{"title":"AMMI and GGE Biplot Analyses for Mega Environment Identification and Selection of Some High-Yielding Cassava Genotypes for Multiple Environments","authors":"Berhanu Bilate Daemo, Derbew Belew Yohannes, Tewodros Mulualem Beyene, Wosene Gebreselassie Abtew","doi":"10.1155/2023/6759698","DOIUrl":null,"url":null,"abstract":"Cassava (Manihot esculenta Crantz) is a staple food and generates income for smallholder farmers in southern Ethiopia. The performance of cassava genotypes varies in different growing environments; thus, the evaluation of genotypes tested in various environments plays an essential role in developing strategies to delineate environments, explore unstable genotypes in target environments, and identify stable genotypes for multiple environments. In this regard, there needs to be more information on the identification of mega-environments and stable genotypes with high yields for wide adaptation. Thus, this study aimed to identify mega-environment and high-yielding cassava genotypes for multiple environments using AMMI and GGE biplots. A total of 25 genotypes were evaluated in six environments using a RCBD during the 2020–2021 cropping season. The AMMI analysis of variances revealed that environments, genotypes, and genotype-environment interaction had a significant (\n \n P\n ≤\n 0.001\n \n ) influence on cassava fresh storage root yield (t·ha−1), showing genetic variability among genotypes by changing environments. The genotype-by-environment interaction showed a 61.36% contribution to the total treatment SS variation, while the environment and genotype effects explained 28.16% and 10.48% of the total treatment SS, respectively. IPCA1 and IPCA2 accounted for 33.42% and 23.5% of the GE interactions SS, respectively. The GGE biplot showed that the six environments used in this study were delineated into three mega-environments, namely, the first (Tarcha and Disa), the second (Wara and Areka), and the third (Jimma and Bonbe). Those mega-environments could be helpful for genotype evaluation and effective breeding. The GGE biplot indicated that the vertex genotypes were G16, G17, and G25. They are regarded as specifically adapted genotypes since they are more responsive to environmental change. The GGE biplot also revealed that Tarcha was ideal, having the most discriminating and representative environment, while G10 was the ideal and the overall winning genotype for the current study. Moreover, the genotypes G10 and G14 were identified as being the most stable, with a higher fresh storage root yield than the grand mean. Thus, G10 and G14 were selected as superior genotypes that could be promoted to advanced yield trials to develop stable cultivars with better storage root yield of cassava.","PeriodicalId":13844,"journal":{"name":"International Journal of Agronomy","volume":" ","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2023-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Agronomy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2023/6759698","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AGRONOMY","Score":null,"Total":0}
引用次数: 4
Abstract
Cassava (Manihot esculenta Crantz) is a staple food and generates income for smallholder farmers in southern Ethiopia. The performance of cassava genotypes varies in different growing environments; thus, the evaluation of genotypes tested in various environments plays an essential role in developing strategies to delineate environments, explore unstable genotypes in target environments, and identify stable genotypes for multiple environments. In this regard, there needs to be more information on the identification of mega-environments and stable genotypes with high yields for wide adaptation. Thus, this study aimed to identify mega-environment and high-yielding cassava genotypes for multiple environments using AMMI and GGE biplots. A total of 25 genotypes were evaluated in six environments using a RCBD during the 2020–2021 cropping season. The AMMI analysis of variances revealed that environments, genotypes, and genotype-environment interaction had a significant (
P
≤
0.001
) influence on cassava fresh storage root yield (t·ha−1), showing genetic variability among genotypes by changing environments. The genotype-by-environment interaction showed a 61.36% contribution to the total treatment SS variation, while the environment and genotype effects explained 28.16% and 10.48% of the total treatment SS, respectively. IPCA1 and IPCA2 accounted for 33.42% and 23.5% of the GE interactions SS, respectively. The GGE biplot showed that the six environments used in this study were delineated into three mega-environments, namely, the first (Tarcha and Disa), the second (Wara and Areka), and the third (Jimma and Bonbe). Those mega-environments could be helpful for genotype evaluation and effective breeding. The GGE biplot indicated that the vertex genotypes were G16, G17, and G25. They are regarded as specifically adapted genotypes since they are more responsive to environmental change. The GGE biplot also revealed that Tarcha was ideal, having the most discriminating and representative environment, while G10 was the ideal and the overall winning genotype for the current study. Moreover, the genotypes G10 and G14 were identified as being the most stable, with a higher fresh storage root yield than the grand mean. Thus, G10 and G14 were selected as superior genotypes that could be promoted to advanced yield trials to develop stable cultivars with better storage root yield of cassava.