Chemical and biological responses in a Mediterranean sandy clay loam soil under grain legume-barley...

Agrochimica, Vol. LVII - N. 1 January-March 2013

Received 18 July 2012 – Received in revised form 7 January 2013 – Accepted 21 January 2013

Chemical and biological responses in a Mediterranean sandy clay loam soil under grain legume-barley intercropping

D. TORTORELLA*, A. SCALISE, A. PRISTERI, B. PETROVIČOVÁ, M. MONTI, A. GELSOMINOUniversità degli Studi Mediterranea di Reggio Calabria, Dipartimento di Agraria, Salita Melissari, 89124 Reggio Calabria, Italy

Keywords: agro-ecological services, biological fertility index, legume crops, soil N pools, soil C dynamics, weed control

Introduction. – In modern agriculture, the introduction of legu-minous intercrops can represent an interesting farming strategy to sup-port low input arable systems. This helps to mitigate the decline in soil fertility, to reduce the agricultural intensification and possibly to prevent the abandonment of arable lands. This becomes particularly true under rain-fed conditions in a Mediterranean environment where severe climatic conditions constitute a critical factor for the conservation of soil fertil-ity and the maintenance of acceptable crop productivity. Legumes are known to provide several key agroecological services. They increase the utilisation of renewable resources through greater reliance on biologi-cally fixed N, reduce N losses with increased N storage in soil, improve soil structure (particularly forage legumes), control weed infestation and pests, and increase crop diversity. Intercropping, or the simultaneous cul-tivation of two or more species simultaneously on the same area of land, is widespread in tropical agriculture but less widely used in temperate regions (Vandermeer, 1989). It relies on the complementary use of soil resources meaning that interspecific competition between the two crops is weaker than intraspecific competition for growth factors (i.e. light, water and nutrients) (Willey, 1979). Intercropped grain legumes and non-legumes can complement each other and have shown a more efficient use of environmental resources for plant growth due to a reduced competi-tion for soil N (Knudsen et al., 2004; Hauggaard-Nielsen et al., 2003 and 2006), an increased water and nutrient use efficiency (Hauggaard-Nielsen et al., 2009b), a greater yield stability and higher N concentration

* Corresponding author: [email protected]

D. TORTORELLA ET AL.2

Fig. 1. – Layout of one block of the field experiment showing the cropping treatments.

in cereal grain (Hauggaard-Nielsen et al., 2009a). An enhanced weed suppression and pest control was also reported (Liebman and Dyck, 1993). Although legume-based intercropping was primarily introduced into tropical agriculture and then proposed for temperate climates, it can be also of great significance in Mediterranean croplands, where severe climatic conditions and high-input cropping systems may decrease soil fertility and exacerbate soil degradation. Current research on intercropped legumes grown in a Mediterranean area has provided evidence of an effi-cient use of soil resources thus resulting in increased cereal grain yield and quality (Bedoussac and Justes, 2010; Tosti and Guiducci, 2010). Nevertheless, soil N dynamics in intercrops has been largely studied since it represents a key prerequisite to understand the functional link between soluble N supplied to crops and mineral N taken up by the crop. However, C and N pools are functionally interconnected in soil, and little attention has been devoted to the microbial processes promoting soil C and N dynamics in legume-based intercropping systems. Our aim was therefore to investigate microbial processes that underline changes in soil C and N pools in a Mediterranean sandy clay loam soil grown with grain legume-cereal intercropping under rain-fed conditions. To this aim, a number of soil chemical (pH; EC; Corg; Nt; NH4

+-N; NO3--N; TSN, total soluble N;

EON, extractable organic N), biochemical (MBC, microbial biomass C; Rbas, soil basal respiration; C0, potentially mineralizable C; MBC/Corg, microbial quotient; qM, mineralisation coefficient; qCO2, metabolic quo-

P100 Pea (90 plants m-2)

P100/B50Pea (90 plants m-2)

Barley (150 plants m-2)

P50/B50Pea (45 plants m-2)

Barley (150 plants m-2)

B100 Barley (300 plants m-2)

F100 Faba bean (40 plants m-2)

F100/B50Faba bean (40 plants m-2)Barley (150 plants m-2)

F50/B50Faba bean (20 plants m-2)Barley (150 plants m-2)

SOIL RESPONSES TO INTERCROPPED LEGUMES 3

tient; qCO2/Corg ratio; MBN, microbial biomass N; PMN, potentially min-eralizable N) variables were measured following cultivation with grain legume (pea, faba bean)-barley intercropping at different plant densities as compared with the respective sole crops. The soil fertility status was esti-mated by combining a number of soil C-related chemical and biochemical variables to calculate the index of biological fertility (IBF) (Benedetti et al., 2006). Compositional shifts in the genetic structure of indigenous soil bacterial community were investigated by denaturing gradient gel electro-phoresis (DGGE) community fingerprinting of soil-extracted DNA. Crop and weed biomass production at an aboveground level was monitored. We hypothesised that newly introduced legume-based cropping systems: alter soil C and N cycling and the genetic structure of the bacterial community (H1); affect the soil biological fertility status (H2); are able to provide significant agroecological services such as a greater crop biomass accu-mulation and weed growth suppression (H3).

Materials and Methods. – Study site and experimental design. – The field experi-ment was carried out in the 2010/11 cropping season at the experimental centre of the Regional Agency for Agriculture “ARSSA” located at San Marco Argentano, Cosenza, Italy (39°38’N, 16°13’E), at about 100 m above sea level. The study site evolves over the alluvial deposits from the river Follone within the Crati valley. The arable soil is classi-fied as a Fluventic Haploxerept, coarse silty, mixed, thermic (Soil Survey Staff, 2010). Mean annual rainfall and air temperature are, respectively, 709 mm and 16.1°C (averages over the 1995-2009 period) with mild rainy winters and hot, dry summers. The coldest month is February (mean temperature +3.0°C) and the hottest is August (mean tempera-ture +33.4°C). Soil thermal and moisture regimes are thermic and xeric, respectively. Soil depth is > 120 cm and total available water holding capacity (AWC, available moisture between field capacity and wilting point) equals 20.0 cm. Main soil characteristics are listed in Table 1. Before starting the trial the arable soil was tilled according to the tra-ditional tillage practices: mouldboard ploughing at 25-30 cm depth (October 15, 2010), fertilization with 200 kg ha-1 of a complex 18N-46P2O5 fertiliser (diammonium phos-phate, DAP) supplying 36 kg N ha-1 and 40 kg P ha-1, and disc harrowing (10 cm depth) immediately before seeding. A precision line seeder did seeding on December 22, 2010.

