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Changes in composition and activity of soil microbial communities in peach and kiwifruit Mediterranean orchards under an innovative management system.

Introduction

The loss of soil quality is a process that mostly affects areas where intensive agriculture and an indiscriminate use of external energetic inputs (fertilisers, pesticides, water) are adopted (Lal 1997). For this reason, the optimisation and innovation of agricultural techniques with a low negative environmental impact can allow recovery of the normal levels of total fertility in agro-ecosystems and have positive effects on both soil and yield quality (Gruhn et al. 2000).

In semi-arid Mediterranean agricultural lands, conventional, non-sustainable techniques, such as frequent and intensive cultivation, zero organic matter input, and use of excessive water and chemical fertilisers, can reduce soil organic matter and increase groundwater contamination, soil accumulation of mineral elements (in particular phosphorus and nitrogen), alkalinisation/salinisation, and nutritional imbalances in plants (Lal 1997; Gruhn et al. 2000). To obtain high yields of good quality and preserve environmental sustainability, chemical and biological soil fertility should be maintained through the choice of innovative, sustainable agricultural techniques (Kushwaha et al. 2000; Jagadamma et al. 2008). Agricultural management practices such as minimum tillage or no tillage, recycling of the carbon sources internal to the fruit grove (pruning material, spontaneous or/and seeded cover crops, compost amendments), and adequate irrigation, fertilisation, and pruning are recommended to save conventional water, restore soil organic matter, reduce erosion and environmental pollution, and increase the C[O.sub.2] sequestration processes from the atmosphere into the soil (Lal 2004). These practices also have positive effects on soil microbiota, increasing microbial biomass, activity, and complexity (Kushwaha et al. 2000; Widmer et al. 2006). Soil microbiota, in turn, influence soil fertility and plant growth by regulating nutrient availability and increasing their turnover (Gruhn et al. 2000; Borken et al. 2002; Govaerts et al. 2008). Whereas an improvement of soil physicochemical properties is more evident in long-term (>10 years) adequate soil treatments (Brady and Weil 2008), molecular and biochemical patterns, microbial biomass, and metabolism of soil microbial communities change significantly in response to both long-term and short-term soil management (Bending et al. 2002; Marschner et al. 2003).

One of the most useful molecular techniques to reveal qualitative changes in the structure of soil bacterial and fungal communities is based on the characterisation of conserved and variable regions of the bacterial 16S rRNA gene (16S rDNA) and fungal 18S rRNA gene (18S rDNA) by denaturing gradient gel electrophoresis (DGGE). Metabolic diversity of a soil bacterial community can be estimated using the Biolog[R] EcoMicroplates metabolic assay (Insam 1997), based on the ability of microbial isolates to oxidise different carbon sources with a high discriminatory power among soil communities (Zak et al. 1994). The community-level physiological profile (CLPP) obtained by the Biolog[R] method is used to differentiate microbial populations from various soil environments or from soil subjected to various treatments (Calbrix et al. 2005).

In the Mediterranean area, 16% of the total cultivable land is used for fruit orchards (Olesen and Bindi 2002). In Italy alone, peach (Prunus persica L.) and kiwifruit (Actinidia deliciosa cv. Hayward) groves cover an area of ~l.0 x [10.sup.5] and 2.0 x [10.sup.4] ha, respectively (ISTAT 2000). Peach and kiwifruit are 2 of the most economically important fruit species of the Mediterranean basin but most of the recent studies have focused on plant physiological behaviour (Montanaro et al. 2006; Dichio et al. 2007), without considering molecular and metabolic aspects of soil microbial community at orchard level.

A new approach in fruit orchard management is imposed by environmental issues such as soil degradation as a result of erosion and desertification, water shortage, and the greenhouse effect (Hochstrat et al. 2006). Sustainable and innovative soil management systems in fruit growing can determine optimal plant nutritional equilibrium, avoid nutrient accumulation and leaching risks, improve irrigation efficiency, and prevent soil erosion and root asphyxia (Xiloyannis et al. 2005; Montanaro et al. 2006; Dichio et al. 2007). However, research into the definition of appropriate agricultural techniques and soil management in order to preserve soil quality, positively affect soil microbial activity and composition, and maintain high yields of high quality in Mediterranean fruit orchards is scarce. Therefore, the aim of this study was to explore the short-term effects of 2 different management systems on microbial genetic, functional, and metabolic diversity, evaluated by a combination of culture-dependent and culture-independent methods.

