Microb Ecol DOI 10.1007/s00248-014-0383-8
SOIL MICROBIOLOGY
Changes in Soil Microbial Biomass and Residual Indices as Ecological Indicators of Land Use Change in Temperate Permanent Grassland Rajasekaran Murugan & Ralf Loges & Friedhelm Taube & André Sradnick & Rainer Georg Joergensen
Received: 25 September 2013 / Accepted: 30 January 2014 # Springer Science+Business Media New York 2014
Abstract The relationship between microbial biomass, residues and their contribution to microbial turnover is important to understand ecosystem C storage. The effects of permanent grassland (100 % ryegrass—PG), conversion to modified grassland (mixture of grass and clover—MG) or maize monoculture (MM) on the dynamics of soil organic C (SOC), microbial biomass, fungal ergosterol and microbial residues (bacterial muramic acid and fungal glucosamine) were investigated. Cattle slurry was applied to quantify the effects of fertilisation on microbial residues and functional diversity of microbial community across land use types. Slurry application significantly increased the stocks of microbial biomass C and S and especially led to a shift in microbial residues towards bacterial tissue. The MM treatment decreased the stocks of SOC, microbial biomass C, N and S and microbial residues compared with the PG and MG treatments at 0–40 cm depth. The MM treatment led to a greater accumulation of saprotrophic fungi, as indicated by the higher ergosterol-tomicrobial biomass C ratio and lower microbial biomass C/S ratio compared with the grassland treatments. The absence of a white clover population in the PG treatment caused a greater accumulation of fungal residues (presumably arbuscular mycorrhizal fungi (AMF), which do not contain ergosterol but
Electronic supplementary material The online version of this article (doi:10.1007/s00248-014-0383-8) contains supplementary material, which is available to authorized users. R. Murugan (*) : A. Sradnick : R. G. Joergensen Department of Soil Biology and Plant Nutrition, University of Kassel, Nordbahnhofstr. 1a, 37213 Witzenhausen, Germany e-mail: [email protected] R. Loges : F. Taube Institute of Crop Science and Plant Breeding, Grass and Forage Science/Organic Agriculture, Christian-Albrechts-University of Kiel, Hermann-Rodewald-Str. 9, 24118 Kiel, Germany
glucosamine), as indicated by the significantly higher fungal C-to-bacterial C ratio and lower ergosterol-to-microbial biomass C ratio compared with the MG treatment. In addition to these microbial biomass and residual indices, the community level physiological profiles (CLPP) demonstrated distinct differences between the PG and MG treatments, suggesting the potential of these measurements to act as an integrative indicator of soil functioning.
Introduction Land use change from permanent grassland to crop land has led to an average loss of soil C stocks by 59 % [20] and may accelerate soil erosion [42]. Soil organic C (SOC), macroaggregates, catabolic diversity and microbial biomass decreased remarkably under cotton (Gossypium hirsutum L.) monoculture [1] and prolonged cultivation of maize in grassland soils [31, 32]. Prolonged cultivation of arable crops and tillage causes a shift in the microbial community structure towards Gram-positive bacteria [54, 58]; the fungal-to-bacterial biomass ratio of permanent grassland soils was twice that of arable land [6]. However, very limited information is available on changes in fungal and bacterial residues and microbial functional diversity following conversion of permanent grassland to monoculture or modified grassland with different mixture of grass species [19, 32]. Soil microbial community plays an important role in the maintenance of soil ecosystem function, such as C sequestration [15, 40]. Crop mixtures in grassland resulted in greater plant biomass, microbial biomass and C utilisation by microbial communities than single species, due to variations in the quantity and quality of root exudates [12, 19]. In contrast, permanent grassland with a single grass species showed greater functional diversity than a modified grassland soil [58].
