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Europ. J. Agronomy 41 (2012) 18– 27

Contents lists available at SciVerse ScienceDirect

European Journal of Agronomy

jo u rn al hom epage: www.elsev ier .com/ locate /e ja

hanges of soil properties and tree performance induced by soil managementn a high-density olive orchard

iccardo Guccia,∗, Giovanni Carusoa, Claudio Bertollaa, Stefania Urbanib, Agnese Taticchib,onia Espostob, Maurizio Servili b, Maria Isabella Sifolac, Sergio Pellegrinid, Marcello Pagliaid,adia Vignozzid

Dip. di Coltivazione e Difesa delle Specie Legnose, Università di Pisa, Via del Borghetto 80, 56124, Pisa, ItalyDip. di Scienze Economico-Estimative e degli Alimenti, Università di Perugia, Via San Costanzo 1, 06126, Perugia, ItalyDip. di Ingegneria Agraria e Agronomia del Territorio, Università di Napoli Federico II, Via Università 100, 80055, Portici, ItalyCRA-Centro di Ricerca per l’Agrobiologia e la Pedologia, Piazza D’Azeglio 30, 50121, Firenze, Italy

r t i c l e i n f o

rticle history:eceived 12 September 2011eceived in revised form 20 February 2012ccepted 1 March 2012

eywords:lea europaea L.il qualitylant coveroil macroporosityillageater infiltration

a b s t r a c t

Long-term effects of plant covers on yield and oil quality in olive orchards are poorly known. We comparedperformance of Olea europaea trees grown under either tillage (CT) or permanent natural cover (NC) ina sandy-loam soil over five years and determined changes in soil properties. The soil was tilled from theyear of planting until the end of the second growing season, when both soil management treatmentswere established. The CT treatment was kept weed-free using a harrow with vertical blades (0.10 mdepth), whereas the NC was obtained by letting the natural flora grow. Trees were fully irrigated untilyear 3 after planting, when deficit irrigation (about 50% of full) was started for both soil treatments.Trunk cross sectional area (TCSA) of NC trees was 77 and 87% to that of CT trees at the end of the 2006and 2010 growing seasons, respectively. Fruit yield and oil yield of NC trees were 65 and 69% to those ofCT ones, respectively (means of five years), however, when expressed on a TCSA basis, they resulted 87and 95%, respectively. The fruit number of NC trees was lower than CT ones, whereas the oil content was

similar. There were no differences in free acidity, peroxide value, spectrophotometric indexes, and fattyacid composition, but phenolic concentrations of the NC treatment were slightly higher than those of CToils. Soil macroporosity in the topsoil was 5.2 and 2% for the NC and CT treatments, respectively. Waterinfiltration rate in CT plots was lower than in NC ones because of soil surface crusting; NC had highervalues of total organic carbon and total extractable carbon than CT, whereas the humic carbon contentwas unaffected.

. Introduction

Water scarcity and soil degradation are major threats to agri-ultural production in the Mediterranean basin, where over 95% ofotal olive trees are grown. Soil management can markedly affectoil properties (Gómez et al., 1999, 2009; Hernández et al., 2005)

nd moisture (Hernández et al., 2005) although responses varyepending on soil type, slope, equipment used, and environmentalonditions.

Abbreviations: ANOVA, analysis of variance; CT, tillage; DW, dry weight; ET0, ref-rence evapotranspiration; FW, fresh weight; HC, humic carbon; Kfs, field saturatedydraulic conductivity; LAI, leaf area index; LSD, least significant difference; MI,aturation index; NC, natural cover; PLWP, pre-dawn leaf water potential; TCSA,

runk cross section area; TEC, total extractable carbon; TOC, total organic carbon;OO, virgin olive oil.∗ Corresponding author. Tel.: +39 050 2216138; fax: +39 050 2216147.

E-mail address: rgucci@agr.unipi.it (R. Gucci).

161-0301/$ – see front matter © 2012 Elsevier B.V. All rights reserved.oi:10.1016/j.eja.2012.03.002

© 2012 Elsevier B.V. All rights reserved.

Conventional tillage causes soil losses, runoff, structuredegradation, acceleration of organic matter mineralization withconsequent formation of compacted layers and negative effect onporosity along the profile (Gómez et al., 2004, 2009; Moreno et al.,2009; Pagliai et al., 2004; Rodrıguez-Lizana et al., 2008). Compactedlayers decrease water infiltration which, in turn, increases runoffon slopes and waterlogging in flat areas. The effects of tillage aretime dependent: after tillage porosity and water infiltration initiallyincrease, but the loose structure does not persist due to compaction,aggregate instability, and surface sealing driven by external andinternal forces (Zhai et al., 1990). It has been shown that posi-tive effects of tillage on water infiltration in the interrow are lostwithin eight weeks, but they last longer in the zone beneath thetree canopy in a clay-loam soil (Gómez et al., 1999). All these pro-

cesses inevitably lead to plant stress, depletion in soil fertility, andincreasing dependence on chemical inputs for plant protection andfertilization with potentially negative effects on yield and productquality.

