沙漠研究 19-1, 221- 224 (2009 )Journal of Arid Land Studies“Desert Technology IX ”
Refereed Paper

Tunisian South Cultivated Grapevine (Vitis vinifera L.)-Foliar Composition on Major Mineral ElementsSana GHAFFARI1, Nejib HASNAOUI2, Ali FERCHICHI1
Abstract: The diversity of the local grapevine varietal set of Tunisia was remarkably noted inareas of Sfax, Kerkhennah, Gabes, Tozeur, Nafta, Degache, Zarzis and Djerba. Some varieties
showed requested characteristics at the level of the production quality, its lateness and its
fitness to take on tree, and its adaptation to various Tunisian pedoclimatic conditions. In anattempt to explore the genetic diversity of 25 autochthones cultivars of grapevine(Vitis vinifera L.) in South East of Tunisia, the determination of leaves composition on major
mineral elements (Na, K, Ca, Mg, Cu, Zn, Fe and Mn) have permitted to appreciate the geneticvariability inter-cultivars and its structuring in this collection. Data (PCA plot, cluster analysis)
provide that some of these parameters were designed suitable to discriminate significantly few
cultivars (ARCz, KORg, BAKz, MGB, BAK, TON, SAKj…). In fact, the recorded differences
could be due to sever environmental conditions such as: nature of the soil, culture and climaticconditions, irrigation, salinity, position of leaves on the branches, rootstock etc.
Key words: Cluster analysis, Genetic diversity, Leaves mineral nutrients, PCA plot,Vitis vinifera L.

1. Introduction
Several native grapevine (Vitis vinifera L.) genotypes, highly appreciated for their organoleptic
characteristics and commercial potential, are widely cultivated in Tunisia, from the Kroumirie-ogodsmountains (North-West, humid climate) to the desert region of Rjim-maâtoug (South-West, arid climate)(Zoghlami et al., 2001). Developing viticulture requires the conservation of autochthonous varieties thathave evolved several mechanisms enabling them to cope with the local bioclimatic and edaphic conditions(Ben Abdallah et al., 1998).
The survey of the genetic distinctness of Tunisian grapevine cultivars can provide some information
to enhance the evaluation, the conservation and the improvement of this phytogenetic patrimony. The
present study was interested to the mineral composition of leaves to appreciate the genetic diversity and its
structuring among the 25 studied grapevine genotypes.
2. Materials and Methods
2.1. Plant tissue samples and mineral analysis
Adult leaves of 25 local grapevine cultivars(Table 1, Fig. 1) were sampled. A total of 30
Djerbaleaves per cultivar were taken from lower,
[R4]
[R3]
Gabès
middle and upper regions of the plant, bulked
[R1]
N
[R2]
together and transported to the laboratory.
MédenineEW
ZarzisLeaves were taken out from 1, 2 or 3 individuals
S
depending on number of individuals per cultivars
available. The concentrations of the mainFig. 1. Sampling localities of the studied cultivars.
mineral elements (Na, K, Ca, Mg, Cu, Zn, Fe andMn) were determined using an atomic absorption spectrophotometer (SHIMADZU AA 6800). Before theanalysis, leaves were oven-dried for 48 h at 70 ˚C and ground to pass through a 1 mm diameter sieve. Fourgrammes were calcinated for 6 hours at 500 ˚C and digested by adding 5 ml of concentrated HCl and 3 ml
H2O and boiled. Each sample was filtered and brought up to the final volume of 100 ml with deionised water.
12Arid and Oases Cropping Laboratory, Institute of Arid Area of Medenine, 4119 Medenine, Tunisia;
Laboratory of Molecular Genetics, Immunology & Biotechnology, Faculty of Sciences of Tunis, Campus University,
2092 El Manar, Tunis, Tunisia;*Corresponding Author: Fax: +21675633006, Tel: +21675633005, E-mail: sana.ghaffari@yahoo.fr

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2.2. Statistical analysis
In order to show the relationships among the 25 studied grapevine genotypes, the recorded data(mineral levels) were analyzed by numerical taxonomic technique using the procedure of principal
component analysis (Sneath and Sokal, 1973). Eigenvectors and eigenvalues of the first four principal
component axes were calculated on the basis of a similarity correlation matrix. Hierarchical clustering wasthen carried out using Pearson’s correlation coefficients between groups linkage.
Table 1. List of studied grapevine cultivars, their geographical region of production and designation.
Cultivar

