Investigation of genotype x environment interaction for Hordeum vulgare L. ssp. vulgare recombinant inbred lines in multi-environments of Tigray, Ethiopia
{"title":"Investigation of genotype x environment interaction for Hordeum vulgare L. ssp. vulgare recombinant inbred lines in multi-environments of Tigray, Ethiopia","authors":"Hailekiros Tadesse Tekle , Yemane Tsehaye , Genet Atsbeha , Fetien Abay Abera , Rogério Marcos Chiulele","doi":"10.1016/j.egg.2024.100231","DOIUrl":null,"url":null,"abstract":"<div><p>The study examined the impact of 166 barley genotypes on yield performance in Tigray, revealing that year, environmental, and genotype factors significantly influence grain yield per plant (GYP). The analysis used AMMI and GGE biplot models, revealing environment as the dominant factor (95.3%), followed by genotypes (2.8%). The genotypes G126, G60, G108, G64, G52, G12, G62, G104, G47, G10, G83, G66, G39, and G30 were found to be highly productive genotypes showing low interaction with environments (genotypes centered near the origin) for the AMMI2 biplot for the IPCA1 and IPCA2 in GEI. The GGE biplot analysis also showed that top-performing genotypes outperformed in grain yield per plant, while Saesa and Himblil parental varieties fell below the top genotypes with yield scores of 15.34 gm/plant and 16.55 gm/plant, respectively. The IPCA1 and average environment coordination (AEC) scores at Mekelle_2018/19 (E3 & E7), Aleasa_2019 (E6), and Habes_2018/19 (E4 & E8) revealed the most stable environments. Though unstable and distant from AEC, Ayba_2018/19 (E1 and E5) significantly contributed to genotype-environment interaction. GGE-biplot of the \"which-won-where\" showed the 8 environments grouped into 4 mega-environments, with the winning genotypes of each environment being G112 for Ayba_2018, G82 for Aleasa_2018, G25 for Mekelle_2018, G61 for Habes_2018, and G4 for Ayba_2019. Similarly, AMMI biplot analysis revealed high average yields across test locations, with RIL genotypes G36, G72, G25, G118, and G112 showing genetic advancements and potential for future breeding initiatives.</p></div>","PeriodicalId":37938,"journal":{"name":"Ecological Genetics and Genomics","volume":"31 ","pages":"Article 100231"},"PeriodicalIF":0.0000,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Genetics and Genomics","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2405985424000156","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Agricultural and Biological Sciences","Score":null,"Total":0}
引用次数: 0
Abstract
The study examined the impact of 166 barley genotypes on yield performance in Tigray, revealing that year, environmental, and genotype factors significantly influence grain yield per plant (GYP). The analysis used AMMI and GGE biplot models, revealing environment as the dominant factor (95.3%), followed by genotypes (2.8%). The genotypes G126, G60, G108, G64, G52, G12, G62, G104, G47, G10, G83, G66, G39, and G30 were found to be highly productive genotypes showing low interaction with environments (genotypes centered near the origin) for the AMMI2 biplot for the IPCA1 and IPCA2 in GEI. The GGE biplot analysis also showed that top-performing genotypes outperformed in grain yield per plant, while Saesa and Himblil parental varieties fell below the top genotypes with yield scores of 15.34 gm/plant and 16.55 gm/plant, respectively. The IPCA1 and average environment coordination (AEC) scores at Mekelle_2018/19 (E3 & E7), Aleasa_2019 (E6), and Habes_2018/19 (E4 & E8) revealed the most stable environments. Though unstable and distant from AEC, Ayba_2018/19 (E1 and E5) significantly contributed to genotype-environment interaction. GGE-biplot of the "which-won-where" showed the 8 environments grouped into 4 mega-environments, with the winning genotypes of each environment being G112 for Ayba_2018, G82 for Aleasa_2018, G25 for Mekelle_2018, G61 for Habes_2018, and G4 for Ayba_2019. Similarly, AMMI biplot analysis revealed high average yields across test locations, with RIL genotypes G36, G72, G25, G118, and G112 showing genetic advancements and potential for future breeding initiatives.
期刊介绍:
Ecological Genetics and Genomics publishes ecological studies of broad interest that provide significant insight into ecological interactions or/ and species diversification. New data in these areas are published as research papers, or methods and resource reports that provide novel information on technologies or tools that will be of interest to a broad readership. Complete data sets are shared where appropriate. The journal also provides Reviews, and Perspectives articles, which present commentary on the latest advances published both here and elsewhere, placing such progress in its broader biological context. Topics include: -metagenomics -population genetics/genomics -evolutionary ecology -conservation and molecular adaptation -speciation genetics -environmental and marine genomics -ecological simulation -genomic divergence of organisms