Soil suppressiveness and functional diversity of the soil microflora in organic farming systems

Soil Biology & Biochemistry 40 (2008) 2394–2406

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Soil Biology & Biochemistry journal homepage: www.elsevier.com/locate/soilbio

Soil suppressiveness and functional diversity of the soil microflora in organic farming systems Joeke Postma a, *, Mirjam T. Schilder a, Jaap Bloem b, Wiepie K. van Leeuwen-Haagsma c a

Plant Research International B.V., P.O. Box 16, 6700 AA Wageningen, The Netherlands Alterra, P.O. Box 47, 6700 AA Wageningen, The Netherlands c Applied Plant Research, P.O. Box 430, 8200 AK Lelystad, The Netherlands b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 13 February 2008 Received in revised form 23 April 2008 Accepted 30 May 2008 Available online 2 July 2008

Arable fields of 10 organic farms from different locations in The Netherlands were sampled in three subsequent years. The soil samples were analysed for disease suppressiveness against Rhizoctonia solani AG2.2IIIB in sugar beet, Streptomyces scabies in radish and Verticillium longisporum in oilseed rape. In addition, a variety of microbial, chemical and physical soil characteristics were assessed. All data were correlated by multiple regression and multivariate analyses with the objective to find correlations between soil suppressiveness and biotic or abiotic soil characteristics. Significant differences in soil suppressiveness were found between the fields for all three diseases. Multiple regression indicated a significant correlation between suppressiveness against Rhizoctonia and the number of antagonistic Lysobacter spp., as well as with % active fungi and bacterial diversity. Grass-clover stimulated Rhizoctonia suppression as well as the presence of antagonistic Lysobacter spp. (mainly L. antibioticus and L. gummosus) in clay soils. Streptomyces suppression correlated with the number of antagonistic Streptomyces spp., % of active fungi and bacterial population size. The presence of antagonistic Streptomyces spp. correlated with a high fungal/bacterial biomass ratio. Verticillium suppression was only measured in 2004 and 2005, due to the inconsistent suppressiveness along the years. Nevertheless, a significant correlation with pH, potential nitrogen mineralization and bacterial biomass was found. Bacterial and fungal PCRdenaturing gel electrophoresis fingerprinting of bacterial and fungal communities, in general, did not significantly correlate with disease suppression. Highly significant explanatory factors of the composition of the dominating bacterial and fungal populations were % lutum, pH, C/N quotient, biomass and growth rate of bacteria. Additionally, the % of organic matter and years of organic farming were explaining significantly the composition of the bacterial population. Thus, significant correlations between several soil characteristics and suppressiveness of different soilborne pathogens were found. For two of the three pathogens, suppression correlated with biotic soil characteristics combined with the presence of specific bacterial antagonists. Probably the soil suppressiveness measured in the organic fields is a combined effect of general and specific disease suppression. Ó 2008 Elsevier Ltd. All rights reserved.

Keywords: Disease suppression Rhizoctonia solani AG2.2IIIB Streptomyces scabies Verticillium longisporum Soil communities Soil characteristics PCR-DGGE Antagonistic Streptomyces Lysobacter

1. Introduction Soil-borne fungal and bacterial root pathogens can cause serious losses to agricultural crops and are recognized to be difficult to manage in narrow rotations. Resistant plant varieties are not available for several soil-borne pathogens and chemical control is often insufficiently effective in soil. Moreover, a reduction of pesticide use is envisaged to reduce potential environmental pollution. Enhancement of disease suppressive properties of soils will limit disease development, thus, being of great importance for sustainable agricultural as well as organic farming systems. Disease suppressive soils are already known for many years for various * Corresponding author. E-mail address: [email protected] (J. Postma). 0038-0717/$ – see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.soilbio.2008.05.023

pathogens including Fusarium oxysporum, Gaeumannomyces graminis var. tritici, Pythium sp., Rhizoctonia solani, Streptomyces scabies (Alabouvette et al., 1979; Lifshitz et al., 1984; Schottel et al., 2001; Mazzola and Gu, 2002; Weller et al., 2002). In these soils, pathogens are limited in their ability to establish or to produce disease symptoms. Soil suppressiveness can be due to soil physico-chemical characteristics such as texture, structure, pH, and Ca content (Ho¨per and Alabouvette, 1996). Soil biota can play a key role in soil suppressiveness too, by controlling the pathogen through competition, antibiosis, (hyper)parasitism, or enhancement of plant resistance. Several microbiological soil characteristics have been related with suppressiveness of soil-borne plant diseases: such as microbial activity or soil respiration being an exponent of competition (Hoitink and Boehm, 1999; Van Os and Van Ginkel, 2001), microbial

J. Postma et al. / Soil Biology & Biochemistry 40 (2008) 2394–2406

community diversity and composition (Garbeva et al., 2006; Pe´rezPiqueres et al., 2006), population size of certain microbial groups like actinomycetes or oligotrophic bacteria (Workneh and van Bruggen, 1994; Tuitert et al., 1998). In other cases, the presence of antibiotic genes correlated with soil suppressiveness (Raaijmakers and Weller, 1998; Garbeva et al., 2006). Suppressive soils have also been the source for several antagonistic micro-organisms, i.e. nonpathogenic F. oxysporum (Alabouvette et al., 1979), Verticillium biguttatum (Jager et al., 1979), Pythium nunn (Lifshitz et al., 1984), and 2,4-diacetylphloroglucinol producing Pseudomonas spp. (Raaijmakers and Weller, 1998). However, in many pathosystems, the relevant mechanism behind soil suppressiveness is not yet understood. In an extensive overview, about 40 soil characteristics (biotic and abiotic) and their positive, negative or no correlations with soil suppressiveness have been summarized by Janvier et al. (2007). In most cases, one soil characteristic correlates positive as well as negative with suppressiveness, depending on the pathogen and the agroecosystem involved. As a consequence, the agricultural practices enhancing soil suppressiveness are hard to determine. Generally speaking, crop rotation, tillage, fertilizers and organic amendments, can all influence disease suppressiveness. However, many soil characteristics could interact. Predicting the precise effects of the agricultural practices on suppressiveness for each disease and soil type is still far from reality (Janvier et al., 2007). We clearly need to increase our knowledge concerning the soil characteristics which influence soil suppressiveness of distinct diseases. In general, research on management factors influencing soil suppressiveness is performed in an experimental setup, varying few factors, such as type of manure, crop rotation, or tillage. In other studies, the mechanism and organisms involved in suppressive soils were evaluated by eliminating or transferring the suppressive factors (Alabouvette et al., 1985; Wiseman et al., 1996; Weller et al., 2002). In such experiments, only one or few soils are involved. To gain further insight into the effect of soil characteristics in soil suppressiveness, a variety of soil parameters from fields of different farms were compared having different soil types, farming strategies, crop rotation as well as manure application. In such an evaluation with different factors influencing each other, disease suppression might not be correlated with any soil factor at all due to its overall complexity. However, the advantage of this approach, without a pre-distinguished hypothesis, is that not yet known correlations can be discovered. In the present study, soils of 10 organic farms at different locations within The Netherlands, each with their own agricultural practices and soil type, were sampled in four subsequent years. The soil samples were analysed for disease suppressiveness against three economically important soil-borne diseases: R. solani AG2.2IIIB in sugar beet (Beta vulgaris L.), S. scabies in radish (Raphanus sativus subsp. sativus), as a model for potato scab, and Verticillium longisporum in oilseed rape (Brassica napus L.). These are generally occurring pathogens, which are difficult to control. Meanwhile, microbial characteristics concerning composition, diversity, antagonistic properties and activity, as well as chemical and physical properties, were analysed. Multiple regression and multivariate analyses were performed to find significant correlations within the data set. Our final objective was to detect soils which differed in soil suppressiveness, and to identify soil factors as well as microbial characteristics which correlate with the suppressiveness. 2. Materials and methods 2.1. Soil samples Arable fields of 10 organic farms at different locations within The Netherlands, having different soil characteristics, were

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sampled in August of 2003, 2004, and 2005 (see Table 1). These farms were part of a farmers’ network BIOM with experienced as well as starting organic farms (Sukkel et al., 2006). All farms had a six years rotation and all samples in 2003 were taken from the fields which were cropped with grass-clover. In August 2006, a selection of the previously analysed fields with the largest contrasts in disease suppression were sampled again and, where available, compared with a grass-clover field from the same organic farm. From each field four independent soil samples of about 7 kg were taken at 3–20 cm depth with minimally 10 m distance between each sampling site. Several physical and chemical parameters were analysed by a commercial soil lab using standard procedures (Blgg, Oosterbeek, The Netherlands). Disease suppression and biotic characteristics of the samples were analysed, as described below, within one month after sampling using fresh soil. This soil was kept at room temperature (approx. 20  C). Part of the soil was stored at 20  C for molecular analysis. The samples of 2003 were only used to analyse the composition of the Rhizoctonia inhibiting bacteria. 2.2. Soil suppressiveness Three plant-pathogen systems were used to assess disease suppression of the soil samples under standardized environmental conditions. Soil suppressiveness against R. solani Ku¨hn (teleomorph Thanatephorus cucumeris (Frank) Donk) was analysed by measuring the disease spread of R. solani AG2.2IIIB isolate 02-337 from sugar beet (IRS, Bergen op Zoom, The Netherlands). This is a fungal pathogen causing damping-off, black root rot and crown rot of sugar beet (Bakker et al., 2005). The test was performed in a growth chamber at 23/18  C (day/night), 60% humidity and a day/night regime of 8 h dark and 16 h light (230 mMol m2 s2 photo-active light; TL280HF). Tanks with an internal size of 4  25  30 cm were used with florist’s foam blocks (Van Dillewijn Verpakkingen BV, Aalsmeer, The Netherlands; Water holding capacity z 55%) of 4  25  17 cm at the bottom. In each tank, 1.3 l soil was packed on top of the rinsed and water saturated foam blocks. The soil water matric potential was automatically regulated at 50 mbar (pF 1.7) (Oyarzun et al., 1994). Tests were carried out in a randomised block design, each replicate soil sample in a different block. Sugar beet seeds, cultivar Aligator (only treated with hymexazol), were seeded in two rows 2 cm deep and with 2 cm distance. In total 22 seeds were used per tank. The tanks were watered and covered with plastic foil for 1 week. Then the soil in each tank was inoculated with five oat kernels colonised with R. solani, 20 mm in front of the seedling rows, just under the soil. The inoculum was prepared with organically grown oat kernels which were autoclaved (121  C for 20 min) twice with a 24 h interval. The kernels were infested with a plug of a 3-day-old R. solani isolate grown on potato dextrose agar (PDA; 39 g PDA [Oxoid Ltd.] per litre deionised water) and incubated for 3 weeks at 20  C in the dark. Disease spread was determined 7, 14, 21 and 25 days after inoculation by

