Multi-environment trials (METs) represent the final stage of breeding programs prior to the commercial release of new varieties. Optimized analysis of METs impact genetic gains subsequently delivered to cane growers. The information provided by METs can be large and therefore complex to analyze and interpret when considering many environments of selection. An overview and a comprehensive interpretation ofMETs can be laborious on the sole basis of many tables of summary data and quantitative analyses of yield components. A complementary approach to interpreting many tables of figures can be obtained using "Genotype main effect plus Genotype-by-Environment" (GGE) analysis. The two-way data table of adjusted genotype means x locations is first standardized by environment. The resulting "standardized GGE matrix" of genotype main effect (G) and genotype x environment interaction (GE) is then subjected to a singular value partitioning between the genotype and environment eigenvectors. Genotypes and environments are represented on biplots defined by axes representing the most significant principal components (PCs). In order to assess effectiveness of GGE biplots to analyze sugarcane METs of Reunion Island, GGE analysis was performed on 21 sugarcane varieties tested in the MET network of eRcane that consists of seven sites of selection. These sites cover a wide range of ecologies of production representative of the main sugarcane growing areas of the industry. Varieties were assessed during two crop-cycles for tonne cane per hectare (TCH), estimable recoverable sugar (ERS), fiber content (FIB) and an economic index (EI). A biplcit represented by both PCl and PC2 : (i) adequately approximated the total GGE variation of TCH (76.52%) and ERS (71.55%) data, (ii) represented very accurately the GGE data of FIB (90.23%) and (iii) represented less efficiently the GGE data of EI (63.41 %). Such two-dimensional GGE biplots of genotypes and locations permitted to visualize at a glanc