Name: Comparison of Different Methods for Stability Analysis in Rice (en Inglés)
SKU: 25761188
Price: 57.91 USD
Availability: InStock
Author: Dinesh Parmar; Jayantibhai Patel

portada Comparison of Different Methods for Stability Analysis in Rice (en Inglés)

Comparison of Different Methods for Stability Analysis in Rice (en Inglés)

Formato

Libro Físico

Editorial

Lap Lambert Academic Publishing

Autor

Dinesh Parmar; Jayantibhai Patel

Año

2012

Idioma

Inglés

N° páginas

144

Encuadernación

Tapa Blanda

ISBN

3659110965

ISBN13

9783659110962

Categorías

Botánica y ciencias de las plantas
Horticultura comercial

Comparison of Different Methods for Stability Analysis in Rice (en Inglés)

Dinesh Parmar; Jayantibhai Patel (Autor) · Lap Lambert Academic Publishing · Tapa Blanda

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Reseña del libro "Comparison of Different Methods for Stability Analysis in Rice (en Inglés)"

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.