Abstract
Understanding the implication of genotype-by-environment (GE) interaction structure is an important consideration in plant breeding programs. A significant GE interaction for a quantitative trait such as yield can seriously limit efforts in selecting superior genotypes for both new crop introduction and improved cultivar development. Traditional statistical analyses of yield trials provide little or no insight into the particular pattern or structure of the GE interaction. The Additive Main Effects and Multiplicative Interaction (AMMI) statistical model incorporates both additive and multiplicative components of the two-way data structure which can account more effectively for the underlying interaction patterns. Integrating results obtained from biplot graphic displays with those of the genotypic stability analysis enables clustering of genotypes based on similarity of response and the degree of stability in performance across diverse environments. The AMMI model is presented, and its usage in diagnosing the GE interaction structure is discussed. Tai's stability statistics are employed to determine the stability of genotypes tested. Empirical applications are demonstrated using data from a national winter rapeseed variety trial.
Keywords
genotype-by-environment interaction, biplot analysis, stability statistics, yield trials
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Shafii, Bahman and Price, William J.
(1992).
"STATISTICAL ANALYSIS OF GENOTYPE-BY-ENVIRONMENT INTERACTION USING THE AMMI MODEL AND STABILITY ESTIMATES,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1395
STATISTICAL ANALYSIS OF GENOTYPE-BY-ENVIRONMENT INTERACTION USING THE AMMI MODEL AND STABILITY ESTIMATES
Understanding the implication of genotype-by-environment (GE) interaction structure is an important consideration in plant breeding programs. A significant GE interaction for a quantitative trait such as yield can seriously limit efforts in selecting superior genotypes for both new crop introduction and improved cultivar development. Traditional statistical analyses of yield trials provide little or no insight into the particular pattern or structure of the GE interaction. The Additive Main Effects and Multiplicative Interaction (AMMI) statistical model incorporates both additive and multiplicative components of the two-way data structure which can account more effectively for the underlying interaction patterns. Integrating results obtained from biplot graphic displays with those of the genotypic stability analysis enables clustering of genotypes based on similarity of response and the degree of stability in performance across diverse environments. The AMMI model is presented, and its usage in diagnosing the GE interaction structure is discussed. Tai's stability statistics are employed to determine the stability of genotypes tested. Empirical applications are demonstrated using data from a national winter rapeseed variety trial.