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In recent years there is evidence of an increasing occurrencef heavy rainfall events associated with climate change (Brunettit al., 2001; IPCC, 2007) that further exposes the soil to erosion andegradation (Phillips et al., 1993; Nearing et al., 2004). Sandy-loamoils are particularly susceptible to crusting due to the impact ofaindrops when the soil is bare and dry, with resulting clogging ofores by dispersed clay or slaked fragments (Dexter, 1997). It haseen observed that single rainfalls of high intensity are sufficiento determine the above changes, whereas the impact of successivevents is less (Zhang and Miller, 1996). In spite of all these prob-ems, periodic tillage is still the most commonly adopted methodo control weeds in olive orchards (Gómez et al., 2003; Ramos et al.,011).

The use of a plant cover is currently the recommended practiceor protection of the orchard floor. The presence of a cover cropot only has positive effects on soil properties (Gómez et al., 2004,009), but also determines better biochemical fertility (Hernándezt al., 2005) and greater bacterial biomass and diversity (Morenot al., 2009) than tilled soils. A permanent plant cover decreases soilrosion, compaction, surface crusting, improves traffic corridors,nd increases water infiltration and accumulation of organic matterown the soil profile (Gómez et al., 2004, 2009; Pagliai et al., 2004;chutter and Dick, 2002). Olive groves managed with grass coverave lower soil losses and a lower average annual runoff coefficienthan ones where weeds are eliminated by either tillage or herbi-ide applications (Gómez et al., 2004; Taguas et al., 2010). On thether hand, complete sod covering the orchard floor competes withree roots for water and nutrients and, hence, may reduce growthnd yield of trees (Atkinson, 1980). For instance, grasses and weedround covers reduced vegetative growth, yield and leaf nitrogen ofwo peach cultivars compared to herbicide treatment (Tworkoskind Glenn, 2001). Little information is available on the long-termesponse of yield to soil management in olive orchards. Althoughhere is some evidence that a natural cover does not reduce yieldompared to conventional tillage under rainfed conditions (Gómezt al., 1999; Hernández et al., 2005) more studies are needed touantify the effects, if any, of plant covers on yield and oil quality.hese effects are likely to be mediated by water availability. Gómezt al. (1999) reported a significant decrease in yield of olive treeshen the soil was managed by tillage plus herbicide in a year of

ery low precipitation.Olive trees for oil production are traditionally not irrigated, but

n recent years irrigation has been extensively used to stimulaterowth during the training phase and increase yield once treesttain maturity. Deficit irrigation is currently expanding due tohe growing concern about the efficient use of water. Deficit irri-ation consists in supplying less water than that needed to meethe full requirements of the crop. Many recent studies have shownhe advantages of deficit irrigation practices in the olive orchard,s they achieve considerable water savings while maintaining highields (Caruso et al., 2011; Gucci et al., 2007; Lavee et al., 2007;oriana et al., 2003). The controlled distribution of suboptimal

olumes of water is also beneficial to obtain oils with high concen-rations of phenolic compounds and long shelf-life (Motilva et al.,000; Servili et al., 2007).

Most studies on the effect of different soil management practicesave been conducted in traditional, rainfed, mature olive orchards

ocusing mainly on either soil physical or chemical propertiesGómez et al., 1999, 2004). In this work we used a comprehen-ive approach to contrast a high-density olive orchard managedith a natural plant cover with one tilled to 0.1 m depth in terms oflant performance and soil characteristics over five growing sea-

ons. In particular, the objectives were to determine effects on:i) soil (macroporosity, water infiltration rate, fractions of organicarbon content) and (ii) vegetative growth, yield componentsflowering, fruit set, fruit weight, oil accumulation, fruit number),

nomy 41 (2012) 18– 27 19

and oil quality (free acidity, peroxide values, spectrophotometricindexes, phenolic concentrations and fatty acids composition) ofdeficit-irrigated trees cultivated either with a natural plant coveras the orchard floor or tilled to 0.1 m depth in a sandy-loamsoil.

2. Materials and methods

2.1. Plant material and site

We used an olive (Olea europaea L. cv. Frantoio) orchard planted,at a density of 513 trees ha−1 in April 2003, on flat land at theVenturina experimental farm of University of Pisa, Italy (43◦10′N;10◦36′E) between 2004 and 2011. Cultural practices were aimed atkeeping labour and chemical input to a minimum. Minimum prun-ing criteria were used for canopy management (Caruso et al., 2011)and pruned wood was shred and distributed on the soil surfaceusing a VKD 170 mulcher (Nobili, Bologna, Italy).

Prior to planting 147 t ha−1 of cow manure were applied intothe soil profile. In the first year each tree received about 15 g of N,P2O5 and K2O. Since 2005 (3rd year after planting) fertilizers weredistributed only via the irrigation system for a total of 25, 50, 85,25, 50 and 35 g of N, P2O5 and K2O per tree in 2005, 2006, 2007,2008, 2009 and 2010, respectively.