The studied crops were pea (Pisum sativum L. cv Hardy), faba bean (Vicia faba L. cv Sikelia) and six-row barley (Hordeum vulgare L. cv Aldebaran). The experimental setup was established within a 52 x 21 m field area, and 28 plots (3 x 10 m each) were arranged in a randomized complete block design, four-time replicated, to compare the grain legume (pea, faba bean)-barley intercropping in an additive (P100/B50, F100/B50) or replacement design (P50/B50, F50/B50) to the respective sole crops (P100, F100, B100; Fig. 1). No irrigation or weeding was provided.

Soil sampling. – Soil was collected at tillering (February 14, 2011), flowering (April 11, 2011) and harvest of the legume crop (pea: June 7, 2011; faba bean: June 29, 2011). Composite soil samples, each consisting of three randomly collected soil cores per treatment pooled together (sub-replicates), were surface collected (10-15 cm depth) from three out of


the four replicated blocks, amounting to 21 composite soil samples per sampling period. Composite soil samples from unplanted plots were taken as reference (control) soil for molecular analysis. Soil samples were split in two parts: an aliquot (200 g) was immediately processed for biochemical and bacterial community analysis; and a further sub-sample (200 g) was air-dried for 24 h and sieved to < 2 mm particle size before chemical analyses.

Table 1. – Main physical and chemical properties (mean ± SD, n = 3) of the agricultural sandy clay loam (USDA classification) soil investigated in the present study (San Marco Argentano, Cosenza, Italy).

Soil variable

Sand (%) 55 ± 4

Silt (%) 24 ± 5

Clay (%) 21 ± 1

Bulk density (g cm-3) 1.42 ± 0.14

pHCaCl2

7.67 ± 0.05

pHKCl 7.27 ± 0.01

Corg (g kg-1) 9.81 ± 0.25

Nt (g kg-1) 0.95 ± 0.04

C/N 10.33 ± 0.51

EC1:2 (dS m-1)# 0.21 ± 0.03

CEC (cmol(+) kg-1)§ 26.2 ± 1.0

Exchangeable Na (cmol(+) kg-1)‡ 0.3 ± 0.1

Exchangeable K (cmol(+) kg-1)‡ 2.5 ± 0.4

Exchangeable Mg (cmol(+) kg-1)‡ 3.6 ± 0.3

Exchangeable Ca (cmol(+) kg-1)‡ 10.9 ± 0.8

Total CaCO3 (g kg-1) 18.0 ± 0.5

Active CaCO3 (g kg-1) 13.5 ± 0.4

Olsen-P (mg kg-1) 12.5 ± 1.6

NH4+ - N (mg kg-1) 17.0 ± 0.4

NO3- - N (mg kg-1) 6.1 ± 0.5

Fe (mg kg-1)† 7.6 ± 0.1

Mn (mg kg-1)† 7.1 ± 0.1

Cu (mg kg-1)† 1.6 ± 0.1

Zn (mg kg-1)† 0.9 ± 0.1

#electrical conductivity determined in 1:2 (w:v) soil:water extracts (1 dS m-1 = 1000 µS cm-1).§measured by using 0.1 M BaCl2 buffered to pH 8.2 with triethanolamine (TEA) (2.25%, v/v).‡extracted with 1 N ammonium acetate (NH4OAc) at pH 7.0.†extracted with 0.005 M diethylenetriamine pentaacetic acid (DTPA) at pH 7.3.


Soil chemical and biochemical variables. – Soil chemical properties (pH, EC, Corg, Nt, NH4

+-N, NO3--N, TSN, EON) were determined according to Sparks (1996). MBC and

MBN were determined following a chloroform fumigation-extraction procedure using a conversion factor of KEC = 0.38 and KEN = 0.54, respectively (Brookes and Joergensen, 2006). Soil basal respiration was determined according to the procedure described by Öhlinger (1995). The cumulative CO2-C evolved after a 28-day incubation period (gas sampling was done after 1, 4, 7, 14, 21 and 28 days) was assumed as Rbas. The C0 pool was estimated by fitting the 28-day cumulated data to the first-order exponential function Ct = C0 (1-e-kt) (Mocali et al., 2009). The PMN resulting from net mineralization of active soil organic N occurring during the 28-day incubation period for Rbas determination was estimated as the cumulative soil inorganic-N after 28 days minus cumulative inorganic soil N at 0 day (Drinkwater et al., 1996). The following soil ecophysiological indices were then calculated: the microbial quotient (MBC/Corg), the metabolic quotient (qCO2), the mineralization coefficient (qM = Rbas/Corg), and the qCO2/Corg ratio (Mocali et al., 2009). The synthetic IBF was calculated according to Benedetti et al. (2006).

Soil DNA extraction and DGGE analysis. – Total bacterial-community DNA was immediately extracted from 2 g of freshly collected subsamples by using a direct extrac-tion method (van Elsas et al., 1997) after cell lysis with beads followed by sodium dode-cylsulfate (SDS) treatment, phenol extraction, and sequential Cesium chloride precipita-tion followed by GeneClean® Spin Kit (QBiogene/MP Biomedicals, LLC, OH; cat. No. 1101-400) column filtration. For DGGE, bacterial 16S rRNA gene-coding targets were amplified using the primer pair F984GC/R1378, which is suitable for total community fingerprinting. PCR conditions and DGGE community fingerprinting were according to Gelsomino and Cacco (2006).

Crop and weed biomass. – Aboveground biomass (crop and weed) was sampled at two stages: barley heading (April 2011; only weeds) and harvest (June 2011; crops and weeds). Samples were collected from two 5-meter rows from each treatment. Total dry weight was determined after oven drying at 60°C until a constant weight was reached.

