Applied Soil Ecology 47 (2011) 184–194

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Applied Soil Ecology

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Assessing soil quality under intensive cultivation and tree orchards inSouthern Italy

Giuliano Bonanomi a,∗, Rosaria D’Ascoli c, Vincenzo Antignani a, Manuela Capodilupo a,Lucia Cozzolino a, Rossana Marzaioli c, Gerardo Puopolo a, Flora A. Rutigliano c, Rosalia Scelzab,Riccardo Scottib, Maria A. Raob, Astolfo Zoina a

a Dipartimento di Arboricoltura, Botanica e Patologia Vegetale, Università di Napoli Federico II, via Università 100, Portici 80055 (NA), Italyb Dipartimento di Scienze del Suolo, della Pianta, dell’Ambiente e delle Produzioni Animali, Università di Napoli Federico II, via Università 100, Portici 80055 (NA), Italyc Dipartimento di Scienze Ambientali, Seconda Università di Napoli, Via Vivaldi 43, Caserta 81100 (CE), Italy

a r t i c l e i n f o

Article history:

Received 26 March 2010

Received in revised form

16 December 2010

Accepted 19 December 2010

Keywords:

Agricultural management

Soil chemical and biological properties

Enzymatic activities

Microbial diversity

Rhizoctonia solani

a b s t r a c t

Concerns about groundwater contamination as well as pesticide residues in food and soil have fuelled vig-

orous debates about the sustainability of chemical-intensive agriculture. Search has been prompted for

agronomic strategies with lower environmental hazards. In this multidisciplinary study we compared

the characteristics of soils from 20 agricultural farms selected in five geographical areas of Southern

Italy with different soil types. In each farm, fields with management regime classified as high-input

(HIMR, intensive cultivation under plastic tunnels) or low-input (LIMR, tree orchards) were selected.

Soil samples were analyzed for 31 parameters including physical and chemical properties (bulk density,

water holding capacity, texture, pH, limestone, electrical conductivity, organic C to a depth of 0–20 and

20–40 cm, total N, P2O5, Ca2+, Mg2+, K+, Na+, cation exchange capacity), enzymatic activities (dehydro-

genase, arylsulphatase, �-glucosidase, phosphatase and urease) and microbiological features (potential

respiration, functional diversity of microbial populations by BIOLOG EcoPlatesTM, microbial biomass, fun-

gal mycelium, culturable actinomycetes, bacteria and fungi, pseudomonads and bacterial species richness

by 16S rDNA-DGGE). Finally, a soil bioassay was performed in order to evaluate the plant growth of a

biotest plant (Lactuca sativa) and soil suppressiveness of the Rhizoctonia solani–L. sativa pathosystem.

Results showed that many soil properties were influenced by management regime more than by the

sampling area. Compared to LIMR, HIMR soils consistently had reduced soil organic C (−24%), enzymatic

activities, microbial biomass and fungal mycelium (−40% and −18%, respectively), functional diversity

(−18%) and bacterial species richness (−14%). On the contrary, the same soils showed a remarkable

increase in the values of the parameters related to the mineral soil fraction (electrical conductivity +370%;

P2O5 +72%; Na+ +86%). Management regime did not affect cation exchange capacity, pH, limestone and

soil texture. The lettuce bioassay showed a higher plant growth (+17%) in the LIMR compared to HIMR

soils, despite the lower content of mineral nutrients. Suppression of R. solani was not influenced by

management regime, but significant differences were recorded among farms. Differences among the

assessed soil parameters indicate a trend of soil quality deterioration under the high-input management

regime.

© 2010 Elsevier B.V. All rights reserved.

1. Introduction

Soil quality is a complex and multifaceted concept defined by

Doran and Parkin (1994) as “the capacity of a soil to function

within ecosystem boundaries to sustain biological productivity, to

maintain environmental quality and to promote plant and animal

health”. As a consequence, soil quality encompasses many proper-

∗ Corresponding author. Tel.: +39 081 2539379; fax: +39 081 7760104.

E-mail address: giuliano.bonanomi@unina.it (G. Bonanomi).

ties and processes as the structural stability of aggregates (Abiven

et al., 2009), the water retention capacity (Loveland and Webb,

2003), the capability of nutrient cycling (Tiessen et al., 1994), the

ability to store organic carbon (Martens et al., 2003) and to naturally

suppress soilborne plant pathogens (Janvier et al., 2007; Bonanomi

et al., 2010). Soil quality is the outcome of interactions among

physical, chemical and biological characteristics, and its proper

assessment requires the determination of a large number of param-

eters (Bloem et al., 2006a; Marzaioli et al., 2010). In this context,

soil enzymes and microbial-based processes are now considered

particularly important because they usually respond more rapidly

0929-1393/$ – see front matter © 2010 Elsevier B.V. All rights reserved.

doi:10.1016/j.apsoil.2010.12.007

G. Bonanomi et al. / Applied Soil Ecology 47 (2011) 184–194 185

than chemical and physical parameters to environmental changes

and stresses such as heavy metal pollution (Schloter et al., 2003;

Gianfreda and Ruggiero, 2006). In fact, soil microbes are useful indi-

cators of soil quality because they are involved in organic matter

decomposition, nutrient cycling, maintenance of soil structure, and

suppressiveness to plant pathogens.

In the last decades a significant decrease in primary productiv-

ity has been observed worldwide as a consequence of soil erosion,

overgrazing, salinity and/or sodicity induced by irrigation (Sumner,

1995), pollution by heavy metals and xenobiotics (Moolenaar et al.,

1997), reduction of soil organic carbon (Loveland and Webb, 2003)

and loss of natural soil suppressiveness due to the heavy applica-

tion of fumigants and fungicides (Weller et al., 2002). Soil fertility

plays a key role in the development of a sustainable agriculture

and projects have been established in many countries (including

US, Canada, France, Germany, Netherlands, UK, Switzerland, Czech

Republic, Austria and New Zealand) with the aim of monitoring soil

quality in relation to farming practices (Bloem et al., 2006b). Many

studies investigated the impact of conventional vs organic farming

(Drinkwater et al., 1995; Birkhofer et al., 2008), grassland vs arable

soils (Haynes, 2000), and different fertilization approaches, includ-

ing mineral and organic applications (Fließbach et al., 2007) on soil

quality. In a leading study, Mäder et al. (2002) reported a higher soil

quality and ecosystem biodiversity for organically managed plots

compared to conventional cultivation.

