No abstract

Background and justification: Ethiopia is the third largest sorghum producer in Africa next to Nigeria and Sudan. Shortage of widely adapted and stable high yielding variety is one of the major bottlenecks for production and productivity of sorghum in the country. Grain yield performance is not the only parameter for selection as a genotype with the highest grain yield would not necessarily mean stable and adaptable across location and years. Eberhart and Russell (1996) and AMMI model could be the preferable tools to identify stable, high yielding and adaptable genotype (s) for wider or specific environments. Objectives: To identify stable high yielding sorghum varieties that could be adapted for wider and/or specific environments and make recommendations for further demonstration and production in the test environments and similar agro ecologies. Material and methods: A total of 21 released sorghum varieties and a local check were evaluated at three locations in western Ethiopia (Bako, Gute, Biloboshe) and two locations in eastern Ethiopia (Mechara and Mieso) in 2017 main cropping seasons. The trial was arranged in a randomized complete block design (RCBD) in three replications. Summary of results and application of the study: The combined analysis of variance revealed highly significant effect of environment and genotype by environment interactions for grain yield. This indicated that the tested varieties showed inconsistent grain yield performance across locations. Birmash variety gave the highest grain yield with average yield of 3.5 ton ha-1 but specifically adapted to Gute, Biloboshe and Mechara. Baji was the second high yielding variety with mean grain yield of 3.3 ton ha-1 and relatively with wider adaptability. The first two IPCAs accounted for a total of 88.64% of the interaction sum square. In general, deviation from regression coefficient, AMMI stability value and genotype selection index revealed that Baji, Birmash, Emahoy, IS9302 and Gambella-1107 were relatively stable varieties with optimum grain yield and therefore recommended for further demonstration and popularization in the test locations and areas with similar agro-ecologies.

Cowpea is one of the most important grain legumes for human consumption and animal feeding. Despite this importance, its production is hampered by biotic and abiotic constraints. Genotype by environment interaction study was performed to identify the most stable cowpea genotype(s) and the desirable environment(s) for cowpea research in Ethiopia. Twenty‐four cowpea landraces and one standard check were evaluated for grain yield and yield‐related traits at six locations (Sekota, Kobo, Sirinka, Melkassa, Mieso, and Babile) using 5 × 5 triple lattice during 2019. Combined analysis of variance showed that grain yield was significantly affected by environments, genotypes, and GE interactions. AMMI analysis revealed the contribution of environment, genotype, and GEI for 29.79%, 15.6%, and 42.06% of variation on grain yield. The first two principal components explained 57.97% of the total GEI variance. AMMI model selected G24 as 1st and 2nd best genotype at five environments. The polygon view of the GGE biplot identified three mega‐environments (ME1, ME2, and ME3) with winning genotypes: G24, G3, and G16, respectively. The highest productive (2528.8 kg ha−1) environment, miesso has been identified as the most; discriminating and representative testing environment whereas the lowest productive (1676.1 kg ha−1) Sirinka was the least discriminating and representative. The highest yielder G24 (2632 kg ha−1) was identified as the “ideal” and the most stable genotype followed by G16 (2290 kg ha−1) while the least stable and low yielder was G11. Therefore, genotypes G24 and G16 were recommended for verification and commercial production in most cowpea growing areas of Ethiopia.

The study was conducted to estimate the effects of genotype, environment, and genotype × environment interaction on grain yield and yield-related traits and to identify stability genotype. At six environments, twenty-four cowpea landraces and one check were evaluated in a 5 × 5 triple lattice during the 2019 cropping season. Data were collected on yield and yield-related traits. The analysis of variance for each environment and across environments showed significant differences among genotypes, environments, and GEI for most traits including yield. Environment, genotype, and GEI showed 27.45%, 20.9%, and 49.55% contribution to the total sum of squares, respectively, for grain yield. This indicated that the environments were diverse and most of the variation in grain yield was caused due to interaction and environmental means. G24 (2632 kg ha−1) and G16 (2290 kg ha−1) were the highest yielder and stable genotypes with mean grain yields above the grand mean (2049.28 kg ha−1) and standard check (2273 kg ha−1). G24 and G16 were the most stable genotypes according to cultivar superiority, Wricke’s ecovalence, regression coefficient, and devotion from regression stability models.

Developing high yielding bread wheat genotypes with superior agronomic trait requires understanding the extent of genetic variability in existing germplasm. The current study was designed to generate information on genetic variability and inheritance of yield and its component traits using 36 advanced bread wheat lines. Field experiment was conducted in 2018 cropping season at Haramaya and Hirna in eastern Ethiopia using triple lattice design. Fifteen quantitative traits were measured and subjected to analysis of variance and genetic analyses. Analysis of variance revealed that there was highly significant difference among genotypes for all quantitative traits at both locations. The lowest and the highest phenotypic (PCV) and genotypic (GCV) values were obtained for hectoliter weight and number of fertile tillers per plant, respectively at both locations. Heritability in broad sense and genetic advance as percent of mean ranged from 39.1% (number of kernels per spike) to 90.1% (days to heading), and from 2.4% (hectoliter weight) to 48.5% (number of fertile tillers), respectively at Haramaya. At Hirna, these parameters ranged from 29.3% (hectoliter weight) to 91.9% (grain yield), and from 1.5% (hectoliter weight) to 27.6% (total tillers per plant), respectively. high heritability coupled with high genetic advance as percent of mean was observed for total number of tillers per plant, number of fertile tillers per plant, grain yield per hectare, and above ground biomass per hectare at Haramaya. and for spike length, total number of tiller per plant, number of fertile tiller per plant, grain yield per hectare, and above ground biomass per hectare at Hirna. This indicates that improvement of these traits through selection is easier than other traits measured.