Soil microbiological and biochemical properties for assessing the effect of agricultural management practices in Estonian cultivated soils

european journal of soil biology 44 (2008) 231–237

available at www.sciencedirect.com

journal homepage: http://www.elsevier.com/locate/ejsobi

Original article

Soil microbiological and biochemical properties for assessing the effect of agricultural management practices in Estonian cultivated soils Marika Truua,*, Jaak Truua, Mari Ivaskb a

Faculty of Biology and Geography, University of Tartu, 23 Riia Street, 51010 Tartu, Estonia Tartu College of Tallinn University of Technology, 78 Puiestee Street, 51008 Tartu, Estonia

b

article info

abstract

Article history:

A set of soil microbiological and biochemical properties was used to assess the influence of

Received 3 September 2007

agricultural practices such as rotation, usage of pesticides, and fertilizers on the three most

Accepted 19 December 2007

widespread soil types (Calcaric Regosols, Calcaric Cambisols and Stagnic Luvisols) in the

Published online 18 June 2008

fields of horticultural farms throughout Estonia. Microbial biomass, dehydrogenase and alkaline phosphatase activity were significantly higher in Calcaric Regosols, whereas mea-

Keywords:

sured soil chemical parameters showed practically no difference among soil types. Multi-

Agricultural management practice

variate exploratory analysis of soil biochemical and microbiological parameters clearly

Microbial activity

distinguished soils with different management practices when the effect of soil type was

Microbial biomass

taken into account in data analysis. Activity of dehydrogenase, potential nitrification, N-

Soil type

mineralisation, and microbial biomass contributed most strongly to the differentiation of soils from differently managed fields. Soils managed according to organic farming principles were generally characterized by elevated microbiological parameter values, but at the same time the variation of those parameters among soils from these fields was also highest. The application of organic manure positively affected microbial biomass, Nmineralisation, potential nitrification, dehydrogenase and acidic phosphatase activity. Data analysis indicated that the amount of mineral nitrogen fertilizers added over time has a stronger effect on microbial biomass than the amount added in a given year. Legume-based crop rotation increased soil respiration and microbial biomass. ª 2008 Elsevier Masson SAS. All rights reserved.

1.

Introduction

There have been two major changes in Estonian agricultural management practice during the last century. At the beginning of the 1940s land was taken from private owners and large collective farms were established. Fifty years of very intensive agricultural practice with high inputs of mineral

fertilizers and agrochemicals followed. At the beginning of the 1990s, the land was returned to private owners, but during the last decade the economic situation and agricultural policy have been unfavorable for agricultural land users. Since 1989 cropping area has steadily decreased by about half (from 975,000 ha in 1989 to 461,000 ha in 2003). The first organically managed farms were established at the very end of the

* Corresponding author. Tel.: þ372 7375 014; fax: þ372 7420 286. E-mail address: [email protected] (M. Truu). 1164-5563/$ – see front matter ª 2008 Elsevier Masson SAS. All rights reserved. doi:10.1016/j.ejsobi.2007.12.003

232

european journal of soil biology 44 (2008) 231–237

1990s. Nowadays ecologically managed land comprises about 5% of all agricultural land and only 14% of this land is used for field crops. However, there are also still a number of farmers who do not strictly follow any particular (organic or conventional) management practice. Soil is a complex environment, where microorganisms play a crucial role in nutrient cycling and the degradation of different pollutants (herbicides, pesticides, PAH-s, phenols, etc.) contributing in this way to the maintenance of soil quality [8,14,39]. On the other hand, microbial activities are strongly dependent on nutritional and other chemical and physical conditions of the soil and respond rapidly to changes in soil properties. Microorganisms are considered sensible indicators when monitoring changes in soil status affected by agricultural management, but the meaningful set of microbiological indicators still remains an object of debate [3,11,31,35]. Microbial biomass is considered to be an integrative, biologically meaningful, management sensitive, and measurable signal in the soil [30]. Its turnover rate is much faster than that of total soil organic matter, and based on the dynamics of soil microbial biomass content, longer-term trends in soil total organic matter content can be predicted [34]. The influence of soil management on the organic matter C and N turnover capacity of microbial biomass has been pointed out in many studies focussing on microbial biomass and activity measurements in arable soils [4,10,28]. Soil respiration is considered to be one of the well-established parameters for monitoring decomposition, but it is highly variable and can fluctuate

