European Journal of Agronomy 16 (2002) 43 – 55 www.elsevier.com/locate/eja
Decomposition of white clover (Trifolium repens) and ryegrass (Lolium perenne) components: C and N dynamics simulated 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 Uni6ersity, Thor6aldsens6ej 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 module of the DAISY model, and link measurable plant litter fractions (lignin, water-soluble) with the model defined plant litter pools. Shoot and root material from perennial ryegrass and white clover was incubated in a sandy loam soil at 9 °C for 94 days. Accumulated CO2 evolution, soil mineral nitrogen (N) and soil microbial biomass-N were measured during the incubation. Marked differences in decomposition rates between above- and below-ground material as well as 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 were independent of the type of material, to obtain good agreement between observed and simulated values. Measurable quality parameters were evaluated against the sizes of pools in the model and measured fluxes. The size of the slowest decomposing fraction of the DAISY model was proportional to the lignin content of the plant material, but twice as large. The easily decomposable fraction in the model was well correlated with the water-soluble fraction of the plant material (r 2 =0.84). The size of this pool in the model was larger than the water-soluble fraction of the plant material in three of the five plant materials. The initial carbon mineralisation was correlated with water-solubility of the plant material and total mineralisation with the lignin:N ratio. Net N mineralisation was well correlated with the C:N ratio and the N content of the added material. At the end of the experiment, the mineral N content was overestimated by the DAISY model for all treatments, except one. A soil microbial residual pool, consisting of undecomposed microbial tissue is suggested as a possible N-sink during the incubation. The study demonstrated a correlation between the model-defined pools and chemical plant fractions, but also that the pools in the model were larger than their measured counterparts. © 2002 Elsevier Science B.V. All rights reserved. Keywords: DAISY; Litter quality; C and N mineralisation; Ryegrass; White clover
* 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. PII: S 1 1 6 1 - 0 3 0 1 ( 0 1 ) 0 0 1 1 8 - 6
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1. Introduction The importance of white clover– ryegrass (Trifolium repens–Lolium perenne) leys in many temperate cropping systems is due to three valuable characteristics. First, clover– grass leys produce high quality feed for livestock, either fresh or as silage. Dry matter yields may exceed 10 t ha − 1 year − 1 dry matter in unfertilised pastures where 95% 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 et al., 1998; Laidlaw et al., 1996), provide a N rich residue, which, when mineralised, may significantly reduce fertiliser requirements for the succeeding crop. Finally, soil structure is improved during the pasture period (Tisdall and Oades, 1979). In order to fully predict the fertilising potential of residues, insight into the decomposition characteristics is needed. Mineralisation of N and other nutrients must match the demands of the following crop in order to maximise yields and avoid subsequent leaching. In this study, measurable quality parameters of the plant material were compared with the course of decomposition in order to find parameters determining litter quality. The DAISY model (Hansen et al., 1990, 1991) was developed to simulate carbon (C) and N dynamics in agricultural soils, plants and the atmosphere. Most pools and fluxes in the model have distinct counterparts in nature (Mueller et al., 1997, 1998), even though determination of their respective quantities may be difficult. The parameterisation of the soil organic matter module of the DAISY model has originally been validated with long-term field studies. Two papers by Mueller et al. (1997, 1998) have focused on short term dynamics of the soil organic matter, demonstrated certain limitations of the model, and suggested modifications of the parameterisation. This study attempted to test and possibly improve the parameterisation, using data from a decomposition study of shoots and roots of two different plant species. Further development of our understanding of the principal determining
factors of litter decomposition relies on linking experimentally measurable fractions of the plant material (i.e. lignin content) with model defined pools. This linkage was investigated in the study.
2. Materials and methods
2.1. Soil and plant materials The soil used in the incubation was collected frozen in early February 1996 from the plough layer of an arable field at the experimental farm of the Royal Veterinary and Agricultural University, Denmark. It was a sandy loam, containing 21% clay, 21% silt, 41% fine sand and 19% coarse sand. Its total C content was 1.29%, total N content 0.11%, pH (0.01 M CaCl2) 6.6, waterholding capacity 29% (w/w, dry basis). The soil was sieved through a 5-mm mesh and pre-incubated for 19 days at 10 °C. The plants used in the experiment were sampled from 3rd year grass and clover leys (Lolium perenne and Trifolium repens), which had received 100 kg N ha − 1 year − 1. The plant material was collected in early February when the soil was frozen. Above-ground material was isolated by hand. Soil samples including roots were taken to a depth of 25 cm. Then, in the laboratory, they were dispersed in a 5% NaCl solution, floating material was collected and washed gently over a 500-mm sieve, and then dried at 60 °C. The plant material was cut into pieces, 2–3 cm of length, prior to incubation. Ball-milled plant samples were analysed for their C and N content using an elemental analyser (Carlo Erba, EA 1108). The lignin and cellulose content was determined according to van Soest (1963). Determination of the water-soluble fraction of the amended material was performed as follows: plant samples were rotary-shaken in deionised water (1 mg sample ml − 1), at 15 °C for 45 min. Samples were centrifuged and the supernatant removed. The pellet was dried at 65 °C and re-weighed. The water-soluble fractions were calculated as the difference between the C and N content of washed and unwashed plant material.
