Soil Biology & Biochemistry 36 (2004) 877–888 www.elsevier.com/locate/soilbio
Soil organic matter turnover as a function of the soil clay content: consequences for model applications T. Mu¨llera,*, H. Ho¨perb a
Department of Soil Biology and Plant Nutrition, Faculty of Ecological Agricultural Sciences, University of Kassel, Nordbahnhofstraße 1a, D-37213 Witzenhausen, Germany b Institute of Soil Technology Bremen, Geological Survey of Lower Saxony, Friedrich-Missler-Straße 46/50, D-28211 Bremen, Germany Received 14 June 2002; received in revised form 5 September 2003; accepted 3 December 2003
Abstract Based on a literature review including 201 surface soils from wet, mild, mid-latitude climates and 290 soils from the Lower Saxony soil monitoring programme (Germany), we investigated the relationship between soil clay content and soil organic matter turnover. The relationship was then used to evaluate the clay modifier for microbial decomposition in the organic matter module of the soil– plant – atmosphere model DAISY. A positive relationship was found between soil clay content and soil microbial biomass (SMB) C. Furthermore, a negative relationship was found between soil clay content and metabolic quotient (qCO2) as an indicator of specific microbial activity. Both findings support the hypothesis of a clay dependent capacity of soils to protect microbial biomass. Under the differing conditions of practical agriculture and forestry, no or only very weak relationships were found between soil clay content and non-living soil organic matter C (humus C). It is concluded that the stabilising effect of clay is much stronger for SMB than for humus. This is in contrast to the DAISY clay modifier assuming the same negative relationship between soil clay content, on the one hand, and turnover of SMB and turnover of soil humus on the other. There is a positive relationship between SMB and microbial decomposition activity under steady-state conditions (microbial growth < microbial death). The original concept of a biomass-independent simulation of organic matter turnover in the DAISY model must therefore be rejected. In addition to the original modifiers of organic matter turnover, a modifier based on the pool size of decomposing organisms is suggested. Priming effects can be simulated by applying this modifier. When using this approach, the original modifiers are related to specific microbial activity. The DAISY clay modifier is a useful approximation of the relationship between the metabolic quotient (qCO2) as an indicator of specific microbial activity and soil clay content. q 2004 Elsevier Ltd. All rights reserved. Keywords: Metabolic quotient; qCO2; Soil microbial biomass; Clay content; Microbial activity; Soil respiration; DAISY; Soil organic matter turnover
1. Introduction The capacity of soils to protect organic matter against microbial decomposition and microbial biomass against cell death or predation seems to depend on the soil clay content (Ko¨rschens, 1980; Nichols, 1984; Van Veen et al., 1984, 1985; Ko¨rschens, 1998). Under identical annual organic matter input, a slower organic matter turnover, a larger microbial biomass and more organic matter are expected in soils with a high clay content compared to soils with a low clay content within the same climatic area. In classical concepts, stable clay-organic complexes are assumed to be * Corresponding author. Tel.: þ49-5542-98-1504; fax: þ 49-5542-981596. E-mail address: tmuller@wiz.uni-kassel.de (T. Mu¨ller). 0038-0717/$ - see front matter q 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.soilbio.2003.12.015
responsible for an increased formation of stabilised organic matter in clay rich soils (Reuter, 1991). Clay may also influence the turnover of organic substrate in the short term. During a 35-d incubation experiment, clay and soil surface area played a major role in controlling the decomposition of added 14C-labelled glucose through stabilisation and protection of the microbial biomass (Saggar et al., 1999). However, Percival et al. (2000) could not find a general relationship between soil clay content and long-term soil organic matter accumulation for 167 New Zealand grassland soils. In this investigation, pyrophosphate-extractable Al alone or in combination with other factors (allophane, Feoxide, clay) explained the greatest amount of soil C variability. During the first 9 weeks of decomposition in microlysimeters under field conditions, a greater portion of