OriginDesignation

Beldi
RazzaguiSiper abiadh
Miski
Bazzoul kalba
TounsiMguargueb
Arabi
DaliaAkhal
Akhal tawil
Bazzoul kalba
Razzagui
Miski
ArichaNab jmelMguargueb
SakasliTounsiBazzoul kalba
Miski
MédinaKorkobbi
Mlouhi mkarkeb
Saoudi

IRA (El Fjé-Médenine)
IRA (El Fjé-Médenine)
IRA (El Fjé-Médenine)
IRA (El Fjé-Médenine)
IRA (El Fjé-Médenine)
IRA (El Fjé-Médenine)
IRA (El Fjé-Médenine)
IRA (El Fjé-Médenine)
IRA (El Fjé-Médenine)
IRA (El Fjé-Médenine)
IRA (El Fjé-Médenine)
ZarzisZarzisZarzisZarzisZarzisDjerba
DjerbaDjerbaChénini Gabès
Chénini Gabès
Chénini Gabès
Chénini Gabès
Chénini Gabès
Chénini Gabès

BLD
RAZ
SAB
MIS
BAK
TON
MGB
ARB
DAL
AKH
AKT
BAKz
RAZz
MISz
ARCZNAJz
MGBj
SAKj
TONj
BAKg
MISg
MEDg
KORg
MMKg
SADg

Table 2. Mineral composition (mg/100 g dry weight) of 25 accessions of Tunisian grapevine.
Cultivars

NaKCa

Mg

Cu

Zn

Fe

Mn

BLD
RAZ
SAB
MIS
BAK
TON
MGB
ARB
DAL
AKH
AKT
BAKz
RAZz
MISz
ARCZNAJz
MGBj
SAKj
TONj
BAKg
MISg
MEDg
KORgMMKg
SADg

0.038
0.083
0.089
0.084
0.043
0.093
0.058
0.063
0.060
0.052
0.042
0.154
0.065
0.065
0.057
0.077
0.036
0.032
0.048
0.033
0.051
0.045
0.049
0.014
0.040

1.426
0.746
1.091
1.124
1.178
1.199
1.596
0.867
1.292
0.723
1.512
0.579
0.605
0.660
2.582
0.448
0.437
0.270
0.369
0.973
0.532
0.953
1.428
0.645
0.953

1.618
2.363
0.914
1.087
0.777
0.931
1.297
3.761
2.383
1.517
1.598
3.841
3.626
3.496
1.480
2.371
4.336
2.600
3.707
3.134
2.517
4.679
3.742
2.578
2.750

0.146
0.303
0.147
0.190
0.135
0.119
0.130
0.305
0.233
0.165
0.215
0.309
0.350
0.296
0.277
0.392
0.559
0.230
0.546
0.264
0.335
0.475
0.512
0.275
0.341

0.000300.00033
0.00057
0.000390.000270.000150.000360.000450.000440.000380.000290.000610.001390.001340.00188
0.00088
0.000390.000670.000440.00055
0.000370.000540.001120.000410.000820.00150.00250.0015
0.00260.00200.00110.00140.00350.00200.00270.00190.00200.00220.00210.00180.00160.00130.00060.00160.00210.00170.00240.00390.00140.0022

0.027
0.029
0.026
0.019
0.012
0.049
0.022
0.101
0.087
0.029
0.017
0.107
0.055
0.107
0.033
0.066
0.031
0.033
0.051
0.048
0.057
0.068
0.053
0.020
0.054

0.004
0.011
0.003
0.011
0.002
0.002
0.003
0.010
0.005
0.005
0.003
0.006
0.006
0.007
0.004
0.013
0.009
0.005
0.005
0.004
0.013
0.006
0.005
0.003
0.003

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Observation s (F 1 et F 2 : 55 .12 %)IIIF2 (18.48 %)IaIIIIVIIb

II

F 1 (36.64 %)Fig. 2. Plot of the first and second component scoresfor 25 accessions of Tunisian grapevine basedon mineral content.

Fig. 3. Dendrogram of 25 accessions of Tunisiangrapevine following Pearson’s correlation
coefficients.

