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Brazil is the world s leader in sugarcane production, and the largest sugar exporter. Developing new varieties is one of the main factors to increase crop yield, and interpretation of genotype-environment interaction (GEI) in the final selection stage is an important aspect to estimate yield. In order to select the best genotypes, varieties are tested in different environments, and breeders need to estimate the phenotypic performance for principle traits such as tons of cane per hectare. GEI affects the selection of superior genotypes, thus, mathematical or statistical models that consider GEI…mehr

Produktbeschreibung
Brazil is the world s leader in sugarcane production, and the largest sugar exporter. Developing new varieties is one of the main factors to increase crop yield, and interpretation of genotype-environment interaction (GEI) in the final selection stage is an important aspect to estimate yield. In order to select the best genotypes, varieties are tested in different environments, and breeders need to estimate the phenotypic performance for principle traits such as tons of cane per hectare. GEI affects the selection of superior genotypes, thus, mathematical or statistical models that consider GEI are necessary and that are able to efficiently identify such genotypes. Geneticists and biometricians have used different methods and there is no clear consensus of the best method to be adopted. In this book, we present a comparison of three methods, namely (i) Eberhart-Russel; (ii) AMMI; (iii) and mixed model (REML/BLUP), in a simulation study performed in the R computing environment to verify the effectiveness of each method in detecting GEI, and assess the particularities of each method from a statistical standpoint. More than 34 million data points were generated and discussed.
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Autorenporträt
Dr. Guilherme Ferraudo, Statistician, obtained his PhD degree in Agronomy (Genetics and Plant Breeding) in 2013 from São Paulo state University (UNESP). He's been working as Statistician for Monsanto since 2008. Prof. Dr. Dilermando Perecin is a full professor at UNESP and he's published more than 100 papers in the area of experimental statistics.