No abstract

In Ethiopia, the current rice productivity is very low which is attributed to different abiotic and biotic constraints significantly impacting rice productivity particularly in the north western parts of the country. In an effort to improve the productivity of rice, the national rice breeding program of the country is introducing and evaluating different rice germplasms targeting their adaptability and agronomic performance. Likewise, 352 lowland rice genotypes were introduced and evaluated using augmented randomized complete block experimental design (Augmented-RCBD) with a plot area of 2.5 m 2 involving 4 rows per plot. The seeds were drilled in rows with a seed rate of 60 kg per hectare. NPS fertilizer (124 kg per hectare) and Urea (100 kg per hectare) fertilizers were applied. 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, and 1000 seed weight were collected and subjected to descriptive statistics (mean performance), ANOVA and multivariate analysis (principal component analysis and clustering analysis) using SAS 9.4 and XLSTAT 2014.5.03 computer programs respectively so as to determine the extent and pattern of the genetic diversity among the tested lowland rice genotypes. From the ANOVA considering the mean square value of the quantitative traits of the treatments (Mean square (MS) of treatments) it has been observed that there is a significant variation for all the traits confirming the presence of genetic variability among the genotypes. The first three principal components (PC1, PC2 and PC3) were identified with a total cumulative variation of 78.90% showing that the genotypes could be grouped into different classes and from the distribution plot, the tested genotypes were almost uniformly distributed in four quadrants pointing the presence of genetic diversity among the genotypes. The clustering analysis result also strengthened the presence of a genetic diversity among the tested rice genotypes where the genotypes were grouped into five clusters with different Euclidian distances.

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.