Comparing a Preliminary Racial Classification with a Numerical Classification of the Maize Landraces of Uruguay

Abstract

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.

Assessment of genetic divergence among coffee genotypes by Ward-MLM procedure in association with mixed models

“…The MLM procedure, combined with analysis of variance (ANOVA), has been proven to be effective in differentiating maize genotypes (Gutiérrez et al, 2003;Franco et al, 2005;Ortiz et al, 2008), fodder radish (Padilla et al, 2005), tomato (Gonçalves et al, 2009), common bean (Barbé et al, 2010;Cabral et al, 2010), pepper (Sudré et al, 2010), and banana (Pestana et al, 2011). However, high environmental influence on quantitative traits, mostly employed in the selection of genotypes in soybean-breeding programs, makes the results less accurate compared with other techniques.…”

Quantification of the diversity among common bean accessions using Ward-MLM strategy

“…In the second stage, the vector average of the quantitative variable is estimated by MLM procedure, for each subpopulation, regardless of the qualitative variable values. This procedure have been used for different purposes and with various crops such as maize (Gutiérrez et al, 2003;Franco et al, 2005;Ortiz et al, 2008), oilseed radish (Padilha et al, 2005), tomato (Gonçalves et al, 2009), snap bean (Barbé et al, 2010) and pepper (Sudré et al, 2010).…”

Determinación de la aptitud del terreno para maíz mediante análisis espacial multicriterio en el Estado de México

“…Además, María (2008) realiza una evaluación de las unidades de producción de maíz y concluye que se deben de tomar en cuenta las características socioeconómicas, el clima y el suelo para este tipo de estudios. Gutiérrez et al (2003) utilizan una clasificación numérica para delimitar la adaptación de diferentes híbridos y variedades de maíz, la cual produce grupos con características claras en términos de variables numéricas; encontraron una probabilidad de 0.966 en su clasificación. Camas et al (2010) aplicaron la metodología del sistema automatizado de evaluación de tierras de la FAO, donde generaron un modelo para planificar el establecimiento de maíz de temporal y concluyen que el sistema automatizado de zonificación agroecológica permite delimitar el establecimiento del maíz.…”