Despite the large and growing body of available literature, no

studies addressed the long-term impact of intensive cultivation

under permanent plastic tunnels on soil quality. Cultivation under

plastic tunnels is a steadily growing agricultural sector all over the

world, and at present it covers more than 400,000 ha in the Mediter-

ranean Basin alone (Enoch and Enoch, 1999). This type of cultivation

is expected to profoundly affect soil quality because it drastically

modifies water, carbon and nutrient cycles. In fact, the almost

complete rainfall restriction and the consequent requirement of

localized irrigation to support crop water demand is expected to

increase soil salinity. Moreover, the widespread use of mineral fer-

tilizer, the systematic elimination of crop residues to limit plant

diseases (Bonanomi et al., 2007), and the optimal temperature and

water content that promote mineralization of organic matter are all

factors expected to reduce soil organic C content, with a negative

feedback on soil microbial populations. Therefore, our hypothesis

is that the long-term soil cultivation under permanent plastic cover

will reduce soil quality, both in chemical and biological terms. In

detail, we expected to find under intensive cultivation: (i) a reduc-

tion in organic C content, and, consequently, a decrease in microbial

growth and activity; (ii) an increase in soil salinity; (iii) no effect

on some basic chemical and physical parameters as pH, limestone

content, and soil texture.

This study addressed this hypothesis with a multidisciplinary

approach. To achieve this purpose, we compared two different

soil management regimes: 1) high-input management regime

(HIMR), characterized by intensive cultivation under permanent

plastic tunnels; and ii) low-input management regime (LIMR),

constituted by tree orchards. Since soil quality cannot be summa-

rized by a single property or process, its assessment necessarily

requires measurement of a large number of parameters. Therefore,

31 parameters were measured (including physical, chemical, bio-

chemical and biological ones) to assess soil quality in five different

sampling areas cultivated both under HIMR and LIMR.

2. Materials and methods

2.1. Study site description and selection of farms

All selected agricultural farms were located in Salerno district

(Southern Italy), a very productive area with ∼3500 ha cultivated

Table 1

Sampled soils classified according to Keys to Soil Taxonomy (USDA, 1998).

Sampling area Soils types Management regime

1 Pachic Haploxerolls HIMR, LIMR

Pachic Haploxerolls HIMR, LIMR

Mollic Haploxeralf HIMR, LIMR

Mollic Haploxeralf HIMR

2 Vertic Haploxerolls HIMR, LIMR

Mollic Haploxererts HIMR, LIMR

Mollic Haploxererts HIMR, LIMR

Typic Haploxererts HIMR

3 Typic Xeropsamments HIMR, LIMR

Typic Xeropsamments HIMR, LIMR

Mollic Haploxererts HIMR, LIMR

Mollic Haploxererts HIMR

4 Fluvaquentic Haplustolls HIMR, LIMR

Lithic Haplustolls HIMR, LIMR

Lithic Haplustolls HIMR, LIMR

Lithic Haplustolls HIMR

5 Vitrandic Haplusteps HIMR, LIMR

Humic Ustivitrands HIMR, LIMR

Vitrandic Calciustolls HIMR, LIMR

Vitrandic Calciustolls HIMR

under greenhouses. The greenhouse structures used in these areas

are low-cost, unheated polyethylene-covered (height 4–5 m) and

with soil-grown crops. The location has a moderate Mediterranean

climate with a dry summer (84 mm) and a relatively high mean

annual rainfall of 988 mm mainly distributed in winter, spring

and fall (354, 217 and 333 mm, respectively); mean monthly tem-

perature range between 23.6 ◦C in August, and 9.0 ◦C in January

(average of 30 years of observation; Battipaglia meteorological sta-

tion located near the study area).

Two different soil management regimes were studied: inten-

sive cultivation under plastic cover for at least 6 years, classified

as high-input management regime (HIMR), and tree orchard, clas-

sified as low-input management regime (LIMR). In five sampling

areas characterized by different soil types, along a gradient of soil

texture ranging from heavy clay to sandy soil, 20 HIMR farms were

selected (four for each sampling area), and in 15 of them (three for

each sampling area, on the basis of field availability) adjacent fields

cultivated with orchards were chosen as LIMR. The choice of tree

orchards as LIMR was principally due to absence of extensive arable

cultivation in the study area because of its not economic outputs.

Although the selection of tree orchards as LIMR could increase the

potential differences between the two agricultural systems (LIMR

vs HIMR), because of the large difference between the two types

of cultivation, tree orchards were selected as LIMR because such

management regime was much more common compared with less-

intensive cropping systems (e.g. extensive arable cultivations such

as wheat, soybean, sugar beet, etc.) or grassland in the study area.

According to the USDA classification (1998), sandy Entisols,

shallow Mollisols, Mollisols having a thick mollic epipedon, Mol-

lisols with vertic properties, Alfisols and Vertisols having a mollic

epipedon, Mollisols and Inceptisols having vitric properties, and

Vitric Andosols with or without a mollic epipedon were included

in the sampling (Table 1). This sampling strategy minimized the

variability due to soil types and allowed a direct comparison of

management regime effect on soil quality.

2.2. Soil history assessment

The soil history of the six years preceding the sampling was

assessed for all the selected farms. Information was gathered by

interview and questionnaires to the farmers. The number of years

of continuous intensive cultivation was recorded. In addition, the

186 G. Bonanomi et al. / Applied Soil Ecology 47 (2011) 184–194

agronomic activities routinely carried out in the HIMR and LIMR

farms were assessed as follows: (i) crop rotation history; (ii) soil

tillage regime (types, depth and number per year); (iii) application

of mineral and organic amendments (type and application rate per

year); (iv) soil disinfestation treatments (application of chemical

fumigants and soil solarization).

2.3. Soil sampling and analyses

In each HIMR farm, three plastic tunnels were considered, and

in each of them five soil sub-samples were collected following a W

scheme (four sampling plots near to corners and one sampling plot

in the centre of the tunnel) and pooling together, in order to have

three composite soil sample for each farm. In each selected LIMR

farm, a single composite soil sample was collected alone, because in

the chosen farms only one orchard of small size (comparable with

a single plastic tunnel) was available. In order to have a compos-

ite soil sample representing the variability of the field, the sample

was obtained pooling together soil collected in five different plots,

following a W scheme, under the trees row devoid of grass strips.

Consequently, a total of 75 composite soil samples were collected

for analyses (60 from HIMR and 15 from LIMR farms, respectively).

Soil samples (∼2 kg) were collected from the topsoil (0–20 cm)

in the spring 2008 (April–May), considered the best time for soil col-

lection (Bloem et al., 2006a). Samples were packed in polyethylene

bags, transferred to laboratory quickly and sieved at 2 mm mesh.

The biochemical and microbiological analyses and bioassay were

carried out on fresh soils stored at +4 ◦C until time of measurements

(within 10 days). Texture and chemical analyses were carried out on

soil dried at room temperature until constant weight was reached.