widely depending on substrate availability, moisture content and temperature [34]. N-mineralisation reflects the quality and quantity of soil organic nitrogen and links the substrate with the functioning and activity of a range of soil organisms. In order to estimate the effects of soil management, land use and specific conditions on soil microbial activity, short-term laboratory measurements, including enzymatic activities are used [3,12]. The advantage of standardizing environmental factors is that it allows the comparison of soils of different origins, but the results obtained represent the potential activity only and must be interpreted with reservations [31]. The objective of this work was to use a set of soil microbiological and biochemical indicators to evaluate the effect of different management practices in three Estonian soil types.

2.

Materials and methods

2.1.

Soil type and management description

Twenty-three study areas with three most widespread soil types (Calcaric Regosols, Calcaric Cambisols and Stagnic Luvisols) throughout Estonia were selected (Table 1). On Calcaric Cambisols and Stagnic Luvisols eight fields, and on Calcaric Regosols seven fields with different management practices were selected. Three years history of agricultural practices (cover crops, amounts of mineral and organic fertilizers and different kinds of pesticides used) was recorded. Mineral nitrogen was applied on 17 fields and the amounts ranged

Table 1 – Soil and site characteristics of studied fields Site no.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Location

58 160 0800 N 58 190 0800 N 58 370 1300 N 58 580 4500 N 58 580 4700 N 58 580 5400 N 58 390 2700 N 58 430 0300 N 59 090 2700 N 59 100 5100 N 59 100 4200 N 58 580 5800 N 58 580 5700 N 58 580 5200 N 58 410 3500 N 58 410 4800 N 58 370 0300 N 58 330 1000 N 58 330 0300 N 58 330 4300 N 58 420 0300 N 58 260 2500 N 58 420 3000 N

22 030 0200 E 22 000 4500 E 26 310 1500 E 24 430 0100 E 24 420 2500 E 24 420 4200 E 26 370 4000 E 26 390 2800 E 25 450 1900 E 25 460 2100 E 25 460 0900 E 24 420 5300 E 24 430 0000 E 24 430 1000 E 26 340 0300 E 26 330 4900 E 26 300 5900 E 25 330 4400 E 25 340 0200 E 25 330 2300 E 26 350 3800 E 22 010 0800 E 26 390 2200 E

Soil typea

CR CR CR CR CR CR CC CC CC CC CC CC CC CC SL SL SL SL SL SL SL CR SL

Mineral nitrogen, sum of three years (kg ha1)

60 70 100 50 70 315 248 249 175 70 140 146

Organic fertilizers, sum of three yearsb (t ha1) 120 65 GM

Pesticidesc

H H H, F H

GM

40 GM

H, I H, I, F H, I, F H H, F H H

GM GM 25 285 309 245 100 182

a CR – Calcaric Regosols, CC – Calcaric Cambisols, SL – Stagnic Luvisols. b GM – green manure, numbers are shown for brown manure. c H – Herbicides, I – Insecticidas, F – Fungicides.

40 80

H, I, F H, I, F H, I H, I H

Legumes/rape

No/no Yes/no Yes/no Yes/no No/yes Yes/no Yes/no Yes/no No/yes No/yes No/no Yes/no Yes/no Yes/no Yes/no Yes/no Yes/no No/yes No/yes No/no No/yes No/yes No/yes

european journal of soil biology 44 (2008) 231–237

233

from 15 kg ha1 per year (in three cases) to 142.5 kg ha1 per year (in one case during the last three years). Animal manure was applied on seven fields and six fields have received green manure. The amount of brown manure applied per year ranged from 25 t ha1 to 40 t ha1. Three of the studied fields had received both organic and mineral fertilizers simultaneously. Seven fields had oilseed rape in rotation (one or two years during the last three years) and were also treated with different pesticides – herbicides on all seven fields, insecticides and fungicides on six fields. All together 16 fields were treated with herbicides during the last three years. In 11 fields at least once during the three years leguminous crops (mainly clover) were grown. One field had not been treated with any fertilizers or pesticides during the last three years, nor did it have any leguminous crops in rotation.

ammonium sulfate as a substrate and sodium chlorate as an inhibitor preventing nitrite oxidation were incubated for 5 h at 25  C and the released nitrite was extracted with 2 M potassium chloride solution and determined colorimetrically at 520 nm. Acid and alkaline phosphomonoesterase activities were measured using p-nitrophenyl phosphate as a substrate according to Margesin [24]. Samples were incubated for 1 h at 37  C and the released p-nitrophenol was extracted with 0.5 M sodium hydroxide in the presence of calcium chloride. The amounts of extracted products derived from all activity measurements were determined photometrically at 400 nm. All measured microbiological parameters were calculated on dry matter bases.

