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Stability Analysis: Where Do We Stand? 1
Author(s) -
Lin C. S.,
Binns M. R.,
Lefkovitch L. P.
Publication year - 1986
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci1986.0011183x002600050012x
Subject(s) - statistics , stability (learning theory) , similarity (geometry) , nonparametric statistics , cluster analysis , biology , mathematics , regression , index (typography) , gene–environment interaction , regression analysis , econometrics , genotype , computer science , artificial intelligence , genetics , machine learning , world wide web , image (mathematics) , gene
To clarify the apparent confusion arising from the diversity of published stability statistics, and the relationship of these with the clustering of genotypes for similarity of response to environments, the interrelationship of nine stability statistics and nine similarity measures are investigated. The stability statistics fall into four groups depending on whether they are based on the deviations from the average genotype effect or on the genotype ✕ environment (GE) term, and whether or not they incorporate a regression model on an environmental index. These groups of stability statistics are shown to be related to three concepts: A genotype may be considered to be stable (i) if its among‐environment variance is small, (ii) if its response to environments is parallel to the mean response of all genotypes in the trial, or (iii) if the residual mean square from a regression model on the environmental index is small. Unfortunately, these three concepts represent different aspects of stability and do not always provide a complete picture of the response. In the alternative approach of cluster analysis, the similarity measures define complete similarity in three different ways: i) equality of genotype's response across locations, ii) equality of all within location differences, and iii) equality of all within location ratios. The advantage of the nonparametric approach is that a cultivar's response characteristics can be assessed qualitatively, without the need for a mathematical characterization.