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2.2. Incubation procedure
2.4. Modelling
The incubation experiment involved six treatments, (1) grass shoot, (2) grass root, (3) clover shoot, (4) clover root, (5) clover– grass mixture, and (6) control soil, with no added plant material. The clover–grass mixture consisted of 44.4% grass shoots, 22.2% grass roots, 22.2% clover shoots and 11.1% clover roots on a dry-weight basis. At the start of the experiment (day 0), 3-litre storage jars were filled with 1200 g (dry weight) of soil with a water content of 14.5%, and 2.4 g dry plant material was added and mixed thoroughly with the soil. Each jar was equipped with a beaker containing water to maintain a water-saturated atmosphere during the experiment. The jars were stored closed at 9 °C in the darkness, and collected for analyses at day 0, 4, 16, 29 and 94.
The soil–plant–atmosphere model DAISY was used to simulate the experiment. Measured values were used to evaluate the current parameterisation of the model. DAISY is a deterministic model that simulates C and N fluxes in a one dimensional soil–plant– atmosphere system (Hansen et al., 1990, 1991). The model includes several sub-models for: (1) soil water movement; (2) soil temperature; (3) soil organic matter dynamics; (4) soil mineral nitrogen; (5) crop growth; and (6) system management. Due to the simplicity of the experimental set-up (no water movement, no crops, no soil handling and one homogeneous soil layer), we used a simplified version of the model, including only the soil organic matter and the mineral N sub-models. The model operates with three organic matter pools: added organic matter (AOM), soil microbial biomass (SMB), and non-living native soil organic matter (SOM) (Fig. 1). These pools are conceived as a continuum with different decomposition rates. For simplicity, the model divides AOM, SMB and SOM in two pools each, one with a faster turnover (e.g. AOM-2), and one with a slower turnover (e.g. AOM-1). The rate of decomposition of each pool is assumed to follow first-order kinetics. An AOM-0 pool is included as a very recalcitrant fraction of the added plant material, and enters directly into the SOM-2 pool (Fig. 1). Turnover rate coefficients of each pool are defined at standard conditions (10 °C, − 10 kPa, 0% clay). The coefficients are adjusted to experimental conditions by multiplication with modifiers for actual soil temperature, soil water potential and clay content. Decay rates of the SOM-1 and SOM-2, as well as death rates and maintenance respiration of SMB-1 are multiplied by all three modifiers (temperature, soil water potential and clay content). Turnover rates of SMB-2, AOM-1 and AOM-2 are only modified by the temperature and soil water potential. In the current experiment, the value of the temperature, soil water potential and clay modifiers were 0.9; 1 and 0.6 respectively.
2.3. Analysis Release of CO2 was measured with decreasing frequency, ranging from daily to every other week. Carbon mineralisation was measured by passive alkali trapping of the respired CO2. Mineral N and soil microbial biomass-N were measured five times during the incubation. Soil samples were extracted with 0.5 M K2SO4 (soil:solute ratio; 1:4 w/v) and filtered. Extracts were frozen immediately and stored at − 20 °C until analysed. NH4 – N was determined spectrophotometrically, NO3 – N and NO2 – N was measured by flow injection analysis (Keeney and Nelson, 1982). Soil microbial biomass (SMB)-N was determined via the chloroform fumigation-extraction method (Brookes et al., 1985; Vance et al., 1987). Soil samples were fumigated with chloroform for 24 h at 25 °C, and then extracted as described above for mineral N. The total N content of both the fumigated and unfumigated extracts was then measured by persulphate oxidation, modified after Cabrera and Beare (1993). Extractable SMB-N was calculated as the difference between fumigated and unfumigated samples and converted to total SMB-N using the correction factor kEN =0.54 determined by Joergensen and Mueller (1996).