2.3.1. Physical and chemical analyses

Physical and chemical properties of soils were determined by

standard methods (Sparks, 1996). Undisturbed soil cores (one core

for each tunnel and orchard, 0–20 cm soil layer, 20 cm height, 6 cm

diameter), with known volume (565.20 cm3), were analyzed for

water holding capacity and bulk density by gravimetric method,

after drying for 48 h at 105 ◦C. Particle size distribution analysis

was carried out by the pipette method; pH and electrical con-

ductivity were measured in 1:2.5 soil:water suspensions and 1:5

soil:water extracts, respectively; total carbonates (limestone) were

determined by the Dietrich-Fruehling calcimeter method (Loeppert

and Suarez, 1996); organic C content, at 0–20 and 20–40 soil lay-

ers, was assayed (on 1 g of pulverized soil) by chromic acid titration

method; total N was determined (on 30 mg pulverized soil) by

flash combustion with a CNS Elemental Analyser (Thermo FlashEA

1112); available phosphate was measured by bicarbonate extrac-

tion; cation exchange capacity was measured after soil treatment

with a barium chloride and triethanolamine solution at pH 8.2; and

exchangeable bases (Ca2+, Mg2+, K+, Na+) were assayed by flame

atomic absorption spectrometry.

2.3.2. Enzymatic activities

The activity of the following enzymes was measured: dehy-

drogenase, arylsulphatase, phosphatase, �-glucosidase and urease.

Dehydrogenase (DH, E.C. 1.1.) activity was measured with buffered

tetrazolium salts solution, according to Trevors (1984). Arylsul-

phatase (AR, E.C. 3.1.6.1), phosphatase (PHO, E.C. 3.1.3.2) and

�-glucosidase (GLU, E.C. 3.2.1.21) activities were determined using

p-nitrophenyl sulphate (p-NPS), p-nitrophenyl phosphate (p-NPP)

or p-nitrophenyl-�-D-glucopyranoside (p-NG) as the substrates,

respectively. Specific buffers and pHs and reaction stop procedures

were used as reported in Gianfreda et al. (2005). Concentrations of

p-nitrophenol (p-NP) were determined at 405 nm after addition of

NaOH and CaCl2 for PHO and AR, and Tris/NaOH buffer (pH 10.0) and

CaCl2 for GLU. Urease activity (E.C. 3.5.1.5) was assayed as described

by Kandeler and Gerber (1988) using urea as substrate. One unit

of enzyme activity was defined as �moles of product released at

30 ◦C h−1 by 1 g of dried soil. Triplicates were performed for each

activity assay.

2.3.3. Microbiological analyses

Microbial monitoring was performed by a multi-technique

approach that combines both conventional and molecular methods

(Liesack et al., 1997).

2.3.3.1. Total microbial activity. This parameter was evaluated as

soil potential respiration by measuring CO2 evolution, with a gas-

chromatograph (Fisons GC 8000 series), from soil samples (4 g

equivalent dry weight of soil, two replicates per sample) incubated

for 1 h in standard conditions (55% water holding capacity; 25 ◦C;

at dark) after Kieft et al. (1998) modified.

2.3.3.2. Community-level physiological profile of microbial popula-

tions. This analysis was performed by the BIOLOG EcoPlatesTM

method based on carbon substrate utilization. BIOLOG EcoPlatesTM

consists of 96 wells containing 31 different carbon sources, and a

blank in triplicate. As the carbon source is utilized the tetrazolium

violet dye is reduced, developing a purple colour. Absorbance read-

ings were taken at 590 nm by a plate reader every 24 h for 96 h. The

assay was conducted as previously described by Bartelt-Ryser et al.

(2005). Average well colour development was calculated as the sum

of activities measured in all wells of each plate, divided by the 31

carbon sources.

2.3.3.3. Total microbial biomass and fungal mycelium. Total micro-

bial biomass, assayed as microbial biomass carbon, was measured

by using the chloroform fumigation/extraction method (Vance

et al., 1987). Total fungal mycelium was determined by the inter-

section method with an optical microscope after filtration of a

soil–water suspension (1 g of soil in 100 mL of distilled water) by

membrane filters as described in detail by D’Ascoli et al. (2005).

The mass of total mycelia was calculated on the basis of the aver-

age values of cross section (9.3 �m2), density (1.1 g mL−1) and dry

mass of the hyphae (15% of the wet mass) according to Berg and

Söderström (1979).

2.3.3.4. Bacteria, fungi, actinomycetes and pseudomonads enumer-

ation. Population density of culturable bacteria, fungi, actino-

mycetes and fluorescent pseudomonads was assessed by the

dilution plating method on different agar media. Soil suspensions

were prepared from each soil sample by shaking (200 rpm) 5 g of

sieved soil with 50 mL of physiological solution (NaCl 0.9%, w/v) for

30 min at room temperature. After this phase, soil mixtures were

left standing for 10 min and serially diluted. Volumes of 100 �L

were plated in triplicate on different media: potato dextrose agar

amended with lactic acid (25%) for fungi, R2A agar medium (Fluka)

for bacteria, Actinomycete Isolation Agar medium (Difco) for acti-

nomycetes, and Gould S’1 medium for fluorescent pseudomonads.

All the media used for bacteria enumeration were amended with

cycloheximide 100 mg L−1 in order to prevent fungal growth. Bac-

teria and fungal colonies were counted after 1 week of incubation

at 28 ◦C and 24 ◦C, respectively, and results were expressed as log

CFU g−1 of dry soil.

2.3.3.5. Bacterial diversity by DGGE analysis. DNA extraction was

performed from 0.5 g of each soil by using the Power SoilTM DNA

Isolation Kit according to the manufacturer’s instructions (Mo Bio

Laboratories, Inc. Carlsbad CA, USA). The amount of DNA extracted

from each soil was standardized by gel electrophoresis to obtain

10 ng of DNA template in each PCR mixture. PCR mixture and ther-

mal cycles adopted for the amplification of bacterial 16S rRNA gene

G. Bonanomi et al. / Applied Soil Ecology 47 (2011) 184–194 187

fragments were the same as previously reported (Bonanomi et al.,

2008). DGGE analysis was performed by using the PhorU2 sys-

tem (Ingeny, Leiden, The Netherlands). Acrylamide gels (8%, w/v)

contained a denaturing gradient ranging from 40 to 60% denatu-

rant solution, where 100% denaturant solution contained 7 M urea

and 40% deionised formamide. DGGE was performed with 1× Tris

Acetate EDTA buffer at 60 ◦C and a constant voltage of 100 V for 16 h.

After staining with ethidium bromide, gels were observed by using

an UV transilluminator. Banding patterns were photographed by

using the Gel Doc 2000 documentation system (Bio-Rad Laborato-

ries, Hercules, CA, USA).

