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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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