R. Murugan et al.
However, the effects of plant species composition between grassland soils on specific responses of fungal and bacterial residues are completely unknown. The non-living microbial residues represent a significant SOM pool, much greater than the living biomass [51]. The size of the microbial residue C pool in soil may be about 40 times that of the living microbial biomass [34, 51]. However, very few studies investigated the importance of the relationship between microbial biomass, residues and their contribution to microbial turnover and ecosystem C storage. The microbial residues can be assessed by measuring those amino sugars that are exclusively microbial products in soil [2]. Among the four detectable amino sugars, glucosamine (GlcN) is primarily derived from the chitin of both arbuscular mycorrhizal fungi (AMF) and saprotrophic fungal cell walls, whereas muramic acid (MurN) exclusively originates from bacteria [2]. As cell wall components accumulate in SOM [2], soil amino sugar content can be used as a valuable biomarker to indicate the effects of changes in agricultural management on shift of fungal and bacterial residues and their contribution to SOM accumulation within a specific ecosystem [27, 38]. The microbial community structure and its diversity have been suggested as a sensitive means of assessing soil quality [40]. The modified MicroResp™ method [10], based on Degens and Harris [14] substrate-induced respiration with multiple carbon sources (multi-SIR), has been reported to be a reliable and sensitive method for measuring changes in microbial community level physiological profiles (CLPP) caused by land use change and fertilisation [43, 53]. The application of cattle slurry may have considerable positive effects on the nutrient dynamics of ecosystems [57], resulting in a higher catabolic diversity than in inorganically fertilised soil [43, 52]. Slurry application has been found to increase bacterial biomass in grassland soil but to decrease fungal biomass [59]. Further manure application reduced the presence of saprotrophic fungi in both arable [21, 48] and grassland soils [12] and especially promoted the formation of bacterial residues, leading to increased SOC stocks [25]. However, only limited information is available on how slurry application affects fungal and bacterial residues across land use types. This study sought to answer the following research questions: (i) How does different grassland conversion intensities affect the stocks of SOC, total N, microbial biomass, fungal ergosterol and microbial residues in comparison with permanent grassland? (ii) To what extent does manure application affect changes in fungal and bacterial residues and microbial CLPP across land use types? (iii) What is the effect of plant species composition between grasslands on changes in fungal and bacterial residues and microbial CLPP? To study the effects of different land use on soil microbial residues and functional diversity, we hypothesised that (1) slurry application has positive effects on bacterial residues
leading to increased stocks of SOC and especially microbial biomass C. (2) Grassland conversion to maize monoculture leads to a decrease in soil fungi, which react more sensitively to management practices than do bacteria. (3) Differences in plant species composition and species dominance between grasslands are likely to exert strong selective pressures on the soil microbial community and functional diversity.
Material and Methods Study Site and Experimental Layout The experimental site Lindhof is located north of Kiel (54° 27′ N, 9° 57′ E), Germany, near the Baltic Sea. The mean annual temperature is 8.9 °C and precipitation is 768 mm [35]. During 1994, the arable land at this site was converted to permanent grassland (PG) consisting only of perennial ryegrass (Lolium perenne L.). During 2010, a part of the permanent grassland was ‘modified’ by sowing a mixture of different permanent grass species immediately after tillage (0–20 cm), which is called modified grassland (MG). The mixture contained 67 % perennial ryegrass, 17 % timothy-grass (Phleum pratense L.), 10 % smooth