Original research articles

Multivariate analysis and clustering reveal high morphological diversity in Tunisian autochthonous grapes (Vitis vinifera): insights into characterization, conservation and commercialization

Abstract

Aim: To characterize autochthonous grapevine cultivars from a national germplasm collection, to estimate the phenotypic diversity among and within the cultivars, and to identify the traits that contributed to cultivar heterogeneity.

Methods and results: Seventy major ampelographic descriptors comprising shoot, leaf and fruit traits were investigated to determine the overall degree of polymorphism among 61 autochthonous Tunisian grapevine genotypes. Based on the correlation values obtained between the characters, all descriptors must be considered for the characterization/clustering of the grapes, of which 12 descriptors were identified as the most important. Based on principal component analysis (PCA) and hierarchical cluster analysis (HCA), all cultivars were discriminated and high morphological variation was observed among the accessions. ANOVA demonstrated that most of the morphological variation was found within (89.31 %) rather than between the groups (10.69 %). The Khalt Bouchemma Gabès, Blanc 3 and Blanc 2 genotypes were identified as the barycentres of the groups, representing all the morphological variation observed within autochthonous grapes in Tunisia. These genotypes exhibited all the required characteristics to be introduced into the market and commercialized as table grapes and stand out as the most promising for commercial cultivation.

Conclusion: The detailed ampelographic description presented herein highlighted clear morphological differentiation between Tunisian autochthonous grapevines, investigated for the first time using 70 OIV descriptors, and allowed us for the first time to easily split the Tunisian autochthonous grapevine accessions into wine and table grapes. Numerical analyses showed that the number of morphological traits that are effectively contributing to the characterization of the cultivars could be reduced to 12.

Significance and impact of the study: In this investigation, we highlight the importance of importance of breeding programs, commercialization and evaluation of economically valuable characteristics of the highly diverse autochthonous grapevine cultivars from Tunisia.

Introduction

In Tunisia, viticulture is very ancient and the first historical record of grapevines dates back to 6000 BC (Zohary and Hopf, 2000). Autochthonous grapes are often grown by poor farmers in marginal, low-input and drought-stressed environments. These genetic resources, which may represent valuable reservoirs of interesting genes for crop improvement such as adaptation to biotic and abiotic stresses (Brush, 1995), represent a small population with a high risk of extinction due to the introduction of commercial high-yield foreign varieties (Hjalmarsson and Ortiz, 2000). All these factors contribute to the need for a detailed description and evaluation of the Tunisian grapevine genetic resources.

Initial efforts to identify genetic diversity in Tunisian grapevine cultivars were mainly based on molecular tools (Zoghlami et al., 2001; 2009). Despite the use of molecular markers, knowledge of the phenotype given by morphological and agronomical descriptors is still important for breeding programs, conservation and commercialization of new varieties (Franco et al., 2005; Gonçalves et al., 2008; Laurentin, 2009).

The description of the morphological characteristics is the usual methodology accepted from a legal point of view for patenting and registration of varieties (Badenes, 1991). In fact, ampelography is the first step in grapevine identification and selection and for resolving different classification problems (Martinez de Toda and Sancha, 1997). The complete characterization as well as the conservation of autochthonous cultivars is of great importance to prevent the loss of diversity (Rodrigues et al., 2008).

In the last years, morphological data have been used to resolve the complex problem of the definition and classification of crop accessions using multivariate statistical analyses such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) (Manjunatha et al., 2007; Aghaei et al., 2008). These methods assist in the successful management of plant genetic resources and contribute to the determination of the agronomical value of the accessions in the germplasm collection (bank).

Morphological characterization continues to be the first step for the description and classification of germplasm accessions and statistical methods like PCA are useful tools for screening the accessions of a collection (Badenes et al., 2000). PCA transforms the original variables into a limited number of uncorrelated new variables. As reported by Martínez-Calvo et al. (2008), this method allows the visualization of differences among individuals, the identification of groups, and the identification of relationships among individuals and variables.

Keeping in mind the importance of morphological characteristics in varietal identification and registration, the characterization of the worldwide Vitis materials has been homogenized by the use of standardized OIV/IPGRI descriptors (Soylemezoglu et al., 2001; Santiago et al., 2007). These descriptors allow the inventory of the grapevine genetic resources with passport, primary and secondary descriptors, bibliography and photos.

In this study we provide, for the first time, an inventory of the Tunisian autochthonous grapevine genetic resources using 70 major OIV descriptors (OIV, 2007). By applying a multivariate analysis to these data, we characterized the autochthonous grapevine cultivars from the Tunisian national germplasm collection, estimated the phenotypic diversity among and within the cultivars, and identified the traits that contributed to cultivar diversity.

Materials and methods

1. Plant material

Sixty-one Tunisian autochthonous grapevine genotypes were included in this study (Table 1). They were collected from different parts of the country and are kept at the repository of the Centre of Biotechnology of Borj Cédria, Tunisia (CBBC).

Table 1. List of the 61 Tunisian autochthonous grapevine accessions from the collection of the CBBCa.


No. Cultivars Origin No. Cultivars Origin
1 Asli Hadab Rafraf 32 BKB Gabes Hencha
2 Asli Dar Slimane Rafraf 33 Djebbi Hencha
3 Châaraoui Djebba 34 Kahli Sfax Baddar
4 Hencha H1 Rafraf 35 Musc d'Alexandrie Kerkennah
5 Khamri Tozeur Rafraf 36 Khédhiri 1 Baddar
6 Sakasly Baddar Rafraf 37 Arich Dressé Mornag
7 Muscat Rafraf Déguache 38 BKB Sfax Baddar
8 Farrani Djebba 39 Khalt s1 Kerkennah
9 Bidh el Hamem Sfax Tozeur 40 Sakasly Djerba Rafraf
10 Bahbahi Djebba Balta 41 Tounsi Djerba Kerkennah
11 Chaouche Djerba Tozeur 42 Khédhiri 2 Balta
12 Hamri Kerkennah Rafraf 43 Arich Djerba Rafraf
13 Marsaoui Tozeur 44 Bezzoul el Khadem Rafraf Kerkennah
14 Saouadi S4 Djerba 45 Khédhiri 3 Kerkennah
15 Amokrane Djerba 46 Khalt Abiadh Mornag
16 Beldi Baddar Balta 47 Razaki Rafraf Baddar
17 Kahli Kerkennah Baddar 48 Turky Rafraf
18 Medina Rafraf 49 Beldi Local Rafraf Djebba
19 Sfaxi s2 Djerba 50 Bidh el Hamem Baddar Nafta
20 Blanc 1 Baddar 51 Guelb Sardouk s3 Nafta
21 Arbia Djerba 52 Khalt Bouchemma Gabès Nafta
22 Beldi Rafraf Balta 53 Razegui Djebba
23 Jerbi Déguache Gabes 54 El Biodh Baddar
24 Mahdaoui Mornag 55 Bidh el Hamem Rafraf Djebba
25 Blanc 2 Baddar 56 Khalt Mdaouar Nafta
26 Beldi Sayeb Gabes 57 Balta 1 Sfax
27 Dattier de Beyrout Baddar 58 Balta 4 Sfax
28 Hencha H2 Djebba 59 Balta 2 Djebba
29 Meski local Mornag 60 Akhal Mguergueb Sfax
30 Blanc 3 Gabes 61 Balta 3 Djebba
31 Arich Ahmar Gabes      

aCentre of Biotechnology of Borj Cédria, Tunisia.