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Each carbon pool has an utilisation rate, this fraction is incorporated as SMB-tissue when decomposed, 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 by the C:N ratio of the pool. Net mineralisation and immobilisation of N was calculated from the total balance of the N pools. For a further description of the DAISY model, see Hansen et al. (1991), Mueller et al. (1997).
2.5. Model set-up and parameterisation The parameterisation described by Mueller et al. (1997) for a sandy loam soil was initially used for the simulation. These parameters were derived
from experiments using a soil similar to ours. The initial size of the SOM was based on the total soil carbon content of the soil. Parameters were first modified to fit the measured values for the control soil. To simulate the increased turnover of OM during the incubation, we found that 325 mg C g − 1 soil (2.4% of total C) should be transferred from SOM-1 to AOM in the model at day 0. This pool can be visualised as physically protected OM made accessible to microorganisms when the soil aggregates are disrupted. This initial amendment was chosen in preference to increasing SOM decomposition rates and it is justified by the fact that 1 year before the soil sampling, a clover– grass ley was incorporated into the soil. Residual organic matter from this incorporation could still be 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 in the soil organic matter submodel of DAISY. AOM: added organic matter; SMB: soil microbial biomass; SOM: native soil organic matter; fX : partitioning coefficients (Hansen et al., 1990, 1991)
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Table 1 DAISY parameters used in the standard setup, as modified by Mueller et al. (1997) and in this experiment. For all other parameters than the listed, the values were the same as used by Mueller et al. (1997) Standard setup
% of Added Organic Matter (AOM) AOM-0 – AOM-1 – AOM-2 – Water soluble (%) – C:N ratio of total AOM C:N ratio of – AOM-0 C:N ratio of – AOM-1 C:N ratio of – AOM-2
Mueller setup
Control soil
Grass
Clover
Clover–grass mixture
Shoots
Roots
Shoots
Roots
– – – –
0 90 10 – 20
13 67 20 13 23
29 66 5 10 27
15 30 55 41 15
37 25 38 21 18
20 62 18 20 21
–
–
23
27
15
18
21
–
20
25
27
16
19
22
–
20
19
26
15
18
17
Decomposition rate AOM-0 (SOM-2) AOM-1 AOM-2
(day−1) 0.00014 0.005 0.05
0.00014 0.012 0.05
0.00014 0.005 0.06
0.00014 0.005 0.06
0.00014 0.005 0.06
0.00014 0.005 0.06
0.00014 0.005 0.06
0.00014 0.005 0.06
Substrate utilisation AOM-0 (SOM-2) AOM-1 AOM-2
efficiency 0.6 0.6 0.6
0.5 0.13 0.69
0.5 0.4 0.5
0.5 0.4 0.5
0.5 0.4 0.5
0.5 0.4 0.5
0.5 0.4 0.5
0.5 0.4 0.5
later. The C:N of this pool was set at 20, according to Magid et al. (1997), who showed that labile OM approached this value when decomposing. AOM-1 was assumed to account for 90% of the total AOM. As a consequence of the transfer to the AOM pool, the C:N ratio of the total AOM listed in Table 1, differs slightly from the measured values of the plant material, listed in Table 2. The decomposition rate of the AOM-1 was reduced from 0.012 day − 1 to the value used in the standard setup: 0.005 day − 1. The decomposition rate of AOM-2 was increased from 0.05 to 0.06 day − 1 and the utilisation efficiencies of AOM-1 and AOM-2 were changed from 0.12 and 0.69 to 0.4 and 0.5, respectively, in order to reduce the mineralised N late in the incubation (Table 1).
The initial SMB level was set at 1.84% of total C in the soil, which was calculated from the measured SMB-N multiplied by a C:N ratio of 6.7 (Jensen et al., 1997; Mueller et al., 1997). Simulations were then run for each material. The size and C:N ratio of AOM-2 was initially set as the water-soluble fraction of the AOM pool. The relative sizes of the AOM-1 and AOM-2 pools were then modified in order to obtain the best fit. In all treatments, modelled C mineralisation rates were higher than the measured values. Therefore, the AOM-0 pool was included. All other than the mentioned parameters (decomposition rate and utilisation efficiency of AOM pools) were kept constant in the simulation. A relative measure of the difference between the model output and measured values was calculated
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by the root mean square error (RMSE) (Loague and Green, 1991). The optimal case where the model fits the measured value exactly yields the value 0. RMSE =
1 N % (pi − m ¯ i )2 Ni = 1
n
0.5
100 m ¯
soil particles. The acid insoluble ash content determined after extraction of acid detergent solubles and cellulose was lower than the total ash content, since some minerals were lost during the previous extractions.