meadow-grass (Poa pratensis L.) and 6 % white clover (Trifolium repens L.). In addition, another part of the PG was converted to maize monoculture (MM). Each land use plots were split into two and received cattle slurry 240 kg N ha−1 a−1 or no fertilisation. The six treatments were (i) permanent grassland with slurry application (PG+), (ii) permanent grassland without slurry application (PG−), (iii) modified grassland with slurry application (MG+), (iv) modified grassland without slurry application (MG−), (v) maize monoculture with slurry application (MM+) and (vi) maize monoculture without slurry application (MM−). The size of each treatment plot was 3×18 m. All six treatments were arranged in a randomised block design with three replicates. The soil contained 66 % sand, 21 % silt and 13 % clay at 0– 40 cm depth and was classified as Eutric Cambisol [18]. The grass was generally cut four times each year for forage production, and slurry was applied using trail hoses with a range of 6 m to guarantee homogenous application [49]. Since the composition of slurry varied among the applications, the amount of slurry added had to be adjusted to match 240 kg N ha−1 year−1 and it ranged from 73 to 117 t fresh matter ha−1 year−1. The amounts of C and P added with the slurry applications ranged from 1.7 to 2.5 t C ha−1 year−1 and from 30 to 45 kg P ha−1 year−1, respectively. The grasslands received slurry four times each year at N rates of 80, 60, 60 and 40 kg N ha−1 applied in split doses following each cut to each cut. All the six treatments were fertilised during 2007 and 2009 with 100 kg K ha−1, 24 kg Mg ha−1, 68 kg S ha−1 (potassium sulfate with magnesium) and 45 kg P ha−1 (rock phosphate). The site
Novel Ecological Indicators of Land Use Change
has been managed organically since 1993, and all of the fertilisers used are accredited by the German organic growers association ‘Bioland’. Soil Sampling and Chemical Analysis All soil samples were taken on 12 April 2012 at three sampling points from each plot at 0–10, 10–20, 20–30 and 30–40 cm depth, using a steel corer with 4 cm diameter. This resulted in nine samples per treatment and depth. All soil samples were stored at 4 °C for 6 weeks prior to the measurement of soil biological properties [3, 4]. Bulk density was calculated from core dry weight divided by volume. Large amounts of flintstone (sedimentary cryptocrystalline form of the mineral quartz) were observed (approximately >25 % at our study sites), especially in the second and third replicates. Therefore, the volume of soil (cm−3) was calculated by subtracting the volume of stones or the volume of the fraction of fragments >2 mm from the total soil volume taken [50]. A field moist soil sample was used to analyse pH (1:2.5 soil-towater ratio). Total C and N were determined by dry combustion in a CNS Analyser (Vario EL, Elementar, Hanau, Germany). Microbial Indices Fumigated (24 h with ethanol-free CHCl3 at 25 °C) and nonfumigated soil (5 g) samples were extracted with 20 ml of 0.5 M K2SO4 by 30 min horizontal shaking at 200 rev min−1 and filtered (hw3, Sartorius Stedim Biotech, Göttingen, Germany) to measure microbial biomass C and N [9, 56]. Simultaneous determination of total organic C and N was performed with a single injection of extracts by infrared absorption after catalytic high temperature combustion up to 950 °C, using a multi N/C® 2100 automatic analyser (Analytik Jena, Germany). Microbial biomass C was calculated as EC/kEC, where EC=(organic C extracted from fumigated soil)−(organic C extracted from non-fumigated soil) and kEC =0.45 [60]. Microbial biomass N was calculated as EN / kEN, where EN =(total N extracted from fumigated soil)−(total N extracted from non-fumigated soil) and kEN =0.54 [9, 26]. Fumigated and non-fumigated 5-g samples were extracted with 25 ml of 1 M NH4NO3 to measure microbial biomass S [30]. Microbial biomass S was calculated as ES /kES, where ES =(total S extracted from fumigated soil)−(total S extracted from non-fumigated soil) and kES =0.35 [45, 61]. The basal respiration of soil was measured by incubating a 60-g soil sample for 7 days at 22 °C with 40 % WHC. The emitted CO2 was trapped in 0.5 M NaOH, and the excess NaOH was back titrated using 0.5 M HCl after the addition of saturated BaCl2 solution and phenolphthalein as an indicator. The metabolic quotient (qCO2) [4] was calculated by dividing basal respiration rates by microbial biomass, to yield qCO2
