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Abs tract. Additive, Main-effects and Multiplicative Interaction (AMMI) models are being widely used for analysing main-effects and genotypeby-environment (G´E) interactions in multilocation variety trials. This paper presents an AMMI analysis of the reaction to rice blast (Pyricularia oryzae) by 25 rice genotypes grown in unreplicated trials in six locations. The object is to gain insight into G´E in rice blast, and identify genotypes with high and stable resistance to the disease. Our results showed that contributions to treatment main-effects were: genotype (33%), environment (29%), and G´E (38%). Analysis of the interaction revealed decreasing magnitude of contributions to the G´E sum of squares in the order: interaction principal component axis 1 (IPCA1) = 39%, IPCA2 = 22%, IPCA3 = 20%, and IPCA4 = 12%. Correlation analysis related IPCA3 to total rainfall in the first month of experimentation. Considerable diversity was observed among the environments and in the reaction of the differential entries to blast disease. An overlap between two differentials suggest one could replace the other. Four improved upland varieties (IRAT13, LAC23, ITA257, and ITA235) and three improved lowland plant types (ITA116, ITA118, and ITA120) showed high and stable resistance to blast. Compared with their parents, this suggests progress in varietal improvement against blast in Africa. The paper discusses some applications of AMMI in the analysis and interpretation of multilocation blast screening data.

The additive main effects and multiplicative interaction (AMMI) model is used to analyse the grain yield data of 13 rice genotypes grown in 12 rainfed lowland rice environments. The trials were organized by the International Network for Genetic Evaluation of Rice in Africa (INGER-Africa) and conducted in Nigeria. Main effects due to environments (E), genotypes (G) and GxE interaction were found to be significant (P = 0.001). Cross validation analysis suggested that an AMMI model with one interaction principal component axis (IPCA) was most useful predictively , whereas GoUobs' test declared two components, IPCAl and IPCA2, statistically significant (P = 0.01). The IPCAl, however, accounted for most (47.8%) of the GxE sum of squares. Correlation and regression analysis, and relative scatter of genotype and environment points on the AMMI biplot suggest that the interaction partitioned in IPCAl resulted from differences in the days to flowering among the genotypes. The paper discusses these in relation to the occurrence of Fe toxicity at the test sites and varietal tolerance to the stress. When genotypes are tested over a range of environments, identifying the best-performing entry for application over the range of environments is often confounded by genotype x environment (G X E) interaction. In this paper, we used the additive main effects and multiplicative interaction (AMMI) model (Zobel et al. 1988, Gauch 1992 to analyse data from multilocation rainfed lowland rice trials conducted in Nigeria. Our aims were to assess the yield range of top-performing entries identified through a three-tier varietal selection protocol.The grain yield data from 13 entries of Oryza sativa from the 1994 and 1995 African Rainfed Lowland Rice Advanced Yield Trial (ARLRAT) were used for the work. The trials were conducted in Nigeria at two sites in IITA-Ibadan, Edozhigi, Wuya, Gadza, and Anfani. At each location the experiment was a randomized complete-block design with three replications. Unit plot size was 5x4 m". Plant stand geometry, fertilization, and pest control were in accordance with recommended practices in each experimental area. Seeds for all genotypes for trials were produced and processed by INGER-Africa to ensure desirable seed health, purity, and quality. The AMMI statistical model is a combination of customary analysis of variance (ANOVA), and principal component analysis (PCA). H = grand mean, ag = effect of genotype g, ^e = effect of environment e, (/)" = n"" characteristic root of the interaction taking N characteristic roots into account, ^^^ and f/en = nth eigenvector of genotype g and environment e, respectively, rge = residual interaction, and B^^ = the error. AMMI uses ordinary ANOVA to analyse main effects, and principal component to analyse the non-additive residual left over by the ANOVA model (Zobel et al. 1988, Gauch 1992. The AMMI analysis was implemented through the microcomputer software MATMODEL Version 2 (Gauch 1993). In the analysis, a combination of a single year and location was considered as an e...