2. Data collection

Seventy major morphological traits, selected from the OIV list (OIV, 2007) and comprising shoot (12 descriptors), leaf (39 descriptors) and fruit (19 descriptors) descriptors, were measured and used to design a numbered-data matrix (Table 2). Ten specimens per accession were individually evaluated for their morphological diversity across the 70 descriptors. Measurements were performed by the same two persons to avoid errors due to individual variation.

Table 2. Ampelographic characteristics based on OIV descriptors for the 61 Tunisian autochthonous grapevine cultivars (GENRES-081, 2001; OIV, 2007). From 1 to 61: analyzed cultivars (see Table 1 for accession names).


Organ OIV Code Plant organ Description of the character OIV values for the 61 grapevine accessions
        1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61
SHOOT OIV 001 Young shoot form of tip 7 7 7 5 5 5 7 5 7 7 5 7 7 7 7 5 5 7 7 5 5 5 1 5 5 5 1 7 7 5 7 7 5 1 7 5 1 5 7 5 7 7 5 5 5 1 5 1 5 1 5 5 5 1 1 7 5 5 1 7 5
OIV 003* Young shoot anthocyanin coloration of tip 1 1 1 3 5 1 1 1 5 3 1 1 1 3 5 1 3 1 1 5 5 1 3 1 3 3 3 1 1 3 3 3 3 5 1 3 3 1 3 3 5 1 5 3 1 3 3 1 1 7 1 1 1 1 5 7 1 1 3 3 1
OIV 004* Young shoot density of prostrate hairs on tip 5 5 3 3 3 5 1 3 5 3 7 5 3 1 1 3 3 1 5 1 1 7 1 5 1 7 1 5 5 5 3 5 3 1 7 5 1 1 5 1 5 3 1 3 7 1 1 7 5 3 7 5 7 5 3 3 5 7 3 5 5
OIV 006 Shoot attitude (habit) 3 3 5 1 3 1 3 3 3 3 1 3 3 3 1 1 1 5 3 3 3 1 3 3 1 1 1 1 3 5 3 1 3 5 1 5 1 1 3 3 1 5 3 3 3 3 1 3 3 1 5 5 3 1 1 3 3 5 3 1 3
OIV 015-2* Shoot intensity of anthocyanin coloration on the bud scales 1 1 1 1 5 5 1 1 1 1 3 1 1 1 5 1 1 1 1 3 1 1 1 1 5 1 1 3 1 1 1 3 3 1 1 1 1 1 1 7 3 1 1 1 1 1 1 3 1 1 5 5 1 1 1 3 1 1 1 5 1
OIV 015* Shoot distribution of the anthocyanin coloration on the bud scales 1 1 1 1 5 5 1 1 1 1 5 1 1 1 5 1 1 1 1 5 1 1 1 1 5 1 1 5 1 1 1 5 5 1 1 1 1 1 1 9 5 1 1 1 1 1 1 5 1 1 9 5 1 1 1 5 1 1 1 5 1
OIV 155 Shoot fertility of basal buds (basal buds 1-3) 1 1 1 1 1 5 1 1 1 1 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 1 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 1 1 1 1 1 5 5 1 5
OIV 008 Shoot color of ventral side of internodes 2 2 2 2 2 2 2 1 2 2 2 2 2 1 2 1 1 1 1 2 1 1 2 1 2 2 2 2 2 1 3 2 2 1 2 2 2 1 2 2 1 2 2 3 1 2 1 1 2 1 2 5 1 2 1 1 2 1 2 2 1
OIV 007 Shoot color of dorsal side of internodes 2 2 2 3 2 1 3 1 2 1 1 2 2 2 2 1 1 2 2 2 1 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 3 1 1 2 1 2 2 1 2 5 2 2 2 2 2 2 2 2 2
OIV 016 Tendril number of consecutive tendrils 1 1 1 1 1 1 2 2 1 1 2 2 2 2 1 1 1 1 2 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 1 1 2 1 1 1 2 1 2
OIV 017 Tendril length of tendrils 3 3 3 5 3 3 5 3 3 5 7 3 3 3 3 3 3 3 3 1 3 3 3 3 1 1 7 3 3 3 3 3 1 1 3 5 3 5 3 3 3 5 3 3 3 3 3 3 3 3 3 5 1 3 1 3 3 5 3 3 3
OIV 301 Bud burst time of bud burst 1 1 1 1 7 5 1 3 1 3 3 1 1 1 1 1 1 1 1 1 7 1 1 1 1 3 3 3 5 5 1 1 1 3 3 3 5 7 5 5 3 3 5 7 5 5 5 1 5 5 3 5 5 5 5 5 5 5 5 5 5
LEAF OIV 051* Young leaf colour of upper surface 3 3 2 4 1 4 1 1 1 2 3 1 1 1 4 2 4 3 1 4 3 2 4 1 3 2 3 1 3 2 3 4 4 3 3 2 3 3 3 4 3 2 3 4 3 3 3 3 2 3 4 5 3 2 3 4 4 3 2 4 3
OIV 053 Young leaf density of prostrate hairs between veins on lower side of leaf 5 3 1 1 1 1 1 1 1 3 9 1 1 1 1 5 3 1 1 1 1 5 1 1 1 9 1 5 1 3 1 1 5 1 1 5 1 1 3 1 5 1 1 1 3 1 1 7 3 1 3 5 9 1 1 1 1 3 3 1 3
OIV 054 Young leaf density of erect hairs between veins on lower side of leaf 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 1 1 1 1 1 1 1 1 1
OIV 056 Young leaf density of erect hairs on veins 1 1 3 1 3 1 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 1 1 1 1 1 1 1 5 1 5 1 1 1 1 1 1 1 1 1
OIV 055 Young leaf density of prostrate hairs on veins 3 3 1 1 1 1 1 1 1 1 5 1 1 1 1 3 1 1 1 1 1 7 3 3 1 7 1 1 1 3 1 1 3 1 7 5 1 1 5 1 3 1 1 1 5 1 1 5 3 1 3 5 7 3 1 1 3 3 1 1 3
OIV 067 Mature leaf shape of blade 3 1 2 3 3 1 3 5 2 2 1 2 3 2 3 3 2 2 3 2 3 3 1 3 3 3 3 1 2 2 3 2 2 2 2 2 4 3 2 2 2 3 2 2 3 3 2 3 3 3 2 5 2 3 2 2 2 1 3 2 2
OIV 068 Mature leaf number of lobes 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 4 2 2 2 2 2 3 2 2 4 3 2 2 4 2 3 4 2 2 2 3 4 3 4 3 2 4 2 2 2 2 2 3 5 2 2 2 3 4 4 2 2 2