values expressed in millimoles CO2–C per mole microbial biomass C per hour. The fungal cell membrane component ergosterol was extracted from 2 g of moist soil with 100 ml ethanol [16], determined by reverse phase HPLC using 100 % methanol as the mobile phase and detected at a wavelength of 282 nm. The amino sugars MurN, mannosamine (ManN), galactosamine (GalN) and GlcN were determined as described by Indorf et al. [22]. Moist samples of 0.5 g soil were weighed into 20-ml test tubes, mixed with 10 ml 6 M HCl and hydrolysed for 6 h at 105 °C. The HCl was removed by a rotary evaporator; the residue was dissolved in water and centrifuged. The samples were transferred to vials and stored at −18 °C until the HPLC measurement. After derivatisation with ortho-phthaldialdehyde, fluorometric emission of amino sugar was measured at a wavelength of 445 nm after excitation at a wavelength of 330 nm. The HPLC system consisted of a Dionex (Germering, Germany) P 580 gradient pump, a Dionex Ultimate WPS-3000TSL analytical autosampler with in-line split-loop injection and thermostat and a Dionex RF 2000 fluorescence detector. Catabolic Response Profiling The catabolic response profile was obtained by the MicroResp™ method of Campbell et al. [10]. The soil was adjusted to a water holding capacity of 50 %, before being weighed (0.3 g) into each deep well (1.1 ml deep well microtitre plate (Nunc, Thermo Electron LED, Langenselbold, Germany)) and stored for 7 days in the dark at 25 °C, prior to catabolic response profile analysis. Besides water, the following 17 substrates were used: 5 amino acids (γ-aminobutyric acid, L-alanine, DL-aspartic acid, L-glutamine and L-leucine), 2 amino sugars (N-acetyl glucosamine and D-glucosamine), 5 carbohydrates (L-arabinose, D-galactose, D-glucose, D-fructose and D-trehalose) and 5 carboxylic acids (ascorbic acid, citric acid, L-malic acid, protocatechuic acid and oxalic acid). These substrates were chosen to present a cross section of root exudates [10] and microbial components and products [2]. A substrate concentration of 8 mg g−1 dry soil was used by adding 25 μl of solution in the deep well plate before incubating the soil for 4 h at 25 °C. Only 2 mg g−1 soil of L-leucine and L-glutamine and 0.8 mg g−1 soil of protocatechuic acid and DL-aspartic acid were used due to their low solubility at higher concentrations [52]. The indicator gel was prepared using 1 % Noble agar, 150 mM KCl, 2.5 mM NaHCO3 and 12.5 μg g−1 cresol red [10]. The warm gel (150 μl) was applied to a microtitre plate (Nunc), which was stored for 72 h before the measurement in a closed PVC bag, containing soda lime and wet tissue paper. The indicator gel plates were regenerated for reuse by storing them for 36 h in a plastic box containing soda lime and wet tissue paper. For calibrating the CO2 trap, five different soils were incubated with and without
R. Murugan et al.
8 mg g−1 glucose in five replicates each for 4 h at 25 °C in the dark, before measuring the CO2 evolution with a gas chromatograph (Shimadzu) and with the MicroResp™ system. The resulting regression line was fitted to the following power function: μl CO2 g−1 soil = 51 × (0.2 + ABS)3, r=0.98, where ABS is the difference in absorption (572 nm) between T1 and T0.
pairs. Land use and slurry treatments effects on multi-SIR were tested by multivariate statistics using multivariate analysis (MANOVA). Discriminant function analysis (DF) was carried out after canonical variate analysis of all 17 organic substrates and aqua dest as variables and the specific land use and fertilisation treatments as groups using STATISTICA 9.0 (StatSoft). The Shannon diversity index (H) was calculated by the formula H=−Σ pi (ln pi), where pi is the particular activity of the sum of all activities [62]. All statistical analyses were carried out using SPSS statistical software (SPSS 16.0).