Table 1 Characteristics of the sampled fields in 2003 Field

Location (province)

Soil type

CaCO3 (%)

Years of organic farming

A B C D E F G H I J

Engwierum (Friesland) Pietersbierum (Friesland) Barger Compascum (Drenthe) Marknesse (Flevoland) Hensbroek (N-Holland) Garderen (Gelderland) Rijnsaterswoude (Z-Holland) Strijen (Z-Holland) IJzendijke (Zeeland) Groeningen (Limburg)

Marine clay Marine clay Sandy soil Marine clay Marine clay Sandy soil Marine clay Marine clay Marine clay Sandy soil

3.7 1.5 0.1 7.5 3.1 0.0 1.2 3.2 9.5 0.0

0 13 1 1 11 13 7 2 6 9

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scoring seedlings displaying damping-off or brown-gray lesions on the stem at soil level. A control with sterilized soil was included to check pathogenicity of the inoculum. Disease spread 21 days after inoculation was used for comparison of data, since maximum disease spread was reached in some of the tanks in this period. Values of disease spread were transformed into relative disease suppression values by the formula: soil suppressiveness against R. solani ¼ 1  disease spread/maximum disease spread. S. scabies (Thaxter) Lambert and Loria, isolate Scab1 from potato, is a bacterial pathogen causing scab on radish, potato and few other crops. A bioassay with radish was adapted after Wanner (2004). The pathogen was grown on PDA þ100 mg l1 Delvocid to avoid fungal contamination during 3 weeks at 25  C. A spore suspension was prepared in sterile demineralised water with 0.2% Silwet and mixed thoroughly through the different soil samples at a concentration of 1.6  105 (2004), 6.1 105 (2005), and 1.5  106 (2006) CFU g1 soil, which was determined by plate counting. One kilogram of soil was added per pot of 9  9  9 cm and 5 seeds of radish, cultivar Master Red, were seeded per pot. The test was performed in a growth chamber at 23/18  C (day/night), 60% humidity and a day/night regime of 8 h dark and 16 h light (230 mMol m2 s2 photo-active light; TL280HF). Pots were watered twice a week, but kept relatively dry the last weeks before harvest to stimulate symptom expression. Tests were carried out in a randomised block design, each replicate soil sample in a different block. After 6 weeks plants were harvested and scab symptoms on the radishes were scored with the following index: 0 ¼ no scab, 1 ¼1 lesion, 2 ¼ < 10% of the surface with scab, 3 ¼ 10–25% of the surface with scab, 4 ¼ 25–50% of the surface with scab, 5 ¼  50% of the surface with scab. The mean value of the indices was calculated per pot. Values of the disease index were transformed into relative disease suppression values by the formula: soil suppressiveness against S. scabies ¼ 1  disease index/maximum disease index. V. longisporum (Stark) Karapapa et al. (1997), isolate V25 from oilseed rape (Berg et al., 2002), is a fungal pathogen causing wilt. It is a stable heterodiploid, previously named V. dahliae var. longisporum (Domsch et al., 2007). Inoculum of the fungus was prepared in autoclaved (20 min, 121  C) perlite (0.5 cm diameter) which had been saturated with Czapek–Dox medium (Oxoid). This substrate was inoculated with mycelium plugs of V. longisporum from PDA and incubated during 6 weeks at 20  C in the dark, and mixed weekly. Then the perlite with fungus was blended to powder. The density of microsclerotia was checked microscopically after dilution in silver sand. Per litre soil 105 microsclerotia were added and mixed thoroughly. Pots (11 11 10 cm) were filled with 1000 ml soil. A control treatment without V. longisporum was performed in a gamma-sterilized loamy sand soil (Zwaagdijk). For each soil sample two pots both with two oilseed rape plants, cultivar Lambada (W. von Borries-Eckendorf GmbH & Co, Leopoldsho¨he, Germany), were used. Tests were carried out in a randomised block design, each replicate soil sample in a different block. The greenhouse was kept at 23/18  C day/night temperature, 16 h light, 60% RH. Plants were watered 2 times a week up to daily after flowering. After 10 weeks when plants started flowering, the number of leaves and number of wilted leaves were counted regularly up to 13–16 weeks. Soil suppressiveness was expressed as 100  area under disease progress curve (AUDPC). 2.3. Microbial biomass and activity Bacteria were measured by confocal laser scanning microscopy and automatic image analysis, after staining of soil smears with DTAF, a fluorescent dye which binds to proteins (Bloem and Vos,

2004). From the number and cell volumes bacterial biomass was calculated and expressed as mg C g1 soil. Fungi in soil smears were stained with a mixture of two stains: fluorescent brightener (blue) which binds to cell walls (polysaccharides) and europium chelate (red) which binds to nucleic acids (DNA and RNA) (Morris et al., 1997). Thus active and inactive hyphae were distinguished. In addition unstained hyphae were counted by switching to transmitted light. The total hyphal length measured under the microscope was used to calculate fungal biomass in terms of mg C g1 soil (Bloem and Vos, 2004). Bacterial growth rate was determined as the incorporation of [3H]thymidine and [14C]leucine into bacterial DNA and proteins, respectively, during an incubation period of 1 h (Bloem and Bolhuis, 2006). Oxygen consumption was measured weekly by gas chromatography from week 1 up to 6. Soil was homogenized, sieved (5 mm mesh size) and brought to 50–60% of the water holding capacity. Subsamples of 200 g soil were incubated in the dark at 20  C in 1.5 l air-tight jars supplied with a gas septum. The gas chromatograph was a Carlo Erba 6000 with a column switching system, equipped with a 4-m Porapak q and a 2 mmol sieve 5 Å column. The detector (HWD) temperature was 180  C, the column temperature was 50  C, and the injection volume was 1 ml (Bloem et al., 1994). Soil respiration (potential C mineralization) was expressed as mg C respired kg1 soil wk1. In the same jars where respiration was measured, the potential N mineralization rate was determined as the increase in mineral N (ammonium plus nitrate) between weeks 1 and 6. Subsamples of 80 g soil were extracted with 200 ml of 1 M KCl. After shaking the extracts for 1 h, they were filtered over a filter paper. Mineral N contents (ammonium and nitrate) were determined by Skalar Segmented Flow Analysis (Breda, The Netherlands). 2.4. Culturable bacterial populations and antagonistic bacteria Population densities of culturable bacteria and filamentous actinomycetes in the soil were determined by dilution-plating on agar media. A soil suspension was prepared by shaking 10 g of fresh soil in a 250 ml Erlenmeyer flasks with 10 g gravel and 95 ml 0.1% NaPP (tetra-sodium diphosphate; Merck, cat. no. 6591) at 460 rpm for 10 min. The suspension was filtered through sterile cheese cloth, and a 10-fold dilution series was made in Ringers solution (1/ 4 strength: 1 tablet Ringers (Oxoid) in 500 ml distilled water, autoclaved at 121  C). Proper dilutions were plated in 4-fold with the linear mode of a spiral plater (WASP, Don Whitley Scientific Ltd, Shipley, UK) on R2A þ metalaxyl 250 mg l1 for bacterial enumeration. Metalaxyl was added to inhibit fungal growth, without inhibiting R. solani. After 1-day incubation at 20  C in darkness, plugs with R. solani AG2.2IIIB isolate 02-337 were placed in the middle of the plates. Bacterial colonies were counted after one week. Bacteria inhibiting R. solani growth were isolated on R2A þ Delvocit 100 mg l1. Isolates were retested for inhibition of R. solani by dual culture on R2A with 4 bacterial 5 mm from the edge of a plate and R. solani inoculated in the middle. Inhibition zones were measured after 7 days. Filamentous actinomycetes were enumerated on chitin oatmeal agar (COA; 2 g colloid chitin [Sigma] purified in 37% HCl, 0.7 g K2HPO4, 0.3 g KH2PO4, 18 g oatmeal agar [Difco Laboratories, Detroit], 12 g agar, þ100 mg Delvocit per litre deionised water). Proper dilutions were spread in duplicate on a sterilized nitrocellulose filter with 0.2 mm pores (Ø 82 mm, OPTITRAN BA-S 83; Schleicher & Schuell) which was placed on the COA. The LOG mode of the spiral plater was used. Plates were incubated for 5 days at 20  C in darkness. Then the filters were removed from the COA plates and the plates were further incubated for another 5 days before counting.