OIV 070 Mature leaf anthocyanin coloration of main veins on blade upper side 1 1 1 2 5 3 2 3 1 4 3 5 1 3 4 1 3 2 1 4 4 5 1 4 5 2 1 1 2 3 5 2 5 2 4 5 2 2 1 1 1 1 3 2 1 3 2 1 1 1 4 5 3 5 1 3 2 1 1 1 1
OIV 074 Mature leaf profile 4 4 4 1 5 4 1 1 5 1 1 1 1 4 1 1 1 1 1 4 4 4 1 4 1 1 3 4 4 1 1 4 3 4 5 3 4 1 4 1 4 1 1 3 4 1 3 3 1 1 3 5 4 1 3 1 1 1 1 1 1
OIV 075 Mature leaf blistering of upper side 3 1 1 1 3 5 3 1 1 1 3 3 1 3 3 1 1 1 1 7 3 3 1 3 3 3 3 1 1 3 1 1 3 1 1 3 1 1 1 1 1 1 1 1 1 1 3 1 1 3 3 5 3 1 3 1 1 1 1 1 1
OIV 76 Mature leaf shape of teeth 2 2 3 2 2 2 2 4 2 2 5 2 2 2 2 1 2 2 2 2 2 4 2 3 2 3 3 4 5 2 3 2 3 3 2 3 3 2 3 4 2 4 4 2 3 3 3 2 2 2 2 5 3 3 4 3 2 2 5 4 2
OIV 79* Mature leaf general shape of petiole sinus 2 2 1 2 1 3 2 1 1 2 4 2 1 2 2 1 1 2 2 2 2 2 1 2 2 3 2 3 2 2 2 1 2 2 2 2 2 2 2 2 2 2 3 1 4 2 2 1 4 1 1 5 1 2 1 1 2 1 2 6 1
OIV 79-1* Mature leaf opening/overlapping of petiole sinus 3 3 3 3 1 5 3 1 1 3 5 3 1 3 5 1 1 3 3 3 5 3 1 3 5 7 3 3 3 5 5 1 5 3 3 3 3 3 3 3 3 3 3 3 5 3 3 3 7 3 3 5 3 5 3 1 3 1 3 7 1
OIV 80 Mature leaf shape of base of petiole sinus 1 1 1 3 1 2 1 1 1 1 3 1 1 3 1 1 2 1 3 3 3 1 1 3 3 3 3 1 1 3 3 1 3 3 1 1 1 3 1 2 2 2 2 3 2 3 1 3 3 3 1 5 1 1 3 2 2 3 2 3 2
OIV 81-1 Mature leaf tooth at petiole sinus 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 1 1 1 1 1 2 1 1 1
OIV 81-2 Mature leaf petiole sinus limited by veins 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 5 1 1 1 1 1 1 1 1 1
OIV 83-2 Mature leaf presence of teeth at the base of the upper leaf sinuses 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 1 1 1 1 1 1 2 1 1
OIV 601 Mature leaf length of vein N1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 5 3 3 3 3 3 3 3 1 3
OIV 602 Mature leaf length of vein N2 3 3 3 3 5 3 3 3 5 5 5 3 5 3 3 1 3 5 3 3 3 3 3 3 3 3 3 3 3 3 3 5 1 3 3 3 3 3 1 1 3 3 3 3 5 3 3 3 3 3 3 5 3 3 3 5 5 5 3 1 3
OIV 603 Mature leaf length of vein N3 3 3 3 3 5 3 5 5 5 5 5 3 5 3 5 1 3 5 5 3 3 5 3 5 3 3 5 3 5 3 3 5 3 3 5 3 3 3 3 5 3 3 3 3 5 3 5 3 3 5 3 5 3 3 3 5 5 5 3 3 5
OIV 604 Mature leaf length of vein N4 5 5 5 5 7 7 7 7 7 7 7 5 7 5 7 1 1 3 5 5 5 7 1 7 5 5 7 3 5 5 5 7 3 5 5 5 3 5 5 5 5 3 5 5 3 5 5 3 7 5 5 5 5 5 5 3 3 1 1 3 5
OIV 605 Mature leaf length from petiole sinus to upper leaf sinus 3 3 3 1 7 1 3 5 7 3 3 3 1 3 1 1 1 3 3 3 3 5 1 5 3 3 5 1 3 3 3 3 3 3 3 3 5 5 3 1 1 3 3 3 5 5 3 3 3 3 1 5 3 3 3 5 3 1 5 3 3
OIV 606 Mature leaf length from petiole sinus to lower leaf sinus 3 3 3 3 5 1 3 5 3 3 3 7 1 3 1 1 1 3 5 1 1 3 1 5 3 3 5 3 5 3 3 3 3 1 3 3 5 3 1 1 1 5 3 3 3 3 3 3 3 3 1 5 3 3 3 5 3 3 3 3 5
OIV 607 Mature leaf angle between N1 and N2 measured at the first ramification 5 5 5 7 3 7 5 5 3 5 5 5 3 5 3 1 3 3 7 5 5 5 5 7 5 7 3 3 7 7 7 3 7 5 5 5 5 5 3 5 5 5 5 5 7 7 5 3 7 3 3 5 3 5 5 5 3 5 5 5 3
OIV 608* Mature leaf angle between N2 and N3 measured at the first ramification 5 5 5 3 3 7 5 3 3 5 5 5 5 5 3 1 5 3 5 7 7 7 5 7 5 7 7 7 5 7 7 3 7 5 3 7 5 7 3 5 5 5 5 7 5 5 3 5 7 5 5 5 7 5 5 5 3 3 5 7 3
OIV 609 Mature leaf angle between N3 and N4 5 7 5 7 3 5 7 3 5 5 5 5 5 5 5 1 5 5 5 5 5 5 3 5 5 7 3 7 5 5 7 5 7 7 3 7 7 5 5 7 7 7 3 5 7 5 3 5 5 7 7 5 5 7 7 3 5 5 5 5 3
OIV 610 Mature leaf angle between N3 and the tangent between petiole point and N5 tooth tip 7 9 3 7 9 7 7 5 7 5 9 9 5 7 9 1 7 7 7 7 5 7 5 7 7 7 9 7 7 9 9 9 9 7 7 7 9 7 9 5 9 7 7 5 7 7 7 7 7 7 7 5 7 9 7 3 7 7 7 9 7
OIV 612 Mature leaf length of teeth N2 3 3 3 5 5 3 3 1 5 3 1 3 3 3 1 1 5 5 1 3 5 3 3 1 3 1 3 5 3 1 3 3 1 3 3 3 3 3 1 1 5 3 3 3 3 3 3 1 1 1 3 5 1 3 3 3 5 3 1 1 1
OIV 613* Mature leaf width of teeth N2 5 3 3 5 5 5 3 3 5 3 3 3 3 1 3 1 3 3 1 3 5 3 3 1 3 3 5 7 5 3 3 3 3 3 3 3 3 3 1 5 5 3 3 3 5 3 3 3 3 3 1 5 1 3 3 3 5 3 3 3 3
OIV 614 Mature leaf length of teeth N4 1 1 3 3 3 3 5 5 3 3 1 1 3 1 3 1 3 1 1 3 1 1 1 1 3 1 3 3 1 1 1 3 1 3 3 1 3 3 1 1 1 1 1 3 3 3 1 1 1 1 1 5 1 1 3 1 1 1 1 1 1
OIV 615 Mature leaf width of teeth N4 3 3 3 3 3 5 5 3 3 3 3 3 3 1 1 1 3 1 1 3 1 3 1 1 1 3 3 5 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 5 3 3 3 3 3 1 3 3 3