Calculation and Statistical Analysis The stocks of the soil nutrients and microbial indices at different depths were calculated on a volume basis by taking the bulk density of the respective soil layer into account. Fungal glucosamine was calculated by subtracting bacterial glucosamine from total glucosamine as an index for fungal residues, assuming that muramic acid and glucosamine occur at a 1 to 2 ratio in bacteria [17]. Fungal C (μg g−1 dry weight)=(mmol GlcN−2×mmol MurN)×179.2 gmol−1 ×9, where 179.2 is the molecular weight of GlcN and 9 is the conversion value of fungal GlcN to fungal C [5]. Bacterial C (μg g−1 dry weight) was calculated as an index for bacterial residues by multiplying MurN in micrograms per gram dry weight by 45 [5]. The values of three sampling points from three block replicates per tillage trial were used to calculate mean concentrations and stocks (n=3). The results presented in Figs. 1 and 2 are arithmetic means of treatments at the respective depths (n=3), given on an oven dry basis (105 °C, 24 h). Data were checked for normal distribution by Chi-square test. When necessary, the data were ln-transformed. The significance of treatment effects was analysed by a two-way ANOVA, using land use and slurry application as independent factors and depth as repeated measurements. A pairwise LSD test (P
SOIL MICROBIOLOGY
Changes in Soil Microbial Biomass and Residual Indices as Ecological Indicators of Land Use Change in Temperate Permanent Grassland Rajasekaran Murugan & Ralf Loges & Friedhelm Taube & André Sradnick & Rainer Georg Joergensen
Received: 25 September 2013 / Accepted: 30 January 2014 # Springer Science+Business Media New York 2014
Abstract The relationship between microbial biomass, residues and their contribution to microbial turnover is important to understand ecosystem C storage. The effects of permanent grassland (100 % ryegrass—PG), conversion to modified grassland (mixture of grass and clover—MG) or maize monoculture (MM) on the dynamics of soil organic C (SOC), microbial biomass, fungal ergosterol and microbial residues (bacterial muramic acid and fungal glucosamine) were investigated. Cattle slurry was applied to quantify the effects of fertilisation on microbial residues and functional diversity of microbial community across land use types. Slurry application significantly increased the stocks of microbial biomass C and S and especially led to a shift in microbial residues towards bacterial tissue. The MM treatment decreased the stocks of SOC, microbial biomass C, N and S and microbial residues compared with the PG and MG treatments at 0–40 cm depth. The MM treatment led to a greater accumulation of saprotrophic fungi, as indicated by the higher ergosterol-tomicrobial biomass C ratio and lower microbial biomass C/S ratio compared with the grassland treatments. The absence of a white clover population in the PG treatment caused a greater accumulation of fungal residues (presumably arbuscular mycorrhizal fungi (AMF), which do not contain ergosterol but
Electronic supplementary material The online version of this article (doi:10.1007/s00248-014-0383-8) contains supplementary material, which is available to authorized users. R. Murugan (*) : A. Sradnick : R. G. Joergensen Department of Soil Biology and Plant Nutrition, University of Kassel, Nordbahnhofstr. 1a, 37213 Witzenhausen, Germany e-mail: [email protected] R. Loges : F. Taube Institute of Crop Science and Plant Breeding, Grass and Forage Science/Organic Agriculture, Christian-Albrechts-University of Kiel, Hermann-Rodewald-Str. 9, 24118 Kiel, Germany
glucosamine), as indicated by the significantly higher fungal C-to-bacterial C ratio and lower ergosterol-to-microbial biomass C ratio compared with the MG treatment. In addition to these microbial biomass and residual indices, the community level physiological profiles (CLPP) demonstrated distinct differences between the PG and MG treatments, suggesting the potential of these measurements to act as an integrative indicator of soil functioning.
Introduction Land use change from permanent grassland to crop land has led to an average loss of soil C stocks by 59 % [20] and may accelerate soil erosion [42]. Soil organic C (SOC), macroaggregates, catabolic diversity and microbial biomass decreased remarkably under cotton (Gossypium hirsutum L.) monoculture [1] and prolonged cultivation of maize in grassland soils [31, 32]. Prolonged cultivation of arable crops and tillage causes a shift in the microbial community structure towards Gram-positive bacteria [54, 58]; the fungal-to-bacterial biomass ratio of permanent grassland soils was twice that of arable land [6]. However, very limited information is available on changes in fungal and bacterial residues and microbial functional diversity following conversion of permanent grassland to monoculture or modified grassland with different mixture of grass species [19, 32]. Soil microbial community plays an important role in the maintenance of soil ecosystem function, such as C sequestration [15, 40]. Crop mixtures in grassland resulted in greater plant biomass, microbial biomass and C utilisation by microbial communities than single species, due to variations in the quantity and quality of root exudates [12, 19]. In contrast, permanent grassland with a single grass species showed greater functional diversity than a modified grassland soil [58].
