Autor/Autorin: Parmar Dinesh
Dr. Dinesh J. Parmar Ph. D. (Agril. Statistics) is working as Asstt. Prof. and has 18 years of experience. His field of interest is Statistical Genetics and Analysis of Data. He is teaching in UG and PG programme. Dr. J. S. Patel is the Professor, Agril. Statistics, Department of Agril. Statistics, BACA, AAU, Anand.
Rice (Oryza sativa L.) is one of the world´s most important staple cereal food crop growing in at least 114 countries under diverse conditions. The considerable variation in environment has resulted in significant variation in the yield performance of rice genotypes. Thus, genotype x environment interaction (GEI) is an important issue faced by the plant breeders and agronomists. There are two major approaches for studying GEI and adaptation. The parametric approach is based on regression techniques (Eberhart and Russell, 1966; Finlay and Wilkinson, 1963) and univariate parametric stability statistics (Shukla, 1972, Francis and Kannenberg, 1978; Hernandez et al., 1993; Lin and Binns, 1988a and 1988b; Hanson, 1970). Nonparametric measures (Si(1), Si(2), Si(3), Si(6)) based on the ranks of genotypes in each environments, have been proposed to find out the response of genotypes to changing environment. Multivariate statistical methods have been studied in the analysis of GEI i.e. Additive Main effect and Multiplicative Interaction (AMMI) in which the dimensionality of original data matrix is reduced to fewer dimensions by decomposing the original data matrix.
Rice (Oryza sativa L.) is one of the world s most important staple cereal food crop growing in at least 114 countries under diverse conditions. The considerable variation in environment has resulted in significant variation in the yield performance of rice genotypes. Thus, genotype x environment interaction (GEI) is an important issue faced by the plant breeders and agronomists. There are two major approaches for studying GEI and adaptation. The parametric approach is based on regression techniques (Eberhart and Russell, 1966; Finlay and Wilkinson, 1963) and univariate parametric stability statistics (Shukla, 1972, Francis and Kannenberg, 1978; Hernandez et al., 1993; Lin and Binns, 1988a and 1988b; Hanson, 1970). Nonparametric measures (Si(1), Si(2), Si(3), Si(6)) based on the ranks of genotypes in each environments, have been proposed to find out the response of genotypes to changing environment. Multivariate statistical methods have been studied in the analysis of GEI i.e. Additive Main effect and Multiplicative Interaction (AMMI) in which the dimensionality of original data matrix is reduced to fewer dimensions by decomposing the original data matrix.
Über den Autor
Dr. Dinesh J. Parmar Ph. D. (Agril. Statistics) is working as Asstt. Prof. and has 18 years of experience. His field of interest is Statistical Genetics and Analysis of Data. He is teaching in UG and PG programme. Dr. J. S. Patel is the Professor, Agril. Statistics, Department of Agril. Statistics, BACA, AAU, Anand.
Klappentext
Rice (Oryza sativa L.) is one of the world's most important staple cereal food crop growing in at least 114 countries under diverse conditions. The considerable variation in environment has resulted in significant variation in the yield performance of rice genotypes. Thus, genotype x environment interaction (GEI) is an important issue faced by the plant breeders and agronomists. There are two major approaches for studying GEI and adaptation. The parametric approach is based on regression techniques (Eberhart and Russell, 1966; Finlay and Wilkinson, 1963) and univariate parametric stability statistics (Shukla, 1972, Francis and Kannenberg, 1978; Hernandez et al., 1993; Lin and Binns, 1988a and 1988b; Hanson, 1970). Nonparametric measures (Si(1), Si(2), Si(3), Si(6)) based on the ranks of genotypes in each environments, have been proposed to find out the response of genotypes to changing environment. Multivariate statistical methods have been studied in the analysis of GEI i.e. Additive Main effect and Multiplicative Interaction (AMMI) in which the dimensionality of original data matrix is reduced to fewer dimensions by decomposing the original data matrix.