2.3.4. Soil indices calculation

Several indices were calculated as follows: (i) fungi/bacteria

ratio was calculated with CFU of culturable microbes; (ii) microbial

quotient was calculated as the fraction of soil organic C occur-

ring as microbial biomass (Haynes, 2000); (iii) specific respiration

of biomass or metabolic quotient (qCO2) was calculated as CO2-C

evolved per unit of microbial biomass C and time; (iv) coefficient

of endogenous mineralization (CEM) was calculated as fraction of

organic C evolved as CO2 per unit of time.

2.3.5. Soil bioassay

A soil bioassay was carried out to assess the effect of HIMR

and LIMR on plant growth of a biotest plant (Lactuca sativa), the

most common crop in the study area. In addition, suppressiveness

of HIMR and LIMR soils was assessed by measuring the disease

severity in L. sativa–R. solani pathosystem. This system was chosen

because R. solani “damping off” is one of the main phytopathological

problems in the study area.

The experiment was fully factorial with management regimes

(HIMR vs LIMR), pathogen inoculum (no inoculum vs R. solani inocu-

lum), and soil sterilization (untreated vs autoclaved soil) as factors.

Pots, 12 cm diameter and 15 cm height, were filled with 200 g of air

dried soil and planted with 3 pre-germinated 7 days old seedlings

of L. sativa, and were placed in a greenhouse (25 ± 4 ◦C) following

a complete random design with regular rotation every 7 days. Pots

were wetted with distilled water every 2 days until water hold-

ing capacity was reached. Soil inoculum was prepared as follows:

150 g of common millet seeds were placed in 0.5 L flasks, saturated

with Czapeck solution (1/10), autoclaved and inoculated with R.

solani. The flasks were incubated for 21 days at 21 ◦C. The resulting

R. solani millet inoculum was air-dried for 7 days in sterile condi-

tions and added up to 0.5% (dry weight) to the pots. In the control,

non-inoculated common millet was added to pot medium.

After 45 growing days, plant shoots were harvested, washed

with tap water and dry weights measured (105 ◦C for 48 h). The

bioassay was carried out with one target species (L. sativa), 75 soil

samples either sterilized or not, and inoculated or not with R. solani

with 8 replicates per treatment.

2.4. Statistical analyses

Two-way ANOVA was used to examine the effects of soil man-

agement (two regimes: HIMR and. LIMR) and sampling area (five

locations) on physical, chemical, biochemical and microbiological

parameters. Given the unbalanced sample size for the two soil man-

agement regimes (nHIMR = 20, nLIMR = 15), the SS type III was used.

The relationships between soil physical, chemical, biochemical and

microbiological properties were assessed by using Pearson correla-

tion coefficients. For the bioassay, a three-way ANOVA was applied

with management regimes, pathogen inoculum and soil steriliza-

tion as factors. Significance was evaluated in all cases at P < 0.01 and

P < 0.05.

Banding patterns of bacterial DGGE were analyzed by Quantity

One Image Analysis Software (Bio-Rad Laboratories, Hercules, CA,

USA). After applying a rolling disc background subtraction (setting

8) and a sensitivity setting of 10, the software performed the anal-

ysis of each lane: a band was detected if it accounted for greater

than 0.5% of the total lane intensity. The program also provided

the total band number and identification of the bands occupy-

ing common position in the lanes. The clustering of the patterns

was performed by the Unweighted Pair Group Method with Arith-

metic Mean (UPMGA; Dice coefficient of similarity). Band richness

from DGGE profile was used as quantitative assessment of bacterial

species richness.

3. Results

3.1. Soil history

The selected farms had converted their soil to intensive culti-

vation (HIMR) since 20.8 years on average, with a minimum of 6

and a maximum of 41 years. Soil disinfestation treatments were

lacking in the LIMR farms, while they were commonly practiced

in the HIMR farms. On average, soil disinfestation treatments were

applied every two years (0.52 applications year−1) using Methyl-

bromide (until 2005), Metham-Na, Metham-K, Cloropicrin and

1,3-dicloropropen. Soil solarization, alone or in combination with

calcium cyanamide treatment, was also commonly applied. No

exogenous application of organic amendments was done in LIMR

farms. In contrast, organic amendments were applied in 55% of the

HIMR farms about every two years (0.48 applications year−1), with

a corresponding application rate of 0.51 t ha−1 year−1. A positive,

although not significant, correlation (r = 0.39; P = 0.09) was found

between the number of years under intensive cultivation and the

amount of organic matter applied. In contrast, the number of years

under HIMR was not correlated with soil organic C content (r = 0.29;

P = 0.18). The tillage regime was significantly more intense in HIMR

compared to LIMR farms, with an average of 6.05 and 1.6 tillage

treatments every year, respectively (t-test: P < 0.001). The most

common practices in HIMR farms were rototilling, spading and har-

rowing (3.4, 2.5, and 2 treatments year−1, respectively). In contrast,

LIMR farms were usually managed with permanent mowed grass-

land or with a limited number of superficial tillage (10–15 cm) such

as rototilling and harrowing.

In HIMR farms the most common crops were, in a decreasing

order of importance: lettuce (L. sativa), rocket (Eruca sativa), tomato

(Solanum lycopersicon), melon (Cucumis melo), watermelon (Citrul-

lus lanatus), sweet pepper (Capsicum annum) and bean (Phaseolus

vulgaris). Pooled together these crops accounted for 90% of all the

cultivations, but lettuce and tomato alone accounted for 61%. The

most common crop rotation included lettuce in winter and tomato,

sweet pepper or melon in summer. The most common orchards in

LIMR farms were: peach (Prunus persica), orange (Citrus aurantium),

and kiwi (Actinidia chinensis).

3.1.1. Effect of management regimes on soil physical and

chemical properties

Soil organic C content in the bottom layer (20–40 cm) and

exchangeable K+ were the only parameters not significantly

affected either by soil management regime or sampling area

(Table 2). Soil texture (data not shown), pH (7.8 vs 7.7 in HIMR

and LIMR), limestone (114 g kg−1 vs 75 g kg−1), cation exchange

capacity (46.8 cmol(+) kg−1 vs 47.8 cmol(+) kg−1), total N (0.23% vs

0.27%), and exchangeable Ca2+ were not significantly affected by

soil management regime but only by the sampling area (Table 2).

In contrast, water holding capacity, bulk density, organic C content

(0–20 cm), P2O5, exchangeable Mg2+ were significantly affected by

management regime and sampling area. Finally, exchangeable Na+,

and electrical conductivity were significantly influenced only by

188 G. Bonanomi et al. / Applied Soil Ecology 47 (2011) 184–194

Table 2

Summarized results of two-way ANOVAs for chemical and physical parameters. Management regime (HIMR vs LIMR) and sampling area (5 areas along a gradient clay vs

sand texture) were the independent variables. P-value from Duncan test: *** = P<0.001; ** = 0.001 < P < 0.01; * = 0.01 < P < 0.05.