R. Murugan et al.
However, the effects of plant species composition between grassland soils on specific responses of fungal and bacterial residues are completely unknown. The non-living microbial residues represent a significant SOM pool, much greater than the living biomass [51]. The size of the microbial residue C pool in soil may be about 40 times that of the living microbial biomass [34, 51]. However, very few studies investigated the importance of the relationship between microbial biomass, residues and their contribution to microbial turnover and ecosystem C storage. The microbial residues can be assessed by measuring those amino sugars that are exclusively microbial products in soil [2]. Among the four detectable amino sugars, glucosamine (GlcN) is primarily derived from the chitin of both arbuscular mycorrhizal fungi (AMF) and saprotrophic fungal cell walls, whereas muramic acid (MurN) exclusively originates from bacteria [2]. As cell wall components accumulate in SOM [2], soil amino sugar content can be used as a valuable biomarker to indicate the effects of changes in agricultural management on shift of fungal and bacterial residues and their contribution to SOM accumulation within a specific ecosystem [27, 38]. The microbial community structure and its diversity have been suggested as a sensitive means of assessing soil quality [40]. The modified MicroResp™ method [10], based on Degens and Harris [14] substrate-induced respiration with multiple carbon sources (multi-SIR), has been reported to be a reliable and sensitive method for measuring changes in microbial community level physiological profiles (CLPP) caused by land use change and fertilisation [43, 53]. The application of cattle slurry may have considerable positive effects on the nutrient dynamics of ecosystems [57], resulting in a higher catabolic diversity than in inorganically fertilised soil [43, 52]. Slurry application has been found to increase bacterial biomass in grassland soil but to decrease fungal biomass [59]. Further manure application reduced the presence of saprotrophic fungi in both arable [21, 48] and grassland soils [12] and especially promoted the formation of bacterial residues, leading to increased SOC stocks [25]. However, only limited information is available on how slurry application affects fungal and bacterial residues across land use types. This study sought to answer the following research questions: (i) How does different grassland conversion intensities affect the stocks of SOC, total N, microbial biomass, fungal ergosterol and microbial residues in comparison with permanent grassland? (ii) To what extent does manure application affect changes in fungal and bacterial residues and microbial CLPP across land use types? (iii) What is the effect of plant species composition between grasslands on changes in fungal and bacterial residues and microbial CLPP? To study the effects of different land use on soil microbial residues and functional diversity, we hypothesised that (1) slurry application has positive effects on bacterial residues
leading to increased stocks of SOC and especially microbial biomass C. (2) Grassland conversion to maize monoculture leads to a decrease in soil fungi, which react more sensitively to management practices than do bacteria. (3) Differences in plant species composition and species dominance between grasslands are likely to exert strong selective pressures on the soil microbial community and functional diversity.
Material and Methods Study Site and Experimental Layout The experimental site Lindhof is located north of Kiel (54° 27′ N, 9° 57′ E), Germany, near the Baltic Sea. The mean annual temperature is 8.9 °C and precipitation is 768 mm [35]. During 1994, the arable land at this site was converted to permanent grassland (PG) consisting only of perennial ryegrass (Lolium perenne L.). During 2010, a part of the permanent grassland was ‘modified’ by sowing a mixture of different permanent grass species immediately after tillage (0–20 cm), which is called modified grassland (MG). The mixture contained 67 % perennial ryegrass, 17 % timothy-grass (Phleum pratense L.), 10 % smooth meadow-grass (Poa pratensis L.) and 6 % white clover (Trifolium repens L.). In addition, another part of the PG was converted to maize monoculture (MM). Each land use plots were split into two and received cattle slurry 240 kg N ha−1 a−1 or no fertilisation. The six treatments were (i) permanent grassland with slurry application (PG+), (ii) permanent grassland without slurry application (PG−), (iii) modified grassland with slurry application (MG+), (iv) modified grassland without slurry application (MG−), (v) maize monoculture with slurry application (MM+) and (vi) maize monoculture without slurry application (MM−). The size of each treatment plot was 3×18 m. All six treatments were arranged in a randomised block design with three replicates. The soil contained 66 % sand, 21 % silt and 13 % clay at 0– 40 cm depth and was classified as Eutric Cambisol [18]. The grass was generally cut four times each year for forage production, and slurry was applied using trail hoses with a range of 6 m to guarantee homogenous application [49]. Since the composition of slurry varied among the applications, the amount of slurry added had to be adjusted to match 240 kg N ha−1 year−1 and it ranged from 73 to 117 t fresh matter ha−1 year−1. The amounts of C and P added with the slurry applications ranged from 1.7 to 2.5 t C ha−1 year−1 and from 30 to 45 kg P ha−1 year−1, respectively. The grasslands received slurry four times each year at N rates of 80, 60, 60 and 40 kg N ha−1 applied in split doses following each cut to each cut. All the six treatments were fertilised during 2007 and 2009 with 100 kg K ha−1, 24 kg Mg ha−1, 68 kg S ha−1 (potassium sulfate with magnesium) and 45 kg P ha−1 (rock phosphate). The site
Novel Ecological Indicators of Land Use Change
has been managed organically since 1993, and all of the fertilisers used are accredited by the German organic growers association ‘Bioland’. Soil Sampling and Chemical Analysis All soil samples were taken on 12 April 2012 at three sampling points from each plot at 0–10, 10–20, 20–30 and 30–40 cm depth, using a steel corer with 4 cm diameter. This resulted in nine samples per treatment and depth. All soil samples were stored at 4 °C for 6 weeks prior to the measurement of soil biological properties [3, 4]. Bulk density was calculated from core dry weight divided by volume. Large amounts of flintstone (sedimentary cryptocrystalline form of the mineral quartz) were observed (approximately >25 % at our study sites), especially in the second and third replicates. Therefore, the volume of soil (cm−3) was calculated by subtracting the volume of stones or the volume of the fraction of fragments >2 mm from the total soil volume taken [50]. A field moist soil sample was used to analyse pH (1:2.5 soil-towater ratio). Total C and N were determined by dry combustion in a CNS Analyser (Vario EL, Elementar, Hanau, Germany). Microbial Indices Fumigated (24 h with ethanol-free CHCl3 at 25 °C) and nonfumigated soil (5 g) samples were extracted with 20 ml of 0.5 M K2SO4 by 30 min horizontal shaking at 200 rev min−1 and filtered (hw3, Sartorius Stedim Biotech, Göttingen, Germany) to measure microbial biomass C and N [9, 56]. Simultaneous determination of total organic C and N was performed with a single injection of extracts by infrared absorption after catalytic high temperature combustion up to 950 °C, using a multi N/C® 2100 automatic analyser (Analytik Jena, Germany). Microbial biomass C was calculated as EC/kEC, where EC=(organic C extracted from fumigated soil)−(organic C extracted from non-fumigated soil) and kEC =0.45 [60]. Microbial biomass N was calculated as EN / kEN, where EN =(total N extracted from fumigated soil)−(total N extracted from non-fumigated soil) and kEN =0.54 [9, 26]. Fumigated and non-fumigated 5-g samples were extracted with 25 ml of 1 M NH4NO3 to measure microbial biomass S [30]. Microbial biomass S was calculated as ES /kES, where ES =(total S extracted from fumigated soil)−(total S extracted from non-fumigated soil) and kES =0.35 [45, 61]. The basal respiration of soil was measured by incubating a 60-g soil sample for 7 days at 22 °C with 40 % WHC. The emitted CO2 was trapped in 0.5 M NaOH, and the excess NaOH was back titrated using 0.5 M HCl after the addition of saturated BaCl2 solution and phenolphthalein as an indicator. The metabolic quotient (qCO2) [4] was calculated by dividing basal respiration rates by microbial biomass, to yield qCO2
