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Joint regression analysis applied to genotype stability evaluation over years
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Orientador(es)
Resumo(s)
Most genotype differences connected with yield stability are due to genotype environment
interaction. The presence and dimension of this interaction are the factors that determine the
performance of genotypes in distinct environments. The environmental factors, like annual rainfall,
temperature, diseases or soil fertility, can only explain part of this interaction. Many statistical tools
have been developed with the aim to explain the information contained in the GE interaction data
matrix. In our work we use the Joint Regression Analysis (JRA), the Zig-Zag Algorithm to estimate
the regression coefficients and the multiple comparison tests of Scheffé, Tukey and Bonferroni. We
point out not just the limitations of the JRA when used year by year, but also genotype selection
advantage from general JRA over years. Data of the Portuguese Plant Breeding Board were used to
carry the year and over years analyses of yielding stability of 22 different genotypes of oat (Avena
sativa L.) at six different locations in the years 2002, 2003 and 2004.
Descrição
Palavras-chave
Genotype stability Joint regression analysis Oat
Contexto Educativo
Citação
Editora
Biuletyn Instytutu Hodowli i Aklimatyzacji Roslin