2.2.

Spearman rank correlation coefficient was used to relate soil microbiological variables to soil chemical parameters, and to the amount of mineral fertilizers applied annually and cumulatively during the last three years. The Kruskal–Wallis oneway analysis of variance by ranks’ test was used to assess the impact of soil type on soil microbiological and chemical parameters. The data set of soil microbiological variables was analyzed using principal component analysis (PCA) based on a correlation matrix, and the effect of binary coded characteristics on the grouping of samples was assessed using a multivariate randomization test [23] with the computer program ADE-4 [37]. Prior PCA values of microbiological variables were log-transformed. The following management options were binary (presence/absence) coded: use of organic fertilizers, and pesticides (herbicides, fungicides, and insecticides). The Mann–Whitney test was used to verify the impact of coded factors on individual soil microbiological variables. The results of PCA are interpreted using scatters of the sample scores and a correlation plot showing the relationship of variables with PCA axes. Grouping of samples due to binary coded variables is visualised using scatters of the sample scores connected with group centroids (star plot). Group centroid coordinates are calculated as the average of the coordinates of all the group members.

Soil sampling

The samples were taken from the fields with a soil corer (B2 cm) from 0 to 20 cm layer at the end of October 2003 and composite samples were made for each field. For the composite samples (one to three composite samples depending on the size of the field, one composite sample per 2 ha) 25 subsamples were randomly collected from each field. The fresh soil samples were sieved (<2 mm) and stored at 4  C until the analyses were carried out. From the sieved samples, soil organic matter content (the loss on ignition method), dry matter content, pHKCl, total nitrogen, total potassium, and available phosphorus content were determined and microbiological analyses were performed.

2.3.

Microbiological biomass and activities

Substrate-induced respiration (SIR) by Isermeyer technique was applied to measure metabolically active microbial biomass carbon. Glucose (0.4 g 100 g1 soil) was added to 20 g of field moist soil and then incubated in a closed vessel for 4 h at 22  C in the dark. The CO2 produced was absorbed in 0.1 M sodium hydroxide and quantified by titration. The microbial biomass C was calculated according to Beck et al. [2]. Soil microbial respiration rate (basal respiration) was mea¨ hrlinger [41]. Twenty gram sured by titration according to O of soil was incubated in a closed vessel for 24 h at 25  C. The CO2 produced was absorbed in 0.05 M sodium hydroxide, quantified by titration, and the respiration rate was calculated. The microbial metabolic quotient qCO2 was calculated as the ratio between basal respiration and SIR-derived microbial carbon. Dehydrogenase activity was measured using triphenyltetrazolium chloride as a substrate. Samples were incubated for 16 h at 25  C and the triphenyl formazan produced was extracted with acetone and measured photometrically at 546 nm [40]. N-mineralisation was determined under waterlogged conditions according to Kandeler [16]. Waterlogged soils were incubated for seven days at 40  C. The ammonium released from organic nitrogen compounds was extracted with 2 M potassium chloride solution, determined by a modified Berthelot reaction, and measured with a spectrophotometer at 660 nm [17]. In order to evaluate the nitrification capacity of the microbial communities, the potential nitrification method was used [18]. Soil samples with

2.4.

3.