values expressed in millimoles CO2–C per mole microbial biomass C per hour. The fungal cell membrane component ergosterol was extracted from 2 g of moist soil with 100 ml ethanol [16], determined by reverse phase HPLC using 100 % methanol as the mobile phase and detected at a wavelength of 282 nm. The amino sugars MurN, mannosamine (ManN), galactosamine (GalN) and GlcN were determined as described by Indorf et al. [22]. Moist samples of 0.5 g soil were weighed into 20-ml test tubes, mixed with 10 ml 6 M HCl and hydrolysed for 6 h at 105 °C. The HCl was removed by a rotary evaporator; the residue was dissolved in water and centrifuged. The samples were transferred to vials and stored at −18 °C until the HPLC measurement. After derivatisation with ortho-phthaldialdehyde, fluorometric emission of amino sugar was measured at a wavelength of 445 nm after excitation at a wavelength of 330 nm. The HPLC system consisted of a Dionex (Germering, Germany) P 580 gradient pump, a Dionex Ultimate WPS-3000TSL analytical autosampler with in-line split-loop injection and thermostat and a Dionex RF 2000 fluorescence detector. Catabolic Response Profiling The catabolic response profile was obtained by the MicroResp™ method of Campbell et al. [10]. The soil was adjusted to a water holding capacity of 50 %, before being weighed (0.3 g) into each deep well (1.1 ml deep well microtitre plate (Nunc, Thermo Electron LED, Langenselbold, Germany)) and stored for 7 days in the dark at 25 °C, prior to catabolic response profile analysis. Besides water, the following 17 substrates were used: 5 amino acids (γ-aminobutyric acid, L-alanine, DL-aspartic acid, L-glutamine and L-leucine), 2 amino sugars (N-acetyl glucosamine and D-glucosamine), 5 carbohydrates (L-arabinose, D-galactose, D-glucose, D-fructose and D-trehalose) and 5 carboxylic acids (ascorbic acid, citric acid, L-malic acid, protocatechuic acid and oxalic acid). These substrates were chosen to present a cross section of root exudates [10] and microbial components and products [2]. A substrate concentration of 8 mg g−1 dry soil was used by adding 25 μl of solution in the deep well plate before incubating the soil for 4 h at 25 °C. Only 2 mg g−1 soil of L-leucine and L-glutamine and 0.8 mg g−1 soil of protocatechuic acid and DL-aspartic acid were used due to their low solubility at higher concentrations [52]. The indicator gel was prepared using 1 % Noble agar, 150 mM KCl, 2.5 mM NaHCO3 and 12.5 μg g−1 cresol red [10]. The warm gel (150 μl) was applied to a microtitre plate (Nunc), which was stored for 72 h before the measurement in a closed PVC bag, containing soda lime and wet tissue paper. The indicator gel plates were regenerated for reuse by storing them for 36 h in a plastic box containing soda lime and wet tissue paper. For calibrating the CO2 trap, five different soils were incubated with and without
R. Murugan et al.
8 mg g−1 glucose in five replicates each for 4 h at 25 °C in the dark, before measuring the CO2 evolution with a gas chromatograph (Shimadzu) and with the MicroResp™ system. The resulting regression line was fitted to the following power function: μl CO2 g−1 soil = 51 × (0.2 + ABS)3, r=0.98, where ABS is the difference in absorption (572 nm) between T1 and T0.
pairs. Land use and slurry treatments effects on multi-SIR were tested by multivariate statistics using multivariate analysis (MANOVA). Discriminant function analysis (DF) was carried out after canonical variate analysis of all 17 organic substrates and aqua dest as variables and the specific land use and fertilisation treatments as groups using STATISTICA 9.0 (StatSoft). The Shannon diversity index (H) was calculated by the formula H=−Σ pi (ln pi), where pi is the particular activity of the sum of all activities [62]. All statistical analyses were carried out using SPSS statistical software (SPSS 16.0).
Calculation and Statistical Analysis The stocks of the soil nutrients and microbial indices at different depths were calculated on a volume basis by taking the bulk density of the respective soil layer into account. Fungal glucosamine was calculated by subtracting bacterial glucosamine from total glucosamine as an index for fungal residues, assuming that muramic acid and glucosamine occur at a 1 to 2 ratio in bacteria [17]. Fungal C (μg g−1 dry weight)=(mmol GlcN−2×mmol MurN)×179.2 gmol−1 ×9, where 179.2 is the molecular weight of GlcN and 9 is the conversion value of fungal GlcN to fungal C [5]. Bacterial C (μg g−1 dry weight) was calculated as an index for bacterial residues by multiplying MurN in micrograms per gram dry weight by 45 [5]. The values of three sampling points from three block replicates per tillage trial were used to calculate mean concentrations and stocks (n=3). The results presented in Figs. 1 and 2 are arithmetic means of treatments at the respective depths (n=3), given on an oven dry basis (105 °C, 24 h). Data were checked for normal distribution by Chi-square test. When necessary, the data were ln-transformed. The significance of treatment effects was analysed by a two-way ANOVA, using land use and slurry application as independent factors and depth as repeated measurements. A pairwise LSD test (P