Parameter Source d.f. eta squared F P-value

Electrical conductivity Management regime 1 0.27 9.51 <0.001***

Sampling area 4 0.25 2.12 0.11

Interaction 4 0.23 1.89 0.14

Na+ Management regime 1 0.52 26.05 <0.001***

Sampling area 4 0.16 1.17 0.34

Interaction 4 0.16 1.15 0.36

Total N Management regime 1 0.08 2.17 0.15

Sampling area 4 0.61 10.08 <0.001*

Interaction 4 0.33 3.16 0.08

P2O5 Management regime 1 0.37 14.78 <0.001***

Sampling area 4 0.46 5.45 0.003**

Interaction 4 0.35 3.41 0.03*

K+ Management regime 1 0.01 0.14 0.70

Sampling area 4 0.15 1.16 0.35

Interaction 4 0.07 0.47 0.75

Mg2+ Management regime 1 0.26 8.82 0.006**

Sampling area 4 0.44 4.99 0.004**

Interaction 4 0.09 0.62 0.64

Ca2+ Management regime 1 0.05 1.45 0.24

Sampling area 4 0.50 6.48 0.001**

Interaction 4 0.21 1.75 0.17

Organic carbon (0–20 cm) Management regime 1 0.29 10.59 0.003**

Sampling area 4 0.70 14.67 <0.001***

Interaction 4 0.19 1.47 0.23

Organic carbon (20–40 cm) Management regime 1 0.27 0.41 0.52

Sampling area 4 0.21 1.00 0.43

Interaction 4 0.01 0.04 0.99

Cation exchange capacity Management regime 1 0.01 0.36 0.54

Sampling area 4 0.43 4.78 0.005**

Interaction 4 0.13 0.95 0.44

Bulk density Management regime 1 0.34 13.17 0.001**

Sampling area 4 0.74 17.85 <0.001***

Interaction 4 0.40 4.28 0.008**

Water holding capacity Management regime 1 0.25 8.36 0.008**

Sampling area 4 0.73 17.43 <0.001***

Interaction 4 0.37 3.82 0.02*

pH Management regime 1 0.00 0.003 0.95

Sampling area 4 0.61 9.54 <0.001***

Interaction 4 0.16 1.19 0.33

Limestone Management regime 1 0.04 1.15 0.29

Sampling area 4 0.33 3.20 0.03*

Interaction 4 0.20 1.65 0.19

management regime (Table 2). Soil organic C content in the upper

layer (0–20 cm) was reduced by 24% in the HIMR compared to LIMR

farms (11.4 g kg−1 vs 14.9 g kg−1, respectively; Fig. 1a). In contrast,

electrical conductivity, Na+ and P2O5 were 370% (579 mS cm−1 vs

123 mS cm−1), 86% (147 meq 100 g−1 vs 79 meq 100 g−1) and 72%

(206 mg kg−1 vs 115 mg kg−1) higher in HIMR compared to LIMR

farms, respectively (Fig. 1b). Water holding capacity and exchange-

able Mg2+ were slightly higher in HIMR than in LIMR farms, while

bulk density was significantly lower under HIMR respect to LIMR

(1.17 g cm3 vs 1.26 g cm3; Fig. 1).

3.1.2. Effect of management regimes on enzymatic activities

All enzymatic activities were significantly affected by man-

agement regime and sampling area, with significant interactions

between these two factors (Table 3). Dehydrogenase, arylsul-

phatase, �-glucosidase, phosphatase and urease were reduced by

84% (0.89 �g TPF g−1 h−1 vs 5.41 �g TPF g−1 h−1), 87% (0.04 �g p-

NF g−1 h−1 vs 0.35 �g p-NF g−1 h−1), 76% (0.27 �g p-NF g−1 h−1

vs 1.12 �g p-NF g−1 h−1), 49% (0.90 �g p-NF g−1 h−1 vs 1.76 �g p-

NF g−1 h−1) and 46% (1.54 �g NH3 g−1 h−1 vs 2.83 �g NH3 g−1 h−1)

in the HIMR compared to the LIMR, respectively (Fig. 2a). Regard-

ing soil type, enzymatic activities were usually higher in sandy than

in clay soils (see Section 3.3).

3.1.3. Effect of management regimes on microbial parameters

Significant reductions in HIMR compared to LIMR farms

were found for potential respiration (30.2 �g CO2 g−1 h−1 vs

38.7 �g CO2 g−1 h−1), microbial physiological profiles, microbial

biomass (Cmic: 0.15 mg g−1 vs 0.25 mg g−1), fungal mycelium

(0.24 mg g−1 vs 0.30 mg g−1) and culturable fungi (Fig. 2b). Bacte-

ria species richness was significantly reduced from 15.4 to 13.3

bands from LIMR to HIMR, respectively (Table 3, Fig. 2b). Only

culturable actinomycetes were significantly more abundant in

HIMR compared to LIMR farms (Fig. 2b), while culturable bacteria

and fluorescent pseudomonads were not significantly affected by

management regime (Table 3). The effect of sampling area was sig-

nificant for all parameters, with the exception of microbial biomass

and microbial functional diversity (Table 3).

G. Bonanomi et al. / Applied Soil Ecology 47 (2011) 184–194 189

0

100

200

300

400

-30

-20

-10

0

Ca

2+

Wate

r hold

ing c

apacity

Mg

2+

Lim

esto

ne

P2O

5

Na

+

Ele

ctr

ical conductivity

Org

anic

carb

on

(0-2

0)

Tota

l N

Org

anic

carb

on

(20-4

0)

Bulk

density

cation

exchange c

apacity

pH K+

Change c

om

pare

d to c

ontr

ol (%

)

*

ns**

ns

ns

ns ns ns ****

ns *** ***

***

(a) (b)

*

Fig. 1. Effect of soil management regimes on physical and chemical parameters. Values are expressed as percentage decrease (a) or increase (b) in HIMR compared to LIMR

(=0) farms, used as control. Asterisks indicate significant statistical differences (Duncan test: P < 0.001 = ***; P < 0.01 = **; P < 0.05 = *; ns = not significant).

3.1.4. Effect of management regimes on soil indices

C-to-N ratio, microbial quotient, qCO2 and CEM were all not sig-

nificantly affected by soil management regime (Table 4). Only the

fungi/bacteria ratio, based on CFU of culturable microbes, was sig-

nificantly affected by management regime, with lower values for

HIMR compared to LIMR soils (Table 4).

Table 3

Summarized results of two-way ANOVAs for microbiological and biochemical parameters. Management regime (HIMR vs LIMR) and sampling area (5 areas along a gradient

clay vs sand texture) were the independent variables. P-value from Duncan test: *** = P < 0.001; ** = 0.001 < P < 0.01; * = 0.01 < P < 0.05.