(1)
3.2. Carbon mineralisation
Where m is the mean of the measured values at the i-th sampling date, p is the corresponding predicted (simulated) value and N is the number of measurement dates. Three model scenarios were compared: the model configuration presented in this paper (Neergaard scenario); the configuration presented by (Mueller et al., 1997) (Mueller scenario); and the Neergard scenario without allocating any of the AOM to the AOM0 pool (AOM-0 scenario).
3. Results
3.1. Plant material Properties of the incubated plant material are shown in Table 2. The total ash content of both roots and shoots was large, due to the adhesion of
Carbon mineralisation rates varied greatly between materials (Fig. 2a). Generally, the root material was mineralised slower than the shoot material of either plant species, which exhibited two distinct patterns of carbon mineralisation (Fig. 2b). The clover roots and shoots had the highest mineralisation rates at the beginning of the experiment, but after 30–40 days decomposition was much slower and essentially stopped for clover root material which released less than 1% of its added C from day 43 to 94 (Fig. 2b). Grass material had a slower initial mineralisation but the rate did not decrease as fast as for the clover. Initial CO2 evolution was strongly correlated with the water-soluble mass, C, N and C:N ratio of the added material, this correlation dropped sharply between day 22 and 29 (Fig. 3). The lignin content was initially poorly correlated with C
Table 2 Properties of the plant material used in the incubation experiment Grass shoots
Grass roots
Clover shoots
Clover roots
Clover–grass mixture a
Nitrogen (%) Carbon (%) C:N ratio Total ash b (%)
1.16 30.7 26.4 26.2
1.00 34.2 34.1 30.2
2.30 29.9 13.0 38.8
2.26 38.7 17.1 20.7
1.50 34.8 22.8 28.9
Acid detergent soluble (%) Lignin (%) Cellulose (%) Lignin:N ratio Acid insoluble ash c (%) Acid soluble ash (%)
53.8 6.2 17.8 5.2 22.2 4.0
48.4 14.5 16.6 14.5 20.5 9.7
53.3 7.5 12.5 3.3 26.7 12.1
54.8 18.5 15.7 8.0 11.0 9.7
55.3 9.7 18.2 6.5 19.3 9.6
Water soluble (% of ash b-free weight) C:N ratio of water soluble
13.1
10.0
41.3
21.4
19.6
14.2
33.7
12.9
14.8
18.3
a Properties were measured on the clover–grass mixture, except for % water-soluble and C:N of water-soluble which were calculated 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.
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Net N mineralisation was linearly related to the ash-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 also high (r\ 0.91). The soil microbial biomass was essentially unaffected by addition of plant material. The values for the control soil were not significantly lower than the amended treatments, but were among the three lowest values at all sampling dates. There were no changes in size of the SMB during the incubation (Fig. 5).
3.4. DAISY simulation
Fig. 2. (a) Soil respiration rates from all treatments during 94 days of incubation. Values are the mean of six replicates (n= 6) until day 29, thereafter the mean of three replicates (n= 3) 9 S.E. (bars). (b) Accumulated soil respiration, calculated as difference between sample and control soil and expressed as% of carbon added to the soil on day 0.
Modified parameters for the simulation are shown in Table 3. The size and C:N ratio of the AOM-1 pool was initially set as those of the water-insoluble fraction, which underestimated the initial C mineralisation for all materials (data not shown). The AOM-2 pool was thus increased until the shape of the mineralisation curve for simulated data matched the measured values. All treatments overestimated the accumulated carbon mineralisation at this point. The AOM-0 pool was then increased to fit the measured data. C:N ratio of AOM-0 was assumed to be the same as the plant material as a whole. The C:N ratio of AOM-1 was not changed even though the size of AOM-1 was reduced. Differences in C:N ratio of
mineralisation rates, but had the highest correlation of the evaluated parameters at the end of the experiment. Several other parameters (e.g. C:N ratio, lignin:N ratio) were evaluated, and showed poor correlation with the mineralisation rates.
3.3. Nitrogen mineralisation and soil microbial biomass Mineral N consisted almost solely of NO− 3 ; the content decreased from B 20% of inorganic NH+ 4 N to B5% within the first weeks of incubation (data not shown). All treatments showed a steady rise in mineral N, albeit of different magnitudes, during the course of the experiment (Table 3). However, since the control soil had a larger increase than the two grass treatments, these components apparently caused N immobilisation.
Fig. 3. Degree of linear correlation (Pearsons coefficient) between carbon mineralisation rates and initial quality parameters of plant as a function of days after the start of the incubation.