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14
C-labelled ryegrass was retained in clay soils than in silt loam soils (Saggar et al., 1996). After 5 years, however, the amount of remaining 14C did not relate to the amount of clay but correlated with the soil surface area. Due to the findings cited above, soil clay content is used as an abiotic factor modifying microbial decomposition activity or defining the size of protected pools of organic matter in models of soil organic matter (SOM) turnover (Hansen et al., 1990; Franko, 1996; Molina, 1996; Parton, 1996). DAISY is a model of water and energy flows, and of C and N turnover in the system soil – plant – atmosphere (Hansen et al., 1990). In the DAISY model, SOM is split up into six different pools: Added organic matter (AOM), soil microbial biomass (SMB) and non-living SOM, each of which is subdivided into a more recalcitrant pool (AOM1, SMB1, SOM1) and a faster decomposing pool (AOM2, SMB2, SOM2). Our study focuses on the turnover of SMB1, SOM1 and SOM2. SOM1 and SOM2 are decomposed by SMB1, whereas residues of SMB1 and SMB2 are decomposed by SMB2. The turnover of the different pools of organic matter is described by first-order kinetics: dC=dt ¼ kCt
ð1Þ
C is carbon in the pool (kg m23), t is the time step (s) and k is the actual decomposition rate coefficient (s21) for this time step. k is calculated from a decomposition rate coefficient kp (s21), valid under DAISY standard conditions (10 8C, 0% clay, 10 kPa), which is modified due to actual environmental conditions using modifiers for clay content c T C ðFm Þ; soil temperature ðFm Þ; and soil water tension ðFm Þ: c T C k ¼ k £ Fm F m Fm
ð2Þ
It is assumed that soil clay content only affects autochthonous soil microorganisms (SMB1) and (partly) humified organic substance (SOM1 and SOM2). Zymogenous microorganisms (SMB2) are assumed to depend mainly on the available substrate such as freshly added organic matter (AOM1 and AOM2). Consequently, Eq. (2) does not c include a clay modifier ðFm Þ when applied to SMB2, AOM1 and AOM2. Fig. 1 shows the functions of the clay and temperature modifiers. Due to this modifier, the turnover of SOM1, SOM2 and SMB1 in a soil with 25% clay content is only half of the turnover in a soil with 0% soil clay content. Microbial basic respiration (mg CO2 – C kg21 soil dry matter h21) is usually used as an indicator of total microbial activity and total C-turnover in soils under steady-state conditions (microbial death < microbial growth) achieved by a conditioning incubation to remove easily available substrate. Then, microbial activity is mainly due to autochthonous soil microorganisms (SMB1 in the DAISY model) feeding on soil humus (SOM1 and SOM2). In this study, humus will be defined as the non-living part of SOM. C evolved by basic respiration can be assumed to be derived
Fig. 1. Clay and soil temperature modifiers (Fcm and FTm, respectively) of the DAISY model.
from the microbial C-pool as well as from the humus Cpool. The metabolic quotient (qCO2 (mg CO2 –C g21 Cmic 21 h )) is a specific indicator of microbial activity, which relates basic respiration to soil microbial C (Cmic). qCO2 is often used as an indicator for assessing the influence of environmental conditions on soil microbial communities (Anderson and Domsch, 1993). From the above-cited literature, it can be hypothesised that SOM and SMB increase as a result of a decreasing turnover rate of SOM with increasing clay content. We used soil organic C-content (Corg), Cmic, basic respiration and qCO2 to investigate the relationship between soil clay content and SOM turnover, to test this hypothesis and to evaluate the clay modifier function of the DAISY model.
2. Materials and methods 2.1. Acquisition of literature data We examined 30 papers reporting SOM transformations in 201 surface soils from areas located in wet, mild, mid-latitude climates (Ko¨ppen’s Cf-climates) to provide information on soil clay content, soil organic C (Corg), SMB C (Cmic), basic respiration, qCO2 and incubation temperature during respiration measurements (Table 1). In those papers in which the data were presented graphically, these values were measured after photomechanical enlargement of the figures.
