Targeting Cultivars onto Rice Growing Environments Using AMMI and SREG GGE Biplot Analyses

ses were not always effective in analyzing the MET data structure. The ANOVA is an additive model that The identification of the highest yielding cultivar for a specific describes main effects effectively and determines if GE environmentonthebasisofbothgenotype(G)andgenotypeenvironment (GE) interaction would be useful to breeders and producers since interaction is a significant source of variation, but it yield estimates based only on G and environment (E) effects are does not provide insight into the patterns of genotypes insufficient. The objective of this study was to demonstrate the useful- or environments that give rise to the interaction. The ness of additive main effects and multiplicative interactions (AMMI) PCA is a multiplicative model that contains no sources model analysis and G plus GE interaction (GGE) biplots, obtained of variation for additive G or E main effects and does from sites regression (SREG) model analysis in interpreting GE grain not analyze the interactions effectively. The linear reyield data. Replicated grain yield data of six rice (Oryza sativa L.) gression method uses E means, which are frequently a cultivars (Cocodrie, Cypress, Jefferson, Lemont, Saber, and Wells) poor estimate of environments, such that the fitted lines from three main cropping seasons (2000, 2001, and 2002) at four locain most cases account for a small fraction of the total tions in Texas, USA (Bay City, Eagle Lake, Ganado, and Beaumont) GE (Zobel et al., 1988). The AMMI model analysis were obtained and used for this purpose. Through AMMI model analysis, the magnitude and significance of the effects of GE interaction and combines the ANOVA (with additive parameters) and its interaction principal components relative to the effects of G and PCA (with multiplicative parameters) into a single analE were estimated. The stability and adaptability of specific cultivars ysis. The AMMI model analysis is useful in making wereassessedbyplottingtheirnominalgrainyieldsatspecificenviron- cultivar recommendations, specifically by megaenvironments in an AMMI biplot, which aided in the identification of mega- ment analysis, in which the best performing cultivar for environments (environments with the same highest yielding cultivar). each subregion of the crop’s growing region is identified Appropriate check cultivars for all locations or for specific locations (Zobel et al., 1988; Gauch and Zobel, 1997). Gauch and were identified. Through GGE biplots of SREG model analysis re- Zobel (1997) demonstrated the usefulness of AMMI sults, the relative yield performance of cultivars at a specific environ- analysis in supporting breeding program decisions, such ment were illustrated, the performance of a cultivar at different envias in the selection of environments or test site locations. ronments was compared, the performance of two cultivars at different environments were compared, the highest yielding cultivars at the dif- Although AMMI model analysis results are based only ferent megaenvironments were identified, and ideal cultivars and test on yield data (not environmental data), Ebdon and locations were identified. Gauch (2002a) reported that AMMI environmental (interaction) statistics were correlated with environmental factors, such as precipitation, mean daily maximum and