Parameter Source d.f. eta squared F P-value

Bacterial species richness (DGGE) Management regime 1 0.21 6.79 0.015*

Sampling area 4 0.83 31.7 <0.001***

Interaction 4 0.02 0.16 0.95

Microbial biomass C Management regime 1 0.19 6.70 0.02*

Sampling area 4 0.05 0.33 0.85

Interaction 4 0.10 0.69 0.60

Fungal mycelium Management regimes 1 0.29 10.2 0.003**

Sampling area 4 0.76 20.03 <0.001***

Interaction 4 0.13 0.96 0.44

Culturable bacteria Management regime 1 0.03 0.81 0.37

Sampling area 4 0.49 6.11 0.001**

Interaction 4 0.03 0.20 0.93

Culturable fungi Management regime 1 0.40 17.13 <0.001***

Sampling area 4 0.49 6.16 <0.001***

Interaction 4 0.14 1.08 0.38

Culturable actinomycetes Management regime 1 0.32 12.21 0.001**

Sampling area 4 0.78 23.29 <0.001***

Interaction 4 0.64 11.17 <0.001***

Culturable pseudomonads Management regime 1 0.07 1.93 0.17

Sampling area 4 0.37 3.72 0.02*

Interaction 4 0.08 0.57 0.68

Potential respiration Management regime 1 0.15 4.60 0.04*

Sampling area 4 0.75 19.09 <0.001***

Interaction 4 0.56 8.12 <0.001***

Functional diversity Management regime 1 0.24 8.19 0.008**

Sampling area 4 0.25 2.14 0.11

Interaction 4 0.07 0.50 0.73

Arylsulphatase Management regime 1 0.79 94.08 <0.001***

Sampling area 4 0.78 8.93 <0.001***

Interaction 4 0.53 7.07 <0.001***

�-glucosidase Management regimes 1 0.67 50.77 <0.001***

Sampling area 4 0.47 5.65 0.003**

Interaction 4 0.43 4.85 0.005**

Dehydrogenase Management regime 1 0.72 66.19 <0.001***

Sampling area 4 0.71 15.56 <0.001***

Interaction 4 0.60 9.35 <0.001***

Phosphatase Management regime 1 0.66 48.98 <0.001***

Sampling area 4 0.77 21.39 <0.001***

Interaction 4 0.48 5.77 0.002**

Urease Management regime 1 0.51 26.53 <0.001***

Sampling area 4 0.64 11.18 <0.001***

Interaction 4 0.34 3.25 0.03*

190 G. Bonanomi et al. / Applied Soil Ecology 47 (2011) 184–194

-50

-25

0

25

Pote

ntial re

spiration

-100

-75

-50

-25

0

Ary

lsu

lph

ata

se

De

hyd

rog

en

ase

β-g

luco

sid

ase

Phosphata

se

Ure

ase

EcoP

late

s™

(A

WC

D)

Mic

robia

l bio

mass

Fungal m

yceliu

m

Pseudom

onads

Bacte

ria r

ichness (

DG

GE

)

Fungi

Bacte

ria

Actinom

ycete

s

*** ******

*** ***

***

Change c

om

pare

d to c

ontr

ol (%

)

*

**ns *

***

ns **

(a) (b)

Fig. 2. Effect of soil management regimes on enzymatic activities (a) and microbiological parameters (b). Values are expressed as a percentage change in HIMR compared to

LIMR (=0) farms. Asterisks indicate significant statistic differences (Duncan test: P < 0.001 = ***; P < 0.01 = **; P < 0.05 = *; ns = not significant).

3.2. Soil bioassay

In the bioassay experiment, lettuce growth was significantly

higher in LIMR compared to HIMR non-inoculated soils, in both

sterile and non-sterile conditions (Fig. 3a), and soil sterilization

increased lettuce growth with a similar trend for HIMR and LIMR

soils (+20 and +21% respectively; Fig. 3a). R. solani inoculum

severely reduced lettuce growth, in both HIMR and LIMR soils

(Fig. 3b), compared to non-inoculated soils. No significant differ-

ences in lettuce development were recorded between the two

management regimes for lettuce development in inoculated soils,

both in sterile and non-sterile conditions (Fig. 3b). R. solani inocu-

lum of sterilized soils determined a more pronounced reduction of

plant growth, in both the HIMR and LIMR soils (Fig. 3b).

3.3. Relationship between soil parameters, lettuce growth and

Rhizoctonia suppression

The whole cross-correlation matrix, being composed of 39

parameters and indices, produced 741 Pearson correlation coef-

ficients (Table 5; water holding capacity, Ca2+, the effect of

sterilization effect in the bioassay, CEM, microbial quotient and

qCO2 were omitted). The most relevant results are described below.

Soil organic C content (0–20 cm) strongly correlated negatively

with clay and positively with sand content. Moreover, it showed a

strong positive correlation with fungal mycelium but only a weak

not significant correlation with microbial biomass (Table 5). A sig-

nificant negative correlation was also found with exchangeable

Mg2+. When all HIMR farms were considered, a weak positive cor-

Table 4

Indices calculated for comparing soil quality in HIMR and LIMR farms. Values are

average values ± 1 standard deviation for HIMR and LIMR farms. P-value from t-test:

*** = P < 0.001; ** = 0.001 < P < 0.01; * = 0.01 < P < 0.05.

Soil indices Management regimes P-value

HIMR LIMR

C-to-N ratio 5.65 ± 0.27 6.02 ± 0.25 0.63

Fungi/bacteria ratioa 0.68 ± 0.03 0.77 ± 0.01 <0.001***

Microbial quotient (%) 1.46 ± 0.07 1.89 ± 0.11 0.27

qCO2 (mg C-CO2 g−1 Cmic h−1) 84.5 ± 4.92 81.6 ± 6.95 0.91

CEM (�g C-CO2 g−1 Corg h−1) 1.10 ± 0.18 0.82 ± 0.16 0.34

a Based on the population of culturable bacteria and fungi.

relation was found between their organic C and limestone contents

(data not shown), while a strong positive correlation (Pearson coef-

ficient: 0.91; P < 0.01) between these two parameters was observed

when HIMR farms applying organic amendment were excluded

from the analysis.