OIV 616 Mature leaf teeth number between tooth tip and tooth tip of N2 first secondary vein 5 3 3 5 7 3 5 3 5 5 7 7 3 5 5 1 5 3 9 5 7 7 5 3 7 3 3 5 3 3 5 5 5 5 3 5 7 5 5 3 3 3 3 3 5 7 5 5 5 3 3 5 5 3 9 3 5 5 5 3 5
OIV 617 Mature leaf length between N2 tooth tip and tooth tip of first secondary N2 vein 3 3 5 5 3 5 5 3 5 5 5 3 3 3 3 1 5 3 7 3 5 5 3 3 3 3 3 5 5 3 3 5 5 3 3 5 3 3 3 3 3 5 3 3 5 3 3 3 3 3 3 5 3 3 3 3 5 5 3 3 3
OIV 066-5 Mature leaf vein N3, length from petiole sinus to vein N4 3 3 5 3 7 3 5 5 5 5 3 3 5 3 3 1 3 1 5 3 5 3 5 3 5 5 3 1 3 3 3 3 3 3 5 3 3 3 3 1 3 3 3 3 5 5 3 5 5 3 3 5 3 3 3 7 1 5 3 1 3
OIV 066-4 Mature leaf length of vein N5 1 3 1 1 3 1 1 1 1 3 3 3 1 3 3 1 1 3 3 3 1 3 1 3 1 1 3 3 1 3 3 1 1 1 3 1 1 1 1 1 1 1 3 1 3 3 3 3 3 1 3 5 3 3 3 1 3 1 3 3 3
OIV 084* Mature leaf density of prostrate hairs between main veins (lower side) 5 5 1 1 1 5 1 1 1 1 7 1 1 3 1 1 1 1 3 1 1 5 1 3 1 5 1 3 1 3 1 1 5 1 3 3 1 1 1 3 1 5 1 1 1 1 1 1 5 1 3 5 1 1 1 1 1 1 1 1 1
OIV 087 Mature leaf density of erect hairs on main veins (lower side) 5 7 3 1 3 7 1 3 5 1 5 3 1 1 5 1 3 1 3 1 1 3 3 1 1 1 1 7 1 1 1 3 7 1 3 3 1 1 1 5 1 3 1 1 1 3 1 1 3 1 5 5 3 1 1 3 3 1 3 3 1
OIV 452 Mature leaf degree of resistance to Plasmopara 9 7 9 7 9 3 3 7 7 1 1 5 5 7 5 1 1 5 5 7 5 1 1 5 1 1 5 7 5 5 5 5 1 7 5 1 9 1 1 5 7 7 5 3 5 5 5 5 5 5 7 5 5 5 3 5 5 5 5 3 7
OIV 455 Mature leaf degree of resistance to Oidium 5 7 9 9 9 9 9 9 9 3 1 9 9 5 9 1 7 9 9 5 7 3 7 5 7 1 5 7 7 5 9 9 7 5 7 9 9 5 3 5 7 7 9 9 5 7 9 7 9 9 5 5 7 3 9 5 7 5 7 9 7
FRUIT OIV 151 Inflorescence sex of flower 3 3 4 3 3 3 3 3 3 3 4 3 3 3 3 1 4 3 4 4 3 3 4 3 4 3 3 3 3 3 3 3 3 4 3 3 3 3 3 4 3 3 3 4 4 4 3 4 3 4 3 5 3 3 4 4 3 3 4 3 4
OIV 202 Bunch length 5 3 7 3 5 7 3 7 5 7 3 7 7 7 7 1 5 5 7 5 7 7 7 5 5 7 7 7 5 5 9 7 7 5 3 7 3 7 3 3 7 7 9 7 7 7 5 7 7 7 5 5 7 7 7 5 5 7 7 7 5
OIV 204 Bunch density 1 1 3 3 1 1 1 3 5 7 3 5 5 1 7 1 3 5 5 1 5 5 5 5 1 3 1 5 1 3 5 5 5 5 5 1 5 1 1 3 1 3 7 9 7 5 5 5 3 1 1 5 5 1 5 5 5 3 5 1 7
OIV 206 Bunch length of peduncle 3 3 3 3 7 5 3 5 3 5 3 3 3 1 3 1 1 3 1 1 3 3 5 1 1 7 1 5 3 3 5 5 5 3 1 3 1 3 3 3 7 3 3 1 3 5 3 3 3 5 3 5 5 1 3 3 5 3 3 5 1
OIV 208 Bunch shape 1 1 2 2 1 2 2 1 2 3 2 3 3 1 2 1 1 3 2 1 3 3 2 3 2 1 1 3 2 1 1 3 2 3 2 2 3 1 3 3 2 3 3 1 1 1 1 2 3 1 1 5 2 1 1 3 3 1 1 1 2
OIV 209 Bunch number of wings 2 1 2 2 3 3 3 2 2 3 2 3 2 2 3 1 3 2 2 3 3 3 3 2 2 3 3 3 3 2 3 3 3 3 3 3 2 3 3 2 3 3 3 1 3 2 2 3 2 2 3 5 3 3 2 2 3 3 3 3 2
OIV 220 Berry length 5 5 7 7 7 7 7 5 7 7 7 7 7 7 7 1 7 5 7 5 7 5 7 7 5 7 7 5 5 7 7 7 5 5 7 5 7 7 5 7 5 5 9 9 5 7 7 7 5 7 7 5 9 7 7 7 5 7 7 5 7
OIV 221 Berry width 5 3 5 3 5 3 7 5 5 7 7 5 7 7 7 1 5 7 7 5 5 3 7 5 5 7 5 5 5 5 5 3 7 5 7 5 7 5 5 5 5 5 7 7 5 7 7 7 5 7 5 5 5 5 7 5 7 5 7 5 7
OIV 223 Berry shape 2 2 2 2 2 3 5 2 3 1 5 2 2 5 2 1 2 1 6 2 4 2 2 2 2 2 2 2 5 2 2 8 1 2 2 2 1 4 2 4 2 2 2 4 2 1 7 1 2 1 2 5 7 2 1 4 1 7 2 4 1
OIV 225* Berry color of skin 1 1 1 1 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 1 1 1 5 3 1 1 1 1 1 1 5 1 1 1 5 5 1 1 5 1
OIV 230 Berry color of flesh 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 1 1 1 1 1 1 1 1 1
OIV 235 Berry degree of firmness of flesh 1 5 5 1 5 5 5 5 9 5 5 9 5 5 5 1 5 5 5 5 9 5 5 3 5 5 5 5 9 5 5 5 5 5 5 5 5 5 5 9 5 5 5 9 9 9 5 5 5 5 1 5 5 5 5 9 5 5 5 5 5
OIV 236 Berry particular flavor 1 1 4 5 5 4 2 5 5 4 5 4 1 5 4 1 4 4 5 4 4 4 3 4 4 4 3 4 2 4 4 4 4 4 2 5 4 4 1 5 1 1 4 4 4 5 5 4 4 4 4 5 5 1 5 4 5 4 5 4 1
OIV 241 Berry presence of seeds 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 5 3 3 3 3 3 3 3 3 3
OIV 502 Bunch single bunch weight 3 3 5 5 5 3 3 5 3 5 3 5 3 5 3 1 3 5 3 5 3 5 5 5 5 5 5 5 5 5 5 5 3 5 3 5 5 5 5 3 5 5 5 3 3 5 5 5 5 3 5 5 5 5 5 3 5 5 5 3 5
OIV 503 Berry single berry weight 3 3 5 5 5 5 5 5 5 5 5 5 5 7 3 1 5 3 5 5 3 3 5 5 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 5
OIV 505* Must sugar content (%) 7 7 5 5 7 5 5 5 7 5 5 7 5 3 3 1 3 5 5 5 3 7 5 5 5 7 5 5 5 5 5 5 3 5 5 7 5 5 5 5 7 7 5 3 5 5 5 7 5 5 5 5 5 3 5 5 5 3 5 3 5
OIV 506 Must total acid content 3 3 3 3 1 1 3 3 1 3 1 1 3 3 1 1 1 5 3 3 3 1 1 3 3 1 3 3 3 3 3 1 3 1 3 3 3 1 1 1 3 3 3 3 3 3 3 3 3 3 3 5 3 1 3 1 3 1 3 1 3
OIV 508 Must pH of must 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 5 3 3 5 3 3 3 3 3 1 3 3 3

