After reading this article you will learn about the concept and methods of stability.
Concept of Stability:
The term ‘phenotypic stability’, yield stability, and adaptation are often used in quite different senses. Therefore, Dorst (1957) remarked that “the word adaptation has great adaptability” and Lin (1986) amplified that “it (the concept of stability) is defined in many ways depending on, how the scientist wishes to look at the problem.”
In fact, depending on the goal and on the character under consideration, 2 different concepts of stability exist. These are termed as the ‘static concept of stability’ and the dynamic concept of stability.
With regard to static concept a stable genotype possesses an unchanged performance regardless of any variation of the environmental conditions. This stable genotype shows no deviation from the character level, meaning thereby, that its variance among environments is zero.
Unlike this concept, the dynamic concept permits a predictable response to environments and a stable genotype according to this concept, has no deviation from this response to environments.
For each environment the performance of a stable genotype corresponds completely to the estimated level or the prediction. Becker (1981) termed this type of stability, the agronomic concept and distinguished it from the biological concept of stability which is equivalent to the static concept.
Methods of Stability:
For plant improvement programmes, the dynamic concept of stability is of relevance and therefore, only this will be under consideration while mentioning different approaches.
Lin (1986) have summarized stability statistics as follows:
(i) The variance of a genotype across environments (Si2) can be a measure of stability.
(ii) Coefficient of variability (CV). Francis and Kanenberg (1978) used the conventional CV% of each genotype as a stability measure.
(iii) Plaisted and Peterson’s (1959) mean variance component for pairwise GE interaction (θi). The mean of the estimated variance components of the GE interaction for all pairs of genotypes that includes genotype i is the stability measure for genotype i.
(iv) Plaisted’s (1960) variance component for GE interaction (θi). One genotype (i) is deleted from the entire set of data and the GE interaction variance from this subset is the stability index for genotype i.
(v) Wricke’s (1962) ecovalence (Wi2). The GE interaction effects for genotype i, squared and summed across all environments, is the stability measure for genotype i.
(vi) Shukla’s (1972) stability variance (σi2). Based on the residuals in a two-way classification, the variance of a genotype across environments is the stability measure.
(vii) Finlay and Wilkinson’s (1963) regression coefficient (bi). The observed values are regressed on environmental indices defined as the difference between the marginal mean of the environments and the overall mean. The regression coefficient for each genotype is then taken as its stability parameter.
(viii) Perkins and Jinks’ (1968) regression coefficient (βi). Similar to (vii) except that the observed values are adjusted for location effects before the regression.
(ix) Eberhart and Russell’s (1966) deviation parameter (δi2). The residual mean square (MS) of deviation from the regression defined in (vii) or (viii) is the measure of stability for each genotype.
Out of various approaches, Eberhart and Russell’s model has been rather commonly used to estimate stability in plant breeding material.
Analysis of variance table in this model is as given in Table 8.7:
Relevant points emerging from this analysis of variance are as follows:
Variability among environments is a prerequisite to useful regression analysis. The significant variety x environment mean square indicates that there are differences among the regression coefficients for the varieties.
The unexplainable deviations from the regression on the environmental index is accounted for by significant pooled deviations. The significant pooled deviations indicate non-linear responses and include that part of genotype x environment interactions that is unexplainable by additive environmental effects.
In the past, the term stable variety often has been used to mean a variety that does relatively the same over a wide range of environments. This means that a stable variety, by this definition performs relatively better under adverse conditions and not so well in favourable environments.
Yield stability of a genotype is evaluated from estimates of stability parameters. A desirable, stable genotype is one having mean yield higher than the average yield of all genotypes under test, regression coefficient (b) close to unity, and small deviations (Sd2) from regression possibly close to zero.
The growth response index (regression coefficient) measures responses to increments in an improving environment. Genotypes with coefficients greater than one could be adapted to more favourable growing conditions, whereas those, with coefficients less than one would be adapted to less favourable growing conditions.
According to Becker and Leon (1988) the regression approach is of little use if b is included in the definition of stability. Consequently, most authors consider b not as a measure of stability but as additional information on the average response of a genotype to advantageousness of environmental conditions. The approach is schematically presented in Fig. 8.3.
The regression technique has also been suggested by Perkins and Jinks (1968) to characterise the specific response of genotype to varying climatic factors. Perkins and Jinks regression coefficient is similar to regression coefficient of Finlay and Wilkinson’s (1963) regression coefficient except that the observed values are adjusted for location effects before the regression. In this system 1+β is considered as a measure of stability. The genotypes having 1+β = 1.0 are considered stable.