Table 1 Literature data included in the literature study Reference Anderson and Domsch (1985) Brookes and McGrath (1984)
Sampling depth (cm)
Region
Culture
Clay (%)
Corg (% dm)
pH
No. I, 22 and 28 8C Low metal inorganic fertiliser, low metal FYM 1 and 2 Rosdorf (no herbicide) 14b, 14c, 15a, 15b, 17a (1–12), 19 (1–4) Brewton (1–10), Auburn (2–42), Tennessee (2–10) 1– 5, 7, 8
0– 10 0– 23
Germany England
Arable Arable
15 9
10.5 1.1
5.9 7.9
0– 10 0– 15
Germany SE-USA
Arable Arable
26 1–45
1.5 0.4– 1.2
6.9 4.6 –7.8
0– 15
Alabama
Arable
2–27
0.4– 0.9
4.0 –5.7
0– 15
England
21–43
0.8– 3.9
5.3 –8.0
0– 10 0– 20 0– 20 0– 30 0– 10 0– 15 0– 20 0– 30
England Germany Germany Germany England England Switzerland France
20 17 3–43 21 36 36 16 22
4.4 1.6 0.9– 21.1 0.9 5.7– 6.6 4.3 1.5– 1.8 10.8
4.5 7.7 4.2 –7.8 7.1 n.d. n.d. 6.2 –6.9 7.3
0– 10
Oregon
Arable
15
1.3– 1.6
5.3 –6.0
0– 15 0– 25
England Denmark
Arable Arable
10–39 6–14
1.3– 2.8 1.2– 2.6
6.6 –7.1 6.2 –7.0
0– 7.5
New Zealand
Grassland
8–33
6.5– 11.7
5.7 –5.9
4 16
15 and 25 8C Non-contaminated control No. 2 –27 Timmerlah (0–30) Treatment 1–5 Control N1, D2, O2, K2, M2 Spring: F, MN; summer: F, MN, MON LVR: 0 and 280 kg N ha21; TVR 0 and 280 kg N ha21 Soil1, Soil2 Studsgaard burned/incorporated, Rønhave burned/incorporated Ohineapanea, Katikati, Horotiu, Otorohanga Fallow, field, meadow, forest B Me1– Me16
Arable grassland forest Grassland arable Arable Arable, grassland Arable Arable Grassland Arable Arable
0– 10 0– 10, 0–15
Bohemia Germany
Arable grassland forest Arable garden forest
6–10 3–7.4
1.2– 2.4 0.7– 10.5
4.6 –6.0 3.2 –6.6
10 10 2 6 1
1– 8, 10, 11 1– 3, 10–16 Borgloon, Meer 1– 6 Unamended
0– 20 0– 20 0– 10 Top mineral soil 0– 10
Scotland Northern Italy Belgium Northern Germany England
Grassland Arable Arable Forest Grassland
2.9– 9.5 0.9– 2.0 0.6– 3.8 3.2– 9.7 1.6
4.7 –6.1 4.6 –6.4 5.2 –6.9 4.8 –8.3 6.0
2 3
Harden et al. (1992) Insam (1990)
1 22
Insam et al. (1991)
60
Jenkinson et al. (1979) Joergensen et al. (1990) Joergensen et al. (1995) Kaiser et al. (1992) Kaiser and Heinemeyer (1993) Lovell et al. (1995) Lovell and Jarvis (1998) Ma¨der et al. (1993) Menasseri et al. (1994)
7 2 1 26 1 5 1 5 5
Miller and Dick (1995)
4
Ocio and Brookes (1990) Powlson et al. (1987)
2 4
Saggar et al. (1994)
4
Santruckova et al. (1993) Schro¨der and Schneider (1996) and Schneider, oral communication Sparling (1981) Valsecchi et al. (1995) Van Gestel et al. (1993) Wolters and Joergenen (1991) Wu et al. (1993)
2–17 3–18 7–17 25–47 8
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Experimental variants/soils included
No. of soils
FYM: farm yard manure; dm: dry matter.
