The use of the multi-variate statistical methods for the description and recognition of vine breeds is a new preoccupation in the field of ampelography. Besides other well known methods (genetic and/or enzyme pattern analysis) cluster analysis offer the possibility to study genotypes through their phenotypic expressions manifested in vine leaves. 15 authochtonous vine breeds werepart of this study. They belonged to the proles pontica and orientalis. With the help of cluster analysis it was possible to Fig. out three polythetic groups highlighting the degree of phenotypic resemblance among the analyzed breeds. The results and especially the grouping can be only confirmed with the help of an additional genetic analysis

ROTARU, Liliana G.

Notulae Botanicae Horti Agrobotanici Cluj-Napoca

English

Available online at www.notulaebotanicae.roNot. Bot. Hort. Agrobot. Cluj 37 (1) 2009, 249-252Print ISSN 0255-965X; Electronic ISSN 1842-4309Notulae Botanicae Horti AgrobotaniciCluj-NapocaIdentifying the Phenotypic Resemblances of the Vine Breeds by Means of Cluster Analysis Liliana ROTARU 1) 1) University of Agricultural Sciences and Veterinary Medicine Iaşi, 3 M. SadoveanuAl., Iaşi 700490, Romania; lirotaru@univagro-iasi.roAbstract The use of the multi-variate statistical methods for the description and recognition of vine breeds is a new preoccupation in the field of ampelography. Besides other well known methods (genetic and/or enzyme pattern analysis) cluster analysis offer the possibility to study genotypes through their phenotypic expressions manifested in vine leaves. 15 authochtonous vine breeds werepart of this study. They belonged to the proles pontica and orientalis. With the help of cluster analysis it was possible to fig. out three polythetic groups highlighting the degree of phenotypic resemblance among the analyzed breeds. The results and especially the grouping can be only confirmed with the help of an additional genetic analysis. Keywords: vine breed, cluster analysis, ampelometry, dendrogram, phenotypeIntroductionTo cluster or clustering denotes “to form a group”. The cases building a group are linked amongst themselves by objective affinity (kinship) and the agglomeration (settle-ment) within the group is formed hierarchically. The meth-od was first proposed for statistics by Sokal and Sneath (1963) its application was realized by Lance and williams (1967) cited by Biji et al. (1991). Its use in ampelography was proposed by Campastrini f. et al. (1993); Boursiquot J. M. et al. (1989, 1997); Cid Alvarez  Nuria et al. (1994); Rotaru L. (1999); Santiago J. L. et al. (2005, 2007).Theoretical aspects The cluster method admits the existence of the poly-thetic groups whose elements are equivalent or similar for several criteria, but not for all characteristics. The consti-tution of groups is made on the basis of some criteria and decision functions. They measure at the same time the si-militude of the elements from the group and the difference among groups. The principle of this method consists of making a divi-sion in each stage of formation by the aggregation of two elements. “Elements” are individuals or the objects subject-ed to the classification itself, or the individual regroupings generated by the classification algorithm.According to the algorithm a division in q-classes of an assembly of n-individuals, in accordance with a “division hierarchy”. This hierarchy has the shape of a “tree” or “den-drogram” and contains n-1 divisions, and it may give an idea about the number classes existing within one popula-tion (Silvestroni et al. 1996).The fundamental algorithm of ascendant hierarchical classification comprises the following stages (fig. 1):Stage 1 – there is a statistic population comprising n-elements (n-individuals);Stage 2 –the distance matrix among the n-elements is determined and the two elements closest together form a new element. This aggregate forms a new element resulting in the first stage of division in n – 1 classes;Stage 