Abstract

Ten upland New Rice for Africa (NERICA) and three upland non-NERICA rice genotypes were evaluated at three locations of six environments in north western Ethiopia from 2009 to 2011 to identify stable and high yielding genotypes for possible release and to identify 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 yield data. The combined analysis of variance revealed that environment (E) accounted for 32.2% of the total variation while G and GEI captured 20.3% and 21.1%, respectively. The first 2 principal components (PC1 and PC2) were used to create a 2-dimensional GGE biplot and explained 56.9 % and 20.6% 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. Among the tested genotypes 3, 2, 11, 13, 8 were found to be desirable in terms of higher yielding ability and stability in descending order. Based on GGEbiplot analysis, the test environments were classified in to three mega-environments. Mega -1  included environment  WO-1 (Woreta) with  genotype 9 as  a winner; Mega-2 constituted  environments such as  WO-3 and WO-5 (Woreta)  with  genotype 2 as a winner  and  Mega-3 contained  environments including  PA-2,PA-6(Pawe)  and ME-7(Metema) with  genotype 8 as winner. However, it is not justifiable to consider two mega-environments within one specified area. So that Mega environments 1 and 2 should be treated as one. The result of this study can 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 (2, 3 and 8) were selected and verified considering their better performance in terms of grain yield, stability, farmers’ preferences and other desirable agronomic traits. Of which, genotype 2 has been officially released for large scale production with the common name ‘’NERICA-12’