Sesame production under irrigation is limited in Ethiopia because of in availability of high yielding varieties, inadequate and inefficient irrigation schemes, and insignificant awareness of producers. This study, comprising 13 sesame genotypes, was conducted around Humera and Werer during 2018 and 2019 under irrigation. The design was randomized completely block design with three replications and the objectives were to develop high yielding genotypes and identify important agronomic traits. Multivariate statistical methods like Additive Main Effect and Multiplicative Interaction (AMMI) model, Principal Component Analysis, Cluster and factor analyses were used. The genotypes (6.22%), environments (42.62) and Genotype × Environment Interactions (25.09%) were statistically (p < 0.001) significant for the agronomic traits. The grain yield in each observation varied from 383 kg/ha to 2044 kg/ha and the grand mean yield was 820.19 kg/ha. The highest mean yield was recorded from G12 (948.6 kg/ha) followed by G4 (938.9 kg/ha) while the lowest was recorded from G8 (703.1 kg/ha). G1, G4, G12, G5, G8, G11 and G13 are identified as unstable genotypes while G2, G3, G6, and G9 are stable genotypes. The genotypes were grouped in to four clusters and cluster-II was characterized as the high yielding cluster and it was also associated with grain yield, pods per plant, branches per plant and thousand seed weight. Branches per plant, pods per plant and thousand seed weight may be most determinant and crucial in developing high yielding sesame varieties. This finding recommends that G4 and G6 are desirable genotypes and can be used for irrigation production.

The experiment was conducted from 2009-2011 cropping seasons. Six mung bean genotypes viz. SML-668, Black bean, Bored, Local Gofa, SML-32 and Local 2-Sheraro were evaluated to early maturing and high yielding genotypes and the design was RCBD with three replications. The combined analysis of variance reviled that there was highly significant variation (p < 0.01) of grain yield among the genotypes, environments and genotype by environment interaction. The genotypic, environmental and the genotype x environment interaction (GIE) accounted about 30.47%, 45.01% and 11.59% of the total variation. The average grain yield of the genotypes was 2008.17 kg/ha. The highest and the lowest mean yield was obtained from SML-668 (2536.47 kg/ha) and SML-32 (1773.59 kg/ha) respectively. The AMMI bi-plot also depicted that, SML-668 and SML-32 were the high yielding and low yielding genotype, respectively. Similar to the AMMI bi-plot, the GGE bi-plot also confirmed that SML-668 was the winning genotype in most of the environments; whereas, SML-32 and local 2-sheraro, were the low yielding genotypes in some or all of the environments. E1, E2 and E6 are discriminating environments and declared as the most representative than E3, E4 and E5. Generally, SML-668 was the ideal genotype with higher mean yield and relatively good stability; Local-2 Sheraro was the moderately good yielding genotype and the most unstable genotype; Whereas, SML-32 was the poorly yielding and unstable genotype.

The experiment was carried out to evaluate the level of resistance of different genotypes, to study the incidence and severity of bacterial blight, fusarium wilt and phyllody and to select bacterial blight, fusarium wilt and phyllody resistant genotypes across six locations and two years. The experiment was laid out in Randomized Complete Block Designs (RCBD) with three replications across all environments. Assessment was conducted on seventeen sesame genotypes in northern Ethiopia during 2014-2015 main seasons. 75, 70 and 25 percent diseases incidence and 50, 50, 10 percent severity of bacterial blight, fusarium wilt and phyllody was recorded respectively. The assessment result indicated that bacterial blight showed about 62.

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Sunflower is the most important oil crop ranked as fourth edible oil in the world. The study was conducted in Northern Ethiopia during 2017–2019 cropping seasons using randomized completely block design with three replications. The objective was to decipher the genotype-by-environment interaction (GEI) in multi-environment trials (MET) and identify adaptable sunflower genotypes. Combined ANOVA, AMMI ANOVA and Eberhart and Rusell regression were analyzed, and GGE bi-plots, AMMI1 and AMMI2 bi-plots, Principal component Analysis (PCA), multi-trait genotype–ideotype distance index (MGIDI), correlation network plot for sunflower traits were sketched. AMMI stability measures, Best Linear Unbiased Prediction (BLUP) based indexes; parametric and non-parametric statistics were computed using R-statistical software. In the AMMI ANOVA the main effects of the environment (E) (54.18% SS), genotype (G) (16.9% SS) and GEI (23.50% SS) were significant (p < 0.001). The genotypic Likely-hood Ratio Test revealed significant for all traits. The AMMI bi-plot and the GGE bi-plots selected G10 and G2 as the most adaptable genotypes. CV, HMGV, RPGV, HMRPGV, Pi, GAI, KRS, S(3) and S(6) also identified G10 as the most stable genotype. Based on the MGIDI, G10 (MGIDI = 1.45) and G5 (MGIDI = 2.19) are selected and these genotypes are recommended for further cultivation in Tigray.