Statistical analyses

Results

Comparison of soil microbiological parameters based on coefficient of variation (CV) showed that the highest variation among all samples appeared in values of dehydrogenase activity and potential nitrification (CV 148.2% and 143.0%, respectively), followed by alkaline phosphatase (CV 92.4%), microbial biomass (CV 89.3%), basal respiration rate (CV 68.8%). The least variable was acid phosphatase activity (CV 57.9%). The multivariate randomization test showed that soil type has a strong effect on soil microbiological variables (P < 0.01, 10,000 permutations). According to the Kruskal–Wallis test, microbial biomass, activities of dehydrogenase, and alkaline phosphatase had significantly higher values in Calcaric Regosols (Table 2). The measured chemical parameters did not differ between soil types, except for pH (Kruskal–Wallis test, P < 0.05) the values of which were lowest in Stagnic Luvisols (6.03  0.75), and did not differ between Calcaric Regosols

234

european journal of soil biology 44 (2008) 231–237

Table 2 – Mean values and standard deviations of soil microbiological parameters in different soil types Soil type (WRB)

SIR (mgC g1 dw)

Dehydrogenase activity (mgTFP g1 dw 16 h1)

Alkaline phospatase (mgNP g1 dw h1)

0.779  0.219** 0.559  0.121 0.439  0.139

6.397  4.022* 4.104  0.975 2.988  0.705

406.7  185.1* 297.5  123.3 167.4  157.4

Calcaric Regosols Calcaric Cambisols Stagnic Luvisols

Only parameters that were different among soil types according to Kruskal–Wallis one-way analysis of variance by Ranks are shown. Asterisks designate group means that are statistically different according to multiple comparisons of mean ranks. *P < 0.05, **P < 0.01.

and Calcaric Cambisols (6.82  0.27 and 6.38  0.35, respectively). Total nitrogen content showed highest variation (CV 106.69%) among samples and was related to soil microbial biomass, dehydrogenase activity, potential nitrification, N-mineralisation, and alkaline phosphatase activity (Table 3). Soil organic matter content correlated significantly with all measured microbiological parameters, but the relationship was strongest in the case of potential nitrification, dehydrogenase activity, N-mineralisation, alkaline and acid phosphatase activities. The soil pH was related to microbial biomass, basal respiration, and alkaline phosphatase, but was only slightly correlated with potential nitrification. A partial form of principal component analysis was used to estimate the importance of microbiological variables in the grouping of the studied fields. In the case of partial PCA, the analysis was performed on the residual matrix of microbiological variables after regression on soil type, i.e. the effect of soil type was removed from the analysis. The first two principal components accounted for 78.9% of the total variation in the microbiological data set. The first principal component was positively related to dehydrogenase, potential nitrification and N-mineralisation activity, and microbial biomass, while the second principal component was negatively correlated with metabolic quotient and soil respiration (Fig. 1a). According to PCA ordination, the fields of organic farms are located to the right of the origin and are characterized by higher microbial activity values and biomass (Fig. 1b, Table 4). The second group of fields is scattered around the origin and consists mainly of fields that have received both organic and inorganic fertilizers. The third group of fields, which is situated to the left of the origin, comprises mostly of fields that in most cases have received higher amounts of mineral nitrogen as well as other agrochemicals (herbicides, pesticides and fungicides). The multivariate randomization test indicated that

Table 3 – Relationships between soil microbiological and chemical parameters based on Spearman rank correlation coefficient Variable SIR Dehydrogenase activity Potential nitrification N-mineralisation Alkaline phosphatase Acid phosphatase Respiration *P < 0.05, **P < 0.01, ***P < 0.001.

Ntot

TC

pH

0.51** 0.82*** 0.83*** 0.62*** 0.88***

0.45* 0.75*** 0.76*** 0.60*** 0.82*** 0.50** 0.39*

0.69***

0.45*

0.48** 0.83*** 0.80***

application of organic fertilizers had a significant impact on the grouping of fields (P < 0.01, 10,000 permutations) (Fig. 1c). According to the Mann–Whitney test, fields treated with organic fertilizer are characterized by two times higher microbial biomass (P < 0.01) and N-mineralisation values (P < 0.001). Data analysis indicated a tendency for increased respiration and microbial biomass in the case of fields with legume-based crop rotation (Mann–Whitney test, P < 0.01 and P < 0.05, respectively). At the same time, these two parameters were negatively affected by the use of pesticides; respiration activity (mean values 0.21 and 0.10 mg CO2 24 h1 g1 dw, respectively) was repressed in particular. Values of studied microbiological variables were regressed on the amount of mineral fertilizers applied annually and cumulatively during the last three years. From the set of measured microbiological parameters, only microbial biomass was correlated negatively with the amount of mineral nitrogen applied annually (R ¼ 0.53, . 0.57, P < 0.01), but the strongest relationship (R ¼ 0.68, P < 0.001) was found with the cumulative amount of mineral nitrogen applied during the last three years.