All enzymatic activities showed significant positive correla-

tions with soil organic C content and fungal mycelium (except

for �-glucosidase), and only a weak, not significant positive cor-

relation with microbial biomass. Negative correlations with clay

content and exchangeable Mg2+ were observed. Enzymatic activ-

ities had also weak not significant negative correlations with

electrical conductivity, Na+, and pH. Significant positive correla-

0

0,5

1

1,5

2

2,5

3

0

0,5

1

1,5

2

2,5

3

Pla

nt

gro

wth

(g /

po

t)

SterilizedNon sterilized

*

*

No inoculum

Rhizoctonia solani

HIMR

LIMR

(b)

(a)

Fig. 3. Mean values of lettuce dry biomass (g/pot) cultivated in HIMR (empty bars,

n = 60) and LIMR soils (filled bars, n = 15) non-inoculated (a) or inoculated with Rhi-

zoctonia solani (b), in sterile and non-sterile conditions. Values are the average of

HIMR (n = 60) and LIMR (n = 15) agricultural systems. Asterisks indicate significant

differences (t-test: * = 0.01 < P < 0.05) between HIMR and LIMR. Standard deviation

is reported on the bars.

G. Bonanomi et al. / Applied Soil Ecology 47 (2011) 184–194 191

Table 5

Cross-correlation matrix among soil physical, chemical, biochemical, microbial parameters, bioassay results and calculated soil indices for HIMR and LIMR farms. Significant

correlations from regression analysis (P < 0.01) are in bold. S and NS indicate sterile and non-sterile soil, respectively.

tions were found when enzyme activities were cross-correlated

with each other (Table 5). Bacterial species richness assessed with

the DGGE method generally showed only weak not significant

correlations with other parameters. The only significant positive

relationships were found with pH, while significant negative cor-

relations were found with P2O5 and sand content. Soil electrical

conductivity reported not significant correlations with the majority

of soil functions as lettuce growth, microbial quotient and potential

respiration (Table 5).

For lettuce growth without R. solani inoculum only weak, sta-

tistically not significant correlations were found with the soil

parameters (Table 5), except for a negative correlation with pH.

R. solani suppression in non-sterile soil showed a strong positive

correlation with soil EC. Interestingly, this correlation was lost in

sterile soil. Finally, of the 31 parameters assessed, only soil EC, P2O5

and exchangeable K+ reported significant positive correlations with

R. solani disease suppression (Table 5). However, it is important to

keep in mind that many of the observed weak or not significant cor-

relations should be due to the inclusion of many variables into the

HIMR category (e.g. different crop history, fertilization regime, soil

types, etc.) which may mask or counteract more specific effects or

influences that should be only observed in some of these systems,

or sampling areas, and not evident across the whole data set.

4. Discussion

An improved knowledge of agricultural soils is crucial to design

farming systems that effectively maintain or increase soil quality.

In this study intensive management regime produced significant

changes in physical, chemical and biological parameters, and such

results indicate a trend of soil quality changes under the HIMR com-

pared with the selected less intensive cultivation systems (LIMR).

This result, although reported for the first time in relation to inten-

sive cultivation under plastic tunnel, is in agreement with the

general view that intensive agricultural utilization of soil decreases

its quality (Mäder et al., 2002).

4.1. Soil physical and chemical parameters

Total soil organic C content is considered a stable parameter

compared to labile C as light C fractions or microbial biomass

(Haynes, 2000). Usually, several years of different land use are

required to detect significant changes of the total soil organic C

pool (Gregorich et al., 1994). The 24% reduction of soil organic C

in HIMR compared to LIMR farms indicates that the carbon cycle

under this land use is deeply altered. Under HIMR, because of the

systematic elimination of crop residues to limit plant diseases, the

amount of organic input returned to the soil is much less than under

LIMR, where an input of leaves, twigs and root of orchard trees as

well as cutting of inter-row grassland occurs. The C deficit in HIMR

soil is only partially counterbalanced by the application of very

little exogenous organic amendments. In addition, the intensive

tillage regime and the more favourable environmental conditions

(higher temperature in winter and optimal soil water content also

in dry summer months) under the plastic cover of HIMR farms

stimulate a faster decomposition of native and exogenous organic

matter. The intensive tillage regime is probably also responsible

for the reduced bulk density reported in HIMR farms. However, the

reduction of organic C content was different among soil types and,

actually, a strong negative correlation with soil clay content was

found. This is quite surprising considering that a stabilizing effect of

clays has been suggested through the formation of organic-mineral

complexes that protect C from microbial decomposition (Sparling,

1992; Haynes, 2000). Interestingly, organic C content of HIMR farms

that did not use exogenous organic amendments increased almost

linearly with limestone content. This behaviour confirmed the pro-

tective role of carbonates in degradation process of soil organic

matter (Muneer and Oades, 1989). Carbonates slow down microbial

processes since Ca2+ ions bind humic acids to form Ca-humate and

in addition favour more stable aggregates with dispersible clays

involving Ca2+ bridges (Clough and Skjemstad, 2000). A supposed

negative effect of limestone content on bacteria and fungi, which

are the main decomposers of organic C, is not supported by our

192 G. Bonanomi et al. / Applied Soil Ecology 47 (2011) 184–194

regression analysis (Table 5). Further study is needed to clarify this

point.

HIMR soils showed a drastic increase of electrical conductiv-

ity and exchangeable Na+ content compared to LIMR soils. It is

noteworthy that only management regime and not sampling area

significantly affects electrical conductivity and Na+ content, indi-

cating that some ecological factors linked to HIMR cause an increase

of such parameters. Salinity and/or sodicity are widespread prob-

lems in irrigated areas characterized by low rainfall level and high

evapo-transpiration demand that determines salt accumulation

at the soil surface (Sumner, 1995). The increase of soil electrical

conductivity could be due to the almost total rainfall restriction

under the plastic film, especially when HIMR is prolonged for many

years, and/or to the use of poor quality irrigation water (Rietz and

Haynes, 2003). The last factor probably played a minor role because

of the high quality of the water used for irrigation in our study

area. The repeated application of Metham-Na in HIMR soils could

contribute to their high soil Na+ content. This hypothesis is par-

tially supported by the positive correlation between the number

of applications of Metham-Na in the last six years and Na+ con-

tent (Pearson coefficient: 0.47; P < 0.05). Salinity and sodicity are

well known for their detrimental effect on crop yields (Maas and

Hoffman, 1977), microbial activities (Rietz and Haynes, 2003) and

soil structure (Sumner, 1995). Commonly, soils are classified as

saline when they have an electrical conductivity of 400 mS cm−1

or higher (Rietz and Haynes, 2003). Electrical conductivity val-

ues were high (>400 mS cm−1, maximal values of 2983 mS cm−1)

in nine farms and the general trend of electrical conductivity in

HIMR suggests that further increases could be detrimental to crop

productivity.

4.2. Enzymatic activities and microbiological parameters

In HIMR soils microbial biomass and fungal mycelium showed

a drastic reduction, in line with the response of soil organic C con-

tent. The positive correlation between organic C content and both

microbial biomass (not significant) and fungal mycelium suggests

that a reduction of the available organic C occurred and this, in cas-

cade, induced a decrease of the decomposer biomass. In fact, in this

study we found a reduction, although not statistically significant,

of the microbial quotient (the fraction of soil organic C occurring

as microbial biomass) in HIMR soils. A reduction of the microbial

quotient was recorded by Haynes (2000) who compared arable

and pastoral soils and by Rietz and Haynes (2003) that reported a

dramatic decline of this parameter along a gradient of soil salinity.