All trees had been fully irrigated since planting until the 2006growing season, when deficit irrigation was started using sub-surface drip lines (Caruso et al., 2011). Trees received abouthalf the volume needed to fully satisfy their requirements, cor-responding to 469, 677, and 893 m3 ha−1 in 2006, 2007, 2008,respectively; in 2009 and 2010, due to summer rains, the waterapplied was only 23 and 12% to that of well irrigated trees (497 and109 m3 ha−1 in 2009 and 2010, respectively). The water require-ment of well irrigated trees was calculated according to Doorenbosand Pruitt (1997) using a crop coefficient of 0.55. The coeffi-cient of ground cover was adjusted annually according to treesize (0.6, 0.8, 0.9, 1 for 2006, 2007, 2008, and 2009–2010, respec-tively).

The climate at the study site was sub-humid Mediterranean(Nahal, 1981; Caruso et al., 2011). The climatic conditions over thestudy period were monitored using a weather station iMETOS IMT300 (Pessl Instruments GmbH, Weiz, Austria) installed on site sinceMay 2006. Reference evapotranspiration (ET0), calculated accord-ing to the Penman–Monteith equation, was 948, 993, 1101 and1001 mm in 2007, 2008, 2009 and 2010, respectively. Annual pre-cipitation was 708, 1107, 771 and 1140 mm in 2007, 2008, 2009and 2010, respectively (Fig. 1). Rains during summer months were160 mm (2006), 39 mm (2007), 74 mm (2008), 87 mm (2009) and140 mm (2010), as reported in Fig. 1.

2.2. Soil type and management

The soil was a deep (1.5 m) sandy-loam (Typic Haploxeralf,coarse-loamy, mixed, thermic) (Soil Survey Staff, 2006) consist-ing of 600 g/kg sand, 150 g/kg clay and 250 g/kg silt. The pH was7.9, average organic matter 1.84% and cation exchange capacity13.7 meq/100 g, all measured at 0.4 m depth. The soil was high inCa and Mg, medium for N, K, Na and low in P.

The soil was periodically tilled at a depth of 0.1 m until October2004 when two management treatments were started: CT, tillageby a power take off-driven harrow with vertical blades (Breviglieri,Nogara, Italy); NC, permanent plant cover periodically mown with

a VKD 170 Nobili mulcher. Subsequently, treatments were main-tained by either tilling or mowing the green cover three or fourtimes a year. Both treatments received the same amount of watersince planting.

20 R. Gucci et al. / Europ. J. Agro

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Vegetative growth was assessed as trunk cross sectional area

ig. 1. Monthly precipitation (mm) at the experimental site in Venturina, Italy, from007 through 2010.

The percentage of soil surface covered by the natural coveras measured at 10 different positions (below the tree canopy

nd in the interrow) along three transects (total of 30 positions)n February, May, July and October 2007 by using a 1 m2 grid1 m × 1 m) subdivided into 100 squares. The plant cover was com-lete in all the NC plots during the wet months, but typically driedut in the summer to recover naturally upon late summer rainfall.oil moisture at 0.06 m depth was measured at three locations peroil management treatment twice a day in 2007 and 2010 using aL2x ThetaProbe (Delta-T Device, Cambridge, UK).

.3. Soil porosity and structure

In order to characterize soil structure, vertically oriented thinections (55 mm × 85 mm) were obtained from undisturbed soilamples collected in May 2010 at different depths (0–0.1 and.1–0.2 m) along the profile of the two soil management systemssix thin sections per treatment and depth). The undisturbed sam-les were dried by acetone replacement (Miedema et al., 1974) and

mpregnated under vacuum with a polyester resin. The impreg-ated blocks were cut into 60 mm high × 70 mm wide × 30 �mhick vertically oriented thin sections (Murphy, 1986). Two imagesf the 0–0.1 m layer were taken for each soil thin section: one repre-entative of the section as a whole and the other at 0–5 mm depth tovaluate soil crusting. The images were analyzed using the Image-ro Plus software (Media Cybernetics, Silver Spring, MD, USA), totalorosity and pore distribution were calculated from measurementsf pore shape and size (the instrument being set up to measureores larger than 50 �m). A shape factor [perimeter2/(4� area)]as used to divide pores into three classes: regular (rounded, shape

actor 1–2), irregular (shape factor 2–5), and elongated (shapeactor > 5), corresponding approximately to the classification usedy Bouma et al. (1977). Pores of each shape group were furtherubdivided into size classes according to either their equivalentiameter (regular and irregular pores), or their width (elongated

ores) (Pagliai et al., 1984). Thin sections were also examined using

Zeiss ‘R POL’ microscope at 25× magnification to observe soiltructure.

nomy 41 (2012) 18– 27

2.4. Water infiltration rate

Steady-state infiltration tests were performed in situ usinga thin-walled metal ring of 0.3 m diameter, partially inserted(40 mm) into the soil to cause as little disturbance of the surface aspossible. To prevent the clogging of the soil surface due to carelesswater application, one piece of cheesecloth was placed under thewater outlet tip. A Guelph Permeameter (Model 2800 – Soil mois-ture Equipment Corp., Santa Barbara, USA) was used to measurethe rate at which the water entered the soil. The measurementswere carried out in May 2010 with four replicates for each treat-ment, about six months after the last tillage (CT) when the inter-rowsoil surface was sealed due to the compacting effect of winter andspring rainfalls. A hydraulic head of 25 mm was used in each testand field saturated hydraulic conductivity (Kfs) calculated accord-ing to Eq. (2). According to Guelph Permeameter technique, Kfs wascalculated using Richards’ analysis (Reynolds, 1993):

Kfs = C(X, Y)R�[2H2 + a2C + 2H/˛]

(1)

where C is the dimensionless shape factor of the measuring wellthat depends primarily on the H/a ratio and soil texture/structureproperties, (X or Y) R is the steady-state flow rate depending onwhether the combination reservoir (X) or the inner reservoir (Y) ofpermeameter was used, H is the hydraulic head of water in the ring,a is the radius of the ring, and is a soil texture/structure parameter(Elrick et al., 1989). The C factor value (Reynolds, 1993) used in thecalculation was obtained according to the empirical equation ofZhang et al. (1998) for sandy soils.