*The 12 most important morphological descriptors for the classification of the accessions.

3. Statistical analysis based on morphological diversity using shoot, leaf and fruit descriptors

Morphological data were analyzed by multivariate analysis, clustering and ANOVA analysis using XLSTAT software (Addinsoft, www.xlstat.com); PCA was performed to identify accession groups and to determine the axes and the characters significantly contributing to the variation. In this procedure, the similarity matrix was used to generate eigenvalues and scores for the accessions. The first two principal components, which accounted for the highest variation, were then used to plot two-dimensional scatter plots. HCA was carried out using Ward’s minimum variance method as a clustering algorithm (Williams, 1976) and squared Euclidean distances as a measure of dissimilarity (Ward, 1963).

Among the groups identified based on different classifications (shoot, leaf and fruit), we determined the cultivars corresponding to the barycentre, which is a geometrical measurement allowing the concentration of a set of cultivars onto one that best expresses the inherent morphological diversity per variation class.

4. Evidence for integrating shoot, leaf and fruit descriptors

Regression analysis was applied to set up evidence for integrating shoot, leaf and fruit descriptors in distinguishing between autochthonous grape cultivars. Thus, the correlation between Euclidean distances calculated from shoot-leaf, shoot-berry and leaf-berry descriptors was determined from the linear regression plot (Rousset, 1997). A positive correlation is expressed by a positive R² value. The significance of this correlation was tested by a Mantel test using 10000 permutations (Mantel and Valand, 1970).

5. Perspectives for the commercialization of Tunisian autochthonous cultivars: table or wine cultivars?

To assess the vocation of the Tunisian grapevine accessions as table or wine cultivars, the following characters were determined according to the OIV descriptor list (Table 2): bunch weight, 100-berry weight, and sugar content, pH and acidity of the must.

Results

1. Morphological diversity based on individual characteristics

1.1 Shoot characteristics

Twelve descriptors were used for the characterization of the shoots. The discrimination between the accessions under investigation revealed that 53.76% of the variation (Table 3) was explained by the first three axes of the PCA plot (Fig. 1a): the first axis was defined by the distribution and the intensity of the anthocyanin coloration on the bud scales (OIV 015 and OIV 015-2, respectively). These two descriptors were strongly correlated (0.940). The second axis was defined by both the anthocyanin coloration of the tip (OIV 003) and the form of the tip (OIV 001).

Table 3. Estimates of variances (eigenvalues), cumulative variance and eigenvectors of the first three principal components (F1, F2, F3) for shoot, leaf, fruit and total descriptors evaluated on 61 Tunisian autochthonous grapevine accessions.


  Shoot descriptors Leaf descriptors Fruit descriptors Total descriptors
  F1 F2 F3 F1 F2 F3 F1 F2 F3 F1 F2 F3
Eigenvalue 2.298 1.856 2.298 5.187 3.319 2.652 2.731 2.354 2.006 6.052 4.704 4.492
Variability (%) 19.146 15.470 19.146 13.301 8.510 6.799 14.373 12.389 10.560 8.646 6.721 6.418
% cumulated 19.146 34.616 53.762 13.301 21.810 28.610 14.373 26.762 37.322 8.646 15.366 21.784

Based on shoot descriptors, the accessions clustered into three main groups (C1, C2 and C3) as revealed in the dendrogram (Fig. 1b). Group C1 was the largest and comprised the 31 accessions with the highest density of prostrate hairs on the tip (OIV 004), as inferred from the morphological data matrix. Group C2 contained the 16 accessions with the highest distribution and intensity of anthocyanin coloration on the bud scales (OIV 015 and OIV 15-2, respectively). Group C3 included 14 accessions that are grouped by the form of the tip (OIV 001), which varied from “half open to open”.

