamics in agricultural soils, plants and the at-mosphere. Most pools and fluxes in the modelhave distinct counterparts in nature (Mueller etal., 1997, 1998), even though determination oftheir respective quantities may be difficult. Theparameterisation of the soil organic matter mod-ule of the DAISY model has originally beenvalidated with long-term field studies. Two papersby Mueller et al. (1997, 1998) have focused onshort term dynamics of the soil organic matter,demonstrated certain limitations of the model,and suggested modifications of the parameterisa-tion. This study attempted to test and possiblyimprove the parameterisation, using data from adecomposition study of shoots and roots of twodifferent plant species. Further development ofour understanding of the principal determining

factors of litter decomposition relies on linkingexperimentally measurable fractions of the plantmaterial (i.e. lignin content) with model definedpools. This linkage was investigated in the study.

2. Materials and methods

2.1. Soil and plant materials

The soil used in the incubation was collectedfrozen in early February 1996 from the ploughlayer of an arable field at the experimental farmof the Royal Veterinary and Agricultural Univer-sity, Denmark. It was a sandy loam, containing21% clay, 21% silt, 41% fine sand and 19% coarsesand. Its total C content was 1.29%, total Ncontent 0.11%, pH (0.01 M CaCl2) 6.6, water-holding capacity 29% (w/w, dry basis). The soilwas sieved through a 5-mm mesh and pre-incu-bated for 19 days at 10 °C.

The plants used in the experiment were sampledfrom 3rd year grass and clover leys (Loliumperenne and Trifolium repens), which had received100 kg N ha−1 year−1. The plant material wascollected in early February when the soil wasfrozen. Above-ground material was isolated byhand. Soil samples including roots were taken to adepth of 25 cm. Then, in the laboratory, theywere dispersed in a 5% NaCl solution, floatingmaterial was collected and washed gently over a500-�m sieve, and then dried at 60 °C. The plantmaterial was cut into pieces, 2–3 cm of length,prior to incubation.

Ball-milled plant samples were analysed fortheir C and N content using an elemental analyser(Carlo Erba, EA 1108). The lignin and cellulosecontent was determined according to van Soest(1963). Determination of the water-soluble frac-tion of the amended material was performed asfollows: plant samples were rotary-shaken indeionised water (1 mg sample ml−1), at 15 °C for45 min. Samples were centrifuged and the super-natant removed. The pellet was dried at 65 °C andre-weighed. The water-soluble fractions were cal-culated as the difference between the C and Ncontent of washed and unwashed plant material.

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2.2. Incubation procedure

The incubation experiment involved six treat-ments, (1) grass shoot, (2) grass root, (3) clovershoot, (4) clover root, (5) clover–grass mixture,and (6) control soil, with no added plant material.The clover–grass mixture consisted of 44.4% grassshoots, 22.2% grass roots, 22.2% clover shoots and11.1% clover roots on a dry-weight basis.

At the start of the experiment (day 0), 3-litrestorage jars were filled with 1200 g (dry weight) ofsoil with a water content of 14.5%, and 2.4 g dryplant material was added and mixed thoroughlywith the soil. Each jar was equipped with a beakercontaining water to maintain a water-saturatedatmosphere during the experiment. The jars werestored closed at 9 °C in the darkness, and collectedfor analyses at day 0, 4, 16, 29 and 94.

2.3. Analysis

Release of CO2 was measured with decreasingfrequency, ranging from daily to every other week.Carbon mineralisation was measured by passivealkali trapping of the respired CO2.

Mineral N and soil microbial biomass-N weremeasured five times during the incubation. Soilsamples were extracted with 0.5 M K2SO4

(soil:solute ratio; 1:4 w/v) and filtered. Extractswere frozen immediately and stored at −20 °Cuntil analysed. NH4–N was determined spec-trophotometrically, NO3–N and NO2–N wasmeasured by flow injection analysis (Keeney andNelson, 1982).

Soil microbial biomass (SMB)-N was deter-mined via the chloroform fumigation-extractionmethod (Brookes et al., 1985; Vance et al., 1987).Soil samples were fumigated with chloroform for24 h at 25 °C, and then extracted as describedabove for mineral N.