Since the metal ring prevented the field-saturated componentof lateral flow, Eq. (1) was modified as follows:

Kfs = C(X, Y)R�[a2C + 2H/˛]

(2)

2.5. Soil organic carbon fractioning

At the same time and position of undisturbed soil sampling, bulksamples were collected to evaluate organic carbon in both soil man-agement treatments. Total organic carbon (TOC) was determined byoxidation at 170 ◦C, with potassium dichromate in presence of sul-phuric acid. The excess potassium dichromate was measured outby Möhr salt titration (Yeomans and Bremner, 1988).

Total extractable carbon (TEC) and humic carbon (HC) organicmatter fractioning were determined according to the officialmethod of the Italian Society of Soil Science (Sequi and De Nobili,2000). The TEC was obtained by 0.1 M NaOH + 0.1 M Na4P2O7 (1:10soil to solution ratio) at 65 ◦C for 24 h. The humic and fulvicacids (HA and FA, respectively) were separated from the extractby acidification to pH 2.0 with H2SO4. The purification of FAfrom non-humic substances was carried out by adsorption ontopolyvinylpyrrolidone columns. The purified FA fraction was thencombined with the HA fraction to give the humified carbon (HC).The quantification of TEC and HC in the extracts was performed byK2Cr2O7 + H2SO4 hot oxidation (Yeomans and Bremner, 1988).

2.6. Leaf water potential and vegetative growth

Tree water status was determined by measuring pre-dawn leafwater potential (PLWP) on six trees per treatment every 7–10 daysduring the vegetative season using a pressure chamber (Carusoet al., 2011).

(TCSA) and canopy volume. Trunk circumference was measured0.4 m above the ground and the TCSA calculated since the year ofplanting at the beginning and at the end of the growing season.

R. Gucci et al. / Europ. J. Agronomy 41 (2012) 18– 27 21

Table 1The effect of soil management on canopy volume of young olive trees (cv. Frantoio). Values are means ± standard deviations of six or eight trees per treatment. Least significantdifferences (LSD) between soil management systems were calculated after analysis of variance within each year (p < 0.05).

Treatment Canopy volume (m3)

January 2006 November 2007 November 2008 December 2009

Natural cover 7.42 ± 2.13 12.76 ± 2.86 15.19 ± 2.67 21.27 ± 7.153.17

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Tillage 9.69 ± 1.10 23.60 ±

LSD (0.05) 2.22 3.74

he average canopy volume was calculated from measurements ofeight and width of the canopy taken in November 2007, November008 and December 2009, assuming an elliptic shape. The leafrea was determined destructively at two dates in 2007. Four treesere harvested, wood and leaves separated and their fresh and dryeights determined. The leaf area of a subsample was determined,rior to oven-drying at 50 ◦C, by scanning the freshly cut leavesnd using the “UTHSCSA Image Tool” program (University of Texas,ealth Science Center, TX, USA). The regressions between leaf area,

eaf dry weight and wood dry weight and branch diameter weresed to estimate the total leaf area of each tree, from which the leafrea index (LAI) was calculated.

.7. Fruit set, yield components and oil quality

The total number of one-year-old shoots, the number of flow-ring shoots bearing at least one inflorescence and the number ofnflorescences were measured in spring on three selected brancheser tree of six trees per treatment, as previously reported (Carusot al., 2011). Fruitlets present on each selected branch were countedbout 30 days after full bloom and fruit set expressed as the numberf fruits per inflorescence. At harvest, 50–100 fruits were randomlyampled to measure average fruit weight and maturation indexccording to standard methodology (Gucci et al., 2007). The totalumber of fruits per tree was calculated by dividing the crop yieldy the average fruit weight (Caruso et al., 2011).

The oil content of the fruit mesocarp of five fruits per tree waseasured by nuclear magnetic resonance using an Oxford MQC-23

nalyzer (Oxford Analytical Instruments Ltd., Oxford, UK) (Carusot al., 2011). The oil yield of individual trees was calculated aftereasuring the mesocarp oil content on a dry weight basis, the fruit

resh yield, the pulp/fruit ratio, and the ratio between dry and fresheight, as previously reported (Gucci et al., 2007).

Harvest occurred on 20 November in 2006, 6 November 2007, 21ctober 2008, 19 October 2009 and 25 October 2010. Each tree wasarvested individually by hand and final crop yield was expressedn the basis of TCSA to account for differences in tree size andegetative growth.