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Table 3 Extractable mineral N (0.5M K2SO4), in mg g−1 soil at different sampling days Days
Grass shoots
Grass roots
Clover shoots
Clover roots
Clover–grass mixture
Control soil
0 4 16 29 94
15.4bc 14.7bc 14.5c 15.9c 18.7c
14.3c 14.1c 15.6bc 14.3d 17.7c
16.2ab 17.9a 20.7a 22.0a 32.7a
17.1a 18.3a 19.5a 21.5a 25.8b
15.4bc 16.1b 17.2b 18.5b 21.6c
14.3c 13.9c 14.4c 15.2cd 21.2c
In rows, values marked with the same letter are not significantly different.
the water-soluble and insoluble fraction were small for all materials, leading to only minor potential errors in the estimate of the C:N ratio of AOM-1 and AOM-2. After modification of the model set-up, simulated values for carbon mineralisation were in good agreement with those measured for the grass treatments (Fig. 6a). For the clover material, the model tended to overestimate C mineralisation in the latter part of the incubation. The lag phase of the first few days cannot be simulated by DAISY in its present form; therefore a characteristic pattern of initial overestimation followed by underestimation of the carbon mineralisation was seen in all treatments. There was a tendency in the model to underestimate mineral N at the beginning of the incubation (Fig. 6b). At the end of the experiment, mineral nitrogen was overestimated in three of the five treatments with plant material. Using the Mueller et al. (1997) parameterisation gave an even more severe overestimation, due to the lower utilisation efficiency of AOM-1. The model simulated a rapid initial increase in SMB followed by a decline or levelling. The measured values showed a less fluctuating development of the SMB during the incubation (Fig. 6c). Calculations of the RMSE showed that the simulation of the soil respiration rate and accumulated soil respiration was much improved by the new parameterisation for the grass materials, but less so for the clover (Table 4). There were smaller differences between the model scenarios for the simulation of mineral nitrogen, which was simulated best by the Mueller scenario, and the soil microbial biomass. For all measured parameters and treatments the inclusion of the AOM-0 pool increased the fit of the model.
The size of the AOM-0 pool was linearly correlated with the lignin content of the plant materials (r 2 = 0.99); there was also a good correlation between the AOM-2 pool and the watersoluble fraction of the plant materials (r 2 = 0.84) (Fig. 7).
4. Discussion
4.1. Carbon mineralisation Decomposition of native organic material was increased at the start of the experiment, as indicated by the increased CO2 evolution from the control soil. The estimated size of the pool, 325 mg g − 1 soil, corresponding to 2.4% of the SOM, may appear large. However, no other studies are known which contradict this value. The introduc-
Fig. 4. Degree of linear correlation (Pearsons coefficient) between net mineralised nitrogen and initial quality parameters of plant material as a function of days after the start of the incubation.
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sharply, indicating that the water-soluble substances were no longer affecting C mineralisation. According to the DAISY simulation, the AOM-2 pool was exhausted slightly later, between day 30 and 40, suggesting that it also included part of the non-soluble material. Although the size of the AOM-2 pool was not invariably larger than the water-soluble fraction (Table 1), there was a good correlation between the measured water solubility and the AOM-2
Fig. 5. Soil microbial biomass nitrogen in all treatments measured six times during 94 days of incubation. Values are the mean of three replicates (n= 3) 9S.E. (bars).
tion of this pool was preferred in favour on changing the decomposition rate of the SOM, which has been validated with independent data. The recent history of the soil is important for the size of this pool. As mentioned, a clover– grass ley was incorporated in the soil 1 year before sampling. By comparing the Neergaard and the Mueller scenario of the control soil in Table 4 the influence of this pool on the quality of the simulation can be seen. The CO2 evolution showed two different patterns of C mineralisation for grass and clover. It also clearly showed a difference in decomposability of shoots and roots. Clover contained more water-soluble components than the grass material. Root material, containing casparian strips, suberin, lignified tissues and other structural components, provides a more recalcitrant material than shoots. These structures may also physically protect decomposable compounds embedded within them, further decreasing decomposability (Chesson, 1997). Initial CO2 evolution was strongly correlated with the water-soluble mass, C, N and C:N ratio of the added material (Fig. 3). As indicated by Janzen and Kucey (1988), inter-correlation among these factors is also large (correlation coefficient; 0.81 – 0.99), and some of the high correlation values may be due to covariation. Between day 22 and 29, correlation between the CO2 evolution rate and the water-soluble fractions dropped
Fig. 6. Simulated time course of soil respiration (a), mineral N (b) and soil microbial biomass-N (c) in the modified DAISY simulation (lines) and the corresponding measured values (dots) for the control soil, grass shoot and clover shoot treatments.
