http://ir.haramaya.edu.et//hru/handle/123456789/175 2026-10-19T10:34:13Z http://ir.haramaya.edu.et//hru/handle/123456789/8720 Genotype x Environment Interaction and Stability Analysis in Upland Rice (Oryza sativa L.) Varieties in Ethiopia Desta Abebe; Wassu Mohammed (PhD); Abush Tesfaye (PhD) In Ethiopia, rice variety development program was started recently but many rice varieties were recommended for production. However, little information is available about the yield potential of varieties over diverse environments. This research was conducted to assess the magnitude of environment (E) and genotype x environment interaction (GEI) effects on yield and yields related traits, stability of the rice varieties for yield and to evaluate and stratify rice testing environments in Ethiopia. Twenty rice varieties were evaluated at six sites during 2016/17 in randomized complete block design with three replications. The combined analysis of variance over environments showed significant differences among genotypes (G), environments (E) and significant effect of GEI on phenology, growth traits, yield components and grain yield of varieties. Analysis of variance from Additive Main Effects and Multiplicative Interaction (AMMI) model revealed that G, E and GEI contributed 3.03 to 33.58, 25.69 to 72.02, and 7.09 to 30.36 %, respectively, to the total sum squares of traits. The E and GEI had a higher contribution than G to most of the traits, and GEI had larger contribution than G and E to the variations of varieties for yield. The interaction principal component axis one (IPC1) and two (IPC2) contributed 37.94 to 88.85 and 8.92 to 87.13% to sum squares of GEI, respectively. The varieties had mean yield of 3.509 t ha-1, and 9 and 11 varieties had mean yield lower and higher than the average yield of genotypes, respectively. The stability parameters of regression coefficient (bi) and deviation from regression (S2di) from Eberhart and Russell's (ER’s), IPCA1, IPCA 2, AMMI stability value (ASV) from AMMI model, GGE biplot, Francis and Kannenberg's coefficient of variability (CV), Lin and Binns's cultivar performance measure (Pi), Shukla's stability variance (δ2i), Wricke's ecovalence (Wi), Finlay and Wilkinson's (bi), Nassar and Huehn’s mean Absolute rank difference (Si1) and variance of ranks (Si2) were used evaluate the stability of genotypes for yield. NERICA-3, Hidasse and Chewaqa varieties had 15.99, 15.7 and 15.42% higher yield than average yield of genotypes, respectively, and identify as responsive to changing environments, selected as the first four best genotypes across poor to most favorable environments and GGE biplot identified as ideal and stable varieties suggested to be used for production across environments. NERICA-12 and ADET varieties had 12.85 and 8.29% higher yield, respectively, than average yield of genotypes and identify as most stable by most of the stability parmeters and considered as wide adaptable varieties. NERICA-4, NERICA-13 and Getachew varieties had 11.97, 10.83 and 5.7% yield advantages over the average yield of genotypes, identified as stable varieties by most of the stability parameters and selected as the first four best genotypes at poor to average environments suggested to be used in these environments. The GGE biplot identified Pawe and Tepi as most discriminating environments, Manbuk and Maitsebri identified as average environments and Woreta as representative environments that could be used as test environments to develop varieties at different stages of breeding activities. The stability parameters from ER's and AMMI models had positive and significant rank correlations with most of the stability parameters and yield and considered as sufficient to evaluate the stability of genotypes for yield. In conclusion, it is suggested to conduct the experiment over seasons to certain the results due to the significant effect of GEI and its larger contribution to the variations of varieties for yield and other traits. 125 2019-08-01T00:00:00Z http://ir.haramaya.edu.et//hru/handle/123456789/8272 GENETIC VARIABILITY AND ASSOCIATION OF YIELD AND YIELD RELATED TRAITS IN BREAD WHEAT (Triticum aestivum L.) GENOTYPES AT GEDO, WESTERN OROMOIA, ETHIOPIA Debele Bekele Tola; Prof. Wassu Mohammed; Dr. Girma Mengistu West Shewa is one of the major wheat production zones of Oromia Regional State in Ethiopia. Developing varieties in the area is one of the measures to increase the production and productivity of the crop. This research was conducted to assess the genetic variability in bread wheat genotypes for morpho-agronomic traits, grain quality and disease resistance, and determine the association among traits. Sixty-four genotypes including two check varieties were evaluated in an 8 x 8 lattice design at Gedo in 2023. The genotypes showed significant differences for most of the morpho-agronomic traits, grain moisture content, gluten content (%), protein content (%), Zeleny index (ml) and percentage of stem rust severity. The average grain yield of genotypes ranged from 2.36 to 4.67 