The total N content of both the fumigated andunfumigated extracts was then measured by per-sulphate oxidation, modified after Cabrera andBeare (1993). Extractable SMB-N was calculatedas the difference between fumigated and unfumi-gated samples and converted to total SMB-Nusing the correction factor kEN=0.54 determinedby Joergensen and Mueller (1996).

2.4. Modelling

The soil–plant–atmosphere model DAISY wasused to simulate the experiment. Measured valueswere used to evaluate the current parameterisationof the model.

DAISY is a deterministic model that simulatesC and N fluxes in a one dimensional soil–plant–atmosphere system (Hansen et al., 1990, 1991).The model includes several sub-models for: (1) soilwater movement; (2) soil temperature; (3) soilorganic matter dynamics; (4) soil mineral nitrogen;(5) crop growth; and (6) system management. Dueto the simplicity of the experimental set-up (nowater movement, no crops, no soil handling andone homogeneous soil layer), we used a simplifiedversion of the model, including only the soil or-ganic matter and the mineral N sub-models.

The model operates with three organic matterpools: added organic matter (AOM), soil micro-bial biomass (SMB), and non-living native soilorganic matter (SOM) (Fig. 1). These pools areconceived as a continuum with different decompo-sition rates. For simplicity, the model dividesAOM, SMB and SOM in two pools each, one witha faster turnover (e.g. AOM-2), and one with aslower turnover (e.g. AOM-1). The rate of decom-position of each pool is assumed to follow first-or-der kinetics. An AOM-0 pool is included as avery recalcitrant fraction of the added plant mate-rial, and enters directly into the SOM-2 pool (Fig.1).

Turnover rate coefficients of each pool aredefined at standard conditions (10 °C, −10 kPa,0% clay). The coefficients are adjusted to experi-mental conditions by multiplication with modifiersfor actual soil temperature, soil water potentialand clay content. Decay rates of the SOM-1 andSOM-2, as well as death rates and maintenancerespiration of SMB-1 are multiplied by all threemodifiers (temperature, soil water potential andclay content). Turnover rates of SMB-2, AOM-1and AOM-2 are only modified by the temperatureand soil water potential. In the current experi-ment, the value of the temperature, soil waterpotential and clay modifiers were 0.9; 1 and 0.6respectively.

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Each carbon pool has an utilisation rate, thisfraction is incorporated as SMB-tissue when de-composed, and the remainder is respired as CO2.The modifiers do not affect the utilisation rate.

The N pools were calculated for each time step(24 h) by dividing the amount of C in the pool bythe C:N ratio of the pool. Net mineralisation andimmobilisation of N was calculated from the totalbalance of the N pools. For a further descriptionof the DAISY model, see Hansen et al. (1991),Mueller et al. (1997).

2.5. Model set-up and parameterisation

The parameterisation described by Mueller etal. (1997) for a sandy loam soil was initially usedfor the simulation. These parameters were derived

from experiments using a soil similar to ours. Theinitial size of the SOM was based on the total soilcarbon content of the soil. Parameters were firstmodified to fit the measured values for the controlsoil. To simulate the increased turnover of OMduring the incubation, we found that 325 �g Cg−1 soil (2.4% of total C) should be transferredfrom SOM-1 to AOM in the model at day 0. Thispool can be visualised as physically protected OMmade accessible to microorganisms when the soilaggregates are disrupted. This initial amendmentwas chosen in preference to increasing SOM de-composition rates and it is justified by the factthat 1 year before the soil sampling, a clover–grass ley was incorporated into the soil. Residualorganic matter from this incorporation could stillbe present in a physically protected form 1 year

Fig. 1. Fluxes of C (— ) and N (---) between the different pools and sub-pools of organic matter, mineral N and evolved CO2 inthe soil organic matter submodel of DAISY. AOM: added organic matter; SMB: soil microbial biomass; SOM: native soil organicmatter; fX : partitioning coefficients (Hansen et al., 1990, 1991)