879
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If not already given, basic respiration and qCO2 were calculated from the data in the papers. We assumed that measurements of basic respiration were made under steadystate conditions after a conditioning incubation and that the soil water content was optimal during incubation. In some studies, different cultivation and fertilisation practices were investigated at the same site. To represent the resulting site-specific variation of Cmic and basic respiration, each of these experimental treatments was included. We did not include soil treatments with heavy metals, pesticides or other toxic substances and data from treatments where organic substrate or other substances were added to the soil prior to incubation and respiration measurements. Thus, for some studies, only the control treatment was used. 2.2. Soil monitoring data 2.2.1. Study sites We also included data from the Lower Saxony soil monitoring programme (SMP) (Ho¨per and Kleefisch, 2001), 60 different sites each consisting of four (six at one site) sampling areas (each of 256 m2). From each sampling area, 16 soil samples were taken and bulked. At the 48 arable sites, soil samples were taken to 20 cm depth. At the 12 grassland sites, soil samples were taken separately from the soil layers 0 – 10 and 10 –20 cm. Each sampling area and soil layer was included in this study separately, resulting in 290 single soil samples. Soil properties varied widely with clay contents between 0.5 and 67%, organic C contents between 0.7 and 9.8% and soil pH(CaCl2) between 3.8 and 7.7. 2.2.2. Soil management and sampling The establishment of the SMP did not affect agricultural practices and crop rotation, which remained under the control of the landowner. The selected areas have been under the same land use practice for many years and soil biological properties can be expected to have reached an equilibrium. Soils were sampled between 15th February and 15th April each year (arable soils: 1996 –2000, grassland soils: 1997– 2000) for microbial analyses. Mean values of the years 1996 – 2000 or 1997– 2000 were calculated. Some of the sites were first established after 1996 or 1997 and, thus, fewer years were included. By the calculation of mean values over several years, inter-annual variation, e.g. due to different crops and weather conditions, is ironed out in order to obtain a more general pattern of microbial soil characterisation. All soils were sieved (2 mm) before analysis. 2.2.3. Soil analysis The soils were analysed for microbial biomass C by substrate induced respiration (SIR) and for basic respiration using a Heinemeyer-apparatus (Anderson and Domsch, 1978; Heinemeyer et al., 1989; Kaiser et al., 1992). Before analysis, the soil water content was adjusted to 50% of
the maximum water holding capacity. To achieve steadystate conditions and to remove easily available substrate, the samples were conditioned at 22 8C for 7 d. For the SIRmeasurements, 3000 mg glucose kg21 soil were mixed into the soil as a glucose – talcum mixture at a ratio of 3:7. The Heinemeyer apparatus allows for hourly measurements of soil respiration during incubation at 22 8C. The lowest mean respiration rates over a period of 3 h during the first 6 h of incubation were used as SIR. SIR was converted into microbial biomass using a factor of 30 (Kaiser et al., 1992). To measure basic respiration, soil samples were incubated in the same way but without added glucose. Mean respiration rates between 10 and 20 h (pH , 7) or between 30 and 40 h (pH . 7) were used as basic respiration. Each value was calculated as the average of three single measurements. Total C in the soil was measured using a Heraeus elemental C- and N-analyser. To calculate total organic C, carbonate-C (Scheibler) was subtracted from the total C values. Soil texture was measured according to the Ko¨hn pipette method (DIN, 19683-2:04.97, 1997). Organic C and texture were determined once per sampling area. For each sampling area, mean values for all the years investigated were calculated for microbial properties. qCO2 was calculated from basic respiration and microbial biomass C. 2.3. Standardising and calculations Data reported from the literature were based on different methods depending on the authors’ preferences and experiences, restrictions of the local laboratories, and experimental requirements. For example, literature data of Cmic are based on different methods, including modifications of SIR, chloroform fumigation incubation and chloroform fumigation extraction. It seems to be impossible to eliminate variations caused by these methodological differences in a standardised way. Therefore, it was decided to use literature data as reported by the authors. An exception was the incubation temperature. Any difference in incubation temperature would have made it difficult to compare data on soil respiration reported in this study. Therefore, basic respiration and qCO2 were converted to an incubation temperature of 10 8C (DAISY standard condition) using the DAISY temperature modifier (Fig. 1). Soil humus C (Chum) was calculated as the difference between Corg and Cmic. Under steady-state conditions after a conditioning incubation, the easily decomposable (added) organic matter (AOM in the DAISY model) should have been metabolised. Then, Chum can be assumed to be the measured equivalent to the sum of the SOM-C pools in the DAISY model. Considerable differences in amount and depth of annual C input can be expected between soils derived from arable, grassland and forest areas, and between the two layers of the SMP grassland areas. Therefore, the different land use
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types and soil depths were investigated separately. An exception is qCO2 as a relative value related to SMB. The ratio soil basic respiration-to-Chum (mg CO2 –C kg21 Chum h21) was calculated for literature and SMP data as an indicator of the specific turnover of Chum. Regression lines and R2 are shown if Spearman’s rank correlation coefficients (rS) differed significantly ðP # 0:05Þ from zero.
3. Results Positive significant linear relationships between Chum and soil clay content were obtained only for literature data of arable soils (Fig. 2) and for SMP data of grassland surface
881
(0 –10 cm) soil (Fig. 3). However, both relationships were weak, with very low values for R2 : Positive significant linear relationships between Cmic and soil clay content were obtained in all soils (Figs. 2 and 3). R2 varied between 0.22 and 0.72. Significant positive relationships between soil basic respiration and soil clay content were found for grassland surface soils (Fig. 3) and for forest soils (Fig. 2). For arable soils, only SMP data showed the same positive linear relationship (Fig. 2). This relationship was significant but weak, indicated by an R2 of only 0.21. qCO2 decreased with increasing soil clay content (Fig. 4). This significant log –normal relationship was much stronger for SMP-data ðR2 ¼ 0:62Þ than for literature data ðR2 ¼ 0:39Þ: However, the regression equations for these relationships did not differ very much.