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Table 4 Root mean square error (RMSE) of the five treatments and control soil under three different scenarios of the DAISY model Scenario Neergaard
Mueller
AOM-0
Soil respiration rate Grass shoot 17 Grass root 24 Clover shoot 43 Clover root 36 Clover grass 20 Control 19
54 155 43 35 64 93
24 48 51 103 35 –
Accumulated soil respiration Grass shoot 9 Grass root 21 Clover shoot 33 Clover root 27 Clover grass 22 Control 5
81 187 33 30 102 112
20 54 48 113 45 –
Mineral nitrogen Grass shoot Grass root Clover shoot Clover root Clover grass Control
12 8 28 28 20 8
8 8 17 15 17 9
14 11 31 48 24 –
Soil microbial biomass Grass shoot 14 Grass root 6 Clover shoot 21 Clover root 20 Clover grass 13 Control 3
49 34 29 29 41 18
15 8 26 34 16 –
microorganisms colonising the tissue. Parts of these complex molecules will be easily decomposable (sugars, amino acids etc.) and can thus be perceived as part of the easily decomposable AOM-2 pool. Developing a method for determination of the size of this fraction, by chemical extraction or bioassay, may be a key step in the further development of ‘modelling the measurable’. Confirmation of the correlation between the AOM-2 pool and the water-soluble fraction with additional independent dataset will be needed before it can be determined if the correlation is a general trait for plant residues. The fit between measured and simulated values was better for the grass material than for the clover as shown by the RMSE values. The model had difficulties simulating the carbon mineralisation of the clover, which almost stopped after the rapid initial decomposition. In spite that the AOM-0 pool was set as twice the size of the lignin fraction, carbon mineralisation rates were still overestimated at the end of the experiment. The soil organic matter module of the DAISY model has mainly been validated with experiments using mature plant litter, collected at harvest. The clover material used in this study, sampled in late winter, consisted of dead desiccated leaves and roots; and live, frost tolerant tubers and roots containing storage compounds. It is possible that the DAISY parameterisation of plant material
The Neergaard scenario is the parameterisation presented in this paper, the Mueller scenario is the parameterisation presented by Mueller et al. (1997). The AOM-0 scenario uses the Neergaard parameterisation, with the size of the AOM-0 pool set at 0. Optimum of the RMSE is 0.
pool (Fig. 7). From the DAISY simulation, it was clear that the easily decomposable fraction of the plant material, i.e. the AOM-2 pool, was larger than the water-soluble part as also shown by Mueller et al. (1998). Compounds such as polymers, which are not extracted immediately by water, may be cleaved by extracellular enzymes in the early phase of decomposition and be released into the soil solution or absorbed directly by
Fig. 7. Correlation between AOM-0 pool of the DAISY model and lignin content of the plant material, and between the AOM-2 pool and the water soluble fraction of the plant materials.
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(three pools with varying C:N ratio) is too simplistic to include so different plant litters. While the simulation for the clover material was poorer than that of grass (Table 4), the decomposition of shoots and roots were simulated equally well by the model, in spite that it has primarily been calibrated against data from studies of above-ground material (Hansen et al., 1990; Mueller et al., 1997, 1998). The sizes of the AOM-0 pools were proportional to the lignin content, suggesting a strong influence (Fig. 7). However, the AOM-0 pool was twice the size of the lignin content, hence other compounds must be contained in this fraction. Berg et al. (1982) and Chesson (1997) suggested that lignin could reduce decomposition of other compounds by physically protecting the material. According to Chesson (1997), this will occur if the lignin content is above 15%. In this experiment, only clover roots had a lignin content that high. As mentioned, the plant material was collected in the winter and consisted of partly dead and leached material, whereas most other studies have used freshly harvested material. Apparently the lignin content alone cannot explain the low decomposability of the material in the current experiment.