A. de Neergaard et al. / Europ. J. Agronomy 16 (2002) 43–55 47

Table 1DAISY parameters used in the standard setup, as modified by Mueller et al. (1997) and in this experiment. For all other parametersthan the listed, the values were the same as used by Mueller et al. (1997)

Mueller Control Grass Clover Clover–grassStandardsetup soilsetup mixture

Shoots Roots Shoots Roots

% of Added Organic Matter (AOM)– 0 13 29 15 37AOM-0 20–– 90 67 66– 30AOM-1 25 62

–AOM-2 – 10 20 5 55 38 18– – 13 10 41Water soluble (%) 21– 20

20 23 27C:N ratio of total 15 18 21AOM

C:N ratio of – – – 23 27 15 18 21AOM-0

– – 20 25 27 16 19 22C:N ratio ofAOM-1

– – 20 19C:N ratio of 26 15 18 17AOM-2

Decomposition rate (day−1)0.00014 0.00014 0.00014 0.000140.00014 0.00014AOM-0 (SOM-2) 0.00014 0.000140.012 0.005 0.005AOM-1 0.0050.005 0.005 0.005 0.0050.05 0.06 0.06 0.060.05 0.06AOM-2 0.06 0.06

Substrate utilisation efficiency0.5 0.5 0.5 0.50.6 0.5AOM-0 (SOM-2) 0.5 0.5

AOM-1 0.130.6 0.4 0.4 0.4 0.4 0.4 0.40.69 0.5 0.5 0.5 0.5 0.50.6 0.5AOM-2

later. The C:N of this pool was set at 20, accord-ing to Magid et al. (1997), who showed that labileOM approached this value when decomposing.AOM-1 was assumed to account for 90% of thetotal AOM. As a consequence of the transfer tothe AOM pool, the C:N ratio of the total AOMlisted in Table 1, differs slightly from the mea-sured values of the plant material, listed in Table2.

The decomposition rate of the AOM-1 wasreduced from 0.012 day−1 to the value used in thestandard setup: 0.005 day−1. The decompositionrate of AOM-2 was increased from 0.05 to 0.06day−1 and the utilisation efficiencies of AOM-1and AOM-2 were changed from 0.12 and 0.69 to0.4 and 0.5, respectively, in order to reduce themineralised N late in the incubation (Table 1).

The initial SMB level was set at 1.84% of totalC in the soil, which was calculated from themeasured SMB-N multiplied by a C:N ratio of 6.7(Jensen et al., 1997; Mueller et al., 1997). Simula-tions were then run for each material. The sizeand C:N ratio of AOM-2 was initially set as thewater-soluble fraction of the AOM pool. Therelative sizes of the AOM-1 and AOM-2 poolswere then modified in order to obtain the best fit.In all treatments, modelled C mineralisation rateswere higher than the measured values. Therefore,the AOM-0 pool was included. All other than thementioned parameters (decomposition rate andutilisation efficiency of AOM pools) were keptconstant in the simulation.

A relative measure of the difference between themodel output and measured values was calculated

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by the root mean square error (RMSE) (Loagueand Green, 1991). The optimal case where themodel fits the measured value exactly yields thevalue 0.

RMSE=�1

N�N

i=1

(pi−mi)2n0.5100m

(1)

Where m is the mean of the measured values atthe i-th sampling date, p is the correspondingpredicted (simulated) value and N is the numberof measurement dates. Three model scenarioswere compared: the model configuration pre-sented in this paper (Neergaard scenario); theconfiguration presented by (Mueller et al., 1997)(Mueller scenario); and the Neergard scenariowithout allocating any of the AOM to the AOM-0 pool (AOM-0 scenario).

3. Results

3.1. Plant material

Properties of the incubated plant material areshown in Table 2. The total ash content of bothroots and shoots was large, due to the adhesion of

soil particles. The acid insoluble ash content de-termined after extraction of acid detergent solu-bles and cellulose was lower than the total ashcontent, since some minerals were lost during theprevious extractions.