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2.5. Identification of antagonistic isolates Part of 16S ribosomal RNA of all bacteria that inhibited hyphal growth of R. solani were sequenced for identification. The streptomycetes were grown in 1 ml LB medium (10 g Trypton [OXOID], 5 g yeast extract [OXOID], 5 g NaCl [Merck] per litre deionised water) in a 1.5 ml microcentrifuge tube to obtain mainly mycelium. After 6 days growing at 25  C, the pellet was used for DNA isolation (5 min centrifugation at 12,000 rpm). Unicellular bacteria were grown on R2A agar. 20 ml lysis buffer (0.05 M NaOH, 0.2% SDS [sodium dodecyl sulphate]) was added to the pellet or bacterial colonies scraped from agar plates and incubated during 15 min at 95  C. After cooling on ice, 200 ml sterile MQ was added and centrifuged 5 min at 12000 rpm and stored at 4  C. PCR amplification was performed with 1 ml of the lysate (supernatant) plus 49 ml of a solution containing (final concentration) 5 ml of 10x Stoffelbuffer, 3.75 mM MgCl2, 200 mM of dATP, dGTP, dTTP and dCTP each, 0.4 mM of primer U968 (50 -AACGCGAAGAACCTTAC), 0.4 mM of primer R1378 (50 -CGGTGTGTACAAGGCCCGGGAACG), 1.0% formamide and 0.5 ml of Amplitaq DNA polymerase (Stoffelfragment; Applied Biosystems, Nieuwerkerk a/d IJssel, The Netherlands). The thermal cycling program consisted of an initial denaturing step of 94  C for 4 min; 9 cycles of 94  C for 1 min, 60  C for 1 min where every cycle is decreased by 0.5  C, and 72  C for 2 min; 25 cycles of 94  C for 1 min, 55  C for 1 min, and 72  C for 2 min; a final extension step of 72  C for 10 min, followed by cooling to 10  C. The PCR products were purified with High Pure PCR product purification kit (Roche, Almere, The Netherlands). For sequencing 2 ml of the PCR product was added to 8 ml of solution containing 2 ml Amersham dye, 2 ml dilution buffer (Amersham Biosciences; GE Healthcare, Belgium) and 4 ml primer R1378 (concentration of 6 mM). The PCR cycle was 25 cycles of 94  C for 20 s and 50  C for 15 s, followed by 60  C for 1 min. Sequences were blasted with the Ribosomal Database Project II release 9. Phylogenetic cut-off levels used were >97% similarity for species and >95% for genus. 2.6. Microbial diversity and composition with PCR-DGGE DNA was extracted from frozen soil (20  C) using a Mo Bio Ultra Clean soil DNA isolation kit (Mo Bio Laboratories, BIOzymTC, Landgraaf, The Netherlands). Additional 50 mg of glass beads (<106 mm) were added to the microtubes. Cells were lysed by beat beating for 2 times 30 seconds in a cell disrupter (Hybaid Ribolyser, Hybaid, Middlesex, UK), in order to achieve maximal cell lysis. After the bead-beating step, DNA was extracted according to the protocol of the supplier. PCR amplification of bacterial 16S rDNA genes was performed according to Postma et al. (2000). Primers F968 with GC-clamp 50 -CGC CCG GGG CGC GCC CCG GGC GGG GCG GGG GCA CGG GGG G AAC GCG AAG AAC CTT AC and R1378 50 -CGG TGT GTA CAA GGC CCG GGA ACG were subsequently used for the total bacterial PCR (Heuer and Smalla, 1997). PCR amplification of fungal ITS sequences was performed according to Anderson et al. (2003) with a nested PCR generating partial ITS products. Amplifications were performed in a PTC-200 thermal cycler (Mj Research, Inc., Tilburg, The Netherlands). DGGE was performed with the PhorU2 system (Ingeny, Leiden, The Netherlands). PCR products (15 to 20 ml) were applied directly onto 6% (wt/vol) polyacrylamide gels in 0.53 TAE buffer (20 mM Tris-acetate [pH 7.4], 10 mM sodium acetate, 0.5 mM Di-sodium EDTA) containing a linear denaturing gradient from 45 to 65% for bacterial DGGE and from 30–80% for ITS fungal DGGE. The gradients were formed with 6% (wt/vol) acrylamide stock solutions that contained no denaturant and 80% denaturant (the 80% denaturant solution contained 7 M urea and 40% [vol/vol] formamide deionised with AG501-X8 mixed-bed resin [Bio-Rad, Veenendaal, The

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Netherlands]). The gels were electrophoresed for 16 h at 60  C and 100 V. After electrophoresis, the gels were stained for 30 min with SYBR Gold I nucleic acid gel stain (Molecular Probes Europe, Leiden, The Netherlands) and photographed under UV light by using a SYBR Green gel stain photographic filter (Molecular Probes) and a Docugel V system apparatus (Biozym, Landgraaf, The Netherlands). 2.7. Statistical analysis and analysis of DGGE gels Analyses of variance were carried out with Genstat 9.2 (Rothamsted Experimental Station, Harpenden, UK) to test significant differences between the soil characteristics and disease development. A block structure was used for the disease data. After ANOVA, least significant differences (LSD) were calculated at a significance level of P ¼ 0.05. Multiple regression was performed with Genstat 9.2 in order to correlate disease suppression with the soil characteristics. The most relevant soil characteristics were selected using ‘All-subsets Regression – Generalized Linear Models’. The combination of significant characteristics was subsequently fitted with ‘Regression Analysis – Generalized Linear Models’. In all cases mean data per field were used. Model checking was performed to compare the fitted and observed data. Banding pattern analysis was performed by GelcomparII software (version 1.61; Applied Maths, Woluwe, Belgium). Each gel contained three marker lanes for reference purposes. Background correction was adapted to gel quality. Band positions and relative intensity were exported from this database. Shannon diversity indices (H0 ) were calculated on the basis of the intensity of bands in the patterns using the equation: H0 ¼ C/ P N  (N ln N  ni ln ni) in which C ¼ 2.3, N ¼ the total intensity of all DNA bands, ni ¼ the intensity of the ith band. The bacterial and fungal community composition analysed with PCR-DGGE, was correlated with soil suppressiveness and soil characteristics by multivariate analyses with the statistical program CANOCO release 4.5 (Ter Braak, 1995; Salles et al., 2004). The relative intensity of the bands was log transformed and analysed with redundancy analysis (RDA), a canonical form of principal component analysis (Ter Braak, 1995). The structure of the data was linear since the gradient lengths of the data sets were shorter than 3.0. Scaling of the figures was focussed on inter-species correlations. In total 6 gels were needed for all the samples, with replicate soil samples on different gels. These gels were analysed as co-variable (block). Mean values per field of soil characteristics were used. Significance of the environmental factors was analysed with Monte Carlo permutation based on 499 random permutations, assuming the null hypothesis that species data (i.e. bands) are unrelated to environmental data and the alternative hypothesis that the species respond to the environmental factors. Vectors pointing in the same direction are positively correlated and those in opposite directions are negatively correlated. The correlation of the composition of the antagonistic bacteria in soil with soil suppressiveness and soil characteristics was analysed similarly. Data on the number of antagonists were not transformed. RDA was used since the data were linear. Mean values per field of soil characteristics were used. Significance of the environmental factors was analysed with Monte Carlo permutation. 3. Results 3.1. Soil characteristics Soils of the selected fields differed substantially in organic matter, clay and calcium content, pH, carbon and nitrogen content, C/N ratio, and type of manure applied (see Tables 1 and 2). Most fields had clayey soils with 10–30% lutum and a neutral pH. Fields of farm C, F, and J

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Table 2 Physical and chemical characteristics of soil samples Fielda

Year

Crop

Lutum (%)

pH-KCl

Organic matter (%)

C total (g C kg1)

N total (g N kg1)

C/N quotient

A B C D E F G H I J

2004 2004 2004 2004 2004 2004 2004 2004 2004 2004

Wheat Cauliflower Grass-clover Onion Shallot, winter radish Triticale, grass-clover Brussels sprouts Cabbage Grass-clover Pumpkin

11.5 13.8 2.0 11.8 27.5 2.0 19.0 20.5 29.3 1.8

7.4 7.3 5.2 7.5 7.2 4.7 6.8 7.4 7.4 5.4

1.7 1.6 10.2 2.9 6.8 2.8 10.3 2.9 1.9 1.9

13.1 11.4 72.2 22.9 43.0 16.1 60.2 19.6 21.2 10.3

1.0 0.9 2.8 1.2 3.7 0.9 4.5 1.6 1.4 0.5

13.6 12.4 26.1 18.4 11.6 18.9 13.4 12.2 15.7 21.3

A B C D E F G H I J

2005 2005 2005 2005 2005 2005 2005 2005 2005 2005

Carrot Carrot Wheat Broccoli Wheat Grass-clover Wheat Celery Sugar beet Alfalfa

12.0 13.5 1.5 13.3 25.8 2.0 14.0 20.3 27.3 1.8

7.5 7.3 5.1 7.5 7.2 4.9 5.9 7.5 7.6 5.4

2.3 2.5 10.8 3.5 4.1 3.8 12.0 3.5 2.7 2.4

10.5 11.3 48.8 17.3 39.5 16.0 56.0 16.8 13.5 10.0

1.1 1.2 2.7 1.4 3.7 1.0 4.8 1.7 1.4 0.6

9.2 9.7 18.4 12.6 10.6 16.7 11.7 9.8 10.1 18.1

A Aa D Da E Ea F G Ga I J LSDb

2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006

Beetroot Grass-clover Potato Grass-clover Cabbage Grass-clover Wheat, winter radish Parsnip, pumpkin Grass-clover Pea Pumpkin