Materials and methods

Experimental design

The study was conducted in peach (Prunus persica (L.) Batsch Nectarine cv. Supercrimson grafted on GF677) and kiwifruit (Actinidia deliciosa C.F. Liang et A.R.Ferguson var. deliciosa, own-rooted plants) orchards located in Bemalda (Southern Italy, Basilicata Region; 40[degrees]24'N, 16[degrees]48'E). Peach trees were trained to vase (500 plants/ha) with a north-south row orientation, whereas kiwifruit plants were trained onto pergolas (494plants/ha). The climate was semi-arid (UNESCO-FAO classification), with an average annual rainfall of 525 mm.

For 7 years (2003-07), each orchard was divided in 2 parts subjected to 2 different soil management and cropping systems, so called 'innovative' (INN) and 'conventional' (CON). CON orchards included conventional soil tillage, annual chemical fertigation (100kgN, 10kgP, 20kgK/ha), empirical irrigation (without considering soil moisture and evapotranspiration, using excessive amounts of water), and empirical pruning (without considering soil nutrient levels and plant nutrient requirements) based on grower experience, and removal of pruning residues from the field.

In contrast, INN orchards were subjected to a management system consisting of minimum tillage, cover crops (30 kg/ha of Trifolium subterraneum seeds and spontaneous grass), compost application (15 t/ha fresh weight; Eco-Pol SpA, VR, Italy; see Table 1 for compost characteristics) over the whole soil surface in the kiwifruit orchard and along the tree row tilled area in the peach orchard, winter pruning based on the selection of shoots with a high number of floral buds and on a better light interception in the canopy, and fertigation based on plant nutrient demand evaluated by leaf mineral analyses and on soil measured N[O.sub.3.sup.-] levels. At INN sites, the endogenous organic carbon inputs (cover crops and pruning residues) were chopped and mulched on the soil, whereas compost was buried into the soil by a light harrowing (depth 0.10m) in autumn. The INN peach orchard was irrigated by 3 drip emitters per plant along the tree lines with capacity of 4 L/h each, while the INN kiwifruit orchard was uniformly irrigated by a microjet method (120 L/h) over the whole orchard surface. Irrigation scheduling and volumes were calculated on the basis of the equation [ET.sub.c] = [ET.sub.c] x [K.sub.c], where [ET.sub.c] is the evapotranspiration of the system, [ET.sub.o] the Penman--Monteith reference crop evapotranspiration, and [K.sub.c] the crop coefficient (data from ALSIA Agrometeorological Service, Basilicata). Water balance was updated at 2-day intervals in order to schedule irrigation treatments when available water, calculated on the basis of soil texture parameters reported in Table 2, was <50%. Soil nitrogen (N[O.sub.3.sup.-]) was measured by the analytic kit Reflectoquant 10[c] (Merck, NJ, USA), and in INN orchards, N[O.sub.3.sup.-] levels were used to calculate integrative mineral fertilisation amount.

In February 2007, 3 composite samples of bulk soil (20 cores each of 70mm diameter pooled on site per each orchard/ treatment) were collected and immediately stored in sterilised plastic pots at 4[degrees]C after removing visible crop residues. For peach orchard, samples were collected from the top soil layer (0-0.10 m) in 4 different soil management systems/locations combinations: innovative along the inter-rows 2.5 m from the drip emitters (PID) and innovative under drip emitters (PIW), and conventional along the inter-rows (PCD) and conventional under drip emitters (PCW). For kiwifruit orchard, samples were collected from 2 soil layers (0-0.10 and 0.10-0.20 m) for 2 different soil management systems: innovative (KI 0/10 and KI 10/20) and conventional (KC 0/10 and KC 10/20). Kiwifruit is a species with an active root system distributed mainly in the topsoil layer (Montanaro et al. 2006), and therefore, 2 different sampling depths were chosen.