About 250 cc of oil were obtained using a laboratory scale systemrom about 3.5 kg of fruits, which were crushed by a hammer mill,he resulting olive paste malaxed at 25 ◦C for 20 min, and the oileparated by centrifugation (Servili et al., 2007). The oils were thenltered and stored in the dark at 8 ◦C until analysis. The free acidity,eroxide value, fatty acids composition and UV absorption charac-eristics at 232 and 270 nm of the oils were measured in accordanceith the European Official Methods (UE 1989/2003 modifying the

CC 2568/91). The total phenols and ortho-diphenols were deter-ined by the Folin-Ciocalteu method according to Montedoro et al.

1992).

.8. Experimental design and statistical analysis

Each treatment was assigned to 36 trees, divided into three plotsf 12 trees each. Each plot included three rows of trees. To avoidorder effects only the central row of each plot was used and all

27.16 ± 4.07 30.48 ± 6.99

4.17 8.90

measurements and samples were taken on the inner trees of thecentral row. Treatment means were separated by least significantdifference (LSD test) after analysis of variance (ANOVA) using fiveor six replicate trees. Since tree size was not uniform between treat-ments when different soil managements were put into action, theTCSA measured in April 2004 was used as a covariate in the anal-ysis of covariance (MedCalc software, Mariakerke, Belgium). Soilmacroporosity and organic matter fractions data were analysed by2 × 2 factorial ANOVA with six replicates.

3. Results

3.1. Tree performance

The PLWP of CT trees, measured during the irrigation period,was often significantly lower (more negative) than that of NC trees(Fig. 2). In the last four years of the study, the cumulated leaf waterpotential of CT trees was on average 13% lower than that of NC treeswith differences ranging from 7 to 20% in 2009 and 2007, respec-tively. The soil humidity of NC plots measured at 0.06 m depth wassignificantly greater than that of CT ones during summer monthsof 2010, but differences disappeared since autumn 2010 (Fig. 3).These data are consistent with soil humidity values measured at0.5 m depth beneath the tree canopy (1.1 m from the trunk), whichwere higher in the NC than in the CT treatment (data not shown).

The TCSA of NC cultivated trees was smaller than that of treesgrowing in CT plots. Differences were established early after soiltreatments had been put into action and the effect was evident atthe end of each of the five growing seasons (Fig. 4). Significant dif-ferences in leaf area per tree between the two soil managementsystems were found at the beginning (35.3 and 57.2 m2 for NC andCT, respectively) and end (45.8 and 68.6 m2 for NC and CT, respec-tively) of the fourth year after planting. These values correspondedto a LAI of 1.81 and 2.92 for NC and CT, respectively (beginning of2007) and 2.36 and 3.67 for NC and CT, respectively (end of 2007).The canopy volume of CT trees was significantly higher than thatof NC trees by 23, 46, 44 and 30% in 2006, 2007, 2008 and 2009(Table 1).

The number of flowering shoots per branch was similar for bothtreatments. Some differences in fruit set appeared since 2008, butthey were not consistently maintained in the following two years(Fig. 5). The fruit and oil yields of trees managed by tillage weresignificantly higher than those of trees grown with a permanent,natural cover (Table 2). However, when yields were expressed ona TCSA basis, there were no significant differences between thetwo soil treatments both in the initial three years of production(2006–2008) and when full production was attained (2009–2010).The fruit yield of trees with a naturally covered floor was 87% thatof trees under periodic tillage (average of five years). The num-ber of fruits borne by NC trees was about half that by CT ones andremained significantly lower even when the number of fruits per

tree was expressed on a TCSA basis (Table 2). The oil content in thefruit pulp was similar for both treatments (Table 2). There were nosignificant differences in maturation index between the soil treat-ments, but fruits harvested from the NC trees were usually more

22 R. Gucci et al. / Europ. J. Agronomy 41 (2012) 18– 27

Table 2Yield, yield components, yield efficiency (fruit yield/TCSA or oil yield/TCSA), and maturation index (MI) of young olive trees (cv. Frantoio) subjected to two different soilmanagement systems. Values are means of three (2006–2008) or two (2009–2010) years. Least significant differences (LSD) at p ≤ 0.05 were calculated after ANOVA withineach period (n = 4–6 trees per treatment).

Soil management Years Fruit yield(g tree−1)

Fruityield/TCSA(g dm−2)

Fruitsper tree

Fruits/TCSA(no dm−2)

Oil yield(g tree−1)

Oilyield/TCSA(g dm−2)

FruitFW (g)

MI Oil inmesocarp(% DW)

Natural cover 2006–2008 9588 12,602 4617 6077 2269 2903 2.13 3.17 71.4Tillage 16,342 14,818 10,252 8901 3470 3062 1.70 2.47 71.0

LSD (0.05) 3445 4408 2472 2396 805 855 0.24 0.86 1.15

Natural cover 2009–2010 18,013 11,047 8636 5124 3371 2172 2.29 2.48 61.1Tillage 25,148 12,423 14,278 6858 4555 2308 1.87 2.24 65.4

LSD (0.05) 6903 2267 5115 1935 849 565 0.37 1.04 4.90

TCSA: trunk cross sectional area; FW: fresh weight; DW: dry weight.

Table 3Free acidity, peroxide value, K232, K270, total phenols, ortho-diphenols, and fatty acids composition of virgin olive oils (VOO) from olive trees (cv. Frantoio) subjected to twodifferent soil management systems. Values are means of four different VOO replicates (n = 4). Different letters indicate least significant differences at p ≤ 0.05 after analysisof variance (ANOVA) within each year. Data of fatty acids were transformed by arcsine transformation prior to ANOVA.

Soil management Year Free acidity(% oleic acid)

Peroxide value(meq O2 kg−1)

K232 K270 Totalphenols(mg kg−1)

Ortho-diphenols(mg kg−1)

Palmiticacid (%)

Oleic acid(%)

Linoleicacid (%)

Linolenicacid (%)

Natural cover 2006 0.25 10.2 1.775 0.123 520 133 N.A. N.A. N.A. N.A.Tillage 0.25 12.7 1.975 0.125 443 132 N.A. N.A. N.A. N.A.

Natural cover 2008 0.37 9.7 1.730 0.295 605 205 13.3 a 73.5 8.4 0.6 bTillage 0.40 10.2 1.645 0.141 530 192 12.8 b 74.4 7.9 0.7 a

Natural cover 2009 0.34 7.2 2.000 N.A. 702 a 325 a 14.0 73.0 7.9 0.6Tillage 0.31 5.3 1.897 N.A. 505 b 238 b 14.2 73.4 8.0 0.7

Natural cover 2010 0.23 9.3 1.875 0.109 130 65 N.A. N.A. N.A. N.A..107

N

pwft

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3

mpi

TEm

T

Tillage 0.20 9.6 1.888 0

.A.: not available.

igmented than those picked from the CT trees (Table 2). Thereere significant differences in fresh weight between treatments:

ruits from the CT treatment were smaller than those from the NCreatment (Table 2).

Soil management did not influence free acidity, peroxide value,232, and K270 in any of the years of study (Table 3). The fatty acidomposition of the oil showed a significant increase in palmiticcid at the expense of linolenic acid of the NC treatment only inne out of two years. Other fatty acids (myristic, palmitoleic, mar-aric, eptadecanoic, stearic, arachic, eicosenoic, behenic, lignoceric)resent in olive oils are not reported in Table 3, as they did not differetween soil management treatments. Total phenolic concentra-ions of the NC treatment were slightly higher than those of the CTne, although differences were significant only in 2009 (Table 3).

.2. Soil properties and water infiltration

Soil porosity, determined according to micromorphometricethods (Pagliai, 1988), was low in both treatments (Fig. 6). In

articular, NC and CT soils can be classified as dense (macroporos-ty between 5 and 10%) and very dense (macroporosity lower than

able 4ffect of soil management on the different fractions of soil total organic carbon (TOC).

anagement treatments and depths after analysis of variance (p < 0.05).

Soil management Depth (m) TOC

Tillage 0–0.1 1.10.1–0.2 1.0

Natural cover 0–0.1 1.30.1–0.2 1.3

EC: extractable carbon; HC: humic carbon.

119 60 N.A. N.A. N.A. N.A.

5%), respectively. Soil macroporosity was significantly affected bysoil management only at the surface (0–0.10 m) where NC showedhigher values than CT. This difference resulted mainly from thehigher frequency of irregular pores and elongated pores, which dra-matically decreased in CT. Macrophotographs of the upper part ofsoil (0–5 mm) and the corresponding pore size distribution of thetwo soil managements confirmed the above differences and evi-denced the presence of a compact surface crust in the CT treatmentonly (Fig. 7).

The water infiltration rate of NC treatment was similar to whatFAO (1990) considers a standard steady rate for sandy loam soils(20–30 mm h−1) (Fig. 8). On the contrary, the infiltration rate mea-sured in CT plots was about eight times lower than that in the NCtreatment, in agreement with the low value of macroporosity at thesurface of CT soil.

Total organic carbon and TEC in NC plots were higher thanin CT ones, the former at both depths, the latter only at the0–0.1 m depth (Table 4). The TEC values significantly decreased

at 0.1–0.2 m depth in both management systems. The humicfraction, the more resistant pool of soil organic matter (Tate,1987), was quite low and unaffected by soil management(Table 4).

Different letters within each column indicate significant differences between soil

(%) TEC (%) HC (%)

4 b 0.55 b 0.19 a4 b 0.31 c 0.12 b3 a 0.68 a 0.18 a5 a 0.38 c 0.10 b

R. Gucci et al. / Europ. J. Agronomy 41 (2012) 18– 27 23

Fig. 2. Seasonal course of pre-dawn leaf water potential (PLWP) of olive trees sub-jected to different soil management in 2007 (A), 2008 (B), 2009 (C), and 2010 (D).Symbols are means of six trees. Vertical bars represent least significant differencesat p ≤ 0.05, calculated after analysis of variance within each date of measurement.Horizontal lines indicate the irrigation period. H, harvest.

Fig. 3. Seasonal changes in soil moisture, measured at 0.06 m depth, in a high-density olive orchard managed either by natural plant cover or tillage. Symbols aremeans of two measurements (dawn and solar noon) of three replicate trees dur-ing 2010 and 2011. Vertical bars represent least significant differences at p ≤ 0.05,calculated after analysis of variance within each date of measurement.