Figure 1. Shoot descriptor analysis. (a) Principal component analysis (PCA) plotted along the first two axes and (b) UPGMA dendrogram of Euclidean distance illustrating the genetic relationships among the 61 studied grapevine cultivars based on shoot descriptors. Sd1, Sd2 and Sd3: barycentres of the groups.

The variance components within and between the individual groups (C1, C2 and C3) detected with ANOVA showed that most of the morphological variation was partitioned within (62.98%) rather than between (37.02%) the groups. These were both significant at p<0.01 (Table 4). Per variation class, the accessions Asli Hadab, Khalt Bouchemma Gabès and Khalt Abiadh (Sd1, Sd2 and Sd3, respectively) were identified as the barycentres of the groups C1, C2 and C3, respectively.

Table 4. Variance decomposition for optimal classification based on shoot, leaf, fruit and total descriptors (significant at p<0.01).


  Shoot descriptors Leaf descriptors Fruit descriptors Total descriptors
  Absolute Percentage Absolute Percentage Absolute Percentage Absolute Percentage
Intra group variation 17.318 62.98% 55.546 85.58% 21.046 79.12% 106.277 89.31%
Inter group variation 10.179 37.02% 9.356 14.42% 5.556 20.88% 12.723 10.69%
Total variation 27.497 100.00% 64.902 100.00% 26.601 100.00% 119.001 100.00%

1.2 Leaf characteristics

Thirty-nine OIV descriptors were used for the characterization of the leaves (Table 2). According to the PCA plot (Fig. 2a), the first three principal components accounted for 28.61% of the total variation (Table 3). The variables with the greatest weight in the first principal component were the opening (OIV 079-1) and the general shape (OIV 079) of the petiolar sinus and the angle between veins N2-N3 at the first ramification (OIV 608). The second axis was defined by three characters: the length from the petiolar sinus to the upper leaf sinus (OIV 605), the length from the petiolar sinus to the lower leaf sinus (OIV 606) and the shape of the blade (OIV 067). The characters OIV 079-1 and OIV 079 were the most correlated descriptors (0.738%). Based on leaf descriptors, the 61 studied cultivars grouped into three major groups: C1’ (19 genotypes), C2’ (39 genotypes) and C3’ (3 genotypes), as illustrated in the dendrogram (Fig. 2b).

Figure 2. Leaf descriptor analysis. (a) Principal component analysis (PCA) plotted along the first two axes and (b) UPGMA dendrogram of Euclidean distance illustrating the genetic relationships among the 61 studied grapevine cultivars based on leaf descriptors. Ld1, Ld2 and Ld3: barycentres of the groups.

The variance components within and between the individual groups (C1’, C2’ and C3’) detected with ANOVA showed that most of the morphological variation was partitioned within (85.58%) rather than between (14.42%) the identified groups (Table 4). Per variation class, the accessions Khalt Bouchemma Gabès, Arich Djerba and Chaouche Djerba (Ld1, Ld2 and Ld3, respectively) were identified as the barycentres of the groups C1’, C2’ and C3’, respectively.

Further observations of leaf descriptors showed that the shape of the blade was either wedge-shaped or pentagonal (OIV 067) with open petiole sinus (OIV 079-1). From a disease sensitivity point views, the leaves of the accessions Châaraoui, Khamri Tozeur and Arich Dressé exhibited high resistance to both Oidium (OIV 455) and Plasmopara (OIV 452), while the accessions Khédhiri 1, Sakasly Baddar, Muscat Rafraf, Akhal Mguergueb, Bidh el Hamem Rafraf and Bezzoul el Khadem Rafraf displayed lower resistance to Plasmopara but high resistance to Oidium.

1.3 Fruit characteristics

Nineteen ampelographic descriptors were used for the description of the fruits. The discrimination between all cultivars revealed that the first three axes of the PCA plot explained 37.32% of the variation (Table 3). The highest loadings on the first PCA axis corresponded to berry length (OIV 220) and bunch density (OIV 204) (Fig. 3a). The variables with the highest loadings on the second PCA axis were color of the berry flesh (OIV 230) and total acid content of the must (OIV 506). The color of the berry skin (OIV 225) and color of the berry flesh (OIV 230) were the most correlated characters. Three major groups were determined using cluster analysis: group C1” (29 genotypes), C2” (14 genotypes) and C3” (18 genotypes) (Fig. 3b).

Figure 3. Fruit descriptor analysis. (a) Principal component analysis (PCA) plotted along the first two axes and (b) UPGMA dendrogram of Euclidean distance illustrating the genetic relationships among the 61 studied grapevine cultivars based on fruit descriptors. Fd1, Fd2 and Fd3: barycentres of the groups.

The variance components within and between the individual groups (C1”, C2” and C3”) detected with ANOVA showed that most of the morphological variation was partitioned within (79.12%) rather than between (20.88%) the groups (Table 4). Per variation class, the accessions Blanc 3, Arbia and Balta 2 (Fd1, Fd2 and Fd3, respectively) were identified as the barycentres of the groups C1”, C2” and C3”, respectively.

The bunch density character (OIV 204) varied from loose to dense and the cluster analysis showed that the accessions with the lowest bunch density were grouped together. Across all accessions, whatever the size or the shape of the bunch or the berry, the weight of a bunch did not exceed 500 g and the weight of a single berry ranged between 2 and 5 g. The sugar content of the must (OIV 505) varied from low (Saouadi, Amokrane, Arbia, Djebbi) to high (Asli Hadab, Asli Dar Slimane, Beldi Rafraf, Beldi Sayeb, Khédhiri 1, Tounsi Djerba, Khédhiri 2, Turky). A low total acid content of the must (OIV 506) characterized all the studied accessions and most of the accessions displayed low ph (OIV 508). Considerable variation was observed for the fruit shape (OIV 223). The intensity of the skin color (OIV 225) was quite diverse, ranging from very light green-yellow to dark red-violet, with medium to firm flesh (OIV 235).

2. Statistical evidence for integrating all morphological data in discriminating between Tunisian autochthonous grapevines

Different dendrograms were obtained and different relationships between the accesssions were observed when based upon shoot, leaf or berry criteria (Fig. 1b; 2b; 3b). Therefore, we performed statistical analysis to see whether or not all morphological data ought to be integrated in the characterization of Tunisian grapes. Thus, Euclidean distances calculated for shoot/berry, shoot/leaf and leaf/berry were compared by Mantel tests (Rousset, 1997) using 10000 permutations (XLSTAT software). As shown in Figure 4, low correlation values were obtained between shoot and berry descriptors (r=0.023) and between leaf and berry descriptors (r=0.027). However, a higher significant correlation value was obtained between shoot and leaf descriptors (r=0.125). Based on these results (Table 5), all descriptors ought to be integrated in clustering Tunisian autochthonous grapevines.

