The analysis of large scale data taken from the world groundnut (Arachis hypogaea L.) germplasm collection I. Two-way quantitative data

摘要: Data associated with germplasm collections are typically large and multivariate a considerable number of descriptors measured on each many accessions. Pattern analysis methods clustering ordination have been identified as techniques for statistically evaluating the available diversity in data. While used studies, approaches not dealt explicitly computational consequences data sets (i.e. greater than 5000 accessions). To consider application these to evaluation data, 11328 accessions groundnut (Arachis hypogaea L) from International Research Institute Semi-Arid Tropics, Andhra Pradesh, India were examined. nine quantitative rainy post-rainy growing seasons used. The technique principal component was reduce dimensionality identification phenotypically similar groups within scale via computationally intensive hierarchical feasible non-hierarchical had be Finite mixture models that maximise likelihood an accession belonging cluster this collection. patterns response different found highly correlated. However, relating results passport other characterisation descriptors, observed did appear related taxonomy or any well known characteristics groundnut.