Fig. 4. Trunk-cross sectional area (TCSA) of young olive trees grown under eithernatural cover or tillage conditions. The soil was tilled from the year of planting (2003)until October 2004 (A), when two soil management treatments were established.Trees had been fully irrigated until the 2006 growing season (B), when deficit irri-gation was started. Symbols are means of 5–6 trees. Means were transformed afteranalysis of covariance using TCSA measured in April 2004 as covariate.

24 R. Gucci et al. / Europ. J. Agronomy 41 (2012) 18– 27

Fig. 5. Number of flowering shoots and fruit set of olive trees (cv. Frantoio) sub-jected to two different soil management systems. Fruit set was measured about 30days after full bloom and expressed as number of fruits per 100 inflorescences. Mea-surements were made every spring, before the beginning of irrigation. Values arembD

4

e0delastpitr

Fig. 6. Total macroporosity (pores > 50 �m) values (n = 6), expressed as a percentageof area occupied by pores of the three shape groups (regular, irregular and elon-gated pores), at two different depths (0–0.1 m and 0.1–0.2 m) in natural cover (NC)

mechanical disturbance of the 0–0.2 m layer, the use of heavyequipment (mouldboard plough or chisel plough), periodic diskingor harrowing (Gómez et al., 2009; Moreno et al., 2009), whereas we

ean of 5–6 replicate trees. Different letters indicate least significant differencesetween treatments after analysis of variance (ANOVA) within each year at p ≤ 0.05.ata of fruit set were transformed by arcsine transformation prior to ANOVA.

. Discussion

Soil management had a major impact on soil physical prop-rties. The NC treatment had greater soil macroporosity in the–0.1 m upper layer and water infiltration rate than CT plots. Theramatic decrease in soil macroporosity of the CT treatment wasssentially due to a significant reduction in elongated and irregu-ar pores, which are critical for root penetration, water movementnd gas diffusion. The vegetation cover likely protected the soilurface from the raindrop impact, thus reducing mechanical disrup-ion of soil aggregates and preserving the continuity of elongated

ores (Panini et al., 1997). There was also evidence of soil crust-

ng in the CT treatment, which was presumably responsible forhe low values of infiltration rate. These results confirm the occur-ence of surface sealing and low infiltration in tilled soils (Gómez

and tillage (CT) treatments. Different letters indicate least significant differencesbetween treatments and soil depths after analysis of variance within each shapegroup at p ≤ 0.05.

et al., 2004; Moreno et al., 2009) despite the fact that our CTtreatment was intended to be less aggressive than conventionaltillage. Conventional tillage of olive orchards typically involves

Fig. 7. Macrophotographs of vertically oriented thin sections from the surface layerof naturally covered (A) or tilled (B) soil and corresponding pore size distribution,according to equivalent pore diameter for regular and irregular pores and widthfor elongated pores in the upper part of soil (0–0.05 m). The presence of a compactsurface crust is evident in the tilled treatment.

R. Gucci et al. / Europ. J. Agro

Foa

ta

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mcsBtiobIcs

2sefanbmfdtTspc

ig. 8. Water infiltration rate measured in the interrow of a high-density oliverchard during the sixth growing season after establishment of different soil man-gement. Histograms are means of four replicates (bars = standard deviations).

ried to minimize disturbance by limiting tillage to 0.1 m depth andbstaining from using rotary tillers.

The bare soil of the CT plots was vulnerable to crusting and,herefore, susceptible to waterlogging. Olive trees are sensitive toypoxia conditions which may negatively influence tree growthnd production (Aragues et al., 2004; Dat et al., 2006), but the peri-ds of waterlogging that occurred in part of the CT area in theutumn of 2008 and 2010 due to the abundant precipitations ofovember and December were too brief to affect tree performance

Fig. 1). The increasing number of heavy rainfall events (Brunettit al., 2001) exacerbates the problem of soil crusting and com-action in tilled soils. Over the 2006–2010 period there was annnual average of 7.5 heavy rainfall events (intensity between 15nd 40 mm h−1) in the experimental area, at least two of whichn the autumn. In 2010 there were 11 events, five of which inhe autumn. When surface sealing of the CT treatment occurrednd hindered water infiltration, even rainfall events of moderatentensity (3–15 mm h−1) could cause waterlogging.

The difference in infiltration rate between NC and CT treat-ents was much greater than that reported (about 2-fold) between

onventional tillage and a barley crop cover in a clay-loam aftereven years of differentiated soil management (Gómez et al., 2009).esides differences in soil type and time of measurement after lastillage the differences we reported were probably amplified by hav-ng measured infiltration only in the middle of the interrow. In oliverchards soil properties and hydrological parameters in the zoneeneath the tree canopy are distinct from those in the interrow.

n particular, it has been shown that water infiltration beneath theanopy was about four times that of the interrow in a clay-loamoil in southern Spain (Gómez et al., 1999).

