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CROP BREEDING, GENETICS & CYTOLOGY

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

^a Texas A&M Univ. System Agric. Research and Extension Center, 1509 Aggie Drive, Beaumont, TX 77713
^b USDA-ARS, 1509 Aggie Drive, Beaumont, TX 77713

^* Corresponding author (sosamonte{at}aesrg.tamu.edu)

The identification of the highest yielding cultivar for a specific environment on the basis of both genotype (G) and genotype x environment (GE) interaction would be useful to breeders and producers since yield estimates based only on G and environment (E) effects are insufficient. The objective of this study was to demonstrate the usefulness of additive main effects and multiplicative interactions (AMMI) model analysis and G plus GE interaction (GGE) biplots, obtained from sites regression (SREG) model analysis in interpreting GE grain yield data. Replicated grain yield data of six rice (Oryza sativa L.) cultivars (Cocodrie, Cypress, Jefferson, Lemont, Saber, and Wells) from three main cropping seasons (2000, 2001, and 2002) at four locations in Texas, USA (Bay City, Eagle Lake, Ganado, and Beaumont) were obtained and used for this purpose. Through AMMI model analysis, the magnitude and significance of the effects of GE interaction and its interaction principal components relative to the effects of G and E were estimated. The stability and adaptability of specific cultivars were assessed by plotting their nominal grain yields at specific environments in an AMMI biplot, which aided in the identification of megaenvironments (environments with the same highest yielding cultivar). Appropriate check cultivars for all locations or for specific locations were identified. Through GGE biplots of SREG model analysis results, the relative yield performance of cultivars at a specific environment were illustrated, the performance of a cultivar at different environments was compared, the performance of two cultivars at different environments were compared, the highest yielding cultivars at the different megaenvironments were identified, and ideal cultivars and test locations were identified.

Abbreviations: AMMI, additive main effects and multiplicative interactions • E, environment • G, genotype • GE, genotype x environment • GEI, genotype environment interaction • GGE, genotype and genotype x environment • IPCA, interaction principal component analysis • MET, multiple-environment trial • PCA, principal component analysis • SREG, sites regression • SVD, singular value decomposition

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