12.3 24.3 13.8 14.3 26.3 23.5 1.5 16.3 15.5 28.0 2.5 2.4

7.4 7.4 7.4 7.6 7.2 7.2 5.0 7.0 7.4 7.4 4.9 0.4

1.9 2.7 3.0 2.9 7.5 8.9 2.6 9.4 4.0 2.5 1.7 1.5

12.5 16.0 18.5 18.3 43.8 48.3 15.0 49.0 27.3 17.5 10.5 7.2

1.0 1.6 1.4 1.3 3.9 4.1 0.8 3.9 2.1 1.5 0.5 0.6

12.7 10.1 13.3 13.6 11.1 11.9 19.0 12.7 12.7 11.9 23.2 2.3

a b

‘a’ is a field from the same farm but with grass-clover. Least significant differences (LSD) at P ¼ 0.05; mean values for the years 2004–2006.

were sandy soils, with 1.5–2.5% lutum and a lower pH (4.7–5.4). Consequently, microbial characteristics such as biomass, activity, colony-forming units (CFUs) and diversity indices were also different per field (Table 3). Most soils were strongly dominated by bacteria, resulting in a low fungal/bacterial biomass (F/B biomass). Only field J and in some years F and I had a relatively higher F/B biomass ratio, mainly due to a low bacterial biomass. Bacterial growth rate (leucine and thymidine incorporation) and the number of culturable bacteria were low in fields C, F and J. These fields were all sandy soils. Interestingly, field J had a high content of antagonistic bacteria (Table 3). Many other soil characteristics also showed significant differences between fields. However, differences were not always consistent between the different years of sampling. Extreme differences between the years were, for example, present with the % of active fungi.

after grass-clover cropping. Field E which had been one of the least suppressive fields in 2004 and 2005 did not become suppressive by the grass-clover crop in 2006. The fields H, I, and J were most suppressive against Streptomyces in 2004 and 2005, whereas B and D were suppressive only in 2004, and A and F in 2005 (Fig. 2). In 2006 the disease levels were much higher than in 2004 and 2005 and no significant differences were present. The fields I and J, which were suppressive in 2004 and 2005, showed very high disease levels in 2006. Results of 2006 showed that grassclover did not influence the suppressiveness against Streptomyces. Soil suppressiveness against Verticillium showed significant differences between the fields, but the results between 2004 and 2005 were not consistent (Fig. 3).

3.2. Disease development

PCR-DGGE profiles were performed to analyse the diversity and the composition of the dominant bacterial and fungal populations of the different fields. The soil samples of 2006 clearly showed that bacterial patterns between two fields of the same farm with a different crop were very similar, whereas patterns differed between farms (Fig. 4). The results of fungal populations showed somewhat larger variations between the fields of a farm. The Shannon diversity index, calculated from PCR-DGGE profiles of all samples, is presented in Table 3.

Significant differences in disease development between soils were measured in the bioassays with Rhizoctonia, Streptomyces and Verticillium (Figs. 1–3). The fields A, D, and G were most suppressive against Rhizoctonia in 2004 and 2005, whereas H was suppressive only in 2004 and B and J in 2005 (Fig. 1). In 2004 and 2005, most conducive fields were E, F, and I. In 2006 fields with the largest differences in disease suppression were retested and, where available, fields from the same farms with a grass-clover crop were added. The results in 2006 clearly showed the positive influence of grass-clover on disease suppression against Rhizoctonia; in 3 of the 4 farms the Rhizoctonia development was strongly suppressed in the grass-clover fields compared to the fields which had grassclover 3 years before. These 3 fields (A, D, and G) had been suppressive in 2004 and 2005 as well, which was resp. 1 and 2 years

3.3. Bacterial and fungal composition with PCR-DGGE

3.4. Antagonistic isolates Many isolates among the cultured bacteria were found to inhibit R. solani in four subsequent years of sampling. Inhibition zones of 15–20 mm occurred regularly. In total 525 isolates inhibiting mycelial growth of Rhizoctonia were indentified by partial 16S

Table 3 Biological characteristics of soil samples: biomass, activity, culturable populations (CFU), % antagonists, and diversity indices of PCR-DGGE patterns Year

Fungal biomass (mg C g1 dry soil)

Bacterial biomass (mg C g1 dry soil)

Thymidine incorporation (pmol g1 h1)

Leucine incorporation (pmol g1 h1)

Potential N mineralization (mg N kg1 wk1)

Potential C mineralization (mg C kg1 wk1)

Active fungi (% of hyphal length)

O2 consumption (mg C kg1 wk1)

Bacteria (log CFU g1 soil)

Actinomycetes (log CFU g1 soil)

A B C D E F G H I J

2004 2004 2004 2004 2004 2004 2004 2004 2004 2004

32.4 29.7 14.1 20.0 18.4 12.8 16.8 25.4 13.9 11.8

112.4 93.1 26.3 106.3 137.7 39.9 100.3 131.0 77.3 15.6

66 68 42 69 172 28 148 83 81 24

549 417 412 455 757 320 701 540 498 278

2.48 1.40 3.05 2.43 2.38 3.18 4.18 1.78 0.50 2.00

9.2 12.8 44.3 15.4 19.4 22.9 22.4 12.4 11.2 23.9

76.7 79.0 58.1 66.4 38.1 56.8 39.2 77.4 50.5 54.6

19 37 60 35 52 38 47 27 36 39

8.35 8.05 7.77 8.16 8.23 7.73 8.36 8.30 8.17 7.56

nd nd nd nd nd nd nd nd nd nd

A B C D E F G H I J

2005 2005 2005 2005 2005 2005 2005 2005 2005 2005

8.4 10.0 11.7 5.9 11.9 8.1 8.2 13.5 11.0 7.8

16.6 26.3 22.0 19.0 41.6 23.0 62.2 60.8 5.6 3.1

52 50 28 77 140 27 85 96 70 13

315 301 239 461 669 228 480 537 389 152

1.30 1.23 2.45 1.23 1.43 1.91 2.75 1.03 0.65 2.07

9.5 11.7 20.6 10.8 12.7 21.1 24.4 14.5 8.3 10.0

13.5 8.0 8.2 4.4 5.4 7.7 1.8 3.3 6.8 20.9

42 49 47 43 47 42 55 54 35 38

8.88 9.11 8.65 8.95 9.07 8.50 8.90 9.18 9.18 8.64

A Aa D Da E Ea F G Ga I J LSDb

2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006

6.1 6.3 4.4 9.4 11.4 12.8 3.5 5.8 9.1 6.5 12.9 5.7

61.6 26.7 75.6 83.1 102.1 125.3 30.5 64.1 97.8 87.2 21.8 22.9

79 179 125 118 172 209 27 162 181 140 19 40

405 856 583 650 714 861 238 658 733 705 187 203

1.98 0.78 2.18 1.66 2.15 1.90 2.27 2.06 1.57 0.55 1.20 0.66

9.0 7.3 9.3 11.7 13.0 9.4 23.4 14.1 14.7 10.5 6.1 6.7

4.3 1.6 0.0 1.4 0.0 0.0 0.0 3.5 0.0 5.0 10.8 15.9 ns 5.9

31 46 45 56 54 41 49 45 44 54 21 13

6.95 7.11 7.07 7.11 6.99 7.05 6.36 6.82 6.94 7.19 6.39 0.20

a b

Shannon index bacteria

Shannon index fungi

1.89 2.34 1.61 1.56 2.54 1.83 1.74 1.64 1.83 2.92

2.5 2.4 1.9 2.4 2.5 2.1 2.6 2.7 2.4 1.9

2.5 2.3 2.3 2.4 2.5 2.2 2.3 2.4 2.2 2.5

6.46 6.88 6.76 6.66 6.65 6.92 6.88 6.69 6.49 6.77

0.20 0.50 1.04 0.60 0.53 0.85 0.66 0.66 0.69 2.61

2.4 2.5 2.2 2.7 2.7 2.2 2.3 2.6 2.5 2.1

2.3 2.4 2.3 2.5 2.3 2.2 2.1 2.3 2.3 2.4

5.47 6.04 5.89 5.93 5.84 5.94 5.87 5.85 5.98 6.01 5.79 0.33

2.13 4.40 2.47 3.26 1.52 2.10 7.97 3.51 2.54 2.32 10.25 1.35 1.10 4.09

3.0 3.0 3.0 3.0 2.8 2.7 2.7 2.7 2.8 2.9 2.1 0.4

2.7 2.8 2.8 2.9 2.8 2.9 2.9 2.8 2.9 2.8 2.8 0.3

Antagonists (% of bacteria)

J. Postma et al. / Soil Biology & Biochemistry 40 (2008) 2394–2406

Fielda

‘a’ is a field from the same farm but with grass-clover. Least significant differences (LSD) at P ¼ 0.05; mean or separate values for the years 2004–2006; ns ¼ not significant.