t ha-1 and 13 genotypes had significantly higher grain yield in the range between 8.8 and 24.5% than the better-yielding Laku variety yield (3.7 t ha-1). The genotypes grain gluten, protein content and Zeleny index ranged from 6.5 to 23.5%, 8.7 to 12.7% and 3.9 to 41.7(ml), respectively. The final stem rust severity scores for bread wheat genotypes recorded from 5.5 to 20% and 39 genotypes including the two check varieties were found to possess high-level adult plant resistance. The estimates of phenotypic (PCV %) and genotypic coefficient of variation (GCV %) for 16 traits ranged from 0.8 to 58.66% and 0.6 to 41.3%, respectively. The grain yield had positive and significant genotypic and phenotypic correlation coefficients with days to maturity, plant height, harvest Index, biomass yield and percentage of stem rust severity. In addition, grain yield had positive and significant genotypic and phenotypic correlation coefficients with thousand kernels weight and grain filling period, respectively. Moreover, thousand kernel weight, biomass yield and harvest Index had a high direct positive effect on grain yield indicating these suggested for indirect selection of genotypes for yield. The first six principal components (PCs) accounted for 76.39% of the total variability among the genotypes. Days to heading, grain filling period, days to maturity, grain gluten content, grain protein content, grain and biomass yields, harvest index and spikelet per spike had a larger contribution to total variability in the six PCs suggesting the importance of considering these traits in the evaluating genotypes. The Euclidean distance of all possible pairs of genotypes ranged from 2.02 to 10.6. The 64 bread wheat genotypes were grouped into seven distinct clusters, Cluster I, III and II consisted of 27 (42.19%), 31 (48.44%) and 2(3.13%) genotypes, respectively, while the four Clusters, IV, V VI and VII were solitary. The study displayed that variation exists among bread wheat genotypes; however, the experiment was evaluated at one location and one season of cropping; thus, further confirmation is needed in additional seasons, and multi-locations will be required to develop varieties for the western part of Ethiopia and similar agro-ecologies. 75p. 2024-12-01T00:00:00Z http://ir.haramaya.edu.et//hru/handle/123456789/8251 GENETIC VARIABILITY AND ASSOCIATION OF YIELD AND YIELD RELATED TRAITS IN SOYBEAN (Glycine max L.) GENOTYPES AT BOKO, EAST HARARGHE, ETHIOPIA Girma Wakgari Kabata; Prof. Wassu Mohammed; Dr. Bulti Tesso Soybean (Glycine max L.) production is increasing as a multipurpose crop used for oil production, as balanced diet and industry prepared foods and export in Ethiopia, but its production is not common in East Hararghe. Very limited research was conducted on genetic variability of soybean genotypes in Eastern Hararghe. This research was conducted to assess genetic variability among soybean genotypes for yield and yield related traits, and to determine the associations of traits. The 61 introduced soybean genotypes and three check varieties were evaluated for 13 traits in 2023 in 8 x 8 simple lattice design at Boko in East Hararghe. The significant differences among soybean genotypes for all traits were evident from the results of analysis of variance. The grain yield of genotypes ranged from 470.76 to 2727.62 kg ha-1 with a mean of 1488.06 kg ha-1. Six introduced genotypes produced higher (2168.301 to 2727.62 kg ha-1) than yield of Maya variety (2078.86 kg ha-1) that produced highest yield among check varieties. The genotypic (GCV %) and phenotypic (PCV %) coefficient variations ranged from 5.45 to 38.84 and 9.84 to 39.44%, respectively, whereas estimate of heritability in broad sense (H2%) for 13 traits ranged from 30.72 to 96.97% and genetic advance as percent mean (GAM) ranged from 6.23 to 78.9%. High and moderate GCV, PCV, H2 and GAM (%) were estimated for all traits except pod length and days to maturity. This indicated close correspondence between the genotype factors and phenotype expression of the genotypes for these traits and the selection of high performing genotypes could be possible to increase the mean of selected genotypes as compared to the base population. Grain yield had positive and significant correlation with days to maturity, plant height, pod per plant, number of seed/plant, number of branch per plant, number of pods per cluster, number of clusters per plant and hundred seeds weight at both genotypic and phenotypic levels. Hundred seeds weight, number of pods per plant, plant height and number of seeds/plant had moderate and positive direct effects on grain yield at genotypic level indicated the true relationship of these traits and yield suggested simultaneous selection was possible to increase the yield and these traits in soybean genotypes. The first four principal components with Eigen values >1 accounted for about 71% of the total variation observed among genotypes. The 13 traits each had small contribution to the total variability of genotypes suggested all traits could