Consulta: creatorFacets:"Pereira,Dulce Gamito Santinhos"
Registros recuperados: 2
Data/hora: 03/06/2024 06:24:39

A comparison between Joint Regression Analysis and the Additive Main and Multiplicative Interaction model: the robustness with increasing amounts of missing data
Provedor de dados: 63
Autores: Rodrigues,Paulo Canas; Pereira,Dulce Gamito Santinhos; Mexia,João Tiago.
This paper joins the main properties of joint regression analysis (JRA), a model based on the Finlay-Wilkinson regression to analyse multi-environment trials, and of the additive main effects and multiplicative interaction (AMMI) model. The study compares JRA and AMMI with particular focus on robustness with increasing amounts of randomly selected missing data. The application is made using a data set from a breeding program of durum wheat (Triticum turgidum L., Durum Group) conducted in Portugal. The results of the two models result in similar dominant cultivars (JRA) and winner of mega-environments (AMMI) for the same environments. However, JRA had more stable results with the increase in the incidence rates of missing values.
Tipo: Info:eu-repo/semantics/article
Palavras-chave: AMMI models; Genotype by environment interaction; Joint regression analysis; Missing values; Durum wheat.
Ano: 2011
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162011000600012

Analyzing genotype-by-environment interaction using curvilinear regression
Provedor de dados: 63
Autores: Pereira,Dulce Gamito Santinhos; Rodrigues,Paulo Canas; Mejza,Iwona; Mejza,Stanislaw; Mexia,João Tiago.
In the context of multi-environment trials, where a series of experiments is conducted across different environmental conditions, the analysis of the structure of genotype-by-environment interaction is an important topic. This paper presents a generalization of the joint regression analysis for the cases where the response (e.g. yield) is not linear across environments and can be written as a second (or higher) order polynomial or another non-linear function. After identifying the common form regression function for all genotypes, we propose a selection procedure based on the adaptation of two tests: (i) a test for parallelism of regression curves; and (ii) a test of coincidence for those regressions. When the hypothesis of parallelism is rejected,...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Scheffé multiple comparison method; Joint regression analysis; Test for parallelism; Test of coincidence.
Ano: 2012
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162012000600003