Farmers in the highlands of Ethiopia have developed locally adapted maize varieties for more than 300 years. In order to assess the phenotypic diversity among traditional Ethiopian highland maize accessions, a total of 180 accessions were evaluated for agro-morphological traits in a replicated randomized complete block design. The accessions varied significantly for all of the measured traits. Cluster analysis revealed the presence of four major clusters. Accessions collected from the different regions were distributed over all the phenotypic clusters, reflecting wide variation within a particular region, but low differentiation among regions. The first principal component, which explained 40.4% of the total variation, was due to days to tasseling and silking, plant and ear height, leaf length and days'to maturity. Traits directly selected by farmers (yield, kernels per row, rows per ear, and ear height) had the highest phenotypic coefficient of variation (PCV), whereas indirectly selected traits (ear diameter, days to tasseling and silking) showed lower PCV values. Number of kernels per row had high heritability and genetic advance as .percent of the mean and could be used as selection criterion to increase grain yield. Overall, the study indicated the existence of ample trait diversity in highland maize accessions, which can be exploited by hybridization and selection.

No abstract

Cassava is a significant contributor to food security and an income source for smallholder farmers in southern Ethiopia. However, little research effort has been done so far based on designing field experiment samples for the biochemical composition of cassava accession at the country level. The study was conducted to assess the biochemical composition of cassava accessions in southwest Ethiopia. Flour samples from the storage roots of 64 cassava accessions were collected and were run in duplicates. Data on 13 biochemical characters were collected and analyzed using standard methods. The analysis of variance showed significant to very highly significant differences among the tested accessions for biochemical composition. The flour moisture ranged from 4.83–10.11%, dry matter (89.89-95.17%), organic matter (86.71–92.65%), ash (2.1–3.96%), fiber (1.14–3.00%), fat (0.26-1.4%), crude protein (1.28-2.86%), starch (65.1–74.2%), carbohydrate (81.29–87.94%), energy (341.44–367.61 kcal/100g DM), and cyanide (1.67–3.14). The highest GCV = 29.54% was shown for crude fat, followed by GCV = 16.94% for crude fiber, and GCV = 16.11% for tannin, whereas, among the characters, dry matter was observed to be the lowest (GCV = 0.84%). The GAM ranged from protein 0.30% to 54.94% for fat, while heritability ranged from flour moisture and dry matter (17.29%) to 84.88% for cyanide. The first five principal components explained 80.1% of the total variation, with PC I accounting for 37%, PC II 15.4%, PC III 11.6%, PC IV 8.4%, and PC V 8.20% of the total variation. This study found the presence of high biochemical variability among the tested accessions’ roots and could be used to select accessions with desirable biochemical composition in future breeding work.

Cassava (Manihot esculenta Crantz) is a staple food and generates income for smallholder farmers in southern Ethiopia. The performance of cassava genotypes varies in different growing environments; thus, the evaluation of genotypes tested in various environments plays an essential role in developing strategies to delineate environments, explore unstable genotypes in target environments, and identify stable genotypes for multiple environments. In this regard, there needs to be more information on the identification of mega-environments and stable genotypes with high yields for wide adaptation. Thus, this study aimed to identify mega-environment and high-yielding cassava genotypes for multiple environments using AMMI and GGE biplots. A total of 25 genotypes were evaluated in six environments using a RCBD during the 2020–2021 cropping season. The AMMI analysis of variances revealed that environments, genotypes, and genotype-environment interaction had a significant ( P ≤ 0.001 ) influence on cassava fresh storage root yield (t·ha−1), showing genetic variability among genotypes by changing environments. The genotype-by-environment interaction showed a 61.36% contribution to the total treatment SS variation, while the environment and genotype effects explained 28.16% and 10.48% of the total treatment SS, respectively. IPCA1 and IPCA2 accounted for 33.42% and 23.5% of the GE interactions SS, respectively. The GGE biplot showed that the six environments used in this study were delineated into three mega-environments, namely, the first (Tarcha and Disa), the second (Wara and Areka), and the third (Jimma and Bonbe). Those mega-environments could be helpful for genotype evaluation and effective breeding. The GGE biplot indicated that the vertex genotypes were G16, G17, and G25. They are regarded as specifically adapted genotypes since they are more responsive to environmental change. The GGE biplot also revealed that Tarcha was ideal, having the most discriminating and representative environment, while G10 was the ideal and the overall winning genotype for the current study. Moreover, the genotypes G10 and G14 were identified as being the most stable, with a higher fresh storage root yield than the grand mean. Thus, G10 and G14 were selected as superior genotypes that could be promoted to advanced yield trials to develop stable cultivars with better storage root yield of cassava.