Performance of Rain Fed Lowland Rice Genotypes in Multi Environment Trials as Analyzed Using Ggebiplot

Abstract

Twenty rainfed lowland rice genotypes were evaluated at four locations of ten environments in western and north western part of Ethiopia from 2009 to 2011 to identify stable and high yielding genotypes for possible release and to determine mega environments. Randomized complete block design with three replications was used. GGE (G= genotype plus GE= genotype-by-environment interaction) biplot methodology was used for graphically display of grain yield data. The combined analysis of variance revealed that environment (E) accounted for 43.3% of the total variation while G and GEI captured 10.3% and 25.8%, respectively. The first 2 principal components (PC1 and PC2) were used to create a 2-dimensional GGE biplot and explained 34.7% and 22.9% of GGE sum of squares (SS), respectively. Genotypic PC1 scores >0 detected the adaptable and/or higher-yielding genotypes, while PC1 scores <0 discriminated the non-adaptable and/or lower-yielding ones. Unlike genotypic PC1 scores, near-zero PC2 scores identified stable genotypes, whereas absolute larger PC2 scores detected the unstable ones. On the other hand, environmental PC1 scores were related to non-crossover type GEIs and the PC2 scores to the crossover type. Of the tested genotypes, G17, G11, G9, and G20 were found to be desirable in terms of high yielding ability and stability. Based on GGE biplot analysis, the test environments were classified in to three mega-environments (Mega-1, Mega-2 and Mega-3). Mega -1 included environments such as WO-1,WO-4 and WO-7 ( all are representing Woreta) with genotype 4 as a winner; Mega-2 constituted environments such as AZ-2, AZ-10(Addis Zemen) and AS-5 and AS-10 (Assosa) with genotype 17 as a winner and Mega-3 contained environments including PA-3, PA-6 and PA-9 (all are representing Pawe) with genotype 2 as winner. The two testing locations (Addis Zemen and Assosa) were found to be combined in Mega environment-2 and highly correlated, indicating as there is no need to conduct variety trial at both locations as the result in one can represent the other. By doing so research cost can be reduced. The result of this study cab be used as a driving force for the national rice breeding program to design breeding strategy that can address the request of different stakeholders for improved varieties through either exploiting or avoiding the effect of GEI. Among the tested genotypes in this study, three candidate genotypes including genotype 17, 11 and 9 were selected and verified. Of which , considering their performance in terms of grain yield , stability, farmers’ preferences and other desirable agronomic traits , genotype 9 has been officially released as wide adaptable variety with better performance for large scale production with the vernacular name ‘’HIBER’’ meaning unity. Keywords : Multi-Environment Trials, GGE biplot, analysis, G × E interaction, lowland rice.