Fig. 2. Humus C (Chum ¼ Corg 2 Cmic (%)), SMB C (mg Cmic kg21 soil) and basic soil respiration (10 8C) (mg CO2 –C kg21 soil h21) as a function of the soil clay content in arable and forest soils. SMP: lower saxony soil monitoring programme. Regression lines are shown if rS was significantly different from zero ðP # 0:05Þ:
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Fig. 3. Humus C (Chum ¼ Corg 2 Cmic (%)), SMB C (mg Cmic kg21 soil) and basic soil respiration (10 8C) (mg CO2 –C kg21 soil h21) as a function of the soil clay content in grassland soils. SMP: lower saxony soil monitoring programme. Regression lines are shown if rS was significantly different from zero ðP # 0:05Þ:
No significant relationship (Spearman rank correlation) was found between the ratio soil basic respiration-to-Chum and the soil clay content. Soil basic respiration showed a significant linear relationship with Cmic (Fig. 5A, R2 ¼ 0:75). If both basic respiration and Cmic were divided by soil humus C to eliminate any effect of general differences in the C-level between the soils, the linear relationship was less strong ðR2 ¼ 0:30Þ but remained significant (Fig. 5B).
4. Discussion 4.1. Data variability In general, data variability was smaller and the relationships investigated were stronger for SMP data
than for literature data. Reasons may be methodological differences as reported above, differences in soil sampling depth as well as a bigger climatic variability. However, the methodological bias should not be over interpreted. Comparative investigations have shown high correlations between different methods for the estimation of SMB (Kaiser et al., 1992; Anderson and Joergensen, 1997). Different variations in annual substrate input by roots, litter, post-harvest residues and organic fertilisers may be other reasons. This is illustrated by the variability of encircled data in Fig. 4 derived from experimental plots at the same site (Auburn, Alabama) differing in crop rotation and fertilising strategy (Insam et al., 1991). Annual differences in biological properties may occur, although an equilibrium exists in the long term. This was taken into account for the SMP data by using mean values derived from 4 to 5 single years.
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Fig. 5. (A) Basic respiration (10 8C) (mg CO2 –C kg21 soil h21) as a function of SMB C (mg Cmic kg21 soil dry matter). Regression line forced through zero. (B) Basic respiration (10 8C) per unit humus C (mg CO2 –C kg21 Chum h21) as a function of SMB C per unit humus C (g Cmic kg21 Chum). Regression line forced through zero. rS was significantly different from zero in both cases ðP # 0:001Þ:
Fig. 4. qCO2 (10 8C) as a function of the soil clay content. (A) SMP data (arable and grassland soils), (B) literature data (arable, grassland and forest soils), (C) SMP and literature data. rS was significantly different from zero in all cases ðP # 0:001Þ: Encircled data derived from Insam et al. (1991).