Statistical analysis. – Chemical and biochemical data, reported as mean values (n = 3), were expressed on an oven-dry weight (dw) soil basis (105°C, 24 h). Soil variables were firstly checked for deviations from normality (Kolmogorov-Smirnov test) and homogeneity of variances (Levene median test). The block effect in the experimental design was not significant (P > 0.05) and the data were then subjected to a two-way analysis of variance (ANOVA) (time x crop) in order to highlight the main effect of time, crops and their interaction. Tukey’s test at P < 0.05 level of significance was used for multiple pairwise comparison of the means shown in Tables 2 and 3. Chemical and biochemical data were also analysed by principal component analysis (PCA) with no rota-tion. Statistical analyses were run using the Systat 11.0 software (Systat Software Inc., Erkrath, D). Digitized DGGE banding patterns were analysed by the Diversity Database™ Fingerprinting software (Bio-Rad Laboratories, Hercules, CA, USA). A dendrogram of hierarchical classification of DGGE data was generated via an unweighed pair-group method using arithmetic averages (UPGMA) clustering method based on Pearson’s cor-relation coefficients as suggested by Smalla et al. (2007).

Results. – Soil chemical and biochemical variables. – During the growing season, the soil pH remained practically unaffected by the


differing treatments, whereas the EC1:2 slightly but significantly (F = 23.636, P < 0.001) increased over time (ranging between 0.15 and 0.18 dS m-1) and precisely in P100 (+13%), F100 (+20%), and also in F100/B50 (+13%) from sowing to harvest (data not shown).

The two-way ANOVA (time x crop) showed that the sampling period significantly affected the variability of as many as half of the measured soil C-related variables and precisely: Corg, C0, qCO2 and qCO2/Corg ratio (Table 2). In contrast, a reduced number of C-related soil variables were affected by the crop treatment (Corg, Rbas, C0) or by the time x crop interac-tion (Corg and C0). In details, the Corg content varied within the 8.77-11.41 mg g-1 dw soil range in all treatments over the time frame period and was generally lower (by 10% on average) in pea-based treatments and higher in faba bean-based crops (Table 2). Furthermore, marked crop-dependent variations were observed in Rbas, which also showed the largest values (within the 300-400 µg CO2-C g-1 28 d-1 range) in the faba bean-based treatments, and the smallest ones (approx. 200 µg CO2-C g-1 28 d-1) in the B100 treatment (Table 2). Interestingly, the potentially mineralized C (C0) displayed a time-dependent decline in B100 treatment (-9%), it increased considerably in faba bean treatments (+39, +23 and +40%, respectively, for F100, F100/B50 and F50/B50 from tillering to harvest) and remained unaffected in pea-based combinations (Table 2). The qCO2 and qCO2/Corg ratio largely fluctuated during the cropping season in all treatments, being increased consistently at the flowering stage and then decreased to initial values at the harvest time. The MBC, the MBC/Corg and the qM didn’tsignificantly differ among treatments or sampling times (Table 2). Only a few soil N variables were responsive to the experimental factors. Nt was generally higher in faba bean based treatment and lower in the plots with the sole barley (Table 3). Both time and crop-dependent variations were observed in NH4

+-N, which showed very large variations during the growing season and at the harvest stage was larger in faba bean treatments (+26, +32 and +13%, respectively, for F100, F100/B50 and F50/B50) and smaller in barley crop (Table 3). Finally, marked seasonal changes were observed in PMN, which firstly increased (flowering) and then heavily declined (harvest) in all treatments (-66% on average).

Principal component analysis of soil chemical and biochemical vari-ables was able to factorize the variation of the original data in four most significant components (Factor 1, 2, 3 and 4, according to the “eigenvalue > 1.0” criterion), which taken together explained approximately 86.1% of the total variance (Table 4). It is noteworthy that both PC1 (34.17%


of total variance explained) and PC2 (25.95% of total variance) were primarily weighed by soil C-related functional variables (qCO2/Corg ratio, qCO2, MBC and MBC/Corg for PC1 and Rbas, qM and C0 for PC2) and – only for PC1 – also by the NH4

+-N and, at a lesser degree, by the PMN. PC3 (16.00% of total variance) was primarily affected by the active soil N-resources (TSN and EON). PMN was also the main variable affecting the PC4 (Table 4). As the first two components taken together explained 60.12% of the total, we focussed on these. Soil samples from the differ-ing crop treatments appeared separated in the ordination biplot, especially along the PC axis 2, which are primarily weighed by some active C pool-related variables (Fig. 2). In fact, the crop treatments can be broadly clustered in three main groups with the faba bean-based treatments being clearly discriminated from pea-based crops and barley sole crop.

Index of biological fertility of soil (IBF). – At the tillering stage the IBF scored within the III class (reference values 12-18: medium fertility) with an average score of 16.1 considering all treatments. On the con-trary, at harvest, only F100 and F50/B50 ranked to the upper rating class IV (reference values 18-24: good fertility) with scores of 18.5 and 19.0, respectively, leaving the other treatments with the IBF class unchanged (average score of 13.8).

DGGE community fingerprinting. – Genetic fingerprinting by DGGE of PCR-amplified 16S rRNA gene fragments revealed that the molecular

Fig. 2. – PCA ordination plot (PC1 vs PC2) of the seven crop treatments (scores) and 17 soil chemical and biochemical variables (loadings). Biplot has the same origin for scores and loadings. All soil data set were considered with no distinction among sampling times. Crop treatments (symbols) are as in Materials and Methods.


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SOIL RESPONSES TO INTERCROPPED LEGUMES 9F

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tim

e di

ffer

ent

lett

ers

in a

row

ind

icat

e si

gnif

ican

t di

ffer

ence

s am

ong

trea

tmen

ts (

Tuk

ey’s

tes

t at

P <

0.0

5).

b Lev

els

of s

igni

fica

nce:

* P

< 0

.05;

**

P <

0.0

1; *

** P

< 0

.001

; ns

: no

t si

gnif

ican

t.


Ta

ble

3. –

Soi

l N

poo

ls (

mea

n ±

SD

, n

= 3

) m

easu

red

duri

ng t

he c

ropp

ing

seas

ona

and

sign

ific

ant

effe

cts

due

to t

ime,

cro

p an

d th

eir

inte

ract

ion

on t

he v

aria

bili

ty o

f so

il d

ata

(F-v

alue

s fr

om t

wo-

way

AN

OV

A t

ime

x cr

op w

ith

corr

espo

ndin

g P

val

uesb )

.

