{"title":"基因型x环境相互作用及其稳定性措施阿拉比卡咖啡的主要重点:综述","authors":"Lemi Beksisa","doi":"10.7176/alst/89-01","DOIUrl":null,"url":null,"abstract":"Understanding the implication of genotype x environment interaction (GEI) structure is an important consideration in plant breeding programs. The phenotype of an individual is determined by both the genotype and the environment, these two effects are not always additive which indicates that genotype x environment interactions (GEI) are present. The presence of genotype x environment interaction contributes to the unreliability 'of crop yield over a wide range of environments. The occurrence of large genotype x environment interaction makes the selection of superior genotypes difficult and inhibits progress from selection. It prevents the full understanding of genetic control of variability. In the absence of GEI, the superior genotype in one environment may be regarded as the superior genotype in all, whereas the presence of the GEI confirms particular genotypes being superior in particular environments. Therefore, it is important to understand the nature of genotype x environment interaction to make testing and selection of genotypes more efficient. A variety of statistical procedures are available to analyze the results of multi-environment trials. Additive Main Effects and Multiplicative Interaction (AMMI) model which combines the conventional analyses of variance for additive main effects with the principal components analysis (PCA) for the non-additive residuals and Genotypic Main effect plus genotype by environment interaction (GGE) biplot are two popular graphical analysis systems for multi-environment trials. Other method like the regression of genotype means on the environment means is also worthwhile.","PeriodicalId":137891,"journal":{"name":"Advances in Life Science and Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Genotype x Environment Interaction and Its Stability Measures; Major emphasis in Arabica Coffee: A Review\",\"authors\":\"Lemi Beksisa\",\"doi\":\"10.7176/alst/89-01\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Understanding the implication of genotype x environment interaction (GEI) structure is an important consideration in plant breeding programs. The phenotype of an individual is determined by both the genotype and the environment, these two effects are not always additive which indicates that genotype x environment interactions (GEI) are present. The presence of genotype x environment interaction contributes to the unreliability 'of crop yield over a wide range of environments. The occurrence of large genotype x environment interaction makes the selection of superior genotypes difficult and inhibits progress from selection. It prevents the full understanding of genetic control of variability. In the absence of GEI, the superior genotype in one environment may be regarded as the superior genotype in all, whereas the presence of the GEI confirms particular genotypes being superior in particular environments. Therefore, it is important to understand the nature of genotype x environment interaction to make testing and selection of genotypes more efficient. A variety of statistical procedures are available to analyze the results of multi-environment trials. Additive Main Effects and Multiplicative Interaction (AMMI) model which combines the conventional analyses of variance for additive main effects with the principal components analysis (PCA) for the non-additive residuals and Genotypic Main effect plus genotype by environment interaction (GGE) biplot are two popular graphical analysis systems for multi-environment trials. Other method like the regression of genotype means on the environment means is also worthwhile.\",\"PeriodicalId\":137891,\"journal\":{\"name\":\"Advances in Life Science and Technology\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Life Science and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.7176/alst/89-01\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Life Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.7176/alst/89-01","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Genotype x Environment Interaction and Its Stability Measures; Major emphasis in Arabica Coffee: A Review
Understanding the implication of genotype x environment interaction (GEI) structure is an important consideration in plant breeding programs. The phenotype of an individual is determined by both the genotype and the environment, these two effects are not always additive which indicates that genotype x environment interactions (GEI) are present. The presence of genotype x environment interaction contributes to the unreliability 'of crop yield over a wide range of environments. The occurrence of large genotype x environment interaction makes the selection of superior genotypes difficult and inhibits progress from selection. It prevents the full understanding of genetic control of variability. In the absence of GEI, the superior genotype in one environment may be regarded as the superior genotype in all, whereas the presence of the GEI confirms particular genotypes being superior in particular environments. Therefore, it is important to understand the nature of genotype x environment interaction to make testing and selection of genotypes more efficient. A variety of statistical procedures are available to analyze the results of multi-environment trials. Additive Main Effects and Multiplicative Interaction (AMMI) model which combines the conventional analyses of variance for additive main effects with the principal components analysis (PCA) for the non-additive residuals and Genotypic Main effect plus genotype by environment interaction (GGE) biplot are two popular graphical analysis systems for multi-environment trials. Other method like the regression of genotype means on the environment means is also worthwhile.