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Comparing a Preliminary Racial Classification with a Numerical Classification of the Maize Landraces of Uruguay
Author(s) -
Gutiérrez Lucía,
Franco Jorge,
Crossa José,
Abadie Tabaré
Publication year - 2003
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci2003.7180
Subject(s) - germplasm , homogeneous , classification scheme , numerical taxonomy , biology , agriculture , one class classification , genetic resources , race (biology) , statistics , mathematics , machine learning , taxonomy (biology) , computer science , agronomy , microbiology and biotechnology , support vector machine , ecology , botany , combinatorics
Plant genetic diversity is a major component of any agricultural ecosystem. Thus, it is essential to classify genetic resources properly to conserve, evaluate, and enhance germplasm efficiently. In maize ( Zea mays L. ) , many classification systems have been used for delineating maize races. From the 1980s, with the use of computers, numerical taxonomy became increasingly important and multivariate methods began to be used for classifying genetic resources. The objective of this study was to compare two methods of classification of Uruguayan maize landraces: (i) a preliminary racial classification obtained through visual assessment and (ii) a numerical classification. The numerical classification was conducted by means of a two‐stage classification strategy: first, initial groups were formed by the Ward method and, next, the Modified Location Model (MLM) refined those groups. This classification was compared with the preliminary racial classification by four criteria. The Ward‐MLM strategy generated more homogeneous groups than those corresponding to the preliminary racial classification. The numerical classification maintained the structure of the more differentiated races, but divided the Cateto Sulino race into two more homogeneous groups, each with smaller variance and more differentiated than other groups. Numerical classification produced groups with clearly distinct characteristics, in terms of the numerical variables, and better, in terms of the four criteria used, than those formed on the basis of racial classification. These results will be the basis for an improved racial classification of maize landraces of Uruguay.