http://ir.haramaya.edu.et//hru/handle/123456789/43 2026-04-09T11:42:55Z 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 http://ir.haramaya.edu.et//hru/handle/123456789/7975 ASSESSMENT OF PROBLEMS ASSOCIATED WITH ARTIFICIAL INSEMINATION AND ITS EFFICIENCY EVALUATION IN SMALLHOLDER DAIRY FARMS OF WEST WALLAGA ZONE, OROMIA ETHIOPIA Marga Fikadu Duguma; (PhD) Kefelegn Kebede Kefenie; Dr. Yesihak Yusuf Mumed The study was conducted in purposively selected three districts of the West Wallaga zone based on the utilization of AI to assess the major problems of artificial insemination service and its efficiency evaluation in smallholder dairy farmers. Cross-sectional and retrospective study designs were used and a questionnaire survey was prepared for 222 smallholders, 40 animal health professionals, and 30 Artificial insemination technicians were asked about problems of AI services accordingly. Data were analyzed by using JMP pro.16. Among the respondents, 38.5%, 24.32%, and 35.90% used AI service regularly without interruption in Gimbi, Lalo Asabi, and Najo respectively and 61.43%, 75.68%, and 64.10% in respective district didn’t get the service regularly due to discontinuation of service on weekends and holidays, long distance to get the service, shortage of artificial insemination technician, shortage of input. Regarding their level of satisfaction, the majority of the respondents were unsatisfied with the overall AI service in the study area due to different problems including, conception failure (40.32%), heat detection problems (35.48%), insufficiency support from the concerned body (16.13%) and AIT problem (8.05%) for Gimbi. The respective values for Lalo Asabi and Najo were 44.93%, 26.309%, 17.39%, 11.59 and 46.48%, 33.8%, 14.08% and 5.63 respectively. The majority of the animal health professionals revealed that they did not get on-the-job training. On top of that, fifty percent of the AIT did not provide service on weekends and holidays in all districts. The effect of age, parity, and breed on the reproductive performance of cows showed that the breed has a significant effect on reproductive performance at (p-value 0.0001) while age and parity did not. From retrospective data, the number of conceptions increased from 17.3% in 2019 to 23.9% in 2022, and not conceived decreased from 4% in 2019 to 3% in 2022 in Lalo Asabi. In the same way in Gimbi, the conception number increased from 19.8% in 2019 to 22% in 2022, and not conceived increased from 2.6% in 2019 to 4% in 2022. In Najo number of conceived increased from 17.2% in 2019 to 19.3% in 2022 while not conceived increased from 5.1 in 2019 to 8.5% in 2022. The percentage of healthy calf births increased from 19.3% in 2019 to 22.4% in 2022 in Gimbi while it increased from 18.8% in 2019 to 24% in 2022 Lalo Asabi and increased from 19.9% in 2019 to 21% in 2022 for Najo district. Regular training should be given to animal owners and AI technicians on overall AI services as a means of resolving problems. All concerned bodies (government and NGO) should take responsibility for supporting AI services in the study area and AI input should be supplied timely 99p. 2023-06-01T00:00:00Z