Soil structure is positively affected by TOC content (Aranda et al.,011; Hernández et al., 2005; Hernanz et al., 2002), but it is neces-ary to quantify the different fractions of TOC to better evaluate theffect of soil management (Vittori Antisari et al., 2010). In fact, weound that while TOC was different between NC and CT treatmentst both depths (0–0.1 and 0.1–0.2 m), differences in TEC were sig-ificant only in the more superficial layer, and HC was unaffectedy soil management in both layers. The use of plant covers deter-ines an increase of easily mineralizable organic matter, namely

resh herbaceous plant residues such as leaves, root debris and exu-ates (Berry et al., 2002). Such fraction enhances biological activity,hus favouring soil aggregate formation (Tisdall and Oades, 1982).

his is particularly important in weakly structured, coarse texturedoils and was clearly shown by our macroporosity results. Under theedo-climatic conditions of our study, five years of natural plantover were not sufficient to affect the HC content of the 0–0.2 m

nomy 41 (2012) 18– 27 25

topsoil. This is not surprising because longer periods are neces-sary to increase the soil content of organic matter along the profileunder Mediterranean climate conditions. Gómez et al. (1999) didnot find differences in the organic matter of the 0–0.09 m topsoilbeneath olive canopies between conventional tillage and no tillage(plus herbicide) after 15 years. The formation of stable organic com-pounds is largely determined by either soil organic matter turnoveror soil minerals (Buurman et al., 2009). The low value of HC wemeasured in both soil management treatments was likely due tothe rapid turnover of organic matter in the topsoil and to the spe-cific textural characteristics. The abundance of the labile fractionsversus the humified ones suggested that this soil had a poor humi-fication capacity (Vittori Antisari et al., 2010).

The presence of a permanent sod reduced trunk growth andthe number of fruits per tree with respect to CT-cultivated trees.Differences in TCSA and canopy volume between treatments wereapparent every year and determined a greater LAI and fruiting sur-face of the CT management, which can explain why CT trees hadmore fruits than NC ones. In olive trees fruit yield is positivelycorrelated with total fruit number (Gucci et al., 2007; Trentacosteet al., 2010), which is not altered by thinning as in the standardcommercial practice of other fruit trees. The effect on fruit numberwas still significant when the larger size of CT canopies was takeninto account. Although it is impossible from our data to determinewhat caused the drastic reduction in fruit number/TCSA for the NCtreatment, we hypothesize that it was due to reduced shoot lengthrather than fruit set (Fig. 5). Changes in initial fruit set or fruitletabscission have been reported to occur only when severe waterdeficit develops (Gucci et al., 2007), but the differences in PLWP wemeasured between NC and CT trees were too small to affect fruitabscission. The overall negative effect of NC on fruit or oil yield waslargely diminished and no longer significant when yield efficiencies(yield/TCSA) were calculated, indicating that differences in canopysize were mainly responsible for the lower yield of NC-grown trees.Gómez et al. (1999) did not find any yield differences between olivetrees grown with conventional tillage or no tillage under rain-fedconditions.

The increase in fruit weight and maturation index for the NCtreatment are consistent with effects due to crop level rather thantree water status (Gucci et al., 2007; Trentacoste et al., 2010). Inaddition, the small differences in maturation index or plant waterstatus did not appear relevant to affect oil quality. A clear negativecorrelation between tree water status and oil phenolic concentra-tions has been reported (Motilva et al., 2000; Servili et al., 2007)but, in our study, the PLWP of NC trees was never lower thanthat of CT ones (Fig. 2). It remains to be ascertained whetherthe higher polyphenols concentration of the NC treatment mea-sured every year (although significant only in 2009) is confirmedover longer periods and, if so, why this increase since cannot beexplained by tree water status or stage of ripening. Phenols andortho-diphenols are very important for quality characterization ofvirgin olive oil (VOO) since they are closely related to their sensoryand health properties (Servili et al., 2004). Oils of both soil treat-ments exceeded the 200 mg kg−1 value, currently considered thethreshold above which phenolic compounds exert their nutraceu-tical effects as antioxidants, except in 2010, when abundant rainsduring fruit development determined low phenolic concentrationsin the oil (Servili et al., 2007).

In conclusion, the positive effect of NC on the strength of sur-face structural elements (peds and aggregates) can be mainlyattributed to the physical protection against raindrop impact andto the stability enhancement due to enmeshing of soil particles and

microaggregates by grass fine roots rather than to the increase ofTOC content. The marked decrease in vegetative growth for the NCtreatment was likely due to the early establishment of the greencover which competed for water and nutrients against the young

2 J. Agro

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6 R. Gucci et al. / Europ.

oot systems of trees. Hence, the establishment of permanent cov-rs should not be recommended in the first two years after plantingut delayed to the third or fourth year depending on tree growth.

natural plant cover significantly decreased the number of fruitsnd yield, but did not affect yield efficiency, mesocarp oil content oril quality; these effects did not depend on a greater water deficiteveloping in NC trees based on PLWP and soil moisture measure-ents.

cknowledgments

We are grateful to Michele Bernardini, Rolando Calabrò,aurizio Gentili, and Stefania Simoncini for excellent technical

ssistance. We also thank Netafim Italia for the supply of theubsurface irrigation system. Research supported by Unaprol-Italyproject Reg. UE no. 2080/2005 and no. 867/2008) and PRIN 2004Carbon Cycle in Tree Ecosystems” (project no. 2004074422 004).

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