4.

Discussion

Soil type is one of the primary determinants of soil microbial structure, as demonstrated by polyphasic studies of soil bacterial community composition [13,32]. We found that microbial biomass, and activities of dehydrogenase and alkaline phosphatase are dependent on soil type, whereas measured soil chemical parameters showed practically no variation among the three studied soil types. These differences in soil microbial parameters due to soil type may be related to the qualitative structure of soil organic carbon as well as to soil texture [33]. In the study of conventional and organic farms Van Diepeningen with co-workers [38] found that soil type in general has a much stronger effect on the soil characteristics than management type. Our results support such a conclusion and stress the importance of considering soil type in data analysis of soil microbiological variables. In our study we took into account the effect of soil type in the further statistical analysis of microbiological variables. The partial form of PCA based on soil biochemical and microbiological parameters separated soils with different management practices, especially soils from organically managed fields. From measured soil biochemical and microbiological parameters, dehydrogenase, potential nitrification, N-mineralisation activity, and microbial biomass contributed most strongly to the separation of soils in PCA. These microbiological variables are considered

235

european journal of soil biology 44 (2008) 231–237

Fig. 1 – Partial principal component analysis based on the correlation matrix of soil microbiological parameters. (a) Ordination of soil samples along first two PCA axes (F1 3 F2). (b) Correlation of soil microbiological parameters with PCA axes. Abbreviations: AcP – acidic phosphatase, AlP – alkaline phosphatase, D – dehydrogenase, N – N-mineralisation, PN – potential nitrification, Q – metabolic quantient, R – respiration, SIR – microbial biomass. (c) Star plot showing impact of manure application on grouping of soil samples. The results of PCA are interpreted using a scatter of the sample scores connected with group centroids. Group centroids are designated by letters a (no manure used) and b (fields with manure use).

as potential indicators of soil quality and management impact in many papers [3,11,12,27,35]. In our case the soils originating from organically managed farms were generally characterized by elevated microbiological parameter values, but at the same time the within-group variation of soil microbiological parameters was also highest. The reasons for such large deviations among organically managed soils may be the different durations of organic management practice as well as differences in management history among fields, such as different amounts and types of organic fertilizers (green or brown manure) applied and differences in crop rotation. The application of manure affected the functioning of the microbial community and two groups were clearly

distinguished: soils which received manure in one or two years or all the three last years, and those that have not received manure during the three year period. This treatment had a positive effect on microbial biomass and N-mineralisation, potential nitrification, dehydrogenase activity and acidic phosphatase activity. Surprisingly, there was no significant positive impact of manure on soil microbial biomass, which has been shown by several authors [19,21,25]. The consecutive application of manure over three years enhanced respiration rate, probably due to the addition of easily degradable organic fractions in the last year, when soil samples were taken. According to Sparling [34] and Dilly [9] the quality of organic matter greatly determines the amount of CO2 efflux from the soil. The

Table 4 – Minimum and maximum values of soil microbiological parameters in soils of studied fields with different fertilizer use Variable SIR (mgC g1 dw) Dehydrogenase activity (mgTFP g1 dw 16 h1) Potential nitrification (mgN g1 dw 24 h1) N-mineralisation (mgN g1 dw d1) Alkaline phosphatase (mgNP g1 dw h1) Acid phosphatase (mgNP g1 dw h1) Respiration (mgCO2 24 h1 g1 dw)

Only organic fertilizers (n ¼ 5)

Mineral and organic fertilizers (n ¼ 6)

Only mineral fertilizers (n ¼ 11)