Figure 4. Linear regression between shoot/leaf, shoot/fruit and leaf/fruit Euclidean distances in 61 grapevine genotypes.

Table 5. Correlation coefficients (r) between shoot/leaf, shoot/berry and leaf/berry matrices of the 61 Tunisian autochthonous grapevine cultivars (significant at p<0.01).


  Shoot/Leaf Shoot/Berry Leaf/Berry
r(AB) 0.125 0.023 0.027
p-value (bilateral) <0.0001 0.318 0.238
alpha 0.05 0.05 0.05

3. Morphological diversity based on total characters

The use of all the 70 ampelographic descriptors (Table 2) yielded a high number of morphotypes and permitted the discrimination of all cultivars. High morphological variation was recorded among the studied accessions. The majority of the descriptors were significantly correlated, though with very heterogeneous values for the coefficients of correlation.

The first three PCA axes accounted for 21.78% of the total variation (Table 3; Fig. 5a). Besides, 12 descriptors out of 70 were identified as the most useful morphological descriptors for the classification of the accessions. These were the following: the shape of petiolar sinus (OIV 079), the opening/overlapping of the petiolar sinus (OIV 079-1), the density of prostrate hairs between the main veins (lower side) (OIV 084), the angle between N2 and N3 measured at the first ramification (OIV 608), the intensity and distribution of anthocyanin coloration on the bud scales (OIV 015-2 and OIV 015, respectively), the intensity of berry skin color (OIV 225), the width of teeth N2 of mature leaf (OIV 613), the sugar content of must (OIV 505), the anthocyanin coloration of shoot tip (OIV 003), the density of prostrate hairs on shoot tip (OIV 004) and the sex of flower (OIV 151) (Table 2).

Figure 5. Total descriptor analysis. (a) Principal component analysis (PCA) plotted along the first two axes and (b) UPGMA dendrogram of Euclidean distance illustrating the genetic relationships among the 61 studied grapevine cultivars based on total descriptors. Td1, Td2 and Td3: barycentres of the groups.

Using all descriptors, three major groups were identified by the cluster analysis: groups C1”’ (14 accessions), C2”’ (36 accessions) and C3”’ (11 accessions) (Fig. 5b). The variance components within and between the individual groups (C1”’, C2”’ and C3”’) detected with ANOVA showed that most of the morphological variation was partitioned within (89.31%) rather than between (10.69%) the groups (Table 4). These were both significant at p<0.01. Per variation class, the accessions Khalt Bouchemma Gabès, Blanc 3 and Blanc 2 (Td1, Td2 and Td3, respectively) were identified as the barycentres of the groups C1”’, C2”’ and C3”’, respectively. Consequently, these accessions are representative of all the morphological variation within autochthonous grapes in Tunisia.

4. Perspectives for the commercialization of Tunisian autochthonous cultivars: table or wine cultivars?

The question that was dealt with in this section was how these accessions ought to be introduced into the market: are they wine or table cultivars?

The three genotypes that were identified as the barycentres of the groups (Fig. 5b) were considered to determine the use and analyzed for the following characters: weight of the bunch (g), weight of 100 berries (g), must sugar content (brix), must organic acid composition (titratable acidity; TA) and must pH (Table 6). The brix and TA parameters were the most compulsory criteria that ought to be considered when distinguishing between table and wine grapes. As shown in Table 6, the sugar content of the analyzed accessions (between 14.2 and 17.1 brix) was lower than that usually reported for wine grapes (between 24 and 26 brix) and closer to values reported for table grapes (between 17 and 19 brix)".

Table 6. Weight of the bunch, weight of 100 berries, sugar content (brix), pH and tartaric acid (TA) content of the identified barycentres (as obtained using total descriptors).


Barycentres Weight of the bunch (g) Weight of 100 berries (g) Brix pH TA
Khalt Bouchemma Gabès 153.3 511.6 17.1 3.8 4.9
Blanc 2 65.5 257.5 15.7 3.4 6.1
Blanc 3 246.8 384.7 14.2 3.4 9.3

Discussion

The present study revealed the morphological diversity within a collection of autochthonous grapevine germplasms grown in different geographical regions of Tunisia using 70 morphological descriptors. These were used to estimate the phenotypic diversity among and within the cultivars, to identify the traits contributing to the heterogeneity, to classify them using PCA and HCA and to see how these accessions ought to be commercialized.

The use of ampelographic descriptors comprising shoot, leaf and fruit traits yielded a high number of morphotypes and permitted the discrimination of all cultivars. This discrimination was found to be higher than those reported previously using isozyme (Ben Abdallah et al., 1998) and molecular markers (Zoghlami et al., 2001; 2009) for the same varieties. In fact, morphological markers assume higher degree of genomic coverage (Vetelӓinen et al., 2005) and most individual phenotypic markers are multigenic. Therefore, variation at more than one locus is being analyzed.

Conversely, the set of OIV descriptors used here allowed for the first time to easily split the Tunisian autochthonous grapevine accessions into wine and table grapes. These findings were not clearly attained when using molecular tools.

Several authors have analyzed morphological diversity in crop plants (Bozokalfa et al., 2009; Jesus et al., 2009; Aghaee et al., 2010; Sarıkamış et al., 2010). In grapevine, morphological investigations have been carried out on Brazilian (Leão et al., 2011), Croatian (Sladonja et al., 2007), Egyptian (Hassan et al., 2011), Georgian (Ekhvaia and Akhalkatsi, 2010), Italian (Muganu et al., 2009; Alba et al., 2011), Portuguese (Cunha et al., 2009), Spanish (Santiago et al., 2007) and Turkish grapevines (Ates et al., 2011).

Multivariate analyses based on morphological characters provide information allowing the breeder to improve populations by selecting from specific geographical regions (Souza and Sorrells, 1991). The multivariate techniques have been applied in viticulture with several objectives: morphological and agronomical descriptions (Coelho et al., 2004; Borges et al., 2008), management (Intrieri et al., 2001) and disease resistance (Nascimento et al., 2006).

Because grapevine shoot is an important character for the description of cultivars, many studies were conducted using such descriptors (Santiago et al., 2007; Zdunic et al., 2008; Barth et al., 2009; Sabir et al., 2009). A thorough knowledge of the quantitative aspects of shoot development is therefore necessary to understand the determination of crop quality and productivity as a function of environment. In our case, the highest amount of variation was attained using shoot descriptors (PCA; 53.76%) (Table 3; Fig.1a). The number of shoot descriptors (n=12) was higher than those reported previously: 4 descriptors (Santiago et al., 2007; Alba et al., 2011), 5 descriptors (Sabir et al., 2009), and 9 descriptors (Muganu et al., 2009).