Soil characteristics and fruit yield

The peach orchard soil was classified as a sandy clay Typic Xerofluvent (WRB, FAO), a sandy clay with a mean stone content of 16% (Table 2), whereas the kiwifruit orchard soil was a Haplic Luvisol (WRB, FAO), a sandy clay with a very low stone content (<0.5%). At the establishment of the treatments (2003), 3 composite soil sample was taken in autumn by randomly collecting 9 soil cores per each orchard/treatment. On these samples, soil texture and pH were determined according to the methods of the Italian Society of Soil Science (S1SS 2000), and total soil organic carbon (SOC) was determined by dry combustion method using a LECOSC230 apparatus (LECO Instruments, UK). In the peach orchard, soil gravimetric bulk density ([d.sub.b]) was estimated by particle size and organic matter level (Saxton and Rawls 2006) and [d.sub.b] values were corrected for the measured stone content bulk porosity ([PHI]), according to Danielson and Sutherland (1986). In the kiwifruit orchard, [d.sub.b] values were measured by cylinder method (Blake and Hartge 1986).

From 2005 to 2007, total commercial fruit yield on the basis of fruit calibre ([greater than or equal to] 51 mm for peaches and [greater than or equal to] 61 mm for kiwifruits) was yearly measured in both INN and CON treatments of peach and kiwifruit orchards.

Total microbial counts

Three replicates of 5-g subsamples (dry weight equivalent) of each soil sample were suspended in 45 mL sterile 0.1% sodium pyrophosphate--one-quarter strength Ringer solution and sonicated for 2 min to disperse microbial cells. The soil suspension was allowed to settle for 30 min at room temperature. 10-fold serial dilutions of the supernatants were made in sterile Ringer solution. Aliquots were spread plated in triplicate on 1/10 strength TSA (tryptic soy agar) medium amended with cycloheximide 0.1 mg/mL for bacterial counting, and inoculated in MEA (malt extract agar) medium implemented with streptomycin 0.03 mg/mL and tetracycline 0.02 mg/mL (Lurch et al. 1998) in triplicate for funsal counting. Counting took place after suitable incubation period (72 h for bacteria and 120 h for fungi) at 28[degrees]C.

Polymerase chain reaction (PCR) and denaturing gradient gel electrophoresis

A direct method was used for DNA and RNA extraction from soil samples by a bead beater system (Fast Prep System). Samples of 500ms of soil were processed by FastDNA[R] Spin Kit for Soil (MP Biomedicals, OH, USA) and RNA Power Soil Isolation Kit (MoBio, CA, USA). DNA and RNA extracts were stored at -20[degrees]C and -80[degrees]C, respectively. Nucleic acid quantity and quality were assayed on 0.7% agarose gel containing 0.5 [micro]g/mL of ethidium bromide.

Extracted total RNA was retro-transcribed to c-DNA by RETROscript[TM] First Strand Synthesis Kit for RT-PCR (Ambion, TX, USA). DNA and c-DNA were amplified in a PCR thermocycler (Bio-Rad Laboratories, CA, USA) with the following primer pairs (MWG-Biotech AG, Germany): (i) 968F1401R eubacterial universal primers to amplify a ~500 bp region of the 16S rDNA gene (Nubel et al. 1996), and (ii) FF390/FR1GC funsal primers to amplify a ~390 bp region of the 18S rDNA gene (Vainio and Hantula 2000). Amplification products were checked by electrophoresis on 1.5% agarose gel run at 10 V/cm in 0.5 x TBE buffer and stained by ethidium bromide, using a low range ladder (1000-80bp; Fermentas, MD, USA).

DGGE genetic fingerprints were performed by the Bio-Rad DCodeTM Universal Mutation Detection System (Bio-Rad Laboratories, Hercules, CA, USA). PCR products (10 [micro]L) were loaded into 6% (16S rDNA amplicons) or 8% (18S rDNA amplicons) polyacrylamide gel (37.5:1 acrylamide :bisacrylamide) with an urea-formamide parallel gradient (45-60% for 16S rDNA and 30-60% for 18S rDNA amplicons). Bacterial and fungal amplicons were resolved in 1 x TAE buffer at 60[degrees]C and at a constant voltage of 75 V for 15 h and 18 h, respectively. Sybr Green I stained gels were photographed with Bio-Rad Gel Doc 2000 documentation system (Bio-Rad Laboratories).