Tre

atm

ent

Soi

l va

riab

le

Nt

(mg

g-1)

NH

4+-N

(µg

g-1)

NO

3- -N

(µg

g-1)

TS

N(µ

g g-1

)E

ON

(µg

g-1)

MB

N(µ

g g-1

)P

MN

(µg

g-1 2

8 d-1

)

P10

01.

16(0

.11)

17.6

1(0

.17)

a1.

99(1

.51)

169.

8(1

2.4)

152.

1(1

2.2)

44.9

0(2

.79)

14.0

8(3

.44)

P10

0/B

501.

09(0

.13)

14.2

1(0

.01)

b5.

73(0

.90)

199.

5(6

4.4)

185.

3(6

4.4)

43.6

6(3

.96)

13.9

2(0

.61)

P50

/B50

1.14

(0.1

4)14

.19

(0.2

1) b

4.86

(0.2

4)16

2.8

(61.

9)14

8.6

(62.

1)42

.00

(3.9

6)13

.12

(2.2

5)

Til

leri

ng

F10

01.

18(0

.14)

14.4

9(0

.10)

b3.

60(1

.18)

187.

3(6

1.9)

172.

8(6

2.0)

44.2

8(1

.03)

15.7

0(0

.32)

F10

0/B

501.

01(0

.07)

14.0

2(0

.34)

b5.

02(0

.61)

162.

8(1

2.4)

148.

7(1

2.7)

43.5

6(4

.11)

13.0

3(0

.86)

F50

/B50

1.08

(0.1

2)13

.93

(0.5

0) b

3.74

(0.7

9)18

2.0

(29.

7)16

8.1

(30.

2)42

.00

(0.4

4)14

.49

(0.7

4)

B10

01.

10(0

.06)

13.9

2(0

.33)

b5.

48(2

.47)

126.

0(1

4.9)

112.

1(1

5.2)

46.5

6(3

.08)

15.3

8(2

.96)

P10

01.

22(0

.03)

a,b

4.06

(0.1

3) a,

b4.

18(2

.60)

a,b

150.

5(4

4.5)

146.

5(4

4.4)

15.5

6(1

4.67

)18

.22

(6.2

2) b

P10

0/B

501.

16(0

.02)

a,b

3.98

(0.3

0) a,

b4.

20(0

.29)

a,b

159.

3(2

.5)

155.

3(2

.8)

36.3

0(1

4.67

)16

.14

(0.8

2) b

P50

/B50

1.18

(0.1

7) a,

b4.

08(0

.02)

a,b

2.81

(0.1

1) a,

b15

2.3

(12.

4)14

8.2

(12.

4)37

.59

(9.1

7)18

.89

(3.7

9) a,

b

Flo

wer

ing

F10

01.

26(0

.09)

a5.

85(0

.62)

a5.

31(0

.33)

a19

6.0

(29.

7)19

0.2

(30.

3)23

.33

(7.3

3)18

.30

(6.7

6) b

SOIL RESPONSES TO INTERCROPPED LEGUMES 11F

100/

B50

1.25

(0.0

5) a

4.43

(0.9

6) ab

6.02

(2.8

0)11

0.3

(2.5

)10

5.8

(3.4

)54

.44

(34.

83)

18.7

9(0

.03)

b

F50

/B50

1.18

(0.0

1) a,

b4.

26(0

.49)

ab4.

25(1

.50)

131.

3(3

7.1)

127.

0(3

6.6)

59.6

3(2

9.33

)29

.83

(9.1

6) a

B10

00.

97(0

.05)

b3.

54(0

.38)

b3.

41(0

.18)

133.

0(9

.9)

129.

5(9

.5)

47.9

6(2

7.50

)13

.93

(2.0

6) b

P10

01.

24(0

.05)

a,b

15.8

2(4

.60)

5.95

(2.5

2)12

0.8

(17.

3)10

4.9

(21.

9)44

.07

(9.9

7) b

4.28

(2.6

6)

P10

0/B

501.

19(0

.02)

a,b

14.7

1(3

.35)

2.62

(0.3

3)12

2.5

(2.5

)10

7.8

(5.8

)57

.04

(7.3

3) a,

b5.

10(0

.11)

P50

/B50

1.15

(0.0

1) a,

b13

.29

(0.3

8)1.

75(0

.14)

113.

8(2

7.2)

100.

5(2

6.8)

44.0

7(1

8.33

) b

5.60

(2.5

3)

Har

vest

F10

01.

29(0

.05)

a18

.28

(2.2

1)4.

92(0

.53)

169.

8(9

1.0)

151.

5(9

3.2)

36.3

0(1

4.67

) b

5.21

(2.6

9)

F10

0/B

501.

12(0

.01)

a,b

18.5

5(2

.67)

4.32

(1.8

5)18

0.3

(66.

82)

161.

7(6

4.2)

72.5

9(7

.33)

a5.

46(0

.07)

F50

/B50

1.13

(0.0

2) a,

b15

.77

(1.6

4)1.

72(0

.34)

157.

5(1

9.8)

141.

7(1

8.2)

25.9

3(8

.49)

c5.

51(1

.02)

B10

01.

01(0

.09)

b12

.87

(1.3

5)1.

94(0

.73)

112.

0(2

.5)

99.1

(1.1

)20

.74

(7.3

3) c

2.60

(2.0

5)

Tim

e2.

505

ns22

3.90

2**

*2.

023

ns1.

584

ns1.

580

ns1.

338

ns64

.741

***

Cro

p4.

162

**2.

770

*1.

335

ns0.

928

ns0.

848

ns0.

810

ns1.

830

ns

Tim

e x

Cro

p0.

981

ns1.

570

ns1.

836

ns0.

494

ns0.

461

ns2.

206

ns1.

490

nsa F

or e

ach

sam

plin

g ti

me

diff

eren

t le

tter

s in

a r

ow i

ndic

ate

sign

ific

ant

diff

eren

ces

amon

g tr

eatm

ents

(T

ukey

’s t

est

at P

< 0

.05)

.b L

evel

s of

sig

nifi

canc

e: *

P <

0.0

5; *

* P

< 0

.01;

***

P <

0.0

01;

ns:

not

sign

ific

ant.


structure of the soil bacterial community was not affected by either crop type or sampling time (data not shown). This finding is clearly evidenced by the DGGE data cluster analysis that showed a large level of similarity (approximately 84%) among the treatments notwithstanding the sam-pling time (Fig. 3); it is also worth noting that pea-based samples were in most cases discriminated from faba bean-based samples.

