Search

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, P i , 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., Competing Interests: The authors are composition of researchers and instructor in different disciplines like crop production and protection. These authors contributed in different parts of the research planning, implementation, data collection, data analysis, manuscript drafting and editing. Every member contributed in the research is included in this manuscript. Hence, the authors declare that there are no conflicts of interest. Furthermore, this investigation received no specific fund from any funding agency, commercial or not-for-profit sectors., (© 2024 The Authors. Published by Elsevier Ltd.)

Introduction: Intercropping contributes a significant role in sustainable agricultural system as it improves soil conservation, productivity and stability of yield and makes better use of production elements. However, the economic advantages and dis advantages of sesame and legume intercropping is not quantified., Methods: Field experiment, consisted of seven treatments, conducted in 2016 and 2017 cropping seasons using randomized complete block design with three replications. Land equivalent ratio, Competitive Ratio (CR), Relative crowding coefficient (K), Aggressivity (A), Sesame equivalent yield, Legume equivalent yield (LEY), Monetary Advantage Index, Area Time Equivalent Ratio (ATER), Land Saved and correlation of the indexes were competed., Results: The highest total LER (0.63) recorded from sesame intercropped with half as well as full seed rate of mungbean. Sesame intercropped with half seed rate of mungbean showed lower CR (1.57) and the total K of the system was greater than a unity in three of the treatments except in sesame + half seed rate of soybean with total K of 0.81. Sesame intercropped with half seed rate of soybean was dominant with aggressivity (0.13) while it was dominated in both of the other treatments with negative aggressivity. The highest MAI (1181.15 and 1239.1) was gained from sesame intercropped with mungbean at full and half seed rates respectively and the ATER value of sesame + half seed rate of soybean was greater than a unity while sesame + half seed rate of soybean scored lower than unity (0.94). Grain yield showed a positively strong correlation with LEY, SEY, and Ktot while it showed a negative correlation with CRs and As. Based on the PCA biplot the intercropping indexes grouped in to three clusters (I-III): cluster I, II and III consisted of 3, 1 and 9 intercropping indexes respectively., Conclusion: LS, LER and MAI are the best indexes to compute the economic profitability of a mixed cropping. Generally, based on the LS, LER and MAI sesame-legume intercropping practice is economically profitable., Competing Interests: The authors declare no conflict of interest.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

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., (© 2020 The Authors.)