However, most of the literature data are derived from samplings within single years. Nevertheless, both the SMP and the literature data showed the same tendencies for many of the variables investigated here (Figs. 2 and 3). Due to the similarity of the regressions shown in Fig. 4A and B, it was decided to pool the data sets and to calculate a common regression equation and a common R2 (Fig. 4C). Pooled regressions were also displayed for other variables in Figs. 2 – 5. 4.2. Soil organic matter content In contrast to our own results, other authors found a positive relationship between SOM content and soil clay content. Under natural conditions (Nichols, 1984) and under low input conditions (Ko¨rschens, 1998), higher soil clay
contents led to higher SOM contents. The same was shown in long-term experiments, where clay-substrate (e.g. bentonite) was added to sandy soils while keeping the fertiliser input identical (Reuter, 1991). Ko¨rschens (1980) found a significant positive relationship between SOM (Ct and Nt) and the clay and fine silt fractions (, 6.3 mm) in no input plots (nil) of 11 EastGerman long-term field experiments. The amount of organic matter described by the regression equation was defined as “the minimum amount of SOM, which will not be diminished under conditions without fertiliser input”. This boundary was compared with 162 field data from farms in the district of Potsdam (East Germany). The Ct-content of none of the 162 soils was less than the minimum amount of SOM predicted using the regression equation mentioned above. However, for this farm data set, no relationship was found between SOM and mineral fine material (, 6.3 mm). Obviously, other factors such as variability in local climate and organic matter input under the conditions of practical agriculture had a larger influence on SOM than the soil clay content. The SMP soils are almost all under intensive agricultural use with optimum fertiliser input and high yields. In contrast to low or no input agriculture, the high C input into the soil due to a high yield level tends to nullify the effect of clay on humus content. This may explain the lack of a clear
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relationship between soil clay content and Chum shown in Figs. 2 and 3. Furthermore, several sandy soils in Lower Saxony have high C contents due to their land-use history (sand mix culture, consisting of a mixture of sand and bog peat), low soil pH (Podzols) and sometimes high water table (Gleysols and Gleyic Podzols). A high variation in SOM contents at low clay contents was also reported by Springob and Kirchmann (2002). Considerable variations in organic matter input also characterise the literature data used in our investigation and may therefore explain the missing or weak relationship between soil clay content and Chum shown in Figs. 2 and 3. Furthermore, the data set may include soils differing in clay quality. Under the differing conditions of practical agriculture and forestry, no or only very weak relationships can be expected between soil clay content and soil humus C. 4.3. Microbial biomass and specific microbial turnover Our results (Cmic and qCO2) support the hypothesis that soil clay can protect microbial biomass. The decrease of qCO2 with increasing soil clay content was most pronounced at clay contents , 25%, indicating that the protective effect of clay is limited. Obviously, a certain amount of clay is necessary to create protected microhabitats and a further increase of the clay content above this amount does not result in a further improvement of these habitats. However, the mechanism behind the protective effect of clay is not yet fully understood. Franzluebbers et al. (1996) summarised the reasons for a greater specific respiratory activity in coarse-textured soils compared to fine-textured soils. Main reasons may be greater water stress, greater faunal activity (predation) and greater nutrient availability due to less physical protection of the organic matter in coarse-textured soils. Furthermore, microorganisms isolated within aggregates of fine textured soils cannot access inter-aggregate substrates. 4.4. Soil organic matter turnover The positive relationship between Cmic and soil basic respiration (Fig. 5) was also found by Franzluebbers et al. (1996) and Jo¨rgensen (R.G. Jo¨rgensen, unpublished Thesis, Institute of Soil Sciences, Go¨ttingen, 1995). Obviously, the increase of Cmic compensated for the decrease of qCO2 (Fig. 4). If the increase of Cmic per unit clay was small (grassland 10 –20 cm in Fig. 3), basic respiration was unaffected by the clay content. If the increase of Cmic per unit clay was larger (arable and forest soils in Fig. 2, grassland surface soil in Fig. 3), basic respiration increased with increasing clay content. As a consequence of the positive relationship between soil clay content and basic respiration, Chum might be expected to decrease with increasing clay content. But, as already mentioned, Figs. 2 and 3 show virtually no influence of soil clay content on Chum.
This apparent contradiction can be explained by taking the annual C-input into account. A positive relationship between soil clay content on the one hand, and soil fertility and soil fertility dependent crop rotations on the other, may have led to a higher input of rhizodeposits and plant residues in clay-rich soils, leading to higher microbial decomposition activity. In the SMP, silty and loamy soils are typically under a crop rotation of 2 yr of winter wheat followed by 1 yr of oilseed rape or sugar beet, whereas sandy soils are under a crop rotation of 1 yr of winter barley followed by 2 yr of maize, sometimes with 1 yr of potatoes. The latter crop rotation is characterised by a high proportion of low C input crops compared to the cereal rich and high C input crop rotation on silty and loamy soils. Overall, the higher C-input in clay rich soils seems to increase microbial turnover activity and