Aboveground biomass production. – The total aboveground bio-mass production was significantly larger (> 11 t ha-1) in pea-based than in faba bean-based intercropping (Fig. 4). Further insight; in spite of the lower crop density, barley aboveground biomass (approx. 8 t ha-1) was not statistically different in both intercropping and sole crop, except in additive series with faba bean (F100/B50) where a significantly lower value (50% less than sole crop) was observed. Conversely, a signifi-cantly smaller legume biomass production was found in the intercrops when compared to the respective legume sole crop: -42% in the additive and -24% in the replacement design compared with P100, and -43% in the additive and -59% in the replacement design compared to F100 (Fig. 4). Moreover, the legume biomass production behaved oppo-sitely, albeit not significantly, depending on the occurring legume in

Fig. 3. – Dendrogram of hierarchical classification (Pearson’s similarity coefficient, UPGMA clustering method) of banding patterns generated by DGGE analysis of PCR-amplified 16S rRNA gene-coding frag-ments from soil-extracted bacterial community DNA monitored in the crop treatments (B100, P100, P100/B50, P50/B50, F100, F100/B50, F50/B50, bulk reference soil as in Materials and Methods) at three sampling times: sowing (December 2010, 1), barley earing (April 2011, 2) and harvest (June 2011, 3). Scale bar (0-1) indicates the similarity level.


the intercropping system: in pea-based treatments, it was higher in the intercropping system with lower pea plant density (P50/B50), whereas an opposite trend was observed in intercropped faba bean (Fig. 4).

The two-way ANOVA (time x crop) evidenced a significant effect of time, crop type and their interaction on the variability of weed bio-mass data (Fig. 5). Weed aboveground biomass (dry weight basis) was generally smaller in pea-based intercrops when compared to faba bean-based treatments and, above all, to faba bean sole crop. Moreover, there was a significant time x crop interaction. From earing to harvest, dry biomass accumulation increased considerably in B100 (approximately 12-fold increase from 0.069 t ha-1), F50/B50 (ca. 5.2-fold increase from 0.177 t ha-1), F100/B50 (approx. 3.9-fold increase from 0.069 t ha-1), F100 and P50/B50 (approx. 2.5-fold increase from 0.980 and 0.130 t ha-1, respectively). In contrast, it decreased considerably in both P100 and P100/B50, showing the smallest value among all treatments, regard-less of the sampling period (Fig. 5).

Discussion. – In soil, C and N dynamics are functionally inter-connected and the microbial processes driving the nutrient cycling and the organic matter turnover determine soil functioning and expression of soil fertility. In a legume-based intercropping system established in a Mediterranean soil we found that seasonal changes

Fig. 4. – Total aboveground biomass (on a dry weight basis) in crop treatments (B100, P100, P100/B50, P50/B50, F100, F100/B50, F50/B50 as in Materials and Methods) at harvest (June 2011). Error bars represent the standard error of the mean (SE) (n = 4). Asterisks above bars indicate statistically significant differences in total biomass among the treatments (Tukey’s test at P < 0.05). Different letters inside the bars indicate statistically significant variations (Tukey’s test at P < 0.05) for single crop biomass (barley: upper case let-ters; pea: lower case letters; faba bean: underlined lower case letters).


were among the main factors affecting the variability of some soil chemical and biochemical variables. Indeed, this cannot be consid-ered an unexpected result because it is largely known that soil prop-erties are highly sensitive to seasonal fluctuations, especially over the short-term. However, we also observed that some biochemical variables were able to detect changes in C and N-cycling induced by differing crop treatments and these variables were combined to estimate the soil fertility status.

Soil C and N pools and biological fertility status. – The increase of easily mineralizable soil C pools observed in mixed cropping systems, especially at an advanced growth stage, confirms that legumes are able to release into the rhizosphere a large amount of readily mineralizable organic compounds (Jensen, 1996), which stimulate the microbial activity and growth, promote the release of soluble nutrients and affect the plant growth (Hauggaard-Nielsen et al., 2007). Multivariate analysis of soil properties evidenced that several soil C-related bio-chemical variables and some labile N pools were markedly responsive to the differing cropping systems; suggesting that quantity and composi-tion of rhizodeposits released into the soil by pea, faba bean or barley root systems selectively affected the microbial C-use efficiency in the

Fig. 5. – Total aboveground biomass (on a dry weight basis) of weeds in crop treatments (B100, P100, P100/B50, P50/B50, F100, F100/B50, F50/B50 as in Materials and Methods) at two sampling times: barley ear-ing (April 2011) and harvest (June 2011). Error bars represent the standard error of the mean (SE) (n = 4). Significant effects of time, crop treatment and their interaction are presented as F-values and level of signifi-cance (* P < 0.05, ** P < 0.01, *** P < 0.001) estimated by a two-way ANOVA (time x crop). Within each sampling time, different letters indicate significant differences among the treatments (Tukey’s test at P < 0.05).


order barley > pea > faba bean. Moreover, the differing amount of labile C pools found under differing crop combinations would indicate that the amount of root-derived C substrates was dependent on crop species (precisely faba bean > pea > barley), according to what observed by many authors (e.g. Mayer et al., 2004). This finding would suggest that the input of C into the soil via rhizodeposition stimulated the microbial activity rather than growth, and this effect was more pronounced under faba bean than pea-based intercrops. Surprisingly, the leguminous inter-crops were found to influence both soil C and N pools, which appeared slightly increased at the end of the growing season. We hypothesize that given the low content of organic C occurring in the arable soil then the labile C-flow entering the soil at the rhizosphere level is likely to alter, at least temporarily, the C (and N) pool. Indeed, this C input is transient in soil, and may quickly become mineralized over the following months after crop harvest as suggested by the increased Rbas and potentially min-eralizable C (C0). Nevertheless, in the Mediterranean environment high temperatures promote the metabolic activities of the microbial commu-nity, resulting in enhanced decomposition rates of soil organic matter (Moscatelli et al., 2007). The mineralization of N-enriched residues entering the soil has the potential to increase selectively the available-N pool, as evidenced by the increased inorganic N (especially ammonium) found in legume-based treatments. Nevertheless, this latest finding is in line with what previously observed (Jensen, 1996; Hauggaard-Nielsen et al., 2009a,b; Pappa et al., 2011). On the other side, the low nitrate content found in pea-barley intercropping may also suggest that soil N resources were more efficiently exploited in the mixed cropping system especially by barley plants whose biomass production in mixed cropping systems (at 150 plants m-2) was as large as in barley sole crop (at 300 plants m-2).