4.2. Nitrogen mineralisation and soil microbial biomass Final net N mineralisation was linearly related to the ash-free N content (r =0.92), water-soluble mass (r= 0.99) and water-soluble N (r= 0.96) (Fig. 7). The correlation coefficients for C:N ratio and water-soluble C were also high (r\ 0.91). As noted earlier, co-variation between the investigated quality parameters was very large. It is therefore not possible to identify the most influential parameter on the basis of this dataset. Numerous studies on SMB dynamics after incorporation of OM in soil have shown an initial increase in SMB followed by a slower decrease (Aoyama and Nozawa, 1993; Cerri and Jenkinson, 1981; Jensen et al., 1997; Ladd et al., 1981; Ocio et al., 1991a,b). The lacking response of the SMB in this study is partly due to the low amounts and relatively slow decomposition of the
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amended material. Only a few other studies were found not to measure an increased microbial biomass after addition of organic substrates (Appel et al., 1995; Joergensen et al., 1995). Of these the latter of these was carried out in an oil-contaminated soil. Appel et al. (1995) measured no increase in the microbial biomass before 28 days after incubation of the plant litter, in spite of a fast initial mineralisation. No full explanation of the phenomena can be given on the basis of these two datasets. Simulation of the mineral N level was good, even though there was a significant overestimation on the last sampling date in four of the treatments. With the Mueller parameterisation, overestimation was markedly higher, mostly due to the lower utilisation efficiency of the AOM-1 pool (0.12 vs. 0.4). Utilisation efficiency of the AOM-2 pool was subsequently decreased from 0.69 to 0.5 since initial immobilisation was overestimated. Overestimation of the mineral N at the end of the experiment may be caused by the fact that in the DAISY model, all nitrogen from decaying SMB enters the mineral N pool. As suggested by Mueller et al. (1997, 1998), a pool consisting of microbial residuals (SMR), could act as a buffer between the SMB and the mineral pool. The microbial residuals could consist of empty hyphae, cell walls and other recalcitrant substrates. A build up of this pool during the incubation could explain the ‘missing N’. At present, it is not possible to measure the size of this pool. Certain other shortcomings of the DAISY model may explain the deviation between the observed and measured values. The DAISY model operates with a constant C:N ratio, and hence quality, in all pools. Magid et al. (1997) clearly showed that this was not the case for the AOM. The C:N ratio of the SMB pool may also vary with changes in the decomposer community (Bremer and van Kessel, 1992). Likewise, the proportion of extractable components may change with the degree of fungal dominance, thus affecting the kEN value. The modification of the DAISY model parameters increased the models ability to simulate the measured data. The inclusion of the AOM-0 pool also significantly improved the simulation for all
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treatments and all measured parameters. However, the suggested parameterisation has not been validated until tested against an independent set of experimental data. Together with the studies by Mueller et al. (1997, 1998), the repeated testing of the model aims to improve its parameterisation and ability to simulate short term dynamics of soil organic matter. Acknowledgements We thank Dr Torsten Mueller for his help and advice with the DAISY modeling. This study was partly funded by the Research Center for Organic Farming (The Danish Environmental Research Program) and by a grant from Anna and Tage Mellers Memorial Foundation. References Aoyama, M., Nozawa, T., 1993. Microbial biomass nitrogen and mineralization– immobilization processes of nitrogen in soils incubated with various organic materials. Soil Sci. Plant Nutr. 39, 23 – 32. Appel, T., Sisak, I., Hermanns-Sellen, M., 1995. CaCl2 extractable N fractions and K2SO4 extractable N released on fumigation as affected by green manure mineralisation and soil texture. Plant Soil 176, 197 –203. Berg, B., Wesse´ n, B., Ekbohm, G., 1982. Nitrogen level and decomposition in Scots pine needle litter. Oikos 38, 291 – 296. Bremer, E., van Kessel, C., 1992. Seasonal microbial biomass dynamics after addition of lentil and wheat residues. Soil Sci. Soc. Am. J. 56, 1141 –1146. Brookes, P.C., Landman, A., Pruden, G., Jenkinson, D.S., 1985. Chloroform fumigation and the release of soil nitrogen: a rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil Biol. Biochem. 17, 837 –842. Cabrera, M.L., Beare, M.H., 1993. Alkaline persulfate oxidation for determining total nitrogen in microbial biomass extracts. Soil Sci. Soc. Am. J. 57, 1007 –1012. Cerri, C.C., Jenkinson, D.S., 1981. Formation of microbial biomass during the decomposition of 14C labelled ryegrass in soil. J. Soil Sci. 32, 619 –626. Chesson, A., 1997. Plant degradation by ruminants: parallels with litter decomposition in soils. In: Cadisch, G., Giller, K.E. (Eds.), Driven by Nature: Plant Litter Quality and Decomposition. CAB International, Wallingford, UK, pp. 47 – 66. Eriksen, J., Jensen, L.S., 2001. Soil respiration, nitrogen mineralisation and uptake in barley following cultivation of grazed grasslands. Biol. Fert. Soils. 33, 139 –145.