microbial biomass more than the formation of stable humus components. A methodological aspect may also be considered to explain the observed positive relationship between soil respiration and clay content together with constant humus content. Hassink (1992) showed that fine sieving (, 2 mm) leads to an increase in the C-mineralisation rate compared to coarse sieving (8 mm) in loamy and clay soils, while the C mineralisation rate was decreased in sandy soils. He attributed this effect to physical protection of SOM in structured soils by clay, which is destroyed by fine sieving, exposing the organic matter to higher microbial decomposition. However, the effect was only significant in one loam and one clay soil. But it cannot be totally ruled out that higher respiration rates in soils with higher clay contents were induced by the fine sieving of the soils we studied (e.g. SMP were sieved at 2 mm). Nevertheless, a probable respiration flush initiated by sieving was minimised by the conditioning incubation prior to analyses. 4.5. Implications for the DAISY model For the following discussion, it has to be pointed out that data presented here can be assumed to be achieved under steady-state conditions after a conditioning incubation, where decomposing organisms (Cmic) are dominated by autochthonous organisms (SMB1) feeding on the soil humus pools (SOM1 and SOM2). Zymogenous organisms (SMB2) and easily decomposable organic substance (AOM1 and AOM2) can be neglected under these conditions. In the DAISY model, the turnover of any organic matter pool is assumed to be independent of the amount of decomposing organisms (SMB1 and SMB2). Based on this assumption, basic respiration and Chum may be used to c evaluate the clay modifier ðFm Þ for SOM1 and SOM2 in Eq. (2). As argued above, the relationships between clay content on the one hand, and basic respiration and Chum on c the other, are in contrast to the clay modifier (Fm in Fig. 1) assuming a clay-dependent preservation of soil humus
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(SOM1 and SOM2). Furthermore, no relationship was found between the ratio soil basic respiration-to-Chum, as an indicator of the specific humus turnover, and the soil clay c content. Therefore, including the original clay modifier ðFm Þ in Eq. (2) does not make sense when applying this equation to SOM1 and SOM2 turnover. This is true at least for the land use systems and climatic regions investigated here. However, c the inclusion of a clay modifier ðFm Þ in Eq. (2) is justified when applying this equation to SMB1 (Figs. 2 and 3). The assumption that turnover of SOM1 and SOM2 is independent of the amount of decomposing organisms was not supported by our observations that soil basic respiration was positively correlated with Cmic (Fig. 5). Consequently, the amount of SMB1-C may be considered as an additional factor modifying the turnover rate of the SOM-C pools in the DAISY model p c T C SMB1 k ¼ kspec Fmspec Fmspec Fmspec Fm
ð3Þ
Eq. (3) shows a further modification of Eq. (2) to be applied to SOM1 and SOM2. The SMB-modifier may be derived from Fig. 5 after definition of an SMB1-C p concentration as DAISY standard condition. kspec is the decomposition rate coefficient valid under DAISY standard conditions including a specific standard SMB1-C concentration. Consequently, the modifiers in Eq. (3) refer to specific microbial activity (decomposition activity per unit SMB1 indicated as ‘mspec’), which is in contrast to Eq. (2) where all modifiers refer to total microbial activity. Under these circumstances, Eq. (3) includes the clay modifier, which is in accordance with Fig. 4 showing the clay dependency of specific microbial activity. In the DAISY model, the turnover of microbial biomass is subdivided into two components: maintenance respiration and microbial death. The latter includes death of organisms and any form of exudation and is followed by utilisation of the released substrate by other organisms (SMB2). The rates of maintenance respiration and microbial death depend to a great extent on the pool size of the respiring or dying organisms and not so much on the pool size of the decomposing organisms (SMB2). Hence, it may not be adequate to use Eq. (3) for the SMB-pools. Due to the assumption that the majority of SMB can be attributed to SMB1, Eq. (2) refers to specific microbial activity when applied to SMB1 under steady-state conditions. qCO2 is an indicator of the specific microbial activity and it may therefore be useful to evaluate the DAISY clay modifier in Eq. (3) when applied to SOM1 and SOM2, and in Eq. (2) when applied to SMB1. qCO2 changes clearly between 0 and 25% clay (Fig. 4C). However, qCO2 remains fairly constant if examining data above 25% clay alone. In Fig. 6, the logarithmic function for data ,25% clay derived from Fig. 4C was converted to relative values of the DAISY clay modifier (full line in Fig. 6) using the following equation: c Fmspec ¼ ð0:5=qCO2 at 25% clayÞqCO2
ð4Þ
885
c Fig. 6. Original DAISY clay modifier (Fmspec ; scattered line) and the qCO2c function derived from Fig. 4c and converted to values of Fmspec (full line).
c is the converted relative clay modifier correspondFmspec ing to a certain clay content, qCO2 is the metabolic quotient corresponding to a certain clay content, qCO2 at 25% clay is the metabolic quotient corresponding to a clay content of 25% dry weight and 0.5 is the relative clay modifier corresponding to a clay content of 25% dry weight. Assuming that qCO2 is constant above 25% clay, the original clay modifier (scattered line in Fig. 6) is a useful approximation of the function derived from Fig. 4c (full line in Fig. 6). The assumption of an inert SOM pool potentially depending on the soil clay content (Ko¨rschens, 1980) has been included into the current release of the DAISY model, which now has an option to define an inactive SOM-pool. Our data set, however, does not allow an evaluation of this approach.