To assess the impact of the differing cropping systems on the soil fertility status we calculated the synthetic IBF. It allows classify the bio-logical fertility of a soil in five categories, ranging from low (class I) to high (class V) fertility level (Benedetti et al., 2006). It was found that the IBF was not responsive to the differing crop treatments, except in the case of two faba bean-based combinations (F100 and F50/B50) where a transient shift towards the upper class value (class IV, good fertility) was noted. This finding may confirm the greater ability of faba bean root system to affect the microbial activities through rhizodeposits. This brings also to the conclusion that changes in the biological fertility level induced by legume-based cropping systems under rain-fed conditions


were negligible, at least over the short-term. Thus H2 cannot be con-firmed. It also noteworthy that the calculated IBF appeared in all cases overestimated in relation to the low values of singly measured soil vari-ables. According to the criterion for the calculation of the IBF value and the estimate of the fertility class, we noticed that the minimum threshold of class I ranging from zero to six can never be achieved, unless very unfertile soils are being investigated. In our view, a greater reliability of estimates of the IBF can be achieved after having critically revised the class limits rating the five fertility levels.

Bacterial community structure. – These functional changes in soil-C and, to a lesser extent, soil-N dynamics did not appear linked to compo-sitional shifts in the bacterial community structure. In fact, the DGGE data evidenced that the arable soil was conducive to the dominance of particular bacterial ribotypes, which only slightly changed under the dif-ferent crop type. Taken together, these findings suggest that the native bacterial community was fairly resilient to the differing crop treatments and there was no consistent relationship between bacterial community structure and functional properties. Indeed, soil type, plant type and soil management practices have been identified as major determinants in shaping the microbial community structure in soil (Gelsomino and Azzellino, 2011; Kuramae et al., 2011). Even though plants can affect microbial communities in soil – especially in the root vicinities where evidence of the so-called rhizosphere effect becomes remarkable – in our case the plant effect was negligible; or, at least, not notice-able yet after a 1-year cultivation with intercropped legumes. Instead, this result is in line with what reported by Wang et al. (2007), who found that the effects of intercropping on the microbial community composition in the rhizosphere are variable and plant genotypes may maintain their native microbial community. Then, the driving effect of soil physical-chemical properties as major determinants affecting the ecology of microorganisms in soil appears confirmed. Yet, Cookson and colleagues (2006) observed that in water-limited and semi-arid environments farm management had no effect on bacterial community structure, but significantly affected C and N cycling; and especially in these agroecosystems moisture was considered the dominant factor con-trolling microbial activity and composition. Then, we argue that recently introduced cropping systems (as for instance legume-based intercrop-ping) may have represented only a “relatively mild” disturbance in a Mediterranean arable soil, which is characterized by thermic and xeric regimes, and then compositional shifts in soil bacterial community are


primarily dominated by the soil type and severe climatic conditions. To sum up, our first working hypothesis (H1) was partially confirmed: through rhizodeposits, legume-based systems slightly altered the active C pools and C-use efficiency, whereas most N-related variables and bac-terial community structure remained unaffected. Prolonged observations over the long-term have been already planned in order to i) investigate changes in the C-use efficiency under repeated legume-based cropping systems, and ii) reveal whether and to what extent legume-based crop-ping systems are capable of causing compositional changes at least in selected groups of the bacterial community.

Productive response of cropping system. – Aboveground biomass of intercropped legumes was always significantly lower than in sole crop. A markedly reduced production was also observed by Caporali

Table 4. – Principal component analysis (PCA) of 17 soil chemical and biochemical properties measured during the growing season. PC loading variables (values ≥ |0.60| are in bold) and percent of total variance explained by the first four factors are reported. Soil variables are as described in Materials and methods.

Soil variable PC1 PC2 PC3 PC4

qCO2/Corg -0.84 0.27 0.10 0.11

qCO2 -0.84 0.28 0.09 0.11

MBC 0.82 0.02 0.08 0.51

MBC/Corg 0.82 -0.03 0.02 0.54

NH4+-N 0.69 0.11 0.28 -0.33

PMN -0.60 0.18 -0.31 0.60

Rbas 0.05 0.90 0.37 0.03

qM 0.06 0.85 0.34 0.09

C0 0.19 0.74 0.27 -0.13

TSN 0.30 0.54 -0.76 -0.15

EON 0.20 0.53 -0.81 -0.11

pH -0.32 -0.25 0.33 0.31

MBN -0.23 -0.02 -0.20 -0.48

Nt 0.17 0.25 0.40 0.03

EC1:2 0.12 0.16 0.45 0.38

Corg 0.11 0.46 0.35 -0.39

NO3--N 0.04 0.32 -0.37 0.34

Variance explained (%) 34.17 25.95 16.00 9.97


et al. (1998) when grain pea was intercropped with wheat (-24%) or barley (-30%) under semi-arid condition. Then, the lower yield response of intercropping system compared to pure cultures has prob-ably been due to water shortage occurring after flowering when pod filling stage occurred. This environmental stress might have limited the positive effects of the complementary use of mineral nitrogen in the soil by the legume-cereal intercropping system. Furthermore, an increased sensitivity to intraspecific interactions was also observed in intercropped pea (whose productivity appeared strongly influenced by the plant density in mixture), whereas it was not noticed in inter-cropped faba bean. These findings, however, were remarkably coun-terbalanced by the benefits provided by the legumes, particularly pea, to the intercropped barley that showing a resulted similar biomass production among treatments, revealed a strong competition with legumes. These results are consistent with those obtained in previous studies carried out under a different climatic condition (Haymes and Lee, 1999). According to Bedoussac and Justes (2010), the greater interspecific competitive ability of barley could be explained by a fast-er emergence process, a higher crop growth rate, an efficient sunlight interception, and by facilitative root interactions within intercropped species, especially in nutrient poor soils (Hauggaard-Nielsen and Jensen, 2005). Haymes and Lee (1999) also noticed that in faba bean-wheat intercropping at maximum canopy development the sole wheat crop intercepted significantly less light (85%) than faba bean and intercropped treatments (both 94%). In this experiment the higher light interception efficiency in pea-based intercropping compared with respective sole crop and consequently higher light exclusion by canopy has resulted in effective weed suppression by intercropping system. The weed control of intercropping systems was influenced by the level of competitive ability expressed by pea and faba in the different mix-tures. This specifically resulted in a different closing speed of the can-opy and in an efficient architecture to intercept solar radiation. These two characteristics were found in pea-barley intercropping in which the presence of the barley has favoured the development in height of the pea with the result of obtaining a dense canopy with a high light extinction power to the ground. In contrast, slower vegetative growth rate and more erect habit of faba bean have postponed the closing of the canopy in faba-barley intercropping resulting in a greater avail-ability of light for the growth of weeds. Needless to say, weed control by intercropping might also be affected by allelopathic interactions,