Hansen, S., Jensen, H.E., Nielsen, N.E., Svendsen, H., 1990. DAISY — Soil Plant Atmosphere System Model. Miljøstyrelsen, Copenhagen Report No. A10. Hansen, S., Jensen, H.E., Nielsen, N.E., Svendsen, H., 1991. Simulation of nitrogen dynamics and biomass production in winter wheat using the Danish simulation model DAISY. Fert. Res. 27, 245 – 259. Hauggaard-Nielsen, H., de Neergaard, A., Jensen, L.S., HøghJensen, H., Magid, J., 1998. A field study of nitrogen dynamics and spring barley growth as affected by the quality of incorporated residues from white clover and ryegrass. Plant Soil 203, 91 – 101. Høgh-Jensen, H., Schjoerring, J.K., 1994. Measurement of biological dinitrogen fixation in grassland: comparison of the enriched 15N dilution and the natural 15N abundance methods at different nitrogen application rates and defoliation frequencies. Plant Soil 166, 153 – 163. Janzen, H.H., Kucey, R.M.N., 1988. C, N and S mineralization of crop residues as influenced by crop species and nutrient regime. Plant Soil 106, 35 – 41. Jensen, L.S., Mueller, T., Magid, J., Nielsen, N.E., 1997. Temporal variation in C and N mineralization, microbial biomass and extractable organic pools in soil after oilseed rape straw incorporations in the field. Soil Biol. Biochem. 29, 1043 – 1055. Joergensen, R.G., Mueller, T., 1996. The fumigation – extraction method to estimate soil microbial biomass: calibration of the kEN value. Soil Biol. Biochem. 28, 33 – 37. Joergensen, R.G., Schmaedeke, F., Windhorst, K., Meyer, B., 1995. Biomass and activity of microorganisms in a fuel oil contaminated soil. Soil Biol. Biochem. 27, 1137 – 1143. Keeney, D.R., Nelson, D.W., 1982. Nitrogen — Inorganic Forms, second ed. ASA, Madison, WI, pp. 643 – 698. Ladd, J.N., Oades, J.M., Amato, M., 1981. Microbial biomass formed from 14C, 15N labelled plant material decomposing in soils in the field. Soil Biol. Biochem. 13, 119 – 126. Laidlaw, A.S., Christie, P., Lee, H.W., 1996. Effect of white clover cultivar on apparent transfer of nitrogen from clover to grass and estimation of relative turnover rates of nitrogen in roots. Plant Soil 179, 243 – 253. Loague, K., Green, E., 1991. Statistical and graphical methods for evaluating solute transport models: overview and application. J. Contam. Hydrol. 7, 51 – 73. Magid, J., Mueller, T., Jensen, L.S., Nielsen, N.E., 1997. Modelling the measurable: interpretation of field-scale CO2 and N-mineralization, soil microbial biomass and light fractions as indicators of oilseed rape, maize and barley straw decomposition. In: Cadisch, G., Giller, K.E. (Eds.), Driven by Nature: Plant Litter Quality and Decomposition. CAB International, Wallingford, UK, pp. 349 – 362. Mueller, T., Jensen, L.S., Magid, J., Nielsen, N.E., 1997. Temporal variation of C and N turnover in soil after oilseed rape straw incorporation in the field: simulations with the soil – plant – atmosphere model DAISY. Ecol. Model. 99, 247 – 262. Mueller, T., Magid, J., Jensen, L.S., Nielsen, N.E., 1998. C and N turnover after the incorporation of chopped maize,
A. de Neergaard et al. / Europ. J. Agronomy 16 (2002) 43–55 barley straw and blue grass in the field: evaluation of a DAISY-model setup. Ecol. Model. 111, 1 –15. Ocio, J.A., Brookes, P.C., Jenkinson, D.S., 1991a. Field incorporation of straw and its effects on soil microbial biomass and soil inorganic N. Soil Biol. Biochem. 23, 171 –176. Ocio, J.A., Martinez, J., Brookes, P.C., 1991b. Contribution of straw-derived N to total microbial biomass N following incorporation of cereal straw to soil. Soil Biol. Biochem. 23, 655 – 659.
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Tisdall, J.M., Oades, J.M., 1979. Stabilization of soil aggregates by the root systems of ryegrass. Aust. J. Soil Res. 17, 429 – 441. van Soest, P.J., 1963. Use of detergents in the analysis of fibrous feeds. J. Assoc. Agric. Chem 46, 825 – 835. Vance, E.D., Brookes, P.C., Jenkinson, D.S., 1987. An extraction method for measuring soil microbial biomass C. Soil Biol. Biochem. 19, 703 – 707.