Statistical Models Comparison for Genotype x Environment Interaction | Simulation study in a sugarcane perspective

Guilherme M. Ferraudo (u. a.)

ISBN 10: 3659344214 / ISBN 13: 9783659344213

Published by LAP LAMBERT Academic Publishing, 2015

About this Item

Statistical Models Comparison for Genotype x Environment Interaction | Simulation study in a sugarcane perspective | Guilherme M. Ferraudo (u. a.) | Taschenbuch | 124 S. | Englisch | 2015 | LAP LAMBERT Academic Publishing | EAN 9783659344213 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 104895477

Bibliographic Details

• Title: Statistical Models Comparison for Genotype x Environment Interaction | Simulation study in a sugarcane perspective
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2015
• Language: English
• ISBN 10: 3659344214
• ISBN 13: 9783659344213
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 9D millimeters 220 millimeters width by 150 millimeters height by 9 millimeters depth
• Weight: 203 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.

About the Author

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.