which are known to play an important role at the level of rhizosphere (Bhowmik and Inderjit, 2003). In both legume-barley intercropping, the additive design was more effective than the replacement design to weed control until the maturity of crops. Our results are in agreement with what has been found by several authors (Hauggaard-Nielsen et al., 2001; Corre-Hellou et al., 2011) confirming that the biologi-cal control of weeds by intercropping system is an effective ecological service. Taken together these results indicate that the legume’s ability to provide agroecological services in Mediterranean agroecosystems should be considered in relation to a specific crop combination, thus bringing to the conclusion that our third working hypothesis (H3) can-not be generalized.

Conclusions. – In Mediterranean croplands where the decline of organic matter content under the critical threshold of 2% is becoming a major process of land degradation (Zdruli et al., 2004), leguminous intercrops could provide yield stability and help maintain adequate soil fertility levels. Results from the present study indicate that pea-barley intercropping had the potential to provide a more stable yield (in terms of total aboveground biomass) and a greater control of weed infestation. This was particularly true in the replacement design (P50/B50) where the interspecific complementarity provided a better yield performance and allowed a reduced N fertilizer use on winter cereal. However, at the belowground level, the soil fertility status was not significantly increased, and a few soil C and N pools were affected during the crop-ping season. Since soil responses become detectable over the long-term, the field experiment is presently being run using a combination of chemical fertiliser and organic cropping conditions for improving soil fertility and preserving organic matter.

Acknowledgements. – Authors thank “ARSSA” (Regione Calabria) for providing site access and field technical support. The EU research programme Legume Futures (LF) financially supported the research under the 7th Framework Programme (FP7-KBBE-2009-3, pro-posal no. 245216). D. Tortorella was supported by a LF post-doc grant. A. Scalise’s PhD grant was co-financed by the European Social Fund (POR Calabria FSE 2007/2013, Asse IV Capitale Umano, Obiettivo Operativo M.2), and by the Regione Calabria (Dipartimento 11).


REFERENCES

Bedoussac L. and Justes E.: Dynamic analysis of competition and complementarity for light and N use to understand the yield and the protein content of a durum wheat-winter intercrop. Plant Soil 330, 37-54 (2010).

Benedetti A., Dell’abate M.T., Mocali S. and Pompili L.: Indicatori microbiologici e biochimici della qualità del suolo. In: Atlante di Indicatori della Qualità del Suolo (CRA-Istituto Sperimentale per la Nutrizione delle Piante, ed.). Edizioni Delta Grafica, Città di Castello, Perugia (2006).

Bhowmik P.C. and Inderjit: Challenges and opportunities in implementing allelopathy for natural weed man-agement. Crop Prot. 22, 661-671 (2003).

Brookes P.A. and Joergensen R.G.: Microbial biomass measurements by fumigation-extraction. In: Microbiological Methods for Assessing Soil Quality (J. Bloem, D.W. Hopkins and A. Benedetti, eds.). CABI Publishing, Wallingford, UK, pp. 77-83 (2006).

Caporali F., Campiglia E., Paolini R. and Mancinelli R.: The effect of crop species, nitrogen fertilization and weeds on winter cereal/pea intercropping. Ital. J. Agron. 2, 1-8 (1998).

Cookson W.R., Marschner P., Clark I.M., Milton N., Smirk N.M., Murphy D.V., Osman M., Stockdale E.A. and Hirsch P.R.: The influence of season, agricultural management, and soil properties on gross nitro-gen transformation and bacterial community structure. Aust. J. Soil Res. 44, 453-465 (2006).

Corre-Hellou G., Dibet A., Hauggaard-Nielsen H., Crozat Y., Gooding M., Ambus P., Dahlmann C., von Fragstein P., Pristeri A., Monti M. and Jensen E.S.: The competitive ability of pea-barley inter-crops against weeds and the interactions with crop productivity and soil N availability. Field Crops Res. 122, 264-272 (2011).

Drinkwater L.E., Cambardella C.A., Reeder J.D. and Rice C.W.: Potentially mineralizable nitrogen as an indicator of biologically active soil nitrogen. In: Methods for Assessing Soil Quality (Doran J.W., Jones A.J., eds.). Soil Science Society of America, Special Publication 49, Madison, WI, pp. 217-229 (1996).

Gelsomino A. and Cacco G.: Compositional shifts of bacterial groups in a solarized and amended soil as deter-mined by denaturing gradient gel electrophoresis. Soil Biol. Biochem. 38, 91-102 (2006).

Gelsomino A. and Azzellino A.: Multivariate analysis of soil: microbial biomass, metabolic activity, and bacterial-community structure and their relationships with soils depth and type. J. Plant Nutr. Soil Sci. 174, 381-394 (2011).

Hauggaard-Nielsen H. and Jensen S.E.: Facilitative root interactions in intercrops. Plant Soil 274, 237-250 (2005).

Hauggaard-Nielsen H., Ambus P. and Jensen E.S.: Interspecific competition, N use and interference with weeds in pea-barley intercropping. Field Crops Res. 70, 101-109 (2001).

Hauggaard-Nielsen H., Ambus P. and Jensen E.S.: The comparison of nitrogen use and leaching in sole cropped versus intercropped pea and barley. Nutr. Cycl. Agroecosys. 65, 289-300 (2003).

Hauggaard-Nielsen H., Andersen M.K., Jørnsgaard B. and Jensen E.S.: Density and relative frequency effects on competitive interaction and resource use in pea-barley intercrops. Field Crops Res. 95, 256-267 (2006).