3.2. Carbon mineralisation

Carbon mineralisation rates varied greatly be-tween materials (Fig. 2a). Generally, the rootmaterial was mineralised slower than the shootmaterial of either plant species, which exhibitedtwo distinct patterns of carbon mineralisation(Fig. 2b). The clover roots and shoots had thehighest mineralisation rates at the beginning ofthe experiment, but after 30–40 days decomposi-tion was much slower and essentially stopped forclover root material which released less than 1%of its added C from day 43 to 94 (Fig. 2b). Grassmaterial had a slower initial mineralisation butthe rate did not decrease as fast as for the clover.

Initial CO2 evolution was strongly correlatedwith the water-soluble mass, C, N and C:N ratioof the added material, this correlation droppedsharply between day 22 and 29 (Fig. 3). The lignincontent was initially poorly correlated with C

Table 2Properties of the plant material used in the incubation experiment

Grass shoots Clover rootsClover shoots Clover–grassGrass rootsmixture a

1.16Nitrogen (%) 1.00 2.30 2.26 1.5030.7 34.8Carbon (%) 38.729.934.226.4 34.1C:N ratio 13.0 17.1 22.8

28.9Total ash b (%) 26.2 30.2 38.8 20.7

53.3 54.8 55.3Acid detergent soluble (%) 48.453.8Lignin (%) 9.76.2 14.5 7.5 18.5Cellulose (%) 18.215.712.516.617.8

8.03.3 6.514.55.2Lignin:N ratio11.022.2 19.320.5 26.7Acid insoluble ash c (%)

4.0 9.7 12.1Acid soluble ash (%) 9.7 9.6

13.1 10.0Water soluble (% of ash b-free 19.621.441.3weight)

C:N ratio of water soluble 12.914.2 33.7 14.8 18.3

a Properties were measured on the clover–grass mixture, except for % water-soluble and C:N of water-soluble which werecalculated from those measured in the components.

b Ash content determined by combustion of plant samples.c Ash content determined after extraction of acid detergent soluble and cellulose.

A. de Neergaard et al. / Europ. J. Agronomy 16 (2002) 43–55 49

Fig. 2. (a) Soil respiration rates from all treatments during 94days of incubation. Values are the mean of six replicates(n=6) until day 29, thereafter the mean of three replicates(n=3) �S.E. (bars). (b) Accumulated soil respiration, calcu-lated as difference between sample and control soil and ex-pressed as% of carbon added to the soil on day 0.

Net N mineralisation was linearly related to theash-free N content (r=0.92), water-soluble mass(r=0.99) and water-soluble N (r=0.96) (Fig. 4).The correlation coefficient for C:N ratio was alsohigh (r�0.91).

The soil microbial biomass was essentially unaf-fected by addition of plant material. The valuesfor the control soil were not significantly lowerthan the amended treatments, but were among thethree lowest values at all sampling dates. Therewere no changes in size of the SMB during theincubation (Fig. 5).

3.4. DAISY simulation

Modified parameters for the simulation areshown in Table 3. The size and C:N ratio of theAOM-1 pool was initially set as those of thewater-insoluble fraction, which underestimatedthe initial C mineralisation for all materials (datanot shown). The AOM-2 pool was thus increaseduntil the shape of the mineralisation curve forsimulated data matched the measured values. Alltreatments overestimated the accumulated carbonmineralisation at this point. The AOM-0 pool wasthen increased to fit the measured data. C:N ratioof AOM-0 was assumed to be the same as theplant material as a whole. The C:N ratio ofAOM-1 was not changed even though the size ofAOM-1 was reduced. Differences in C:N ratio of

mineralisation rates, but had the highest correla-tion of the evaluated parameters at the end of theexperiment. Several other parameters (e.g. C:Nratio, lignin:N ratio) were evaluated, and showedpoor correlation with the mineralisation rates.

3.3. Nitrogen mineralisation and soil microbialbiomass

Mineral N consisted almost solely of NO3−; the

NH4+ content decreased from �20% of inorganic

N to �5% within the first weeks of incubation(data not shown). All treatments showed a steadyrise in mineral N, albeit of different magnitudes,during the course of the experiment (Table 3).However, since the control soil had a larger in-crease than the two grass treatments, these com-ponents apparently caused N immobilisation.