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J. Postma et al. / Soil Biology & Biochemistry 40 (2008) 2394–2406

2.5 LSD = 4.1

2004

Disease index (0-5)

Disease spread in cm

25 20 15 10 5 0 A

B

C

D

E

F

G

H

I

2004

2

LSD = 0.7 1.5 1 0.5 0

J

A

B

C

D

E

Field

F

G

H

I

J

Field

LSD = 4.9

2.5

2005

20

Disease index (0-5)

Disease spread in cm

25

15 10 5

2005 2 LSD = 1.1 1.5 1 0.5

0 A

B

C

D

E

F

G

H

I

0

J

A

Field

B

C

D

E

F

G

H

I

J

Field LSD = 7.1

2006

5

20

Disease index (0-5)

Disease spread in cm

25

15 10 5 0 A

Aa

D

Da

E

Ea

F

G

Ga

I

J

Field Fig. 1. Disease spread of Rhizoctonia solani AG2.2IIIB in a sugar beet bioassay 21 days after inoculation with the pathogen in soil samples of 2004, 2005, and 2006. Fields with an ‘a’ are from the same farm with a grass-clover crop. LSD is least significant difference at P ¼ 0.05.

sequencing (Table 4). The isolates were affiliated with mainly the genus Streptomycetes (50%), followed by Lysobacter (22%) and Pseudomonas (14%) (average values over 2003–2006). Statistical analysis of the data was not possible due to the variation between the replicate samples of the fields; data were strongly clustered. The genus Streptomyces, including Kitasatospora, was most abundantly present and contained many different species. Strong inhibiting isolates were for example Streptomyces griseus, Streptomyces platensis, Streptomyces rutgersensis, Streptomyces peruviensis, Streptomyces lavendulae and Kitasatospora sp. Strong inhibitors of the Pseudomonas genus belong to P. chlororaphis, P. lundensis, P. brassicacearum, and P. fluorescence. The isolated antagonistic Lysobacter species mainly affiliated with L. antibioticus or gummosus, having yellowish or pink to orange coloured colonies on R2A (Fig. 5) (Reichenbach, 1992). DNA sequences of fifteen of the Lysobacter isolates from different fields are deposited in the EMBL database (http://www.ebi.ac.uk) under accession numbers AM941206–AM941220. The isolates were in general strong inhibitors of Rhizoctonia. Lysobacter was isolated from clay soils, and never from the sandy soils of fields C, F and J (Table 4). Almost no colonies of Lysobacter were isolated in 2005, where sampling followed an extreme dry period. In 2006 five times more Lysobacter colonies were isolated from the fields with grassclover than from the fields which had grass-clover in 2003.

2006

not significant 4 3 2 1 0 A

Aa

D

Da

E

Ea

F

G

Ga

I

J

Field Fig. 2. Disease index of Streptomyces scabies on radish 42 days after inoculation with the pathogen in soil samples of 2004, 2005, and 2006. Fields with an ‘a’ are from the same farm with a grass-clover crop. Disease index 0 is healthy and 5 is >50% coverage with scab. LSD is least significant difference at P ¼ 0.05.

Several other interesting antagonists were present, however, in much lower numbers, i.e. Bacillus, Paenibacillus, Flavobacterium, Rhizobium, Stenotrophomonas, Curvibacter, Microbacterium, Lentzea. We also found four isolates of Collimonas fungivorans in field F, which is an acid sandy soil with a long organic agricultural history. These isolates were isolated in 2003 as well as 2004, when field F was cropped with grass-clover. DNA sequences of these four isolates are deposited in the EMBL database under accession numbers AM941221–AM941224. 3.5. Correlation between soil suppressiveness and soil factors Soil suppressiveness of the three diseases was correlated with the different soil characteristics using multiple regression analysis. Significant linear regression models could be fitted for all three disease systems resulting in formulae containing the most relevant characteristics (Table 5). Disease suppression of Rhizoctonia correlated with the percentage of antagonistic Lysobacter isolated from the bulk soil, the percentage of active fungi, and bacterial diversity analysed with PCR-DGGE. Since Lysobacter did not occur in sandy soils, we also analysed the Rhizoctonia data set for the clayey soils separately. The result was a higher fit (59% compared to 47%) with a combination of

J. Postma et al. / Soil Biology & Biochemistry 40 (2008) 2394–2406

Nr of wilting leaves

4 LSD = 0.91

2004

3 2 1 0 A

B

C

D

E

F

G

H

I

J

hc

Field

Nr of wilting leaves

4 LSD = 1.46

2005

3 2 1 0 A

B

C

D

E

F

G

H

I

J

hc

Field Fig. 3. Mean number of wilting leaves per plant of oilseed rape 13–16 weeks after inoculation with Verticillium longisporum in soil samples of 2004 and 2005. Treatment hc is healthy control in sterilized soil. LSD is least significant difference at P ¼ 0.05.

the same soil characteristics and supplemented with the factor % lutum. Not enough samples were present for a separate regression analysis in the sandy soils. Disease suppression of Streptomyces correlated with the % of antagonistic Streptomyces spp., the amount of active fungi, and the log number of culturable bacteria. Disease suppression of Verticillium could only be analysed for the years 2004 and 2005, since disease suppression was not measured in 2006. Although we could not recognize any consistency between disease development in 2004 and 2005 (Fig. 3), a significant regression model could be fitted. The disease suppression correlated with bacterial biomass, pH and potential N mineralization of the soil. 3.6. Correlation between soil suppressiveness and bacterial and fungal composition Several soil characteristics showed significant correlations with PCR-DGGE profiles when analysed with redundancy analysis.

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However, the main question, ‘‘can disease suppression be explained by the dominant bacterial or fungal population’’, was answered negatively for most of the combinations. The bacterial and fungal profiles did, in general, not significantly correlate with disease suppression against the three plant pathogens. Probability of correlations between bacterial PCR-DGGE profiles of soil samples and disease suppression against R. solani, S. scabies and V. longisporum was, respectively, 0.06, 0.20 and 0.97. Probability of correlations between fungal PCR-DGGE profiles and disease suppression against R. solani, S. scabies and V. longisporum was, respectively, 0.014, 0.49 and 0.51. Thus, only the fungal composition and soil suppressiveness against Rhizoctonia showed a significant correlation. A selection of 14 relevant and most significant soil characteristics correlating with fungal and bacterial community structure is presented in Figs. 6 and 7. Organic matter, lutum, pH, C/N quotient, C, N, years of organic farming, bacterial growth rate (i.e. leucine and thymidine incorporation), bacterial diversity, bacterial biomass and bacterial numbers (CFU) significantly explained the bacterial composition (Fig. 6). Several of these factors are also significantly explanatory for the fungal population, i.e. C/N quotient, pH, lutum, bacterial growth rate (i.e. leucine, thymidine), bacterial and fungal biomass, and fungal diversity (Fig. 7). Since soil suppressiveness against the three diseases was, in general, not a significant explanatory factor for the dominant fungal and bacterial composition, they are presented in the figures as supplementary factors. Interestingly, three of the bacterial bands with the highest fit (>15%) pointed in the same direction as the soil suppressiveness against Rhizoctonia and Streptomyces. For fungi one band correlated (>15%) with soil suppressiveness against Rhizoctonia and Streptomyces. 3.7. Correlation between soil suppressiveness and antagonist composition The correlation between the three most frequently isolated antagonistic groups (Lysobacter, Streptomyces, Pseudomonas spp.) with relevant soil characteristics is shown in Fig. 8. Bacterial growth rate (i.e. leucine and thymidine incorporation), fungal/bacterial biomass ratio, bacterial and fungal diversity and pH were significant explanatory factors for the antagonist composition. Soil suppressiveness against the three plant pathogens were added as supplementary factors. Interestingly, soil suppressiveness against the Streptomyces pathogen correlated with the presence of antagonistic Streptomyces spp., which concomitantly corresponded with a high fungal/bacterial biomass ratio and high C/N ratio. Soil suppressiveness against Rhizoctonia corresponded with the presence of antagonistic Lysobacter spp., which correlated

Fig. 4. PCR-DGGE patterns of bacterial (left) and fungal (right) populations of different fields in 2006. Fields with an ‘a’ are from the same farm with a grass-clover crop. Only one replicate is presented. M ¼ marker.

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J. Postma et al. / Soil Biology & Biochemistry 40 (2008) 2394–2406

Table 4 Number of isolates inhibiting Rhizoctonia solani AG2.2IIIB per genus isolated from bulk soil samples in 2003, 2004, 2005, and 2006 Fielda

Year

A B C D E F G H I J

2003 2003 2003 2003 2003 2003 2003 2003 2003 2003

11 8 20 2 6 23 6 16 13 13

A B C D E F G H I J

2004 2004 2004 2004 2004 2004 2004 2004 2004 2004

4 2 3 6 3 6 3 7 8

A B C D E F G H I J

2005 2005 2005 2005 2005 2005 2005 2005 2005 2005

2 7 7 3 2 4 4 9 7 19

A Aa D Da E Ea F G Ga I J Total %

2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006

3 2 7 6

a b

Streptomycesb

4 6 2 1 6 261 50

Lysobacter

Pseudomonas

3 17

1

14 5

3 2

8 2 2

2

5

3

2 8 2 1

9

7

1

3 4 1 3 2 2 3 2 1 1 2 4 4

1 11 4 15 2 4

Other genus

2 8 1 2 1

3 2

2 1 3

115 22

73 14

3 1 2 2 1 4 3 1 1 2 1 3 2 1

1 4 1 2 4

1 3 5 1 11 3 3 2 1

1 3 76 14

a is a field from the same farm with a grass-clover crop. Including Kitasatospora.

Fig. 5. In vitro inhibition of Rhizoctonia solani AG2.2IIIB on R2A by several bacterial isolates.