4.6. Model behaviour DAISY simulations were carried out to illustrate the effect of replacing Eq. (2) by Eq. (3). We used the mean contents of total organic C (2.54%), Cmic (357 mg C kg21), and mean C-to-N ratios of soil organic matter (12.3) and microbial biomass (6.7) derived from our dataset to initiate soil C and N properties. In accordance with Fig. 5, the SMBc modifier Fmspec was calculated from the actual SMB1-C (SMB1acutal) using a simple linear relationship where c Fmspec ¼ 1 for the initial C-content of SMB1 (SMB1initial ¼ 356 mg C kg21): c Fmspec ¼ ð1=SMB1initial ÞSMB1actual
ð5Þ
Due to our above assumptions, only 1 mg C kg21 was allocated to the initial zymogenous SMB2 pool. All other parameters were initiated with default values. Simulations were carried out at 10 8C (DAISY standard temperature) without and with addition of AOM equivalent to 8 t dry matter straw ha21 (C-to-N ratio of 90) at day 1. Although our investigation does not focus on AOM and zymogenous microbial populations, addition of AOM has been simulated to show the principal effects of Eq. (3) in
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Fig. 7. 370 d DAISY model simulations of total organic C (AOM-C þ SOM-C þ SMB-C), SOM-C and SMB-C at 10 8C (DAISY standard temperature) for a hypothetical soil (15% clay content) without and with addition of AOM equivalent to 8 t straw dry matter ha21 at day 1. The figure shows alternative simulations using Eq. (2) (full line) or Eq. (3) (dotted line).
case of growing SMB, which is different from steady-state conditions with fairly constant or slightly decreasing SMB. Fig. 7 shows simulations done for a soil containing 15% clay. The application of Eq. (3) led to smaller SOM-C losses and to lower SMB-C in the long-term if no AOM was added (Fig. 7C and E). With addition of AOM, the application of Eq. (3) led to larger SOM-C losses and to higher SMB-C (Fig. 7D and F). At the end of the simulations with Eq. (2), SOM-C was higher with AOM addition than without (compare full lines
in Fig. 7C and D). When applying Eq. (3), SOM-C was slightly lower with AOM addition than without (compare dotted lines in Fig. 7C and D). Hence, a priming effect was simulated if using Eq. (3) during the simulation with addition of AOM. Priming effects could not be simulated by the original version of the DAISY model. The application of Eq. (3) had only very little effect on total organic C (Fig. 7A and B). Table 2 shows a comparison of DAISY simulations for soils differing in clay contents. In general, the effects of replacing Eq. (2) by
Table 2 370 d DAISY model simulations of total organic C (Corg), SOM-C and SMB-C at 10 8C (DAISY standard temperature) for three hypothetical soils differing in clay content without (2AOM) and with addition of added organic matter (þ AOM) equivalent to 8 t straw dry matter ha21 at day 1 Soil clay content (%)
2 15 40
SOM-C
Corg
SMB-C
2AOM
þAOM
2AOM
þAOM
2AOM
þAOM
0.08 0.04 0.02
20.16 20.13 20.10
0.14 0.08 0.04
20.26 20.23 20.18
24.6 22.5 21.3
6.0 5.5 4.4
It shows relative changes of pool sizes (%) at day 370 when applying Eq. (3) instead of Eq. (2).
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Eq. (3) as shown in Fig. 7 increase with decreasing clay content. The model simulations shown here can only illustrate the general effects of an application of Eq. (3). Further investigations are necessary to calibrate this new setup with measured data. Our investigation focussed on autochthonous soil organic matter pools (SMB1, SOM1 and SOM2 in the DAISY model). No conclusions can be drawn on easily decomposing organic matter pools such as AOM and on zymogenous microbial biomass (SMB2). However, zymogenous microorganisms feeding on easily decomposable organic matter adapt very quickly to the amount of available substrate by growth or death. Considering only the amount of available substrate and not the amount of decomposing organisms may therefore be sufficient for the simulation of turnover processes related to zymogenous microorganisms, which would be in accordance with the original approach of the DAISY model. However, further investigations are necessary to test this hypothesis.
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