Susceptibility to lodging is a major constraint on teff production in Ethiopia, but efforts to develop lodging-resistant cultivars have not been successful. We studied the mechanical properties of teff culms and associated agro-morphological traits in field experiments with 320 teff accessions at two sites in northwestern Ethiopia during the 2018 and 2019 growing seasons. The results showed significant variability in both mechanical properties and agro-morphological traits among accessions. Traits contributing to lodging resistance, such as internode diameter, pushing resistance, and base failure moment, were significantly positively correlated with each other and with plant height. Similarly, the correlation of those traits with lodging index was significant and positive. In contrast, tiller number showed a significant negative correlation with lodging index. The peduncle–panicle length, which generally accounted for 59% of the plant height, should be a target when breeding for semi-dwarfism. Root system development, which reached a depth of more than 1 m in tall and 57 cm in dwarf teff accessions, signifies the presence of genetic variabilities for future root lodging studies in teff, and it may also explain why teff performs well in drought-prone areas of Ethiopia. Breeding programs for lodging resistance might focus on accessions with good standing ability (high base failure moment) and introgression of stem strength with a semi-dwarf phenotype. Alternatively, selection for a large internode diameter, increased pushing resistance and base failure moment, and a reduced tiller number should be considered.

Teff is the most strategic cereal crop grown from high rainfall to drought prone areas of Ethiopia, where it covers nearly 30% of the land allotted for cereals. However, its productivity remains very low due to lack of knowledge and research interventions. To investigate the grain yield potential, estimate the genetic parameters, and the diversity, a pot experiment with intensive management and a field experiment with conventional management at two contrasting locations for two seasons using the same 317 genotypes and additional 3 improved cultivars in the field experiment were carried out. The results showed highly significant variation among the genotypes for grain yield, biomass, harvest index, and phenological traits under both experiments. The best linear unbiased predictor (BLUP)-adjusted grain yield performance of the genotypes ranged from 4.2 to 8.8 g/plant in the intensive management and 1.8 to 4.3 g/plant in the field growing condition with conventional management. Coefficient of genetic variation, heritability, and expected genetic advance for grain yield were the highest in both experiments. Among the phenological traits, the grain filling period in the intensive growing condition exceptionally showed the highest genetic coefficient of variation and genetic advance. The high grain yield performance and wider range of the harvest index observed under the intensive management condition with moderate to high heritability signifies the genetic potential of teff for further improvement through trait recombination.

Durum wheat (Triticum turgidum L. var durum) is an indigenous predominant tetraploid wheat species in Ethiopia. Ten released durum wheat varieties were evaluated during 2016 and 2017 main cropping seasons at five districts representing various agro-ecologies of northwestern Ethiopia. The experiment was laid-out using Randomized Complete Block Design, replicated three times. Grain yield and protein contents (%) of entries were scored and the ANOVAs were performed for the traits under study. Additive Main effect and Multiplicative Interaction (AMMI) model was used to determine the stability of the genotypes across environments. Analysis of variance revealed a significant difference among the tested varieties for the grain yield in all tested locations. The AMMI analysis of variance of the grain yield showed that the presence of a highly significant difference among genotypes, environments and genotype–environment interaction with the contribution of 10%, 75.6% and 14.4% of the total sum of square variance, respectively. Genotypes Denbi, OBSA and Tate were identified as the most stable genotypes. Mukiye and Utuba recommend for Shebel Berenta; Filakit and TOLTU recommend for Debre; Elias and Mangudo recommended for Wonberema woreda.

This study aims to acquaint breeders of the need to use statistical tools that will help resolve the identification of consistently better performing genotypes across various environmental conditions. It also aim to reveal the relationship among the various statistical methods used to describe genotype × environment interaction (GEI) and cultivar stability. A mixed model with fixed genotypes and random environments were used for the analysis of variance (ANOVA). In the present study, twenty released bread wheat cultivars were evaluated during the 2009 main cropping season using a randomized complete block design (RCBD) with three replications at seven different environments. The combined ANOVA revealed the presence of a highly significant GEI (p < 0.01) for grain yield indicating its influence on cultivar selection and recommendation. Spearman’s rank correlation coefficient revealed a perfect correspondence between Wricke’s ecovalence (Wi) and Shukla’s statbility variance (s2). These stability measures also showed a highly significant positive rank correlation with deviation from regression (S2di), coefficient of determination( ri2), AMMI stability value (ASV), variance of ranks (Si(2)), rank sum (R-sum), and mean absolute rank difference (Si(1)) indicating their similarity in cultivar ranking. The principal component analysis (PCA) clearly showed three groupings of the statistical methods as the static concept of stability, the dynamic concept of stability and yield performance measures. Therefore, it is imperative to consider one stability measure from the dynamic concept and one from the yield performance measures for efficient cultivar recommendation. Key words: Bread wheat, genotype × environment interaction (GEI), principal component analysis (PCA), rank correlation, stability measures.