3 –a new distance matrix is formed after the aggre-gation and the distances between the new element and the remaining are calculated. This is similar stage 1, but with only n – 1 close elements that aggregate. A new division in n – 2 classes is obtained where the first is included too;Stage m –new distances are calculated and the aggrega-tion process is repeated until there is a single element re-sulted from the regrouping of all those n-objects subjected to grouping.This successive grouping is displayed as a dendrogram in which we insert orderly the values of the index or the distances corresponding to different levels of aggregation.The classification technique according to the variance principle or inertia calculation tries to optimize the best di-vision resulted by the aggregation of the elements. According to Huygens’s relation, the decomposition of the total inertia quantity is made in intraclass inertia and interclass inertia (Stefano et al., 1996).In the initial stage, the intraclass inertia is zero and the interclass inertia is equal to total inertia since at this level of aggregation each element constitutes a class. In the final Rotaru, L./  Not. Bot. Hort. Agrobot. Cluj 37 (1) 2009, 249-252250within the proles pontica, subproles balcanica the fol-lowing vine breeds are found: Bătută neagră, zghihară de Huşi, Galbenă de Odobeşti, Mustoasă de Măderat, Berbe-cel, frâncuşă. To the subproles georgica belong the breeds Grasă de Cotnari, Coarnă albă, fetească regală, Tămâioasă românească, Busuioacă de Bohotin. To the proles orien-talis, subproles antasiatica belongs only Coarna neagră, and to the subproles caspica belong the breeds Băbească neagră, fetească neagră and fetească albă. from these au-tochthonous breeds (considered monothetic groups) we harvested during 3 years, 10 adult leaves situated in the middle of summershoots, because in this area the variabil-ity of the ampelographic characters is lowest. 51 reference points in the leaf architecture were de-fined achieving 68 direct ampelometric measurements points. with these data 53 ampelometric values were cal-cultaed (sums, ratios, product) (fig. 2). As for the case of symmetric characters we measured and calculated both values. These allowed the build up from a statistic popula-tion made of simple series (variation series) of 30 values for all 121 characters established and for all breeds analyzed (15). The elaborated dendrogram of breeds constructed stage, the interclass inertia is zero and the intraclass inertia is equal to total inertia since now there is a class contain-ing all elements. Consequently, as aggregations are being executed by regrouping of elements, we notice that the intraclass inertia increases whereas the interclass inertia decreases.Consequently, the principle of the aggregation algo-rithm according to the variance (inertia) resides in looking for the best division for each class where internal variance of every class may be minimal and thus the interclass vari-ance may be maximal.The application of this method in ampelography al-lows the establishment of the phenotypic resemblances of breeds on the basis of the “closeness” among the elements subjected to grouping. So, the smaller the chaining index of two breeds, the bigger their phenotypic similitude (Diaz G. et al., 1991; Martinez de Toda f. et al., 1997).Materials and Methodswe studied 15 autochthonous vine breeds belong-ing to different ecological-geographical groups (proles). Fig. 1. Algorithm of classification – progressive agglomeration of the 5 points - individuals1234513254 41325134251 243 5S t a g e  1 S t a g e  2 S t a g e  3 S t a g e  4 S t a g e  5Fig. 2. Ampelometric measurements effectuated on the leaf of vine breedsRotaru, L./  Not. Bot. Hort. Agrobot. Cluj 37 (1) 2009, 249-252251cuneiform leaves (fetească neagră, Coarnă neagră), reni-form (fetească albă), orbicular reniform (fetească regală), orbicular-cuneiform (Grasă de Cotnari); in contrary the ampelometric data sizes of the breeds were