2019-02-082019-02-082018ISSN: 2415-2382https://www.ajol.info/index.php/ejas/article/view/176748/166129https://hdl.handle.net/10883/19947In multi-location experimental trials, test locations must be selected to properly discriminate between varieties and to be representative of the target regions. The objective of this study were to evaluate test locations in terms of discrimination ability, representativeness, and desirability, and to investigate the presence of mega-environments using AMMI and GGE models and to suggest representative environments for breeding and variety testing purposes. Among 19 maize varieties tested across 11 environments, mean grain yield ranged between 4.47 t/ha (BH545) to 7.49 t/ha (BH546). Both AMMI and GGE models identified G14 and G1 as desirable hybrids for cultivation because they combined stability and higher average yield. Nonetheless, as confirmed by GGE analysis BH546 was most closest to the ideal genotype hence, considered as best hybrid. Environment wise, E9 and E4 were the most stable and unstable test environments, respectively. The 11 test environments fell into three apparent mega-environments. E9 formed one group by its own, E1, E2, E3, E5, E6, E7, E8 and E11 formed the second group and E4 and E10 formed the third group. E3, E5 and, E7 were both discriminating and representative therefore are favorable environments for selecting generally adapted genotypes. E4, E9 and E10 were discriminating but non-representative test environments thus are useful for selecting specifically adapted genotypes. E8 and E11 were nondiscriminating test environments hence little information about the genotypes. The results of this study helped to identify mega-environments, also representativeness and discriminating power of test environments better visualized with the GGE bi-plot model.PDFCIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose.AGRICULTURAL SCIENCES AND BIOTECHNOLOGYAGRICULTURAL SCIENCES AND BIOTECHNOLOGYArticleFIELD EXPERIMENTATIONPLANT BREEDINGVARIETY SCREENINGENVIRONMENTAL FACTORSSTATISTICAL METHODSOpen Access