0.46–1.23 3.32–14.6 0.85–6.64 0.94–2.28 92.5–774 120–336 0.07–0.24

0.54–0.85 2.96–4.49 0.51–2.91 0.81–1.80 90.0–419 90–419 0.07–0.15

0.26–0.80 2.00–5.41 0.56–4.2 0.40–2.23 20.7–565 118–259 0.05–0.09

236

european journal of soil biology 44 (2008) 231–237

increase of qCO2 due to organic amendments was reported also by Leita and co-workers [21]. Higher microbial biomass and activities in the case of organic farming and manure amendment have been described by many authors [1,5,6,15,29]. Bittman et al. [4] have found significantly greater bacterial abundance in the soils where manure has been used compared to minerally fertilized and untreated soils, but fungal biomass responded negatively to both types of fertilization. Long-term organic and mineral fertilizer application is reflected also in altered soil bacterial community structure [36]. Another management option frequently used for the input of organic matter, legume-based crop rotation, has been reported to having a positive effect on the microbial community [22]. Legume-based crop rotation was also proved to increase soil respiration and microbial biomass, although the use of manure had a much stronger overall effect on soil microbial activity. Strong cumulative negative effect of mineral nitrogen application on microbial biomass was revealed in data analysis. This means that the amount of nitrogen fertilizers added over time has a stronger effect on microbial biomass than the amount added in a given year. Such a relationship between microbial biomass and mineral nitrogen application could be partly explained by reduced input of readily available organic matter for soil microbes. Chantigny et al. [7] found that watersoluble organic carbon contents decreased with increasing mineral nitrogen fertilizer application due to elevated C-mineralisation. Same authors also suggest that increased mineral nitrogen application may indirectly affect soil respiration by promoting plant growth and water uptake, which leads to reduced soil water content. In our case, simultaneous application of manure and mineral fertilizers had the same negative effect on soil microbial biomass as the use of mineral fertilizers alone. The amount of mineral nitrogen added over time was also negatively related to soil acidity, which was on average 0.4 units lower in soils of minerally fertilized fields. There was no relationship between the amount of mineral nitrogen added and soil organic matter content among the studied fields. This may be due to the fact that we do not have any baseline data for the studied fields and, in such a situation, comparison between fields may not reveal a decrease of soil organic matter as a result of synthetic N fertilization [20]. Our data did not reveal a negative relationship between the amount of mineral fertilizer used and dehydrogenase activity, although Masciandaro et al. [26] have found that mineral and organic fertilizers affect the kinetic parameters of dehydrogenase, substrate affinity and the maximal rate of the enzyme activity. It could be suggested that the effect on the kinetic parameters of dehydrogenase might be mediated through microbial biomass, which decreases with the amendment of mineral fertilizers; meanwhile the change in substrate affinity reflects an alteration in soil microbial community structure. Our results show that changes in Estonian agricultural management practice during the last decade have already been reflected in soil microbiological properties. In most cases, organic farming practice has led to increased microbial biomass and activities in the soil, but high variation of microbiological parameters in those soils may, at the same time, indicate the ongoing transitions caused by changes in agricultural management practice.

Acknowledgements This study was part of the research project ‘‘Impact of agricultural management practices to the diversity of soil biota in Estonian arable soils’’ funded by Estonian Science Foundation grant No. 5571. The authors are grateful to the head of the Laboratory of Soil Biology at Hohenheim University, Prof. Ellen Kandeler, for the opportunity provided for M. Truu to study soil biochemical methods in their laboratory. We also thank Dr. Dagmar Tscherco and Dr. Kerstin Mo¨lter for their help and advice.