medium;Group C has the highest homogeneity because the val-ue of the dissimilarity index is  6.6457. It includes breeds with big leaves which had the highest values of the am-pelometric data. The shape of the leaves is close together: orbicular-cuneiform (Galbenă de Odobeşti, Mustoasă de Măderat), orbicular cordiform (zghihară de Huşi) or cor-diform (Bătută neagră).The graphic representation of the chaining index val-ues may be seen in fig. 4.CONCLUSIONSCluster analysis admits the existence of polythetic groups (similar groups of breeds but not for all characters) and therefore allow the division of breeds under study in branches according to the existing dissimilarity or simili-with the help of the cluster analysis is based on the estab-lished groups (branches) which depend on the size of Eu-clidian distance of the elements subjected to grouping.The data assembly was represented by the average value of each character analyzed. Results and discussions The elaboration of the dendrogram of hierarchized classification of breeds (fig. 3) was achieved to the prin-ciple of minimal inertia loss (ward criterion generalized). from the analysis of the dendrogram the existence of 3 optimal groups (branches) could be identified and the ex-istence of a level “rupture” between knots 12-13. Branch A is made of the breeds: Coarnă albă, Berbecel, frâncuşă, Tămâioasă românească, Busuioacă de Bohotin, Băbească neagră. Branch B results from the aggregation of: Coarnă neagră, fetească regală, Grasă de Cotnari, fetească albă, fetească neagră. The third main branch C is formed by the chaining among the breeds Galbenă de Odobeşti, Mustoasă de Măderat, zghihară de Huşi and Bătută neagră. The three main groups (branches) may be character-ized as follows (Tab. 1):Group A, is the least homogenous because it aggregates at the highest dissimilarity index 12.1520 and is made from breeds having leaves of different shapes: orbicular slightly cuneiform (Tămâioasă românească, Busuioacă de Bohotin, frâncuşă, Băbească neagră); orbicular (Coarnă albă); taper (Berbecel), but generally most of the ampelo-metric data had the lowest values;Group B is a little more sTab. because the value of the dissimilarity index is less than 8.9445 In this group with Fig. 3. Dendrogram of hierarchized classification of breedsTab. 1. Levels of breed chaining in the elaboration of dendrogramKnot making No. of breeds in the knotValue of chaining indexTamâioasa româneasca - Busuioaca de Bohotin 2 1.3897Galbena de Odobesti - Mustoasa de Maderat 2 1.5740Feteasca regala - Grasa de Cotnari 2 2.1244Frâncusa - Tamâioasa româneasca - Busuioaca de Bohotin 3 2.4340Galbena de Odobesti - Mustoasa de Maderat - Zghihara de Husi 3 2.7144Frâncusa - Tamâioasa româneasca - Busuioaca de Bohotin - Babeasca neagra 4 2.9856Coarna neagra - Feteasca regala - Grasa de Cotnari 3 4.0913Feteasca alba - Feteasca neagra 2 5.7765Coarna alba - Berbecel 2 5.9572Galbena de Odobesti - Mustoasa de Maderat - Zghihara de Husi - Batuta neagra 4 6.6457Coarna neagra - Feteasca regala - Grasa de Cotnari - Feteasca alba - Feteasca neagra 5 8.9445Coarna alba - Berbecel - Frâncusa - Tamâioasa româneasca - Busuioaca de Bohotin - Babeasca neagra6 12.1520Coarna alba - Berbecel - Frâncusa - Tamâioasa româneasca - Busuioaca de Bohotin - Babeasca neagra - Coarna neagra - Feteasca regala - Grasa de Cotnari - Feteasca alba - Feteasca neagra11 21.9544Total inertia 15 34.1896Coarna albaCoarna n.Galbena Zghihara BerbecelMustoasa Fet. regalaFet. albaGrasa FrâncusaTamâioasa Busuioaca Batuta n.Babeasca n.Fet. neagra0 5 10 15 20 25 30 35 40chaining indexBreed dendrogramRotaru, L./  Not. Bot. Hort. Agrobot. Cluj 37 (1) 2009, 249-25225252.Boursiquot, J. M. and P. This (1997). Les nouvelles techniques utilisées en ampelographie: informatique et marquage. Journal International des Sciences de la Vigne et du Vin. 40(1):13-23.Campastrini, F., R. Anzani, O. Failla, F. Iacono and A. Scienza (1993). Application de l’analyse phyllometrique a la clasification geographique de la