Microbial community metabolic profiles (Biolog[R])

Sole carbon source utilisation patterns of soil microbial communities, also called community-level physiological profiles (i.e. CLPPs), were assessed using the Biolog[R] 96-well EcoMicroplates (AES Laboratoire, France), containing 31 different carbon sources replicated 3 times (Zak et al. 1994). Data were analysed to determine average well colour development (AWCD), the mean of the blanked absorbance values for all the substrates that provides a measure of total cultural bacterial activity, and substrate richness (S), the number of utilised substrates. Metabolic diversity parameters, such as Shannon's substrate diversity index (H') and substrate evenness (E), equitability of activities across all utilised substrates were calculated according to Zak et al. (1994).

The microplates were incubated at 25[degrees]C in the dark and colour development was measured as optical density (OD) every 24h over a 144-h period using a Microplate E-Max Reader (Bio-Rad) with a E590-nm wavelength filter, and the data were collected by Microlog 4.01 software (Biolog, CA, USA). To avoid the intrinsic absorbance of some carbon sources, raw OD data were corrected by blanking each response well against its own first reading at the start of the incubation (Calbrix et al. 2005). In addition, individual absorbance values of the 31 different substrates were subtracted from the control well containing no carbon source (water) to correct for any respiratory activity due to carbon added with the inocula, and negative values were set to zero (Widmer et al. 2006).

Fingerprints and statistical analyses

Genetic fingerprints were analysed by Bionumerics software version 4.5 (Applied Maths, Belgium). Normalisation of the profiles in each lane was carried out by loading a standard reference pattern in 3 different points of the denaturing gel. Profile comparison and clustering were performed by applying the unweigthed pair-group method using arithmetic average (UPGMA) algorithm, based on the Pearson correlation coefficient (Rademaker and de Bruijn 2004). This curve-based coefficient does not suffer from typical peak/shoulder mismatches (Boon et al. 2002).

The values of fruit yield, total microbial counts, and Biolog[R] metabolic diversity indices (H', S, E, and AWCD) were treated by analysis of variance (ANOVA) using the SAS software (SAS Institute, NC, USA) in order to detect significant differences among the effects due to the different agricultural practices (PROC GLM). Principal component analysis (PCA) was applied (PROC FACTOR) on Biolog[R] absorbance values via the correlation matrix in order to characterise the structure of bacterial communities by classifying treatments according to their substrate utilisation patterns. Each principal component was a linear combination of the original variables with coefficients equal to the eigenvectors of the correlation matrix. According to Kaiser's criterion, only components with eigenvalue >1 were retained. A graphic interpretation was obtained by biplot on the 2 dimensions of the main principal components.

Results

Fruit yield and total microbial counts

In both peach and kiwifruit orchards, the average commercial fruit yield per hectare during 2005-2007 was significantly higher (P<0.05) in INN than in CON (Table 3). Average commercial yield was 21.60 and 15.09t/ha for peach INN and CON, respectively, whereas the values were 39.34 and 24.64t/ha for kiwifruit INN and CON, respectively.

In the peach orchard, there was a significantly higher number of cultivable bacteria in the INN than the CON treatments (F=0.55, P<0.05), while no significant differences were found among the samples taken under drip emitters and along the inter-rows (Fig. 1a). Neither soil treatment nor location affected total fungal number for peach orchard (Fig. 1a). The number of cultivable bacteria isolated from soil samples of kiwifruit orchard was significantly higher in INN than in CON (F = 23.17, P < 0.001), whereas no significant differences were related to soil depth (Fig. 1b). Fungal counts in the kiwifruit orchard were significantly affected by both soil treatment (F = 16.39, P < 0.05) and depth (F = 18.16, P < 0.05), being higher in the surface layer (0-0.10m) and in CON sites (Fig. 1b).