references

[1] R. Albiach, R. Canet, F. Pomares, F. Ingelmo, Microbial biomass content and enzymatic activities after the application of organic amendments to a horticultural soil, Bioresource Technol. 75 (2000) 43–48. ¨ hlinger, A. Baumgarten, Substrate-induced [2] T. Beck, R. O ¨ hlinger, E. Kandeler, respiration, in: F. Schinner, R. O R. Margesin (Eds.), Methods in Soil Biology, Springer-Verlag, Berlin, 1996, pp. 64–68. [3] G.D. Bending, M.K. Turner, F. Rayns, M.-C. Max, M. Wood, Microbial and biochemical soil indicators and their potential for differentiating areas under contrasting agricultural management regimes, Soil Biol. Biochem. 36 (2004) 1785–1792. [4] S. Bittman, T.A. Forge, C.G. Kowalenko, Response of the bacterial and fungal biomass in a grassland soil to multi-year applications of dairy manure slurry and fertilizer, Soil Biol. Biochem. 37 (2005) 613–623. [5] L.R. Bulluck, M. Brosius, G.K. Evanylo, J.B. Ristaino, Organic and synthetic fertility amendments influence soil microbial, physical and chemical properties on organic and conventional farms, Appl. Soil Ecol. 19 (2002) 147–160. [6] L. Bo¨hme, U. Langer, F. Bo¨hme, Microbial biomass, enzyme activities and microbial community structure in two European long-term field experiments, Agric. Ecosyst. Environ. 109 (2005) 141–152. [7] M.H. Chantigny, D.A. Angers, D. Prevost, R.R. Simard, F.-P. Chalifour, Dynamics of soluble organic C and C mineralisation in cultivated soils with varying N fertilization, Soil Biol. Biochem. 31 (1999) 543–550. [8] R.P. Dick, Soil activities as integrative indicators of soil health, in: C. Pankhurst, B.M. Doube, V.V.S.R. Gupta (Eds.), Biological Indicators of Soil Health, CABI Publishing, 1997, pp. 121–156. [9] O. Dilly, Microbial respiratory quotient during basal metabolism and after glucose amendment in soils and litter, Soil Biol. Biochem. 33 (2001) 117–127. [10] O. Dilly, H.-P. Blume, J.C. Munch, Soil microbial activities in Luvisols and Anthrosols during 9 years of region-typical tillage and fertilisation practices in northern Germany, Biogeochemistry 65 (2003) 319–339. [11] Z. Filip, International approach to assessing soil quality by ecologically-related biological parameters, Agric. Ecosyst. Environ. 88 (2002) 169–174. [12] F. Gil-Sotres, C. Trasar-Cepeda, M.C. Leiro´s, S. Seoane, Different approaches to evaluating soil quality using biochemical properties, Agric. Ecosyst. Environ. 37 (2005) 877–887. [13] M.S. Girvan, J. Bullimore, J.N. Pretty, A.M. Osborn, A.S. Ball, Soil type is the primary determinant of the composition of

european journal of soil biology 44 (2008) 231–237

[14]

[15]

[16]

[17]

[18]

[19]

[20]

[21]

[22]

[23]

[24]

[25]

[26]

[27]

the total and active bacterial communities in arable soils, Appl. Environ. Microbiol. 69 (2003) 1800–1809. R.L. Haney, S.A. Senseman, L.J. Krutz, F.M. Hons, Soil carbon and nitrogen mineralisation as affected by atrazine and glyphosate, Biol. Fertil. Soils 35 (2002) 35–40. D.G. Hole, A.J. Perkins, J.D. Wilson, I.H. Alexander, P.V. Grice, A.D. Evans, Does organic farming benefits biodiversity? Biol. Conserv. 122 (2005) 113–130. E. Kandeler, N-mineralisation under waterlogged conditions, ¨ hlinger, E. Kandeler, R. Margesin (Eds.), in: F. Schinner, R. O Methods in Soil Biology, Springer-Verlag, Berlin, 1996, pp. 141–143. ¨ hlinger, E. E. Kandeler, Ammonium, in: F. Schinner, R. O Kandeler, R. Margesin (Eds.), Methods in Soil Biology, Springer-Verlag, Berlin, 1996, pp. 406–408. E. Kandeler, Potential nitrification, in: F. Schinner, R. ¨ hlinger, E. Kandeler, R. Margesin (Eds.), Methods in Soil O Biology, Springer-Verlag, Berlin, 1996, pp. 147–149. E. Kandeler, M. Stemmer, M.-M. Klimanek, Response of soil microbial biomass, urease and xylanase within particle size fractions to long-term soil management, Soil Biol. Biochem. 31 (1999) 261–273. S.A. Khan, R.L. Mulvaney, T.R. Ellsworth, C.W. Boast, The myth of nitrogen fertilization for soil carbon sequestration, J. Environ. Qual. 36 (2007) 1821–1832. L. Leita, M. De Nobili, C. Mondini, G. Muhlbachova, L. Marchiol, G. Bragato, M. Cotin, Influence of inorganic and organic fertilization on soil microbial biomass, metabolic quotient and heavy metal bioavailability, Biol. Fertil. Soils 28 (1999) 371–376. N.Z. Lupwayi, W.A. Rice, G.W. Clayton, Soil microbial diversity and community structure under wheat as influenced by tillage and crop rotation, Soil Biol. Biochem. 30 (1998) 1733–1741. B.F.J. Manly, Randomization, Bootstrap and Monte Carlo Methods in Biology, Chapman and Hall, London, 1997, pp. 172–204. R. Margesin, Acidic and alkaline phosphomonoesterase activity with the substrate p-nitrophenyl phosphate, in: ¨ hlinger, E. Kandeler, R. Margesin (Eds.), F. Schinner, R. O Methods in Soil Biology, Springer-Verlag, Berlin, 1996, pp. 213–217. P. Marschner, E. Kandeler, B. Marschner, Structure and function of soil microbial community in a long-term fertilizer experiment, Soil Biol. Biochem. 35 (2003) 453–461. G. Masciandaro, B. Ceccanti, V. Ronchi, C. Bauer, Kinetic parameters of dehydrogenase in the assessment of the response of soil to vermicompost and inorganic fertilisers, Biol. Fertil. Soils 32 (2000) 479–483. P. Ma¨der, A. Fließbach, D. Dubois, L. Gunst, P. Fried, U. Niggli, Soil fertility and biodiversity in organic farming, Science 296 (2002) 1694–1697.

