Statistic	Symbol	Definition
Mean variance component	θ i	Plaisted and Peterson (1959) proposed the variance component of genotype‐by‐environment interactions (GEI) for interactions between each of the possible pairs of genotypes. This statistic considers the average of the estimate for all combinations with a common genotype to be a measure of stability. Accordingly, the genotypes that show a lower value for θ i are considered more stable.
GE variance component	θ (i)	This statistic is a modified measure of stability parameter. In this approach, the ith genotype is deleted from the entire set of data and the GEI variance from this subset is the stability index for the ith genotype. According to this statistic, the genotypes that show higher values for the (i) are considered more stable.
Wricke's ecovalence stability index	W i 2	Wricke (1962) proposed the concept of ecovalence as the contribution of each genotype to the GEI sum of squares. The ecovalence (W i) of the ith genotype is its interaction with the environments, squared and summed across environments. Thus, genotypes with low values have smaller deviations from the mean across environments and are more stable.
Regression coefficienta	b i	The regression coefficient (b i) is the response of the genotype to the environmental index that is derived from the average performance of all genotypes in each environment (Finlay and Wilkinson, 1963). If b i does not significantly differ from 1, then the genotype is adapted to all environments. A b i > 1 indicates genotypes with higher sensitivity to environmental change and greater specificity of adaptability to high‐yielding environments, whereas a b i < 1 describes a measure of greater resistance to environmental change, thereby increasing the specificity of adaptability to low‐yielding environments.
Deviation from regression	S di 2	In addition to the regression coefficient, variance of deviations from the regression (S di 2) has been suggested as one of the most‐used parameters for the selection of stable genotypes. Genotypes with an S di 2 = 0 would be most stable, while an S di 2 > 0 would indicate lower stability across all environments. Hence, genotypes with lower values are the most desirable (Eberhart and Russell, 1966).
Shukla's stability variance	σ i 2	Shukla (1972) suggested the stability variance of genotype i as its variance across environments after the main effects of environmental means have been removed. According to this statistic, genotypes with minimum values are intended to be more stable.
Environmental coefficient of variance	CV i	The coefficient of variation is suggested by Francis and Kannenberg (1978) as a stability statistic through the combination of the coefficient of variation, mean yield, and environmental variance. Genotypes with low CV i, low environmental variance (EV), and high mean yield are considered to be the most desirable.
Nassar and Huhn's non‐parametric statistics and Huhn's statisticsb	S (1) S (2) S (3) S (6)	Huhn (1990) and Nassar and Huhn (1987) suggested four non‐parametric statistics: (1) S (1), the mean of the absolute rank differences of a genotype over all tested environments; (2) S (2), the variance among the ranks over all tested environments; (3) S (3), the sum of the absolute deviations for each genotype relative to the mean of ranks; and (4) S (6), the sum of squares of rank for each genotype relative to the mean of ranks. To compute these statistics, the mean yield data have to be transformed into ranks for each genotype and environment, and the genotypes are considered stable if their ranks are similar across environments. The lowest value for each of these statistics reveals high stability for a certain genotype.
Thennarasu's non‐parametric statistics	NP (1) NP (2) NP (3) NP (4)	Four NP (1–4) statistics are a set of alternative non‐parametric stability statistics defined by Thennarasu (1995). These parameters are based on the ranks of adjusted means of the genotypes in each environment. Low values of these statistics reflect high stability.
Kang's rank‐sum	Kang or KR	Kang's rank‐sum (Kang, 1988) uses both yield and σ i 2 as selection criteria. This parameter gives a weight of 1 to both yield and stability statistics to identify high‐yielding and stable genotypes. The genotype with the highest yield and lower σ i 2 is assigned a rank of 1. Then, the ranks of yield and stability variance are added for each genotype, and the genotypes with the lowest rank‐sum are the most desirable.

^aTo determine stability using this parameter, the significance test (H0: B ≠ 1) must be conducted. For more detail, see Finlay and Wilkinson (1963).

^bIn addition to S ⁽ⁱ⁾ statistics, two significance tests for S ⁽¹⁾ and S ⁽²⁾, namely Z ₁ and Z ₂, are calculated.