Evaluation of Upland Rice Genotypes and Mega Environment Investigation Based on GGE-Biplot Analysis

Abstract

Though a recent introduction, rice is becoming the most important food and cash crop in Ethiopia. However, its productivity is being constrained mainly by lack of improved varieties. Identification of high yielding and stable genotype(s) and desirable environment(s) has been the most important objectives of multi-environment trials. The objectives of the present study were to explore the effect of genotype and genotype × environment interaction on grain yield of upland rice genotypes and to examine the possible existence of different mega environments. Grain yield performances of 13 upland rice genotypes were evaluated under rainfed conditions at three locations (Woreta, Pawe, Metema) in North Western Ethiopia from 2011 to 2013. The study was laid out in a randomized complete block design with three replications. The first two principal components (IPC1 and IPC2) were used to create a twodimensional GGE biplot that accounted for 76.80% and 20.05%, respectively. A combined analysis of variance showed that both main effects (environment and genotype) and GE interactions were highly significant. The polygon tool of the biplot suggested the existence of two upland rice mega-environments with wining genotypes G3, G9, G8 and G12. Visualizing the mean and stability parameters of the genotypes in the biplot indicated that genotypes NERICA-13 (G3), FOFIFA-3730 (G11) and NERICA-12 (G2) are adapted to rainfed upland rice growing areas of North Western Ethiopia. According to the ideal-genotype biplot, genotypes G3 followed by G2 and G11 were better genotypes demonstrating their high mean yield and stability performance.Based on discrimination and representation, Woreta was identified as the ideal location for generally adaptable upland rice genotypes and Metema for selecting specifically adaptable upland rice genotypes. Finally, it was concluded that genotypes; G2, G3 and G11 showed high grain yield and stability performance across environments and were promoted to variety verification. Of which, G2 has been officially released for large scale production with the common name "NERICA-12''.

Agronomic Performance Evaluation and Yield Stability Analysis of Upland Rice (<i>Oryza sativa</i> L.) Varieties Using AMMI and GGE biplot

“…A highly significant difference (P ≤ 0.01) was observed for genotype x location effects on days to heading, days to maturity, number of filled grains per panicle, plant height, and grain yield, but a significant difference (P ≤ 0.01) was detected for panicle length. The presence of a threeway interaction leads to the stability analysis for identifying which rice genotype adapted well in which location [10,11]. As presented in Table 3, varieties respond differently for all measured agronomic traits for each location.…”

“…Environments with low IPCA1 and IPCA2 scores which were placed near to the origin in the GGE biplot graph have low discriminating ability for genotypes evaluation and high contribution to the stability of the genotypes [15,16]. According to the figure presented below (Figure 3), the corner genotypes which have the longest vectors are the highest yielding genotypes for the environments that fall with in the sector [10,11]. The genotype with high yield in E3, E6, E5, E4, and E2 is genotype G12 followed by G10, G5 G11 and G9.…”

Multi-environment evaluation of foxtail millet advanced lines for forage yield stability

“…If one experimental site is similar to another one, then to delete one of the two experimental sites did not affect selecting genotypes when the test cost was less (YAN et al, 2007). Several authors have a lay emphasis on removing of similar environments according to GGE biplot analysis, however the decision on removing an environment or keeping it is very complicated (RAKSHIT et al, 2012;ZHANG et al, 2016;TARIKU, 2017). Non-genetic variations consisted of predictable and non-predictable components.…”

Yield Stability of Soybean Genotypes in Tropical Environments based on Genotype and Genotype-by-Environment Biplot

“…Desember 2018 A multi-environment trial over several environments and years is a way to overcome the GEI problem, to recognize the selected genotype with high and stable performance over a wide range of environments (Gedif et al, 2014;Rincent et al, 2017;Tariku, 2017).…”