Determining the level of genotypic variability of upland rice genotypes using cluster analysis

Abstract

Determining the extent and degree of germplasm diversity and genetic relationships among breeding materials is an important aid in crop improvement research strategies with an understanding that genetic variability is the base for crop improvement providing an opportunity for plant breeders to develop new and improved cultivars with desirable traits and it is a key to reliable and sustainable production of crops through breeding. It has been also confirmed that measuring the available genetic diversity of crops is important for effective evaluation and utilization of germplasms to explore their variability so as to identify necessary agronomic traits. For eradicating the problem of rice production, the national rice breeding and genetics research program of Ethiopia is introducing and evaluating different rice germplasms for their environmental adaptability and agronomic performance with increasing the crops’ genetic diversity. Likely, 100 upland rice genotypes were introduced and evaluated with 3 nationally released upland rice varieties as standard checks using an augmented-RCBD experimental design. Each genotype was planted on a plot area of 2.5 m2 involving 4 rows per plot with 0.25m spacing between each row. The seeds were drilled in rows with a seed rate of 60 kgha-1. Nitrogen-phosphorus-sulfur (NPS) and Urea fertilizers were applied in the amount of 124 kg ha-1 and 100 kgha-1 respectively. This experiment was conducted to evaluate the introduced upland rice genotypes’ environmental adaptability and agronomic performance (their yield and yield related traits performance, and their reaction to different pests) to be used for further breeding. To be used in the next rice breeding research program, the extent of their genetic diversity needs to be estimated. Thus, the extent and pattern of genetic diversity of the tested upland rice genotypes based on their quantitative traits has been determined using cluster analysis. The quantitative traits such as days to 50% heading, days to 85% maturity, plant height, panicle length, number of filled grains per a panicle and number of unfilled grains per a panicle, grain yield and 1000 seed weight were measured and were subjected to clustering analysis using XLSTAT 5.03 statistical software. During clustering analysis, the genotypes were grouped into five different clusters with different Euclidian distances confirming the presence of genetic variability among the genotypes. The genotype with the highest grain (6298 kgha-1) yield was obtained and included under cluster-III.