Joint Regression Analysis applied to Genotype Stability Evaluation over years Abstract Most of genotype differences connected with yield stability is due to genotype * environment interaction. The presence and dimension of this interaction are factors that determine the performance of genotypes in distinct environments. Besides environmental factors, like annual rainfall, temperature, diseases or soil fertility- which can 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 will use Joint Regression Analysis (JRA), the Zig-Zag Algorithm to estimate the regression coefficients and the multiple comparison tests of Scheffé, Tukey and Bonferroni. We will point out not just the limitations of JRA when used year by year, but the advantage of general JRA over years, on genotype selection. Data of the Portuguese Plant Breeding Board are used to carry out genotype stability analysis, year by year and over years: a sample of 22 different genotypes of oat (Avena sativa L.) grain yield in six different locations during the years 2002, 2003 and 2004 is considered.
|Number of pages||11|
|Journal||Biuletyn Instytutu Hodowli i Aklimatyzacji Roślin|
|Publication status||Published - 1 Jan 2008|
- joint regression analysis
- genotype stability