All enzymatic parameters showed a dramatic decline in HIMR

compared to LIMR soils. This result is consistent with the findings of

many studies that reported a decline of enzymatic activities in cul-

tivated soils when compared to the corresponding uncultivated or

less-disturbed soils (e.g. Drinkwater et al., 1995; Gianfreda et al.,

2005; Acosta-Martínez et al., 2008). In our study all enzymatic

activities were positively correlated only with total soil organic

C content and with fungal mycelium, while only a weak posi-

tive relation was found with microbial biomass. This suggests a

major role of soil fungi, as compared to other microbial groups,

in promoting enzymatic activities. However, other soil factors also

significantly affect enzymatic activities. Rietz and Haynes (2003)

found strong negative correlations between soil electrical conduc-

tivity and enzymatic activities (hydrolysis of fluoresceine diacetate,

�-glucosidase, alkaline phosphatase and arylsulphatase). In our

study the weak, but not significant, negative correlation between

enzymatic activities and both electrical conductivity and Na+ con-

tent could be explained by the narrower range of soil salinity values

measured in our farms compared to those reported by Rietz and

Haynes (2003). The consistent negative correlations between enzy-

matic activities and clay content and exchangeable Mg2+ could be

accounted for by the high affinity of proteic molecules for clay sur-

faces. This could strongly reduce their catalytic activity because of

induced conformational changes, inter-layering phenomena, and

inhibition effects (Gianfreda and Ruggiero, 2006). In general the

lower enzymatic activities in the HIMR soils indicate a reduced

microbial activity and, probably, a lower capability to cleave organic

P, S and to degrade organic matter compared to LIMR soils. More-

over, the consistent decline of enzymatic activities across the soil

texture gradient indicates that such parameters provide sensitive

indicators of the impact of intensive cultivation on soil quality.

Among culturable microbes, bacteria cultured on R2A were the

most abundant followed by fungi and actinomycetes as expected.

Bacterial and fungal culturable populations responded differently

to HIMR, and a significant decrease was observed only for fungal

populations. The fungi/bacteria ratio, based on CFU of culturable

microbes, was significantly lower in HIMR soils, suggesting a shift to

bacterial dominated community under this land use. A progressive

shift from fungal to bacterial dominated microbial communities has

been reported in relation to fertilization intensity (de Vries et al.,

2006), tillage regime (Frey et al., 1999) and pasture management

(de Vries et al., 2007).

The lack of relationships between culturable fungi and fungal

mycelium and between microbial biomass and both culturable fun-

gal and bacterial populations (Table 5) confirms the limitations of

the cultivation methods. In contrast, the monitoring of bacterial

and fungal communities using a multi-technique approach that

combines conventional cultivation-based methods, gene-based

molecular analysis of soil community DNA, physiological profil-

ing and measurements of overall microbial biomass and activity

gives a more realistic picture of the structure, growth and functions

of the soil microbial community (Liesack et al., 1997; Bonanomi

et al., 2008). According to previous results (Lupwayi et al., 1998),

the community-level physiological profile obtained with the BLG

method was able to differentiate HIMR and LIMR farms, pointing

out a lower metabolic capability and functional diversity of the soil

under HIMR.

A positive correlation between microbial species richness and

broad soil functions like soil fertility (Coleman and Whitman, 2005)

has been often proposed, but rarely tested. Although a significant

correlation between species richness and the indicators of soil func-

tions were not found, it seems that the observations of reduced

species richness in HIMR soils support the hypothesis of Coleman

and Whitman (2005).

4.3. Lettuce growth and Rhizoctonia solani suppression

The higher lettuce growth in LIMR compared to HIMR soils

agrees with the general decrease of soil quality assessed with chem-

ical, biochemical and microbial parameters. However, none of the

measured parameters alone was able to predict lettuce growth

(Table 5), except for pH that negatively affects it. Such effect could

be related to the high pH values recorded in some farms (pH values

ranged between 6.5 and 8.3), which may reduce the availability of

some mineral nutrients. This result indicates the complexity of the

soil quality concept. Interestingly, HIMR soils support a lower plant

growth in spite of their higher content of some nutrients (P2O5

and Mg2+) and a comparable level of other main nutrients (N, K+

and Ca2+). Presumably, other negative factors overcome the higher

nutrient availability. Soil salinity and sodicity may be an explana-

tion, but only weak negative correlations were found with plant

growth (Table 5). Also the reduction of enzymatic activities in HIMR

soils cannot explain this pattern because weak positive correlations

were found with lettuce growth. The positive response of lettuce

growth to soil sterilization, in non-inoculated plants, suggests the

built-up of a negative microbial community in such soils (Mazzoleni

et al., 2007; Kulmatisky et al., 2008). However, it is known that also

G. Bonanomi et al. / Applied Soil Ecology 47 (2011) 184–194 193

soil chemistry can be altered substantially during autoclaving treat-

ment with changes in labile organic matter and mineral nitrogen

forms.

Predicting R. solani suppressiveness is difficult, in spite of the

large number of available studies, no substantial results have

emerged (Termorshuizen et al., 2007; Bonanomi et al., 2010). In

this study electrical conductivity showed a fair positive correlation

with R. solani disease suppression, but only in non-sterile soils. This

result suggests a high sensitivity of R. solani to soil salinity, but

only in the presence of a native microbial community capable to

compete with the pathogen.

5. Conclusions

This study indicates that long-lasting soil cultivation under

plastic cover (HIMR) negatively affects chemical, biochemical and

biological soil quality, at least compared with the selected less-

intensive crop cultivation system (tree orchards). The reduction in

soil C content, microbial biomass, soil enzymatic activities, func-

tional and species diversity and the drastic increase in soil salinity

are the result of such land use. The full consequences of such

changes on crop productivity are still unknown, but they proba-

bly determine a multiple stress to plants with potential negative

consequences on crop yield. Phytopathological disorders with com-

plex, often unidentified ethyology are growing under the HIMR,

and farmers continuously increase the cultivation inputs (mainly

soil fumigations and mineral fertilizers) to overcome such prob-

lems and maintain crop productivity. Further studies are needed to

identify agronomic strategies that will mitigate the negative effects

of HIMR cultivation on soil quality.

Acknowledgements

Authors are grateful to the many owners who allowed access

to their farms and to Dr. Pietro Caggiano for its invaluable techni-

cal support. The study was funded by the “Camera di Commercio,

Industria, Artigianato e Agricoltura di Salerno”.

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