caa a22 4500 477135307 CHVBK 20180405111716.0 cr unu---uuuuu 170330e19970501xx s 000 0 eng 10.1023/A:1002971207770 doi (NATIONALLICENCE)springer-10.1023/A:1002971207770 The analysis of large scale data taken from the world groundnut (Arachis hypogaea L.) germplasm collection I. Two-way quantitative data [Elektronische Daten] [B.D. Harch, K.E. Basford, I.H. DeLacy, P.K. Lawrence] Data associated with germplasm collections are typically large and multivariate with a considerable number of descriptors measured on each of many accessions. Pattern analysis methods of clustering and ordination have been identified as techniques for statistically evaluating the available diversity in germplasm data. While used in many studies, the approaches have not dealt explicitly with the computational consequences of large data sets (i.e. greater than 5000 accessions). To consider the application of these techniques to germplasm evaluation data, 11328 accessions of groundnut (Arachis hypogaea L) from the International Research Institute for the Semi-Arid Tropics, Andhra Pradesh, India were examined. Data for nine quantitative descriptors measured in the rainy and post-rainy growing seasons were used. The ordination technique of principal component analysis was used to reduce the dimensionality of the germplasm data. The identification of phenotypically similar groups of accessions within large scale data via the computationally intensive hierarchical clustering techniques was not feasible and non-hierarchical techniques had to be used. Finite mixture models that maximise the likelihood of an accession belonging to a cluster were used to cluster the accessions in this collection. The patterns of response for the different growing seasons were found to be highly correlated. However, in relating the results to passport and other characterisation and evaluation descriptors, the observed patterns did not appear to be related to taxonomy or any other well known characteristics of groundnut. Kluwer Academic Publishers, 1997 Arachis hypogaea nationallicence clustering nationallicence genetic diversity nationallicence multivariate data nationallicence ordination nationallicence peanut nationallicence Harch B.D. CSIRO, Mathematical & Information Sciences, PMB 2, 5064, Glen Osmond, SA, Australia aut Basford K.E. Department of Agriculture, The University of Queensland, 4074, St Lucia, Qld, Australia aut DeLacy I.H. Department of Agriculture, The University of Queensland, 4074, St Lucia, Qld, Australia aut Lawrence P.K. Australian Tropical Field Crops Genetic Resource Centre, P.O. Box 201, 4072, Biloela, Qld, Australia aut Euphytica Kluwer Academic Publishers 95/1(1997-05-01), 27-38 0014-2336 95:1<27 1997 95 10681 https://doi.org/10.1023/A:1002971207770 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1023/A:1002971207770 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Harch B.D. CSIRO, Mathematical & Information Sciences, PMB 2, 5064, Glen Osmond, SA, Australia aut NATIONALLICENCE 700 1- Basford K.E. Department of Agriculture, The University of Queensland, 4074, St Lucia, Qld, Australia aut NATIONALLICENCE 700 1- DeLacy I.H. Department of Agriculture, The University of Queensland, 4074, St Lucia, Qld, Australia aut NATIONALLICENCE 700 1- Lawrence P.K. Australian Tropical Field Crops Genetic Resource Centre, P.O. Box 201, 4072, Biloela, Qld, Australia aut NATIONALLICENCE 773 0- Euphytica Kluwer Academic Publishers 95/1(1997-05-01), 27-38 0014-2336 95:1<27 1997 95 10681 Metadata rights reserved Springer special CC-BY-NC licence nationallicence SWISSBIB 477135307 BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer