The use of genotype main effect (G) plus genotype‐by‐environment (GE) interaction (G+GE) biplot analysis by plant breeders and other agricultural researchers has increased dramatically during the past 5 yr for analyzing multi‐environment trial (MET) data. Recently, however, its legitimacy was questioned by a proponent of Additive Main Effect and Multiplicative Interaction (AMMI) analysis. The objectives of this review are: (i) to compare GGE biplot analysis and AMMI analysis on three aspects of genotype‐by‐environment data (GED) analysis, namely mega‐environment analysis, genotype evaluation, and test‐environment evaluation; (ii) to discuss whether G and GE should be combined or separated in these three aspects of GED analysis; and (iii) to discuss the role and importance of model diagnosis in biplot analysis of GED. Our main conclusions are: (i) both GGE biplot analysis and AMMI analysis combine rather than separate G and GE in mega‐environment analysis and genotype evaluation, (ii) the GGE biplot is superior to the AMMI1 graph in mega‐environment analysis and genotype evaluation because it explains more G+GE and has the inner‐product property of the biplot, (iii) the discriminating power vs. representativeness view of the GGE biplot is effective in evaluating test environments, which is not possible in AMMI analysis, and (iv) model diagnosis for each dataset is useful, but accuracy gain from model diagnosis should not be overstated.

Utilization of genotype x environment (GE) interadion encountered in crop performance trials is an important issue among plant breeders and agronomists. Practical integration of yield and stability

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Diallel mating designs provide to breeders useful genetic information, such as general combining ability (GCA) and specific combining ability (SCA), to help them devise appropriate breeding and selection strategies. Here we report a much‐improved version of DIALLEL‐SAS that was originally released in 1997. The new program, DIALLEL–SAS05, has a clear and user‐friendly interface that was designed to meet users’ needs for various diallel‐cross design experiments. DIALLEL‐SAS05 has major advantages over DIALLEL‐SAS in that: (i) it analyzes not only all four Griffing's diallel methods (both fixed and random models), but it also computes Gardner–Eberhart's Analyses II and III; (ii) it provides desired results from diallel experiments with parent number from 4 to 12, (iii) it can analyze diallel data from any number of environments, and (iv) for a random‐effects model, it provides estimates of GCA (σ2g) and SCA (σ2s) variances, which can be used to estimate additive (σ2A) and dominance (σ2D) variances, and ultimately narrow‐sense heritability (h2). DIALLEL‐SAS05 also provides information on GCA × ENV, SCA × ENV, reciprocal × ENV, maternal × ENV, and nonmaternal × ENV interactions, when applicable. DIALLEL‐SAS05 should greatly improve researchers’ efficiency in analyzing and interpreting diallel‐cross data. The program code is available on a CD from the corresponding author.