AMMI and GGE Biplot Analyses for Mega-Environment Identification and Selection of Some High-Yielding Oat (Avena sativa L.) Genotypes for Multiple Environments

This paper reports an evaluation of eleven oat genotypes in four environments for two consecutive years to identify high-biomass-yielding, stable, and broadly adapted genotypes in selected parts of Ethiopia. Genotypes were planted and evaluated with a randomized complete block design, which was repeated three times. The additive main effect and multiplicative interaction analysis of variances revealed that the environment, genotype, and genotype–environment interaction had a significant (p ≤ 0.001) influence on the biomass yield in the dry matter base (t ha−1). The interaction of the first and second principal component analysis accounted for 73.43% and 14.97% of the genotype according to the environment interaction sum of squares, respectively. G6 and G5 were the most stable and widely adapted genotypes and were selected as superior genotypes. The genotype-by-environment interaction showed a 49.46% contribution to the total treatment of sum-of-squares variation, while genotype and environment effects explained 34.94% and 15.60%, respectively. The highest mean yield was obtained from G6 (12.52 kg/ha), and the lowest mean yield was obtained from G7 (8.65 kg/ha). According to the additive main effect and multiplicative interaction biplot, G6 and G5 were high-yielding genotypes, whereas G7 was a low-yielding genotype. Furthermore, according to the genotype and genotype–environment interaction biplot, G6 was the winning genotype in all environments. However, G7 was a low-yielding genotype in all environments. Finally, G6 was an ideal genotype with a higher mean yield and relatively good stability. However, G7 was a poor-yielding and unstable genotype. The genotype, environment, and genotype x environment interaction had extremely important effects on the biomass yield of oats. The findings of the graphic stability methods (additive main effect and multiplicative interaction and the genotype and genotype–environment interaction) for identifying high-yielding and stable oat genotypes were very similar

Wzrost, plonowanie i jakość ostryżu długiego (Curcuma longa L.) w odpowiedzi na poziom nawożenia azotem oraz termin jego aplikacji

Soil fertility decline is one of the factors that result in low productivity of turmeric (Curcuma longa, Zingiberaceae Lindl.,) in Ethiopia. An experiment was conducted to determine the effects of N rate and time of application on growth, yield, and quality of turmeric crops in Ethiopia. The trial consisted of five N rates: 0, 46, 69, 92, 115 kg ha−1, and five split application times: full dose at emergence, two times (1/2), three times (1/3), four times (1/4), and five times (1/5) equally split applications, arranged in a split plot design with three replications. Plant heights, tiller number per plant, pseudo-stem girth, mother and finger rhizome numbers and weights, fresh rhizome yield, oleoresin and essential oil contents – all were significantly affected by the interaction effects of N rate and time of application. The three times split application of 115 kg N ha−1 produced higher values of these crop characteristics. This application rate also produced a better yield and quality than did two times of application, the most commonly used practice. Therefore, turmeric producers in southwestern Ethiopia should apply 115 kg N ha−1 in three equally split applications to improve turmeric yield and quality

Phenotypic characterization of Amaro coffee (Coffea arabica L.) local accessions using multi-variate techniques at Awada, southern Ethiopia

Open Access Article; Published online: 17 May 2022As a country of origin of coffee, Ethiopia is endowed with an immense diversity of the crop in its diverse coffee-growing agro-ecologies. Amaro Kelo is one of the major coffee production agro-ecologies in Ethiopia, where the genetic diversity of its landrace coffee germplasm was not properly characterized previously. The study aimed to characterize 64 Amaro Kelo local coffee accessions to understand the potential of the accessions for utilization in future coffee genetic improvement efforts. The experiment was laid out in an 8 × 8 simple lattice design with two replications at Awada Agricultural Research Sub-Center. Data were collected on 19 quantitative and 10 qualitative traits, and subjected to multivariate analyses, i.e. cluster and principal component analyses. The cluster analysis identified five clusters based on the quantitative characters, and the distances between most of the clusters were highly significant at P < 0.01. Principal component analysis revealed the first six principal components with Eigenvalues greater than one accounted for 77.7% of the total variation. The first two principal components with respective contributions of 23.32 and 18.85% cumulatively accounted for 42.2% of the total variation in the accessions. In addition, high values of Shannon-diversity index were found for the qualitative traits: branching habit, growth habit, fruit shape, overall appearance and stem habit. In general, the multivariate analyses confirmed the presence of high variation among the studied Amaro-Kelo coffee accessions that might serve as an important genetic resource for future coffee genetic improvement or conservation efforts