Verticillium needs living host tissue for its proliferation. Additionally, the pathogens belong to entirely different taxonomic groups: S. scabies is a bacterium, R. solani and V. longisporum are fungi belonging to, respectively, Basidiomycota and Ascomycota. By analysing chemical, physical and many different biological soil characteristics, we were able to correlate soil suppressiveness of the three soil-borne plant diseases and identify a combination of the most relevant soil characteristics, mainly biotic factors. Interestingly, suppressiveness correlated with few general (biotic) soil characteristics combined with the presence of specific bacterial antagonists. We assume that the evaluated organic farms, which use organic manure and do not use chemical inputs, have a basic general suppression based on microbial activity and competition, but differ in their level of specific disease suppression. Probably the soil suppressiveness is a combined effect of general and specific suppression, where the first relates to activity, biomass, diversity, and the second is the result of the presence of specific antagonistic groups.

positively with fungal diversity and negatively with the number of years after grass-clover. 4.2. Rhizoctonia suppression 4. Discussion 4.1. Soil suppressiveness In order to find correlations between soil characteristics and disease suppressiveness of soils, a variety of microbial, chemical and physical soil characteristics as well as soil suppressiveness data were assessed on 10 organic farms. Significant differences between suppressiveness of soils were detected between the evaluated organic farms, as well as between fields within single farms. However, none of the soils was suppressive for all three pathogens tested. This indicates that the variation in soil suppressiveness was pathogen specific. Previous studies with a variety of pathogens also showed soil suppressiveness to be different per pathogen (Oyarzun et al., 1997; Termorshuizen et al., 2006; Ghini et al., 2007). Specificity in disease suppression can be expected, since the pathogens differ largely in their ecology. Rhizoctonia and Streptomyces have a saprophytic stage and can grow on organic matter in soil, whereas

Disease suppression against R. solani AG2.2IIIB in sugar beet, measured in three subsequent years, correlated positively with the presence of antagonistic Lysobacter spp. Although the number of years of organic cropping in itself had no influence, cropping grassclover, which is common within the rotation cycle of organic farming, showed a strong increase in suppressiveness against Rhizoctonia. Suppressiveness lasted until 2 years after the grass-clover was grown, but had disappeared after 3 years. Also the number of Lysobacter, mainly L. gummosus and L. antibioticus, increased drastically (about 5 times) after cropping grass-clover. Lysobacter was only isolated from clayey soils, and not from the assessed sandy soils. Lysobacter spp. was never before correlated with soil suppressiveness. Only in few very recent publications it has been described that an antagonistic isolate of Lysobacter sp. was obtained from an extensive soil sampling in (i) the famous Fusarium-suppressive soil of Chateaurenard in France (Adesina et al., 2007), (ii) Klein Wanzleben in Germany (Zachow et al., 2008), and (iii) two

J. Postma et al. / Soil Biology & Biochemistry 40 (2008) 2394–2406

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Table 5 Regression models that best explain soil suppressiveness against Rhizoctonia solani, Streptomyces scabies and Verticillium longisporum with the most relevant soil characteristics Soil suppressiveness (ss) per pathogen

Multiple regression model

Variance accounted (%)

Standard error of observations

Rhizoctonia Rhizoctonia (only clayey soils) Streptomyces Verticilliuma

90.0 þ 0.5 antagonistic Lysobacter  0.5 active fungi  23.4 bacterial diversity 155.0 þ 0.5 antagonistic Lysobacter  0.5 active fungi  38.3 bacterial diversity  1.2% lutum 80.9 þ 0.2 antagonistic Streptomyces þ 0.6 active fungi þ 15.2 log(number of bacteria) 29.6 þ 14.6 pH-KCl þ 9.1 potential N mineralizetion  0.3 bacterial biomass

47 59 81 50

13.0 12.3 11.4 10.1

Data: 2004, 2005, and 2006. a Only data of 2004 and 2005 were available.

soils from the North-east polder in The Netherlands (Van Overbeek and Van Elsas, 2008). These soils were all clay or silt soils. Besides the correlation with the presence of Lysobacter spp., also fungal activity and bacterial diversity correlated with the disease suppression. In other Rhizoctonia suppressive soils, correlations with Pseudomonas spp. (Mazzola and Gu, 2002), Streptomyces spp. (Tuitert et al., 1998), Trichoderma spp. (Wiseman et al., 1996), V. biguttatum (Velvis et al., 1989), respiration (Croteau and Zibilske, 1998), antibiotic genes (Garbeva et al., 2004) and microbial diversity (Garbeva et al., 2006) have been suggested. However, in general, the mechanism of suppression against Rhizoctonia is not well understood. 4.3. Streptomyces suppression Disease suppressiveness against scab (S. scabies) correlated with the high number of antagonistic bacteria, in particular with

1.0 C/N*

antagonistic Streptomyces spp., as well as with the percentage of active fungi and the number of bacteria. Also earlier studies had shown a correlation between antagonistic streptomycetes and common scab (including potato) (Schottel et al., 2001; Wiggins and Kinkel, 2005). High populations of antagonistic Streptomyces spp. were present in soils with a high fungal/bacterial biomass ratio, a high C/N quotient, and low bacterial growth rate (low leucine and thymidine incorporation). Thus, disease suppression against scab is likely to be stimulated by agricultural practices that enhance the population of antagonistic Streptomyces spp., and/or increase the fungal/bacteria ratio. The application of acidifying manure (e.g. ammonium sulphate, urea), which is known to control common scab (Sturz et al., 2004), can be expected to stimulate fungal development, since fungi proliferate better in acid environments than bacteria. On the other hand, applying calcium as well as high levels of nitrogen-rich manure is mentioned to stimulate common scab (Veenman and van den Boogert, 2003). Likely, these practices lower the fungal/bacterial ratio. Antagonistic Streptomyces spp. are probably stimulated by the addition of recalcitrant organic matter, since streptomycetes are well known for their potential to use a wide range of organic compounds (Locci, 1989) including complex and recalcitrant organic matter. In contrast with Rhizoctonia suppression, growing grass-clover did not have a clear influence on the occurrence of scab. 4.4. Verticillium suppression

years organic*

years after grass-clover N total *

ssVertic ssStrept ssRhizoc

organic matter*

42.5 68.1 21.2

0.8

16.5 logBacteria*

18.3 B biomass* Thymidine *

B diversity *

years organic

C total *

RDA2 (13.8 %)

RDA2 (20.0%)

Suppressiveness against wilt caused by V. longisporum seemed to be variable in the 2 years of sampling. Nevertheless, a significant

Leucine* lutum*

F biomass * Leucine * Thymidine * lutum * 56.5 pH-KCl * B biomass*

years after grass-clover C total

F diversity * ssRhizoc ssStrept

ssVertic

organic matter N total *

C/N *

58.7

pH-KCl *

-1.0

-0.6

-0.6

1.0

RDA1 (26.2 %) Fig. 6. Correlation between bacterial composition and most relevant soil characteristics of soil samples from 2004, 2005 and 2006 analysed with RDA (redundancy analysis). Soil characteristics (arrows with solid lines) with * have a significant correlation with the bacterial composition. Arrows with dotted lines present bacterial bands in PCR-DGGE profiles with a fit >15%. Disease suppression of the three pathogens (ssRhizoc, ssStrept, ssVertic) is presented as supplementary factor (small gray arrows). Values on the axes present the species – environmental relation.

-1.0

1.0

RDA1 (29.6 %) Fig. 7. Correlation between fungal composition and most relevant soil characteristics of soil samples from 2004, 2005 and 2006 analysed with RDA (redundancy analysis). Soil characteristics (arrows with solid lines) with * have a significant correlation with the fungal composition. Arrows with dotted lines present fungal bands in PCR-DGGE profiles with a fit >15%. Disease suppression of the three pathogens (ssRhizoc, ssStrept, ssVertic) is presented as supplementary factor (small gray arrows). Values on the axes present the species  environmental relation.

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J. Postma et al. / Soil Biology & Biochemistry 40 (2008) 2394–2406

0.8

F/B biomass * Streptomyces

RDA2 (23.1 %)

ssRhizoc Lysobacter

C/N ssStrept

F diversity years organic B diversity *

ssVertic

pH-KCl *

N total years after grass-clover

Thymidine *

C total organic matter

lutum Pseudomanas

-0.8 -1.0

Leucine *

1.0

RDA1 (64.5 %) Fig. 8. Correlation between three main antagonists and most relevant soil characteristics of soil samples from 2004, 2005 and 2006 analysed with RDA (redundancy analysis). Soil characteristics (arrows with solid lines) with * have a significant correlation with the antagonist composition. Arrows with dotted lines present the antagonists Lysobacter, Streptomyces and Pseudomonas with, respectively, 65, 40 and 29% fit. Disease suppression of the three pathogens (ssRhizoc, ssStrept, ssVertic) is presented as supplementary factor (small gray arrows). Values on the axes present the antagonistic species  environmental relation.

correlation could be fitted, in which pH, potential N mineralization, and bacterial biomass correlated with suppressiveness. A specific antagonistic component could not be included in the formula, since antagonists of Verticillium had not been assessed. In literature, antagonistic bacteria (Berg et al., 2006) as well as antagonistic or parasitic fungi (Nagtzaam et al., 1998) of Verticillium have been described. For a better description of the soil suppressiveness against Verticillium specific antagonists or parasites should be assessed. 4.5. Microbial composition and diversity The importance of microbial diversity for suppression of diseases has been suggested (Garbeva et al., 2006; Brussaard et al., 2007). It is clear that sterilized soil loses its suppressiveness. Thus, adding micro-organisms to such a sterilized system will enhance suppressiveness. However, a natural agricultural soil is estimated to contain over 6000 bacterial species per g (Curtis et al., 2002), and it can be questioned if increasing this biodiversity enhances its suppressiveness. Moreover, PCR-DGGE has a technical limit of maximally 60 bands per profile, thus only diversity indices based on dominant genotypes can be used and not absolute diversity data. In our evaluation of the 10 organic farms we assessed biodiversity using the Shannon index of bacterial as well as fungal populations with PCR-DGGE. In most combinations, bacterial or fungal diversity indices were not contributing to suppressiveness. A changed composition of bacteria and fungi assessed with PCRDGGE (Garbeva et al., 2006) or T-RFLP (Pe´rez-Piqueres et al., 2006; Benı´tez et al., 2007) was correlated with increased soil suppressiveness. In these studies, an experimental setup of 1 or 2 soil types with specific treatments (i.e. rotation, compost, tillage strategies) were used. In our study, with a variety of soil types, crops, and management practices, the composition of the dominantly present bacteria and fungi assessed with PCR-DGGE did, in general, not correlate significantly with suppressiveness of the three diseases. Only the altered fungal composition and Rhizoctonia