Fig. 3. Degree of linear correlation (Pearsons coefficient) be-tween carbon mineralisation rates and initial quality parame-ters of plant as a function of days after the start of theincubation.

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Table 3Extractable mineral N (0.5M K2SO4), in mg g−1 soil at different sampling days

Grass roots Clover shoots Clover rootsGrass shoots Clover–grass mixtureDays Control soil

14.3c 16.2ab 17.1a 15.4bc0 14.3c15.4bc14.1c 17.9a 18.3a14.7bc 16.1b4 13.9c15.6bc 20.7a 19.5a16 17.2b14.5c 14.4c14.3d 22.0a 21.5a15.9c 18.5b29 15.2cd17.7c 32.7a 25.8b 21.6c 21.2c94 18.7c

In rows, values marked with the same letter are not significantly different.

the water-soluble and insoluble fraction weresmall for all materials, leading to only minorpotential errors in the estimate of the C:N ratio ofAOM-1 and AOM-2.

After modification of the model set-up, simu-lated values for carbon mineralisation were ingood agreement with those measured for the grasstreatments (Fig. 6a). For the clover material, themodel tended to overestimate C mineralisation inthe latter part of the incubation. The lag phase ofthe first few days cannot be simulated by DAISYin its present form; therefore a characteristic pat-tern of initial overestimation followed by underes-timation of the carbon mineralisation was seen inall treatments.

There was a tendency in the model to underesti-mate mineral N at the beginning of the incubation(Fig. 6b). At the end of the experiment, mineralnitrogen was overestimated in three of the fivetreatments with plant material. Using the Muelleret al. (1997) parameterisation gave an even moresevere overestimation, due to the lower utilisationefficiency of AOM-1. The model simulated arapid initial increase in SMB followed by a de-cline or levelling. The measured values showed aless fluctuating development of the SMB duringthe incubation (Fig. 6c).

Calculations of the RMSE showed that thesimulation of the soil respiration rate and accu-mulated soil respiration was much improved bythe new parameterisation for the grass materials,but less so for the clover (Table 4). There weresmaller differences between the model scenariosfor the simulation of mineral nitrogen, which wassimulated best by the Mueller scenario, and thesoil microbial biomass. For all measured parame-ters and treatments the inclusion of the AOM-0pool increased the fit of the model.

The size of the AOM-0 pool was linearlycorrelated with the lignin content of the plantmaterials (r2=0.99); there was also a good corre-lation between the AOM-2 pool and the water-soluble fraction of the plant materials (r2=0.84)(Fig. 7).

4. Discussion

4.1. Carbon mineralisation

Decomposition of native organic material wasincreased at the start of the experiment, as indi-cated by the increased CO2 evolution from thecontrol soil. The estimated size of the pool, 325 �gg−1 soil, corresponding to 2.4% of the SOM, mayappear large. However, no other studies areknown which contradict this value. The introduc-

Fig. 4. Degree of linear correlation (Pearsons coefficient) be-tween net mineralised nitrogen and initial quality parametersof plant material as a function of days after the start of theincubation.

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Fig. 5. Soil microbial biomass nitrogen in all treatmentsmeasured six times during 94 days of incubation. Values arethe mean of three replicates (n=3) �S.E. (bars).

sharply, indicating that the water-soluble sub-stances were no longer affecting C mineralisation.According to the DAISY simulation, the AOM-2pool was exhausted slightly later, between day 30and 40, suggesting that it also included part of thenon-soluble material.

Although the size of the AOM-2 pool was notinvariably larger than the water-soluble fraction(Table 1), there was a good correlation betweenthe measured water solubility and the AOM-2

Fig. 6. Simulated time course of soil respiration (a), mineral N(b) and soil microbial biomass-N (c) in the modified DAISYsimulation (lines) and the corresponding measured values(dots) for the control soil, grass shoot and clover shoottreatments.

tion of this pool was preferred in favour onchanging the decomposition rate of the SOM,which has been validated with independent data.The recent history of the soil is important for thesize of this pool. As mentioned, a clover–grass leywas incorporated in the soil 1 year before sam-pling. By comparing the Neergaard and theMueller scenario of the control soil in Table 4 theinfluence of this pool on the quality of the simula-tion can be seen.