[FIGURE 1 OMITTED]

Genetic and functional fingerprinting

The similarity dendrograms of bacterial 16S DGGE fingerprints from peach orchard showed that bacterial genetic diversity in the sites under drip emitters (PIW and PCW) were discriminated from patterns of sites along the inter-rows (PID and PCD), with Pearson similarity coefficients ranging from 94.2 to 98.7 for 16S rDNA (Fig. 2a). In contrast, the DGGE patterns of fungal 18S rDNA in the peach orchard did not show a clear separation between drip emitter and inter-row sites and clustered separately at lower values of similarity (73.8) (Fig. 2c). Functional diversity of the microbial soil community of peach orchard, evaluated by rRNA DGGE fingerprints, showed a clear discrimination between drip emitter and inter-row sites for 16S rRNA, with Pearson similarity coefficients ranging from 92.0 to 95.6 (Fig. 2b), but not for 18S rRNA (Fig. 2d).

DGGE dendrograms of kiwifruit orchard showed that INN sites (KI 0/10 and KI 10/20) almost always clustered separately from CON sites (KC 0/10 and KC 10/20) for bacteria as well as fungal ribosomal genes (Fig. 3). In particular, the genetic fingerprint relative to 18S rDNA was grouped in 2 clearly separated clusters (INN/CON) with Pearson similarity coefficient of 76.7 (Fig. 3c). The electrophoretic profiles relative to the metabolically active bacterial (16S rRNA) and fungal (18S rRNA) community of INN treatments clustered separately from CON soils (Fig. 3b-d). The dendrogram of bacterial 16S rDNA in the kiwifruit orchard was the only one not to show a clear separation between the two soil management systems (Fig. 3a).

[FIGURE 2 OMITTED]

[FIGURE 3 OMITTED]

Metabolic fingerprinting

The F values obtained from statistical analysis showed that, in the peach orchard, H' was significantly affected (P<0.05) by soil treatment (INN/CON), whereas the values of S, E, and AWCD showed no significant differences (Table 4). In the kiwifruit orchard, soil depth (0-0.10 and 0.10-0.20m) significantly influenced H' (P< 0.01), while the interaction of soil management system and soil depth determined significant changes in S, E, and H' values (Table 4).

The PCA for both orchards identified 3 components with eigenvalue >1: in peach orchard, PC 1, PC 2, and PC 3 were 14.51, 9.36, and 7.11, respectively; in kiwifruit, PC 1, PC 2, and PC 3 were 17.00, 8.74, and 5.25, respectively. The biplot in Fig. 4 showed the first 2 components accounted for most variance (77% for peach and 87% for kiwifruit orchard). In the peach orchard, the 4 datasets (2 managements x 2 irrigation systems) were quite clearly separated from each other. In particular, PC 1 (accounting for 47% of total variance) separated soils from different locations (drip emitter/inter-row), while PC 2 (30% of the total variance) clearly discriminated soils of the 2 different management systems (INN/CON) (Fig. 4a). In kiwifruit orchard, PC 1 and PC 2 accounted for 59% and 28% of total variability, respectively discriminating the innovative from the conventional managed systems, as well as taking into consideration soil depths; in particular, the conventional cropping system in the deeper soil layer (KC 10/20) was markedly separated from the other 3 treatments (Fig. 4b). Figure 4 also reports the different carbon sources and how they contribute to discriminate among treatments; metabolic diversity of some soil samples (e.g. KI 0/10) is clearly due to the different utilisation of a great number of carbon sources, while metabolic diversity of other microbial communities (e.g. KC 0/10) seems to be affected by few substrates.

Discussion

Our data reveal significant differences between the innovative, high carbon input soil management system (INN) and the conventional, low carbon input system (CON). In particular, the higher fruit yield of both peach and kiwifruits orchards can be clearly attributed to the INN system (Table 3). On the basis of statistical critical F values, in both peach and kiwifruit orchards, total bacterial number was higher in INN (Fig. 1), likely because soil bacteria rely on external available nutrients and respond promptly to changes in organic nutrient matter (Borken et al. 2002; Peixoto et al. 2006). In contrast, total fungal counts showed no significant differences between soil management systems or sample location/soil depths (Fig. 1b). In a long-term study of the consequences of tillage and residue management on selected micro-flora groups, Govaerts et al. (2008) reported that total bacterial count was generally higher when residue was retained than when residue was removed and minimum tillage occurred. The positive effects of minimum tillage and organic carbon input on soil bacteria are due to increased soil aeration, cooler and wetter conditions, temperature and moisture buffering capacity of the soil, as well as higher carbon content in surface soil (Brady and Weil 2008).

