HIERARCHICAL-CLUSTERING OF PERENNIAL RYEGRASS POPULATIONS WITH GEOGRAPHIC CONTIGUITY CONSTRAINT

An algorithm of automatic classification is proposed and applied to a large collection of perennial ryegrass wild populations from France. T his method is based on an ascendant hierarchical clustering using the Euclidian distance from the principal components extracted from the va riance-covariance matrix between 28 agronomic traits. A contiguity con straint is imposed: only those pairs of populations which are defined as contiguous are grouped together into a cluster. The definition of c ontiguity is based on a geostatistical parameter: the range of the var iogramme, i.e. the largest distance above which the variance between p airs of population no longer increases. This method yields clusters th at are generally more compact than those obtained without constraint. In most cases the contours of these clusters fit well with known ecoge ographic regions, namely, for macroclimatic homogeneous conditions. Th is suggests that selective factors exert a major influence in the gene tic differentiation of ryegrass populations for quantitatively inherit ed adaptive traits. It is proposed that such a method could provide us eful genetic and ecogeographic bases for sampling a core collection in widespread wild species such as forage grasses.