237

[28] R.L. Parfitt, G.W. Yeates, D.J. Ross, A.D. Mackay, P.J. Budding, Relationships between soil biota, nitrogen and phosphorus availability, and pasture growth under organic and conventional management, Appl. Soil Ecol. 28 (2005) 1–13. [29] J.A. Parham, S.P. Deng, W.R. Raun, G.V. Johnson, Long-term cattle manure application in soil. I. Effect on soil phosphorus level, microbial biomass C, and dehydrogenase and phosphatase activities, Biol. Fertil. Soils 35 (2002) 328–337. [30] D.S. Powlson, The soil microbial biomass: before, beyond and back, in: K. Ritz, J. Dighton, G.E. Giller (Eds.), Beyond the Biomass, Wiley, Chichester, UK, 1994, pp. 3–20. [31] M. Schloter, O. Dilly, J.C. Munch, Indicators for evaluating soil quality, Agric. Ecosyst. Environ. 98 (2003) 255–262. [32] D. Seghers, S.D. Siciliano, E.M. Tot, W. Verstraete, Combined effect of fertilizer and herbicide applications and the abundance, community structure and performance of the soil methanothrophic community, Soil Biol. Biochem. 37 (2005) 187–193. [33] A.V. Spain, R.F. Isbell, M.E. Propert, Soil organic matter, in: Soils: Australian Viewpoint, CSIRO/Academic Press, Melbourne/London, 1983, pp. 551–563. [34] G.P. Sparling, Soil microbial biomass, activity and nutrient cycling as indicators of soil health, in: C. Pankhurst, B.M. Doube, V.V.S.R. Gupta (Eds.), Biological Indicators of Soil Health, CABI Publishing, 1997, pp. 97–120. [35] G.P. Sparling, L.A. Schipper, W. Bettjeman, R. Hill, Soil quality monitoring in New Zealand: practical lessons from a 6-year trial, Agric. Ecosyst. Environ. 104 (2004) 523–534. [36] H.Y. Sun, S.P. Deng, W.R. Raun, Bacterial community structure and diversity in a century-old manure-treated agroecosystem, Appl. Environ. Microbiol. 70 (2004) 5868– 5874. [37] J. Thioulouse, D. Chessel, S. Doledec, J.-M. Olivier, ADE-4: a multivariate analysis and graphical display software, Stat. Comput. 7 (1997) 75–83. [38] A.D. Van Diepeningen, O.J. de Vos, G.W. Korthals, A.H.C. van Bruggen, Effects of organic versus conventional management on chemical and biological parameters in agricultural soils, Appl. Soil Ecol. 31 (2006) 120–135. [39] K. Watanabe, N. Hamamura, Molecular and physiological approaches to understanding the ecology of pollutant degradation, Curr. Opin. Biotechnol. 14 (3) (2003) 289–295. ¨ hlinger, Dehydrogenase activity with the substrate TTC, [40] R. O ¨ hlinger, E. Kandeler, R. Margesin (Eds.), in: F. Schinner, R. O Methods in Soil Biology, Springer-Verlag, Berlin, 1996, pp. 241–243. ¨ hlinger, Soil respiration by titration, in: F. Schinner, [41] R. O ¨ hlinger, E. Kandeler, R. Margesin (Eds.), Methods in Soil R. O Biology, Springer-Verlag, Berlin, 1996, pp. 95–98.