The CO2 evolution showed two different pat-terns of C mineralisation for grass and clover. Italso clearly showed a difference in decomposabil-ity of shoots and roots. Clover contained morewater-soluble components than the grass material.Root material, containing casparian strips,suberin, lignified tissues and other structural com-ponents, provides a more recalcitrant materialthan shoots. These structures may also physicallyprotect decomposable compounds embeddedwithin them, further decreasing decomposability(Chesson, 1997).

Initial CO2 evolution was strongly correlatedwith the water-soluble mass, C, N and C:N ratioof the added material (Fig. 3). As indicated byJanzen and Kucey (1988), inter-correlation amongthese factors is also large (correlation coefficient;0.81–0.99), and some of the high correlation val-ues may be due to covariation. Between day 22and 29, correlation between the CO2 evolutionrate and the water-soluble fractions dropped

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Table 4Root mean square error (RMSE) of the five treatments andcontrol soil under three different scenarios of the DAISYmodel

Scenario

MuellerNeergaard AOM-0

Soil respiration rate5417 24Grass shoot

24Grass root 155 4843Clover shoot 51433536 103Clover root

20Clover grass 64 35Control 19 93 –

Accumulated soil respirationGrass shoot 819 20

187 54Grass root 213333 48Clover shoot

27Clover root 30 11322Clover grass 102 45

1125 –Control

Mineral nitrogenGrass shoot 812 14

88 11Grass root17 31Clover shoot 281528 48Clover root

20Clover grass 17 24Control 98 –

Soil microbial biomassGrass shoot 4914 15

34 8Grass root 62921 26Clover shoot

20Clover root 29 3441Clover grass 161318 –3Control

The Neergaard scenario is the parameterisation presented inthis paper, the Mueller scenario is the parameterisation pre-sented by Mueller et al. (1997). The AOM-0 scenario uses theNeergaard parameterisation, with the size of the AOM-0 poolset at 0. Optimum of the RMSE is 0.

microorganisms colonising the tissue. Parts ofthese complex molecules will be easily decompos-able (sugars, amino acids etc.) and can thus beperceived as part of the easily decomposableAOM-2 pool. Developing a method for determi-nation of the size of this fraction, by chemicalextraction or bioassay, may be a key step in thefurther development of ‘modelling the measur-able’. Confirmation of the correlation between theAOM-2 pool and the water-soluble fraction withadditional independent dataset will be needed be-fore it can be determined if the correlation is ageneral trait for plant residues.

The fit between measured and simulated valueswas better for the grass material than for theclover as shown by the RMSE values. The modelhad difficulties simulating the carbon mineralisa-tion of the clover, which almost stopped after therapid initial decomposition. In spite that theAOM-0 pool was set as twice the size of the ligninfraction, carbon mineralisation rates were stilloverestimated at the end of the experiment. Thesoil organic matter module of the DAISY modelhas mainly been validated with experiments usingmature plant litter, collected at harvest. Theclover material used in this study, sampled in latewinter, consisted of dead desiccated leaves androots; and live, frost tolerant tubers and rootscontaining storage compounds. It is possible thatthe DAISY parameterisation of plant material

Fig. 7. Correlation between AOM-0 pool of the DAISY modeland lignin content of the plant material, and between theAOM-2 pool and the water soluble fraction of the plantmaterials.

pool (Fig. 7). From the DAISY simulation, it wasclear that the easily decomposable fraction of theplant material, i.e. the AOM-2 pool, was largerthan the water-soluble part as also shown byMueller et al. (1998). Compounds such as poly-mers, which are not extracted immediately bywater, may be cleaved by extracellular enzymes inthe early phase of decomposition and be releasedinto the soil solution or absorbed directly by

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(three pools with varying C:N ratio) is too sim-plistic to include so different plant litters.

While the simulation for the clover materialwas poorer than that of grass (Table 4), thedecomposition of shoots and roots were simulatedequally well by the model, in spite that it hasprimarily been calibrated against data from stud-ies of above-ground material (Hansen et al., 1990;Mueller et al., 1997, 1998).

