Estimate of genotypic value: A proposed method

Abstract

Genotypic differences in yield between breeding lines or cultivars may be estimated with the aid of functions of the regression of their individual yields, in different environments, on the mean yields of all the lines tested in the respective environments. CONSIDERATIONSThe progress made in any breeding programme is dependent on the recognition of superior genotypes. Selection for disease-and insect-resistance, maturity, height, shape or colour of fruits etc. can be successfully done in a few nursery tests. However, the expression of some quantitative characters, and in particular that of yield, are strongly influenced by genotype × environment interaction effects. Therefore, selection for these characters has to be based on evaluations at many different environments. This can be obtained by conducting the tests at various sites and under different managements, e.g., crop rotation, fertilization, irrigation, seeding dates and rates, etc.If these environments would comprise a representative sample of the population of environments for which the breeding programme is intended then the mean phenotypic performance of any line, averaged over these environments, should provide a reliable estimate of its genotypic value (CoMsTOC~: and MOLL, 1963). In practice such a representative sample of environments can hardly be achieved. It seems, however, feasible to conduct a series of tests under environmental conditions which include the range of conditions for which selection is being done. PROPOSED METHODIt is suggested to fit for each line a function of the regression of its individual yields, at the different environments, on the mean yields of all the tested lines at the respective environments. Data presented by FINLAY (1968) and our own investigations indicate that a good fit to a linear regression may be obtained either from the actual yield data or from their transformed values.The regression functions for a breeding line i and for an established cultivar cv, included in the trials as check, would be Yt = at + btx and Ycv = acv + bc~x, respectively. The value of Yt at that level of x for which x~ c~ equals the average yield of this cultivar, under the conditions which the breeding programme is aiming at, may be regalded as an estimate of the genotypic value of line i (Fig. 1). 121

Use of AMMI and other stability statistics in the simultaneous selection of rice genotypes for yield and stability under direct-seeded conditions

“…Various statistical methods/ models (parametric and non-parametric), concepts, and definitions of stability have been described over the years by many researchers (Lin et al, 1986;Becker and Léon, 1988;Crossa et al, 1990;Lin and Binns, 1994;Hussein et al, 2000;Mohammadi and Amri, 2008;Mohammadi et al, 2010). The model used for GEI is similar to a static or biological concept of stability (Becker and Léon, 1988) and the parameters used in the study are the coefficient of determination (Ri 2 ) (Pinthus, 1973), coefficient of variability (CVi) (Francis and Kannenberg, 1978), and the genotypic variances across environments (Si 2 ) (Roemer, 1917). Similarly, another model is the dynamic or agronomic concept of stability (Becker and Léon, 1988) where the parameters used are the regression coefficient (bi) (Finlay and Wilkinson, 1963) and Shukla's stability variance (σi 2 ) (Shukla, 1972), the regression coefficient (bi), and deviation from regression (Sd i 2 ) (Eberhart and Russell, 1966;Perkins and Jinks, 1968).…”

Development of Potential Aerobic Rice Cultivar through Participatory Varietal Selection Employing Aerobic Response Index

“…Accordingly, genotypes CR 2624 was the most stable for grain yield because their regression coefficients were almost equal to unity and they had lower deviations from regression with high R i (88%) values (Pinthus, 1973), conforming their stability. In contrast, genotype IR 78875-131-B-1-4 for grain yield had regression coefficients greater than one, and so was regarded as sensitive to environmental changes.…”

ISSR Markers-Trait Associations and Stability Analysis in Bread Wheat Varieties

“…Coefficient of Variance (CV%) was estimated according to Francis and Kannenbert (1978). The coefficient of determination (r 2 ) was proposed to use by Pinthus (1973). Relative Yield Reduction (RYR%) = (1-yield under drought / yield under normal)*100.…”

“…Eberhart and Russell (1966) model has been widely used in studies of adaptability and stability of plant materials. Also, the coefficient of determination (r 2 ) used by Pinthus (1973) measures the proportion of a genotype's production variation that is attributable to the linear regression as an index of production stability over different environments. According to Crossa et al (1988), the selection of superior genotypes in a plant-breeding program is based mainly on their yield potential and stable performance over a wide range of environmental conditions.…”