population italienne de la vigne sauvage. Journal International de la Vigne et du Vin. 27(4):225-262.Cid Alvarez, N., J. M. Boursiquot, S. Otero, M. Pilar and L. Romani Martinez (1994). Differenciation des cepages autochtones du Nord-Ouest de l’Espagne (Galice) et elaboration d’une cle de determination basse sur l’ampelometrie. Journal International de la Vigne et du Vin. 28(1):1-17.Diaz, G., M. Setzu, A. Diana, I. Loi, B. De Martis, M. Pala and M. Boselli (1991). Analyse de Fourier de la forme de la feuille de vigne. Premiere application ampelometrique sur un echantillon de 34 cépages implantés en Sardaigne. Journal Internat. des Sciences de la Vigne et du Vin. 25(1):37-49.Martinez de Toda, F. and J. C. Sacha (1997). Caractérisation ampélographique des cultivars rouge de Vitis vinifera L consérves en Rioja. Bull. OIV. 70(793-794), 221-234.Rotaru Liliana, 1999-Analiza cluster în ampelometrie. Lucr. St. ale USAMV Iasi. seria Hortic. 1(42):53-60.Santiago, J. L., S. Boso, M. Vilanova and M. C. Martínez (2005). Characterisation of cv. Albarín Blanco (Vitis vinifera L.). Synonyms, homonyms and errors of identification. Journal Internat. des Sciences de la Vigne et du Vin. 39:57-65.Santiago, J. L., S. Boso, P. Gago, V. Alonso-Villaverde and M. C. Martínez (2007). Molecular and ampelographic characterisationof Vitis vinifera L. ‘Albariño’, ‘Savagnin Blanc’ and ‘Caíño Blanco’ shows that they are different cultivars. Spanish Journal of Agricultural Research. 5(3):333-340.Silvestroni, O., C. Intrieri and D. Domizio (1996). Impiego di metodi fillometrici per la caracterizzazione di alcuni vitigni dell’Emilia Romagna. Rev. Vitic Enol. nr. 1:17-26.Stefano, A., N. Vignozzi and M. A. Vignini (1996). AmpeloCADs (Ampelographic Computer-Aided Digitizing System): an integrated system to digitize, file and process biometrical data from Vitis ssp. leaves. American Journal Enol. and Vitic. 47(3):257-267.tude. There were three polythetic groups of breeds (A, B, C):- Group A with the breeds Tămâioasă românească, Busuioacă de Bohotin, frâncuşă, Băbească neagră, Coarnă albă and Berbecel, as the least homogenous, with the high-est value of the dissimilarity of 12.1520;- Group B with the breeds fetească neagră, Coarnă neagră, fetească albă, fetească regală and Grasă de Cot-nari, with a medium stability, and a dissimilarity index of 8.9445.- Group C with the breeds Galbenă de Odobeşti, Mustoasă de Măderat, zghihară de Huşi and Bătută. It is the most homogenous, and a dissimilarity index of 6.6457.The use of the cluster analysis allowed the examina-tion of the hypothesis if the breeds Galbenă de Odobeşti, zghihară de Huşi and Bătută neagră have a common ge-netic origin. This is certified because these three breds are located within the same knot. It is also possible to add Mustoasă de Măderat to this group. It belongs to the same ecologic-geographical group (proles pontica, subproles balcanica). Only the genetic analysis may confirm the kin-ship of these breeds.The cluster analysis may also be extended to other plants provided that one should use the biometric meth-ods of investigation for the phenotype expression.AcknowledgementsThis study is financed by the National Council for Higher Education Research (CNCSIS), the Executive Unit for the funding of Higher Education and Academic Research (UEfISCSU), Romania, project PCE-IDEI, code CNCSIS 1141.ReferencesBiji, E. T. Baron, L. Tovissi, P. Wagner, Al. Isac Maniu, M. Korka and D. Porojan (1991). Statistică teoretică si economică. Editura Didactică si Pedagogică, Bucuresti. Boursiquot, J. M., L. Vignau and J. C. Boulet (1989). Ricerche sull’utilizzazione dell’ampelometria. Rev. Vitic. Enol. 42:37-Fig. 4. Histogram of breed chaining

Identifying the Phenotypic Resemblances of the Vine Breeds by Means of Cluster Analysis

Liliana G. ROTARU

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Identifying the Phenotypic Resemblances of the Vine Breeds by Means of Cluster Analysis

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