suppressiveness showed a significant correlation. However, the bacterial and fungal communities were clearly different per farm. The bacterial and fungal communities were mainly dependent on general soil characteristics such as lutum content, pH, C/N quotient, bacterial activity and specific respiration. These data were mirrored by Kowalchuk et al. (2003), who concluded that PCR-DGGE profiles of bacteria and fungi were poor predictors of Pythium suppression. Although compost-amended soil had a different microbial community, dominant microbial populations remained mostly intact after rigorous soil treatments such as fumigation and flooding, which destruct Pythium suppression. We therefore conclude that assessing the dominantly present bacterial and fungal communities can be used to describe general changes in soil; e.g. microbial changes due to organic amendments or stress factors. But disease suppression is likely to depend on more specific interactions between pathogens and certain groups of micro-organisms, which are not necessarily dominant in their presence. If we consider the antagonistic Lysobacter spp., they represented only 0.03% of the bacterial population as analysed with PCR-DGGE (assuming that 10% of bacteria is culturable). As a consequence, antagonistic Lysobacter spp., which might play a key role in disease suppression of Rhizoctonia, could not be visualized by PCR-DGGE fingerprinting profile of the bacterial community. 4.6. Antagonistic bacteria Searching for specific traits of suppressive microbial populations, we identified 525 culturable antagonists against Rhizoctonia by a combination of classical isolation and identification through partial 16S rRNA sequencing. Most frequently occurring antagonists inhibiting Rhizoctonia appeared to be Streptomyces, Lysobacter and Pseudomonas spp.. Streptomyces and Pseudomonas are known for their capacity to inhibit several fungal pathogens (Tuitert et al., 1998; Berg et al., 2002; Weller et al., 2002; Wiggins and Kinkel, 2005). The antagonistic potential of Lysobacter was described for the first time in 2003 against plant pathogens (Folman et al., 2003; Sullivan et al., 2003; Postma and Willemsen-de Klein, 2004), as well as nematodes (Nour et al., 2003). The potential role of Lysobacter spp. in soil suppressiveness was never mentioned before. Another newly described bacterial antagonist of fungi is Collimonas fungivorans (de Boer et al., 2004). Until now, this mycophagous bacterium has only been isolated from (semi) natural grassland (Ho¨ppener-Ogawa et al., 2007). In our study it was isolated in two subsequent years from a farm with an arable rotation on an acid sandy soil when grass-clover was grown (field F). Thus, this species can potentially play a role in disease suppression in agricultural systems having grass in its rotation. 5. Conclusion The current descriptive approach, without pre-distinguished hypotheses, resulted in new concepts for soil suppressiveness. We found a significant correlation between Rhizoctonia suppression in natural soils and the presence of two species of Lysobacter (L. gummosus and L. antibioticus), which would be a new mode of action of Rhizoctonia suppression. This Rhizoctonia suppression and presence of Lysobacter spp. were found to be stimulated by cropping grass-clover and was isolated only from clay soils. Suppressiveness of scab (S. scabies) in soils sampled at different farms correlated with the presence of antagonistic Streptomyces spp., which is a confirmation of previous studies (Schottel et al., 2001; Wiggins and Kinkel, 2005). The detected correlation of these antagonistic Streptomyces spp. with high C/N quotient and a high fungal/bacterial biomass ratio gave new tools to enhance these antagonistic populations in soil. These new hypotheses allow

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further studies on the causal relations between the microbial populations and soil suppressiveness, followed by designing and testing management tools to enhance suppressive microbial populations and suppressiveness of soils. Acknowledgements We thank the organic farmers for allowing us to take soil samples, Hans Schneider and Gabriele Berg for handling their pathogenic isolates, An Vos, Popko Bolhuis and Meint Veninga for performing microbiological analyses, and Arjan Reijneveld for analysis of soil samples. Aad Termorshuizen, Arjen Speksnijder and Saskia Burgers are acknowledged for fruitful advises on the manuscript, molecular methods, and statistical methods. This research was supported by the Dutch Ministry of Agriculture, Nature and Food Quality in the research program DWK432. References Adesina, M.F., Lembke, A., Costa, R., Speksnijder, A., Smalla, K., 2007. Screening of bacterial isolates from various European soils for in vitro antagonistic activity towards Rhizoctonia solani and Fusarium oxysporum: Site-dependent composition and diversity revealed. Soil Biology and Biochemistry 39, 2818–2828. Alabouvette, C., Rouxel, F., Louvet, J., 1979. Characteristics of Fusarium wilt-suppressive soils and prospects for their utilization in biological control. In: Schippers, B., Gams, W. (Eds.), Soil-borne Plant Pathogens. Academic Press, New York, pp. 165–182. Alabouvette, C., Couteaudier, Y., Louvet, J., 1985. Soils suppressive to Fusarium wilt: mechanisms and management of suppressiveness. In: Parkere, C.A.e.a., Rovira, A.D., Moor, K.J., Wong, P.Y.W., Kollmorgen, J.F. (Eds.), Ecology and Management of Soilborne Plant Pathogens. Am. Phytopathological Society. Minnesota, St. Paul, pp. 101–106. Anderson, I.C., Campbell, C.D., Prosser, J.I., 2003. Diversity of fungi in organic soils under a moorland – Scots pine (Pinus sylvestris L.) gradient. Environmental Microbiology 5, 1121–1132. Bakker, Y., Van Loon, F.M., Schneider, J.H., 2005. Soil suppressiveness to Rhizoctonia solani and microbial diversity. Communications in Agricultural and Applied Biological Sciences 70, 29–33. Benı´tez, M.-S., Tustas, F.B., Rotenberg, D., Kleinhenz, M.D., Cardina, J., Stinner, D., Miller, S.A., McSpadden Gardener, B.B., 2007. Multiple statistical approaches of community fingerprint data reveal bacterial populations associated with general disease suppression arising from the application of different organic field management strategies. Soil Biology and Biochemistry 39, 2289–2301. Berg, G., Roskot, N., Steidle, A., Eberl, L., Zock, A., Smalla, K., 2002. Plant-dependent genotypic and phenotypic diversity of antagonistic rhizobacteria isolated from different Verticillium host plants. Applied and Environmental Microbiology 68, 3328–3338. Berg, G., Opelt, K., Zachow, C., Lottmann, J., Go¨tz, M., Costa, R., Smalla, K., 2006. The rhizosphere effect on bacteria antagonistic towards the pathogenic fungus Verticillium differs depending on plant species and site. FEMS Microbiology Ecology 56, 250–261. Bloem, J., Vos, A., 2004. Fluorescent staining of microbes for total direct counts. In: Kowalchuk, G.A., De Bruijn, F.J., Head, I.M., Akkermans, A.D.L., Van Elsas, J.D. (Eds.), Molecular Microbial Ecology Manual, second ed. Kluwer Academic Publishers, Dordrecht, pp. 861–874. Bloem, J., Bolhuis, P.R., 2006. Thymidine and leucine incorporation to assess bacterial growth rate. In: Bloem, J., Hopkins, D.W., Benedetti, A. (Eds.), Microbiological Methods for Assessing Soil Quality. CABI, Wallingford, UK, pp. 142–149. Bloem, J., Lebbink, G., Zwart, K.B., Bouwman, L.A., Burgers, S.L.G.E., de Vos, J.A., de Ruiter, P.C., 1994. Dynamics of microorganisms, microbivores and nitrogen mineralisation in winter wheat fields under conventional and integrated management. Agriculture. Ecosystems and Environment 51, 129–143. de Boer, W., Leveau, J.H.J., Kowalchuk, G.A., Klein Gunnewiek, P.J.A., Abeln, E.C.A., Figge, M.J., Sjollema, K., Janse, J.D., van Veen, J.A., 2004. Collimonas fungivorans gen. nov., sp. nov., a chitinolytic soil bacterium with the ability to grow on living fungal hyphae. International Journal of Systematic and Evolutionary Microbiology 54, 857–864. Brussaard, L., de Ruiter, P.C., Brown, G.G., 2007. Soil biodiversity for agricultural sustainability. Agriculture, Ecosystems and Environment 121, 233–244. Croteau, G.A., Zibilske, L.M., 1998. Influence of papermill processing residuals on saprophytic growth and disease caused by Rhizoctonia solani. Applied Soil Ecology 10, 103–115. Curtis, T.P., Sloan, W.T., Scannell, J.W., 2002. Estimating prokaryotic diversity and its limits. Proceedings of the National Academy of Sciences of the United States of America 99, 10494–10499. Domsch, K.H., Gams, W., Anderson, T.-H., 2007. Compendium of soil fungi, second ed. IHW-Verlag Eching, Germany, 672 pp. Folman, L.B., Postma, J., Veen, J.A., 2003. Inability to find consistent bacterial biocontrol agents of Pythium aphanidermatum in cucumber using screens based on ecophysiological traits. Microbial Ecology 45, 72–87.