The tip of the young shoot was open for all the cultivars. This character allows the differentiation among the Vinifera and other Vitis species. Additionally, the coloration of the shoot tip, which defines the second axis of the PCA plot, seems to be an important character in distinguishing between the grapevine cultivars (Morton, 1979). Moreover, this character may vary in relation to exposure to light (Kara, 1990). The accessions Asli Hadab, Khalt Bouchemma Gabès and Khalt Abiadh were established as being the barycentres of the identified groups (Fig. 1b). This means that using shoot descriptors all the morphological variation could be explained by these accessions.

Leaf descriptors have been generally used as powerful tools for identifying grapevine genotypes (Santiago et al., 2007; Celik et al., 2008; Sabir et al., 2009; Gago et al., 2009; Harbi-Ben Slimane et al., 2010). Thirty-nine leaf descriptors were used in a set of 61 autochthonous grapes. This number was higher than those reported previously by Sabir et al. (2009), Santiago et al. (2007) and Gago et al. (2009) using 12, 17 and 19 descriptors, respectively. According to the PCA plot (Fig. 2a), although using 39 descriptors, the first three principal components accounted for only 28.61% of the total variation.

The discrimination between all grapevines, based on fruit descriptors, revealed that the first three axes of the PCA plot explained 37.32% of the variation using only 19 ampelographic descriptors (Fig. 3a). The number of descriptors was higher than those used by Ates et al., 2011 (6 descriptors), Alba et al., 2011 (11 descriptors) and Leão et al., 2011 (12 descriptors). It was observed that all accessions had low acidity values, which is one characteristic of cultivated grapevine (Navarro et al., 2001; Liu et al., 2006).

Based on the differences in clustering that occurred with shoot, leaf or berry descriptors (Fig. 1b; 2b; 3b) and the correlation values registred between shoot and berry (r=0.023), leaf and berry (r=0.027) and shoot and leaf descriptors (r=0.125) (Table 5), all descriptors ought to be integrated in clustering for the discrimination of the Tunisian autochthonous cultivars. Significant correlations were detected between all descriptors (Fig. 4), confirming the results of Ocampo et al. (2006), who found positive correlations between all morphological traits.

Therefore, a total number of 70 major OIV descriptors has been used to discriminate among autochthonous grapes. This number appears to be high if compared to previous studies in grapevine cultivars: Leão et al., 2011 (12 descriptors); Sabir et al., 2009 (17 descriptors); Cunha et al., 2009 (22 descriptors); Alba et al., 2011 (30 descriptors); Muganu et al., 2009 (34 descriptors); Ekhvaia and Akhalkatsi, 2010 (43 descriptors); Sladonja et al., 2007 (50 descriptors); and Celik et al., 2008 (61 descriptors).

As inferred from the PCA plot (Fig. 5a), when using the 70 descriptors to draw a final cluster scheme, the eigenvalues of the first, second and third axis of the principal components accounted for 8.65, 15.37 and 21.78% of the total variation, respectively (Table 3).

The relative magnitude of the first three PCA eigenvectors showed that 12 descriptors out of 70 were identified as the most important morphological descriptors for the classification of the accessions. These were OIV 079, OIV 079-1, OIV 084, OIV 608, OIV 015-2, OIV 015, OIV 225, OIV 613, OIV 505, OIV 003, OIV 004, and OIV 151 (Table 2). These descriptors ranked within the primary descriptor list established by the OIV for the characterization of cultivars (OIV, 2012). Iezzoni and Pritts (1991) mentioned that associations between traits uncovered by PCA may correspond to a genetic linkage between loci controlling traits or a pleiotropic effect.

To access the grapevine fresh fruit market, we must characterize the cultivar as table or wine grapes. The overall flavor is one of the most important qualities for establishing a continuous consumer preference. Flavor composition has been defined as a complex quality attribute, in which the mix of sugars, acids, and volatiles plays a primary role (Baldwin, 2002). Among the flavor metabolites already mentioned, sugar and organic acid compositions, which are measured through total soluble solids (TSS) and titratable acidity (TA), are most commonly associated with the taste of fruits, including table grapes (Shiraishi et al., 2010). In our case, the accessions Khalt Bouchemma Gabès, Blanc 2 and Blanc 3 were identified as table grapes (Table 6), according to their flavor composition and bunch weight, which meet the market requirements for table grapes previously set up by the United Nations Economic Commission for Europe (UNECE) (Norme CEE-ONU FEV-19, 2010). These indicate that table grapes must have a refractive index of at least 16 to 19 brix and a minimum bunch weight of 75 g. Based on the aforementioned statements, Khalt Bouchemma Gabès possess the required characteristics to be introduced into the market as table grapes. Their overall flavor composition that is associated with their taste (Baldwin, 2002; Shiraishi et al., 2010) may therefore strongly satisfy consumer preference.

Conclusion

The detailed ampelographic description presented in this study highlights clear morphological differentiation between 61 Tunisian autochthonous grapevines characterized for the first time using 70 OIV descriptors.

Morphological traits were shown to enable cultivar comparison and classification in germplasm collection. Numerical analyses showed that the number of morphological traits that are effectively contributing to the characterization of the cultivars could be reduced to 12. Furthermore, three cultivars out of 61 were found to represent the morphological variability observed in Tunisian grapes, representing the barycentres of the individual groups in cluster analysis.

Finally, the autochthonous Tunisian grapes ought to be classified as table grapes according to the standards of the UNECE (Normes CEE-ONU FEV-19, 2010).


Acknowledgements: The authors are thankful to the Ministry of Higher Education and Scientific Research (Tunisia) for financial support.

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Authors


Myriam Lamine

Affiliation : Laboratory of Plant Molecular Physiology, Biotechnology Centre of Borj-Cédria, BP 901, Hammam-Lif 2050, Tunisia

myriam_lamine@yahoo.fr

Hassène Zemni

Affiliation : Laboratory of Plant Molecular Physiology, Biotechnology Centre of Borj-Cédria, BP 901, Hammam-Lif 2050, Tunisia


Sana Ziadi

Affiliation : Laboratory of Plant Molecular Physiology, Biotechnology Centre of Borj-Cédria, BP 901, Hammam-Lif 2050, Tunisia


Asma Chabaane

Affiliation : Laboratory of Plant Molecular Physiology, Biotechnology Centre of Borj-Cédria, BP 901, Hammam-Lif 2050, Tunisia


Imen Melki

Affiliation : Laboratory of Plant Molecular Physiology, Biotechnology Centre of Borj-Cédria, BP 901, Hammam-Lif 2050, Tunisia


Samiha Mejri

Affiliation : Laboratory of Plant Molecular Physiology, Biotechnology Centre of Borj-Cédria, BP 901, Hammam-Lif 2050, Tunisia


Nejia Zoghlami

Affiliation : Laboratory of Plant Molecular Physiology, Biotechnology Centre of Borj-Cédria, BP 901, Hammam-Lif 2050, Tunisia

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