3. Results and Discussion
3.1. Mineral composition
Among the 8 parameters studied (Table 2), the calcium represents the major element ranging from0.77 to 4.68 mg/100 g, registered in BAK and MEDg cultivars, respectively. It is followed by thepotassium content. ARCz had the highest content (2.58 mg/100g) and the least proportion (0.27 mg/100g)
had been registered in SAKj. The magnesium content has middling levels, with an average of 0.3 mg/100g.
All the remaining elements had levels less than 0.16 mg/100g, copper was detected on traces.
3.2. Principal component analysis
In order to assess the patterns of variations, Principal Components Analysis (PCA) was done considering
all the 8 variables simultaneously (Fig. 2). The first 4 principal components (PCs) accounted for 82.95% of
the variability amongst the 25 accessions. The first PC (PC1) accounted for 36.64% of the total qualitative
variation and had calcium, magnesium, iron and manganese with high positive and potassium with highnegative coefficients. The second component (PC2) contributed for an additional 18.48% of the total
variation, depicted primarily the patterns of variation in potassium, copper and zinc all having high positivecoefficients. PC3 accounted for 16.60% of the variability among the 25 accessions and described the patternsof variation in sodium and manganese contents, which increased at the expense of calcium and magnesium
since these 2 had negative coefficients. PC4 constituted 11.23% of the variation and had zinc and manganese,both with high positive coefficients, copper and iron with negative signs.
The patterns of divergence between the 25 accessions for the first 2 principal components are given inFigure 1. For ease of understanding the space is divided into 4 quadrants (I–IV) each originating from thepoint of intersection of the lines passing through the respective averages of the component axes. It can bediscerned from the figure that the mineral diversity of the accessions was large. Four accessions (KORg,ARB, MISz and RAZz) occupied the first quadrant that designates positive values of both the components.
The IInd quadrant had three accessions. Opposing on SAB and MGB, ARCz showed the highest positivecoefficient of the IInd component. Three accessions occupied the IVth quadrant. TON and BAK showednegative coefficients of the Ist component. SAKj diverge considerably from the other and had negativecoefficients of the IInd component. However, five accessions occupied the IIIrd quadrant, which
designates positive coefficient of Ist component and negative value of the second (Fig. 2).
3.3. Cluster analysis
The accessions were broadly grouped into 2 clusters (Fig. 3) following Pearson’s correlation coefficients,
whereby the individuals in any one cluster are more closely related than the individuals in different clusters.
The Ist cluster had 17 accessions and can be subdivided into two sub groups Ia and Ib. It was interesting tonote that cluster I contains accessions from Djerba, Zarzis and Gabes in most. Cluster Ia was the largest,included 11 accessions characterised by high content in calcium and magnesium. Cluster Ib contained 6accessions that had most amount of zinc. 8 accessions were included in cluster II that was subdivided into IIa

224and IIb. These accessions are in most from IRA Medenine. BLD, AKT and MIS, in cluster IIa, werecharacterised by moderate amount of all mineral contents. Cluster IIb (TON, MGB, SAB, BAK and ARCz)
was characterized by slight amount of calcium and magnesium and quiet high levels of potassium, ARCz was
the richest cultivar in this latter (Fig. 3).
Dendrogram clustering showed the same accessions grouping displayed by the PCA plot. In fact, the twoclassification methods prove that there is a strong relationship between diversity and geographical origin.
Recorded variability could be explained both with regard to genetically differences and environmental
conditions such as: nature of the soil, culture and climatic conditions, irrigation, salinity, position of leaves on
the branches, rootstock etc. (Bouillet, 1996). Earlier, several works was interested in the study of the origin of
these differences. Among the most reported factors, we noted the effect of the physicochemical compositionof the ground on the plant content of mineral elements (Wallace, 1943).
Indeed plant nutrition depends on soil mineral nutrients, but also on its pH which has a strong influenceon the assimilation of these biogenic salts (Vilain, 1993) and on its fertilization (Christensen, 2005). Anothercomponent of the grounds of extreme importance is water. In fact, any deficiency or excess of water can playa determining role on roots function, culture production and tissue mineral contents (Keller, 2005). The roleof the mode of irrigation has been evidenced on the growth and the mineral nutrition of the grapevine(Paranychianakis et al., 2006). Moreover, the effect of the rootstock on the content of mineral elements has
been reported on many researches (Spring et al., 1999).
4. Conclusions
The Tunisian grapevine accessions studied displayed considerable level of variability for all theminerals. In fact, some of these parameters were designed suitable to discriminate significantly few
cultivars (ARCz, KORg, BAKz, MGB, BAK, TON, SAKj…). PCA plot and cluster analysis have proved
to be an effective method in grouping accessions. In fact, it is important to ascertain the extent of genetic
diversity available in the material for its effective utilization in breeding programs.
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