2405

Garbeva, P., Voesenek, K., van Elsas, J.D., 2004. Quanitative detection and diversity of the pyrrolnitrin biosynthetic locus in soil under different treatments. Soil Biology and Biochemistry 36, 1453–1463. Garbeva, P., Postma, J., van Veen, J.A., van Elsas, J.D., 2006. Effect of above-ground plant species on soil microbial community structure and its impact on suppression of Rhizoctonia solani AG3. Environmental Microbiology 8, 233–246. Ghini, R., Patricio, F.R.A., Bettiol, W., de Almeida, I.M.G., Maia, A.d.H.N., 2007. Effect of sewage sludge on suppressiveness to soil-borne plant pathogens. Soil Biology and Biochemistry 39, 2797–2805. Heuer, H., Smalla, K., 1997. Application of denaturing gradient gel electrophoresis and temperature gradient gel electrophoresis for studying soil microbial communities. In: van Elsas, J.D., Trevors, J.T., Wellington, E.M.H. (Eds.), Modern Soil Microbiology. Marcel Dekker, New York, pp. 353–373. Hoitink, H.A.J., Boehm, M.J., 1999. Biocontrol within the context of soil microbial communities: a substrate-dependent phenomenon. Annual Review of Phytopathology 37, 427–446. Ho¨per, H., Alabouvette, C., 1996. Importance of physical and chemical soil properties in the suppressiveness of soils to plant diseases. European Journal of Soil Biology 32, 41–58. Ho¨ppener-Ogawa, S., Leveau, J.H.J., Smant, W., Van Veen, J.A., De Boer, W., 2007. Specific detection and real-time PCR quantification of potentially mycophagous bacteria belonging to the genus Collimonas in different soil ecosystems. Applied and Environmental Microbiology 73, 4191–4197. Jager, G., ten Hope, A., Velvis, H., 1979. Hyperparasites of Rhizoctonia solani in Dutch potato fields. Netherlands Journal of Plant Pathology 14, 86–91. Janvier, C., Villeneuve, F., Alabouvette, C., Edel-Hermann, V., Mateille, T., Steinberg, C., 2007. Soil health through soil disease suppression: Which strategy from descriptors to indicators? Soil Biology and Biochemistry 39, 1–23. Karapapa, V.K., Bainbridge, B.W., Heale, J.B., 1997. Morphological and molecular characterization of Verticillium longisporum comb. nov., pathogenic to oilseed rape. Mycological Research 101, 1281–1294. Kowalchuk, G.A., Os, G.J., Aartrijk, J., Veen, J.A., 2003. Microbial community responses to disease management soil treatments used in flower bulb cultivation. Biology and Fertility of Soils 37, 55–63. Lifshitz, R., Stanghellini, M.E., Baker, R., 1984. A new species of Pythium isolated from soil in Colorado. Mycotaxon 20, 373–379. Locci, R., 1989. Streptoycetes and related genera. In: Williams, S.T., Sharpe, M.E., Holt, J.G. (Eds.), Bergey’s manual of Systematic Bacteriology. Williams & Wilkins, Baltimore, pp. 2451–2492. Mazzola, M., Gu, Y.H., 2002. Wheat genotype-specific induction of soil microbial communities suppressive to disease incited by Rhizoctonia solani Anastomosis Group (AG)-5 and AG-8. Phytopathology 92, 1300–1307. Morris, S.J., Zink, T., Conners, K., Allen, M.F., 1997. Comparison between fluorescein diacetate and differential fluorescent staining procedures for determining fungal biomass in soils. Applied Soil Ecology 6, 161–167. Nagtzaam, M.P.M., Bollen, G.J., Termorshuizen, A.J.,1998. Efficacy of Talaromyces flavus alone or in combination with other antagonists in controlling Verticillium dahliae in growth chamber experiments. Journal of Phytopathology 146, 165–173. Nour, S.M., Lawrence, J.R., Zhu, H., Swerhone, G.D.W., Welsh, M., Welacky, T.W., Topp, E., 2003. Bacteria associated with cysts of the soybean cyst nematode (Heterodera glycines). Applied and Environmental Microbiology 69, 607–615. Oyarzun, P.J., Van Der Zalm, T., Roelofsen, H.W., Dijst, G., 1994. Computerized equipment to control soil water matric potential and soil temperature in inoculum potential assays of soil-borne pathogens. Plant and Soil 166, 125–135. Oyarzun, P.J., Dijst, G., Zoon, F.C., Maas, P.W.T., 1997. Comparison of soil receptivity to Thielaviopsis basicola, Aphanomyces euteiches, and Fusarium solani F. sp. pisi causing root rot in pea. Phytopathology 87, 534–541. Pe´rez-Piqueres, A., Edel-Hermann, V., Alabouvette, C., Steinberg, C., 2006. Response of soil microbial communities to compost amendments. Soil Biology and Biochemistry 38, 460–470. Postma, J., Willemsen-de Klein, M.J.E.I.M., 2004. Biocontrol of Pythium aphanidermatum in cucumber with combined applications of bacterial antagonists with chitosan. IOBC/wprs Bulletin 27, 101–104. Postma, J., Willemsen-de Klein, M.J.E.I.M., Van Elsas, J.D., 2000. Effect of the indigenous microflora on the development of root and crown rot caused by Pythium aphanidermatum in cucumber grown on rockwool. Phytopathology 90, 125–133. Raaijmakers, J.M., Weller, D.M., 1998. Natural plant protection by 2,4-diacetylphloroglucinol-producing Pseudomonas spp. in Take-all decline soils. Molecular Plant-Microbe Interactions 11, 144–152. Reichenbach, 1992. The genus Lysobacter. In: Balows, A., Tru¨per, H.G., Dworkin, M., Harder, W., Schleifer, K.-H. (Eds.), The Prokaryotes, second ed., pp. 3256–3275. Salles, J.F., Van Veen, J.A., Van Elsas, J.D., 2004. Multivariate analyses of Burkholderia species in soil: effect of crop and land use history. Applied and Environmental Microbiology 70, 4012–4020. Schottel, J.L., Shimizu, K., Kinkel, L.L., 2001. Relationships of in vitro pathogen inhibition and soil colonization to potato scab biocontrol by antagonistic Streptomyces spp. Biological Control 20, 102–112. Sturz, A.V., Ryan, D.A.J., Coffin, A.D., Matheson, B.G., Arsenault, W.J., Kimpinski, J., Christie, B.R., 2004. Stimulating disease suppression in soils: Sulphate fertilizers can increase biodiversity and antibiosis ability of root zone bacteria against Streptomyces scabies. Soil Biology and Biochemistry 36, 343–352. Sukkel, W., van Leeuwen-Haagsma, W., van der Wel, C., 2006. BIOM, Organic Farmers Network for Research, Development and Innovation. In: Joint Organic Congress, Odense, Denmark. Available from: http://orgprints.org/7399/.

2406

J. Postma et al. / Soil Biology & Biochemistry 40 (2008) 2394–2406

Sullivan, R.F., Holtman, M.A., Zylstra, G.J., White Jr., J.F., Kobayashi, D.Y., 2003. Taxonomic positioning of two biological control agents for plant diseases as Lysobacter enzymogenes based on phylogenetic analysis of 16S rDNA, fatty acid composition and phenotypic characteristics. Journal of Applied Microbiology 94, 1079–1086. Ter Braak, C.J.F., 1995. Ordination. In: Jongman, R.H.G., ter Braak, C.J.F., van Tongeren, O.F.R. (Eds.), Data Analysis in Community and Landscape Ecology, second ed. Cambridge University Press, Cambridge, UK, pp. 91–173. Termorshuizen, A.J., van Rijn, E., van der Gaag, D.J., Alabouvette, C., Chen, Y., Lagerlof, J., Malandrakis, A.A., Paplomatas, E.J., Ramert, B., Ryckeboer, J., Steinberg, C., Zmora-Nahum, S., 2006. Suppressiveness of 18 composts against 7 pathosystems: variability in pathogen response. Soil Biology and Biochemistry 38, 2461–2477. Tuitert, G., Szczech, M., Bollen, G.J., 1998. Suppression of Rhizoctonia solani in potting mixtures amended with compost made from organic household waste. Phytopathology 88, 764–773. Van Os, G.J., Van Ginkel, J.H., 2001. Suppression of Pythium root rot in bulbous iris in relation to biomass and activity of the soil microflora. Soil Biology and Biochemistry 33, 1447–1454. Van Overbeek, L., Van Elsas, J.D., 2008. Effects of plant genotype and growth stage on the structure of bacterial communities associated with potato (Solanum tuberosum L.). FEMS Microbiology Ecology 64, 283–296.

Veenman, A., van den Boogert, P.H.J.F., 2003. Inventarisatie van nieuwe en bestaande mogelijkheden voor de bestrijding van schurft in aardappelen. Praktijkonderzoek Plant & Omgeving B.V., AGV. Velvis, H., van den Boogert, P.H.J.F., Jager, G., 1989. The role of antagonism in decline of Rhizoctonia solani inoculum in soil. Soil Biology and Biochemistry 21, 125–129. Wanner, L.A., 2004. Field isolates of Streptomyces differ in pathogenicity and virulence on radish. Plant Disease 88, 785–796. Weller, D.M., Raaijmakers, J.M., McSpadden Gardener, B.B., Thomashow, L.S., 2002. Microbial populations responsible for specific soil suppressiveness to plant pathogens. Annual Review of Phytopathology 40, 309–348. Wiggins, B.E., Kinkel, L.L., 2005. Green manures and crop sequences influence alfalfa root rot and pathogen inhibitory activity among soil-borne streptomycetes. Plant and Soil 268, 271–283. Wiseman, B.M., Neate, S.M., Keller, K.O., Smith, S.E., 1996. Suppression of Rhizoctonia solani anatomosis group 8 in Australia and its biological nature. Soil Biology and Biochemistry 28, 727–732. Workneh, F., van Bruggen, A.H.C., 1994. Suppression of corky root of tomatoes in soils from organic farms associated with soil microbial activity and nitrogen status of soil and tomato tissue. Phytopathology 84, 688–694. Zachow, C., Tilcher, R., Berg, G., 2008. Sugar beet-associated bacterial and fungal communities show a high indigenous antagonistic potential against plant pathogens. Microbial Ecology 55, 119–129.