Mega-environment targeting of maize varieties using Ammi and GGE bi-plot analysis in Ethiopia
In 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.
| Main Authors: | , , , , , |
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| Format: | Article biblioteca |
| Language: | English |
| Published: |
Ethiopian Institute of Agricultural Research
2018
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| Subjects: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, FIELD EXPERIMENTATION, PLANT BREEDING, VARIETY SCREENING, ENVIRONMENTAL FACTORS, STATISTICAL METHODS, |
| Online Access: | https://www.ajol.info/index.php/ejas/article/view/176748/166129 https://hdl.handle.net/10883/19947 |
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