[FIGURE 4 OMITTED]

The number of cultivable microorganisms isolated from soil samples represents a minimal portion of the total bacteria and fungi inhabiting soil (Nannipieri et al. 2003). For this reason, it is necessary to combine culture-based microbiological methods with molecular techniques. In fact, different soil management systems, such as tillage modalities and organic farming practices, can induce a genetic alteration of soil microbial communities and changes in microbial community structure as assessed by DGGE analysis of PCR amplified 16S rDNA (Crecchio et al. 2004). In our study, this behaviour is clear in both 16S rRNA and 18S rDNA/rRNA DGGE dendrograms from kiwifruit orchard, which revealed a clear discrimination between INN and CON systems (Fig. 3b-d). For bacterial counts, the effects on microbial community structures were due to the concomitant effects of crop residue management, cover crops, compost application, and adequate irrigation. It is noteworthy that the fungal community evaluated by DGGE was affected by INN management, although no increase in size was observed (Fig. 1b). In fact, compost amendments strongly influence the composition of soil microbial communities because of the direct influence of the bacteria in the compost and the promoting effect of the compost on the bacterial activity and growth (Perez-Piqueres et al. 2006; Chu et al. 2007).

Differences in DGGE dendrograms of peach orchard soils were less marked, as indicated by the generally high values of similarity Pearson coefficient (Fig. 2). It seems that the irrigation regime is the main factor inducing changes in bacterial communities of sites under drip emitters and of sites 2.5 m from the emitters. Qualitative changes of soil bacterial communities in relation to water availability can be important in Mediterranean peach orchards, where the adoption of appropriate and adequate irrigation techniques, based on drip irrigation, is worthwhile to save water and improve plant water use efficiency (Boland et al. 2000; Dichio et al. 2007). These qualitative differences were less clear for fungal communities (Fig. 2c, d), as 18S DGGE dendrograms showed a lower discrimination among the 4 different soil treatments/locations combinations. Lower response of fungal communities to different soil treatments was also observed by Marschner et al. (2003), who demonstrated that bacterial diversity was affected by different organic and inorganic soil amendments, while no changes occurred in fungal community structure.

Crop residue retention in the field and changes in soil organic matter can affect the metabolic diversity of the soil microbial communities evaluated by Biolog[R] CLPP (Bending et al. 2002; Govaerts et al. 2008). Mader et al. (1996) observed low microbial diversity in long-term, conventionally managed areas, leading to the predominance of few groups of microorganisms. Soil bacterial metabolic diversity indices, as indicated by carbon substrate utilisation patterns, were also higher in sustainable than in conventional farms (Milder et al. 1996). Our results show that in peach orchard, Shannon's diversity index (H') was significantly increased by INN management, whereas in kiwifruit orchard it was influenced by soil depth (Table 4). In addition to compost and available water, cover crops could be an important discriminating element for bacterial substrate utilisation between peach orchard INN and CON treatments, as recently observed by Carrera et al. (2007). Considering that univariate measures such as CLPP indices reduce the complexity to a single number, multivariate measures have been necessary to take into account the complexity of the datasets. So, visualisation of the scores and loadings by PCA biplot (Fig. 4) allowed us to highlight significant differences in the metabolic capability of soil microbial communities subjected to the 2 different management practices (INN or CON) in both the orchards.

Our study revealed qualitative (genetic and metabolic) and quantitative changes in soil microbial communities in response to an innovative, sustainable agricultural management. In Mediterranean orchards, under semi-arid climatic conditions, the adoption of exogenous (compost) and endogenous (cover crops, cultural and pruning residues) sources of organic matter and the measures used to slow down its mineralisation, as well as a correct water management, can be key factors for preserving or restoring soil bacterial metabolic diversity, so enhancing soil quality and fertility. At the same time, adoption of sustainable practices could help to obtain good-quality fruits, to preserve natural resources, mainly soil and water, and to avoid detrimental effects on the environment.

10.1071/SR09128

Acknowledgments

This research was supported by the national research projects FISRMESCOSAGR and BRIMET.