be used to group genotypes in different clusters. The genetic distance for all possible of pairs 64 soybean genotypes ranged from 3.02 to 13.89 and the genotypes were grouped into eight distinct clusters. Cluster VI, III and I consisted of 18, 10 and 9 genotypes, respectively, and these clusters accounted 57.81% of the genotypes and the other five clusters consisted of 3 to 8 genotypes. The four clusters, Cluster VIII, VII, III and I consisted 27 (42.19%) of the genotypes and had higher mean grain yield in the range between 10.94 and 22.39% than the overall mean yield of genotypes. The mean genotypes in these clusters was also higher than the overall mean of genotypes for most of agronomic traits and had higher inter-cluster distances between each other and with other four clusters suggested the development of varieties is possible by selection and/or crossing of genotypes from these clusters. The research results suggested the possibility of developing varieties for high yield and agronomic traits through selection and/or crossing of distant genotypes and further evaluation to East Hararghe 80p. 2024-09-01T00:00:00Z http://ir.haramaya.edu.et//hru/handle/123456789/8131 GENETIC VARIABILITY AND ASSOCIATION OF YIELD AND YIELD RELATED TRAITS IN SOYBEAN (Glycine max L.) GENOTYPES AT BOKO, EAST HARARGHE, ETHIOPIA GIRMA WAKGARI KABATA; Prof. Wassu Mohammed (PhD); Dr. Bulti Tesso (PhD) Soybean (Glycine max L.) production is increasing as a multipurpose crop used for oil production, as balanced diet and industry prepared foods and export in Ethiopia, but its production is not common in East Hararghe. Very limited research was conducted on genetic variability of soybean genotypes in Eastern Hararghe. This research was conducted to assess genetic variability among soybean genotypes for yield and yield related traits, and to determine the associations of traits. The 61 introduced soybean genotypes and three check varieties were evaluated for 13 traits in 2023 in 8 x 8 simple lattice design at Boko in East Hararghe. The significant differences among soybean genotypes for all traits were evident from the results of analysis of variance. The grain yield of genotypes ranged from 470.76 to 2727.62 kg ha-1 with a mean of 1488.06 kg ha-1 . Six introduced genotypes produced higher (2168.301 to 2727.62 kg ha-1 ) than yield of Maya variety (2078.86 kg ha-1 ) that produced highest yield among check varieties. The genotypic (GCV %) and phenotypic (PCV %) coefficient variations ranged from 5.45 to 38.84 and 9.84 to 39.44%, respectively, whereas estimate of heritability in broad sense (H2%) for 13 traits ranged from 30.72 to 96.97% and genetic advance as percent mean (GAM) ranged from 6.23 to 78.9%. High and moderate GCV, PCV, H2 and GAM (%) were estimated for all traits except pod length and days to maturity. This indicated close correspondence between the genotype factors and phenotype expression of the genotypes for these traits and the selection of high performing genotypes could be possible to increase the mean of selected genotypes as compared to the base population. Grain yield had positive and significant correlation with days to maturity, plant height, pod per plant, number of seed/plant, number of branch per plant, number of pods per cluster, number of clusters per plant and hundred seeds weight at both genotypic and phenotypic levels. Hundred seeds weight, number of pods per plant, plant height and number of seeds/plant had moderate and positive direct effects on grain yield at genotypic level indicated the true relationship of these traits and yield suggested simultaneous selection was possible to increase the yield and these traits in soybean genotypes. The first four principal components with Eigen values >1 accounted for about 71% of the total variation observed among genotypes. The 13 traits each had small contribution to the total variability of genotypes suggested all traits could be used to group genotypes in different clusters. The genetic distance for all possible of pairs 64 soybean genotypes ranged from 3.02 to 13.89 and the genotypes were grouped into eight distinct clusters. Cluster VI, III and I consisted of 18, 10 and 9 genotypes, respectively, and these clusters accounted 57.81% of the genotypes and the other five clusters consisted of 3 to 8 genotypes. The four clusters, Cluster VIII, VII, III and I consisted 27 (42.19%) of the genotypes and had higher mean grain yield in the range between 10.94 and 22.39% than the overall mean yield of genotypes. The mean genotypes in these clusters was also higher than the overall mean of genotypes for most of agronomic traits and had higher inter-cluster distances between each other and with other four clusters suggested the development of varieties is possible by selection and/or crossing of genotypes from these clusters. The research results suggested the possibility of developing varieties for high yield and agronomic traits through selection and/or crossing of distant genotypes and further evaluation to East Hararghe. 80 2024-09-01T00:00:00Z