Hauggaard-Nielsen H., Jørnsgaard B., Kinane J. and Jensen E.S.: Grain legume-cereal intercropping: The practical application of diversity, competition and facilitation in arable and organic cropping systems. Renew. Agr. Food Syst. 23, 3-12 (2007).

Hauggaard-Nielsen H., Mundus S. and Jensen S.E.: Nitrogen dynamics following grain legumes and sub-sequent catch crops and the effect of succeeding cereal crop. Nutr. Cycl. Agroecosys. 84, 281-291(2009a).

Hauggaard-Nielsen H., Gooding M., Ambus P., Corre-Hellou G., Crozat Y., Dahlmann C. et al.: Pea-barley intercropping and short-term subsequent crop effects across European organic cropping conditions. Nutr. Cycl. Agroecosys. 85, 141-155 (2009b).

Haymes R. and Lee H.C.: Competition between autumn and spring planted grain intercrops of wheat (Triticum aestivum) and field bean (Vicia faba). Field Crops Res. 62, 167-176 (1999).

Jensen E.S.: Rhizodeposition of N by pea and barley and its effect on soil N dynamics. Soil Biol. Biochem. 28, 65-71 (1996).

Knudsen M.T., Hauggaard-Nielsen H., Jornsgard B. and Jensen E.S.: Comparison of interspecific competi-tion and N use in pea-barley, faba bean-barley and lupin-barley intercrops grown at two temperate locations. J. Agr. Sci. 142, 617-627 (2004).

Kuramae E.E., Yergeau E., Wong L.C., Pijl A.S., van Veen J.A. and Kowalchuk G.A.: Soil characteristics more strongly influence soil bacterial communities than land-use type. FEMS Microbiol. Ecol. 79, 12-24 (2011).

Liebman M. and Dyck E.: Crop rotation and intercropping strategies for weed management. Ecol. Appl. 3, 92-122 (1993).

Mayer J., Bruegger F., Jensen E.S., Schloter M. and Hess J.: Turnover of grain legume N rhizodeposits and effect of rhizodeposition on the turnover of crop residues. Biol. Fert. Soils 39, 153-164 (2004).


Mocali S., Dentice A., Marcucci A. and Benedetti A.: The impact of post-harvest treatments of transgenic eggplant residues on soil quality and microbial diversity. Agrochimica 53, 296-307 (2009).

Moscatelli M.C., Di Tizio A., Marinari S. and Grego S.: Microbial indicators related to soil carbon in Mediterranean land use systems. Soil Till. Res. 97, 51-59 (2007).

Öhlinger R.: Soil respiration by titration. In: Methods in Soil Biology (Schinner F., Öhlinger R., Kandeler E., Margesin R., eds.). Springer, Berlin, Germany, pp. 95-98 (1995).

Pappa V.A., Rees R.M., Walker R.J., Baddeley J.A. and Watson C.A.: Nitrous oxide emissions and nitrate leaching in an arable rotation resulting from the presence of an intercrop. Agric. Ecosys. Environ. 141, 153-161 (2011).

Smalla K., Oros-Sichler M., Milling A., Heuer H., Baumgarte S., Becker R., Neuber G., Kropf S., Ulrich A. and Tebbe C.C.: Bacterial diversity of soils assessed by DGGE, T-RFLP and SSCP finger-prints of PCR-amplified 16S rDNA gene fragments: Do the different methods provide similar results? J. Microbiol. Meth. 69, 470-479 (2007).

Soil Survey Staff: Keys to Soil Taxonomy. USDA-NRCS, Washington D.C., 11th ed. (2010).Sparks D.L.: Methods of Soil Analysis, Part 3 – Chemical Methods. SSSA Book Series No. 5. SSSA-ASA,

Madison, WI (1996).Tosti G. and Guiducci M.: Durum wheat-faba bean temporary intercropping: Effects on nitrogen supply and

wheat quality. Eur. J. Agron. 33, 157-165 (2010).van Elsas J.D., Mäntynen V. and Wolters A.C.: Soil DNA extraction and assessment of the fate of

Mycobacterium chlorophenolicum strain PCP-1 in different soils by 16S ribosomal RNA gene sequence based most-probable-number PCR and immunofluorescence. Biol. Fert. Soils 24, 188-195 (1997).

Vandermeer J.: The Ecology of Intercropping. Cambridge University Press, Cambridge, UK (1989).Wang D., Marschner P., Solaiman Z. and Rengel Z.: Belowground interactions between intercropped wheat

and Brassicas in acidic and alkaline soils. Soil Biol. Biochem. 39, 961-971 (2007).Willey R.W.: Intercropping: its importance and research needs. Competition and yield advantage. Part 1. Field

Crop Abstract 32, 1-10 (1979).Zdruli P., Jones R.J.A. and Montanarella L.: Organic Matter in the Soils of Southern Europe. European Soil

Bureau Technical Report, EUR 21083 EN. Office for Official Publications of the European Communities, Luxembourg (2004).

Summary. – Grain legume-cereal intercropping can be of a great interest in Mediterranean croplands where severe climatic conditions and intensive agricultural practices exacerbate the process of soil degradation and decline of soil fertility. This study aimed to investigate the impact of leguminous intercrops in soil dynamics of vari-ous C and N pools and bacterial community structure. A field experiment including grain legume (pea, faba bean)-barley intercropping as compared to the respective sole crops (P100, F100 and B100) was carried out on a sandy clay loam soil in a Mediterranean environment. Row intercrops were established in an additive (P100/B50 or F100/B50) or replacement (P50/B50 or F50/B50) design. Soil chemical, biochemical and ecophysi-ological variables together with shifts in the bacterial community structure by DGGE community fingerprinting were determined at three sampling times over the cropping season. The impact of intercropped legumes upon the soil fertility status was assessed by calculating the IBF. Crop biomass production and weed suppression at the harvest stage were also determined. Results showed that the soil fertility status was not significantly increased and a few soil C and N-related biochemical variables were affected by the legume-based intercropping during the cropping season, suggesting that legume root-derived substrates stimulate microbial mineralization thus leading to an increase of the available-N pool in soil. The bacterial community structure was resilient to crop treat-ments. Pea-barley intercropping in replacement design provided a more stable yield (in terms of aboveground biomass production) and a lower weed infestation.

Chemical and biological responses in a Mediterranean sandy clay loam soil under grain legume-barley...

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