Manuscript received 17 July 2009, accepted 21 October 2009

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Adriano Sofo (A,C), Giuseppe Celano (A), Patrizia Ricciuti (B), Maddalena Curci (B), Bartolomeo Dichio (A), Cristos Xiloyannis (A), and Carmine Crecchio (B)

(A) Dipartimento di Scienze dei Sistemi Colturali, Forestali e dell'Ambiente, Universita degli Studi della Basilicata, Via dell'Ateneo Lucano 10, 85100, Potenza, Italy.

(B) Dipartimento di Biologia e Chimica Agroforestale e Ambientale, Universita degli Studi di Bari, Via Orabona 4, 70126 Bari, Italy.

(C) Corresponding author. Email: adriano.sofo@unibas.it
Table 1. Compost characteristics and elements soil inputs
from a yearly compost application of 15 t/ha

Units are g/kg dry matter, except for water
(g/kg fresh weight)

Parameter              Value    Added to soil (kg/ha)

Water                    248              --
pH ([H.sub.2]O)         7.98              --
Total nitrogen            18             203
Organic carbon           338            5100
Organic matter           583            8700
Humus                    104            1600
CM                      22.2              --
[P.sub.2][O.sub.5]       6.8             100
[K.sub.2]O                14             210
Zn                     0.112             1.7
Fe                      5.53           82.95
Cu                     0.065            0.98
Mn                     0.114            1.71

Table 2. Physico-chemical characteristics of the soil layers
investigated in peach and kiwifruit orchards at the establishment
of the treatments

Each value represents the average [+ or -] standard deviation
(n = 3). [d.sub.b], Soil gravimetric bulk density

Soil layer   Sand  Silt  Clay       Total N            Organic C
(m)                          (g/kg)

Peach

0-0.10       685   175   140   0.9 [+ or -] 0.05   8.1 [+ or -] 0.03

Kiwifruit

0-0.10       622   183   195   1.3 [+ or -] 0.02   13.4 [+ or -] 2.4
0.10-0.20    600   206   194   1.0 [+ or -] 0.32   11.8 [+ or -] 3.3

Soil layer    pH ([H.sub.2]O)         [d.sub.b]
(m)                                 (t/[m.sup.3])

Peach

0-0.10       8.07 [+ or -] 0.02   1.57 [+ or -] 0.48

Kiwifruit

0-0.10       7.64 [+ or -] 0.08   1.52 [+ or -] 0.18
0.10-0.20    7.79 [+ or -] 0.17   1.78 [+ or -] 0.37

Table 3. Total commercial fruit yield (ttha) divided in peach and
kiwifruit orchards from 2005 to 2007

Means (n = 3) of total fruit yield with the asterisk are
significantly different between the 2 treatments (P<0.05). INN,
Innovative soil management; CON, conventional soil management

Treatment    2005    2006    2007    Total     Mean

Peach

INN          19.32   21.53   23.96    64.81   21.60 *
CON          12.12   16.53   16.61    42.56   15.09

Kiwifruit

INN          49.68   45.79   22.55   118.02   39.34 *
CON          19.67   38.39   15.86    73.92   24.64

Table 4. Effects of the different soil management systems and
soil sample locations on critical F values of Shannon's substrate
diversity index (H'), substrate richness (S), substrate evenness
(E), and average well colour development (AWCD) in soils from
peach and kiwifruit orchards (n = 3)

* P < 0.05; ** P < 0.01; *** P < 0.001. INN, innovative soil
management; CON, conventional soil management; Wet, soil under
drip emitters; Dry, soil along the inter-rows

Experimental factor                 H'         S         E       AWCD

Peach

Management (INN/CON)              8.38 *     0.13     2.00       1.90
Location (WET/DRY)                0.50       0.00     0.14       4.93
Management x location             0.00       1.24     1.32       3.97

Kiwifruit

Management (INN/CON)              0.08       0.04     1.73       0.00
Location (0-0.10/0.10-0.20 m)    11.01 **    1.75     1.49       3.50
Management x location            15.69 ***   6.04 *   12.99 **   2.87
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Author:Sofo, Adriano; Celano, Giuseppe; Ricciuti, Patrizia; Curci, Maddalena; Dichio, Bartolomeo; Xiloyanni
Publication:Australian Journal of Soil Research
Article Type:Report
Geographic Code:8AUST
Date:May 1, 2010
Words:6034
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