The sizes of the AOM-0 pools were propor-tional to the lignin content, suggesting a stronginfluence (Fig. 7). However, the AOM-0 pool wastwice the size of the lignin content, hence othercompounds must be contained in this fraction.Berg et al. (1982) and Chesson (1997) suggestedthat lignin could reduce decomposition of othercompounds by physically protecting the material.According to Chesson (1997), this will occur if thelignin content is above 15%. In this experiment,only clover roots had a lignin content that high.As mentioned, the plant material was collected inthe winter and consisted of partly dead andleached material, whereas most other studies haveused freshly harvested material. Apparently thelignin content alone cannot explain the low de-composability of the material in the currentexperiment.

4.2. Nitrogen mineralisation and soil microbialbiomass

Final net N mineralisation was linearly relatedto the ash-free N content (r=0.92), water-solublemass (r=0.99) and water-soluble N (r=0.96)(Fig. 7). The correlation coefficients for C:N ratioand water-soluble C were also high (r�0.91). Asnoted earlier, co-variation between the investi-gated quality parameters was very large. It istherefore not possible to identify the most influen-tial parameter on the basis of this dataset.

Numerous studies on SMB dynamics after in-corporation of OM in soil have shown an initialincrease in SMB followed by a slower decrease(Aoyama and Nozawa, 1993; Cerri and Jenkin-son, 1981; Jensen et al., 1997; Ladd et al., 1981;Ocio et al., 1991a,b). The lacking response of theSMB in this study is partly due to the lowamounts and relatively slow decomposition of the

amended material. Only a few other studies werefound not to measure an increased microbialbiomass after addition of organic substrates (Ap-pel et al., 1995; Joergensen et al., 1995). Of thesethe latter of these was carried out in an oil-con-taminated soil. Appel et al. (1995) measured noincrease in the microbial biomass before 28 daysafter incubation of the plant litter, in spite of afast initial mineralisation. No full explanation ofthe phenomena can be given on the basis of thesetwo datasets.

Simulation of the mineral N level was good,even though there was a significant overestimationon the last sampling date in four of the treat-ments. With the Mueller parameterisation, overes-timation was markedly higher, mostly due to thelower utilisation efficiency of the AOM-1 pool(0.12 vs. 0.4). Utilisation efficiency of the AOM-2pool was subsequently decreased from 0.69 to 0.5since initial immobilisation was overestimated.Overestimation of the mineral N at the end of theexperiment may be caused by the fact that in theDAISY model, all nitrogen from decaying SMBenters the mineral N pool. As suggested byMueller et al. (1997, 1998), a pool consisting ofmicrobial residuals (SMR), could act as a bufferbetween the SMB and the mineral pool. Themicrobial residuals could consist of empty hy-phae, cell walls and other recalcitrant substrates.A build up of this pool during the incubationcould explain the ‘missing N’. At present, it is notpossible to measure the size of this pool.

Certain other shortcomings of the DAISYmodel may explain the deviation between theobserved and measured values. The DAISYmodel operates with a constant C:N ratio, andhence quality, in all pools. Magid et al. (1997)clearly showed that this was not the case for theAOM. The C:N ratio of the SMB pool may alsovary with changes in the decomposer community(Bremer and van Kessel, 1992). Likewise, theproportion of extractable components maychange with the degree of fungal dominance, thusaffecting the kEN value.

The modification of the DAISY model parame-ters increased the models ability to simulate themeasured data. The inclusion of the AOM-0 poolalso significantly improved the simulation for all

A. de Neergaard et al. / Europ. J. Agronomy 16 (2002) 43–5554

treatments and all measured parameters. How-ever, the suggested parameterisation has not beenvalidated until tested against an independent setof experimental data. Together with the studies byMueller et al. (1997, 1998), the repeated testing ofthe model aims to improve its parameterisationand ability to simulate short term dynamics of soilorganic matter.

Acknowledgements

We thank Dr Torsten Mueller for his help andadvice with the DAISY modeling. This study waspartly funded by the Research Center for OrganicFarming (The Danish Environmental ResearchProgram) and by a grant from Anna and TageMellers Memorial Foundation.

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