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 and mega environments. Randomized complete block design with three replications was used. GGE 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. Among the tested genotypes in this study, three candidate genotypes (2, 3 and 8) were selected and verified considering their better performance. Of which, genotype 2 has been officially released for large scale production with the common name ‘’NERICA-12’’.
Highlights
Among the target commodities that have received due attention in promotion of agricultural production, rice is considered as the “millennium crop” expected to contribute in ensuring food security in Ethiopia (MoARD, 2010)
The significant genotype × environment interaction effects demonstrated that genotypes responded differently to the variation in environmental conditions of location indicating the necessity of testing rice varieties at multiple locations
The result of this study indicated that upland rice yield performance was influenced by the environment effect followed by genotype × environment interaction (GEI) and genotype
Summary
Among the target commodities that have received due attention in promotion of agricultural production, rice is considered as the “millennium crop” expected to contribute in ensuring food security in Ethiopia (MoARD, 2010). The general rice breeding scheme includes evaluating a number of genotypes at various stages and testing selected ones at several environments. The multienvironment testing usually results in genotype-by-environment interactions that often complicate the interpretation of results obtained and reduces efficiency in selecting the best genotypes. This interaction is the result of changes in cultivar’s relative performance across environments, due to differential responses of the genotypes to various edaphic, climatic and biotic factors. Gauch and Zobel (1996) explained the importance of GEI as: “Were there no interaction, a single variety any other crop would yield the most the world over, and the variety trial need be conducted at only one location to provide universal results’’ Information on genotype × environment interaction leads to successful evaluation of genotypes and test environments. Analysis of genotype –by- environment data from multienvironment trials has been an important component of plant breeding and cultivar recommendation (Yan, 2011)
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