Multivariate analysis of groundnut (Arachis hypogaea L.) cultivars based on oil traits

Abstract

The existence of genetic variation is essential in plant breeding for increasing yield, wider adaptation, selection of parents for hybridization, desirable quality, and pest or disease resistance. This study was planned to investigate genetic variability based on cluster analysis of groundnut cultivars based on morpho-agronomic traits. The crop was sown in randomized complete block design (RCBD) during the 2015 wet season across four locations in Ethiopia. The cluster analysis based on average linkage (UPGMA) of 16 groundnut genotypes, measured for 17 oil traits showed that the most distinct genotypes were Werer-963 and Behagudo (D=10.48), between Werer-963 and Tole-1 (D=10.32), and between Tole-1 and Sedi (D=9.86). The cluster mean analysis based on oil traits and quality parameters has grouped genotypes into 3 clusters. The first cluster constituted 9 genotypes that showed non-significant above average performance for oleic acid, eicosenoic acid, lignoceric acid, total monounsaturated fatty acids (TMUS), total unsaturated fatty acids (TUS) and oleic to linoleic acid ratio (O/L). Thus, such genotypes can be used for improvement of oil quality traits. The second cluster consisted of three genotypes that showed significant above average performance for stearic acid, arachidic acid, behenic acid, grain yield (GY) and oil yield (OY), but non-significant and above average performance for oil content, oleic acid, total saturated fatty acids (TS), TMUS and O/L ratio indicating that genotypes in the second cluster can be used for improvement of GY, OY and also oil quality traits. Key words: Cluster means, agro-morphological traits, unweighted pair group method with arithmetic mean (UPGMA), hybridization, genetic variability.