Comparison of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in variable environments

Twenty parametric and non-parametric measures derived from grain yield of 15 advanced durum genotypes evaluated across 12 variable environments during the 2004–2006 growing seasons were used to assess performance stability and adaptability of the genotypes and to study interrelationship among these measures. The combined ANOVA and the non-parametric tests of Genotype × environment interaction indicated the presence of significant crossover and non-crossover interactions, and of significant differences among genotypes. Principal component analysis based on the rank correlation matrix indicated that most non-parametric measures were significantly inter-correlated with parametric measures and therefore can be used as alternatives. The results also revealed that stability measures can be classified into three groups based on static and dynamic concepts of stability. The group related to the dynamic concept and strongly correlated with mean grain yield of stability included the parameters of TOP (proportion of environments in which a genotype ranked in the top third), superiority index (P _i) and geometric adaptability index. The second group reflecting the concept of static stability included, Wricke’s ecovalence, the variance in regression deviation (S ² _di), AMMI stability value, the Huehn’s parameters [S _i⁽¹⁾, S _i⁽²⁾], Tennarasua’s parameter [NP_i⁽¹⁾], Kang’s parameter (RS) and yield reliability index (I _i) which were not correlated with mean grain yield. The third group influenced simultaneously by grain yield and stability included the measures S _i⁽³⁾, S _i⁽⁶⁾, NP_i⁽²⁾, NP_i⁽³⁾, environmental variance (S ² _xi), coefficient of variability and coefficient of regression (b _i). Based on the concept of dynamic stability, genotypes G6, G4, and G3 were found to be the most adapted to favorable environments, whereas genotypes G8, G9, and G12 were more stable and are related to the concept of static stability.     

Evaluation of soybean cultivars using parametric and nonparametric stability estimates

High and stable yield is a very desirable attribute of soybean (Glycine max (L.) Merr.) cultivars. Stable yield of a cultivar means that its rank relative to other cultivars remains unchanged in a given set of environments. To characterize 12 soybean cultivars chosen from performance trials, data were obtained from 10 environments (five locations in 2 years). Six stability parameters from four statistical models were derived for each cultivar. Regression coefficients were significantly and positively correlated only with coefficients of variation; they are useful in characterizing whether cultivars responded well in favourable or poor environments. Nassar and Huhn's nonparametric measures, S_i⁽¹⁾ and S_i⁽²⁾, were significantly and positively correlated with Eberhart and Russell's s_di² and Wricke's ecovalence (W_i). The stability measures are useful in characterizing cultivars by showing their relative performance in various environments. Results revealed that high-yielding cultivars also can be stable cultivars. Correlations between stability parameters obtained from individual years over the same set of locations and cultivars were very low and nonsignificant, suggesting that single-year data are not reliable as basis for selection. To provide an additional guide for selection, Kang's rank-sum approach was applied, in which both yield (in rank) and measured nonparametric stability (in rank) were considered. In general, selection for yield only would sacrifice stability to some degree, and selection for stability only would sacrifice a certain amount of yield. The rank-sum approach reconciles the two and appeared to provide a useful means to characterize soybean cultivars.     

Genotype × environment interactions for tea yields

Several methods were used to evaluate phenotypic stability in 20 tea (Camellia sinensis) genotypes, many of which are cultivated widely in East Africa. The genotypes were evaluated for annual yields at two sites over a six year period. Data obtained were used to compare methods of analysis of G × E interactions and yield stability in tea. A standard multi-factor analysis of variance test revealed that all first order interactions (genotypes × sites; genotypes × years; sites × years) as well as second order interactions (sites × genotype × years) were significant. Regression analysis was used to assess genotype response to environments. Regression coefficients (b_i) obtained ranged from 0.78 to 1.25. Deviations from regression (S²d) were significant (p < 0.05) from 0.0 for all the test genotypes. Analysis for sensitivity to environment change (SE² _i) revealed that the test genotypes differed in their level of sensitivity. The hierarchical cluster analysis method was used to assemble the test genotypes into groups with similar regression coefficients (b_i) and mean yield, which proved useful for the identification of high yielding genotypes for breeding purposes as well as for commercial exploitation. Rank correlation between yield and some stability parameters were significant. Mean yield was significantly correlated to b_i (r = 0.80^***) and SE² _i(0.74^***) which is an indication that selection for increased yield in tea would change yield stability by increasing b_i and SE² _i leading to development of genotypes that are specifically adapted to environments with optimal growing conditions. Genotypes differed in response to years and sites. As stand age increased, genotype yields generally increased though annual yield fluctuations were more pronounced in some genotypes than others. This response was not consistent across the sites for all genotypes indicating the need to test clones at multiple sites over longer periods of time. This revised version was published online in August 2006 with corrections to the Cover Date.     

Stability Analysis of Yield in Maize,Wheat and Sorghum and its Implications in Breeding Programs*

Six stability statistics: (b_i, s²_di, , , and ) were estimated for maize, wheat and sorghum in different environments by using three statistical models. The significant linear portion of genotype × environment interaction for maize indicates different hybrids responded differently to environments, whereas the non-significant genotype × environment interaction (linear) were found for wheat and sorghum suggest that all genotypes responded similarly as the environments change. However, the highly significant pooled deviations (deviation from regression) for all three crops make yield predictions from the model less reliable. When regression coefficients (b_i) were non-significant, s²_di, became an important statistic in estimating stability. It appears that the regression coefficient, b_i, was best used to estimate genotypic adaptability, whereas s²_di, for stability. Maize and sorghum had negative correlations between the mean yield and stability statistics, s²_di, , and , suggesting that high yield and stability are not mutually exclusive in the range of environments used in this study; however, such correlations did not occur in wheat. Thus, maize and sorghum hybrids with high yield potential and high stability could be identified and selected. Correlations between mean yield and b_i, or , were positive and significant for maize and sorghum but were non-significant for wheat, indicating that such relationships may be species specific. Under a given set of testing environments, the stability ranking associated with each maize hybrid is correlated to and depends on other hybrids included in the analysis.     

Yield stability in common bean (Phaseolus vulgaris L.) genotypes

An experiment was undertaken to determine the stability of seed yield in 21 common bean genotypes representing three growth habits. Seven genotypes in each growth habit (determinate bush, indeterminate bush and indeterminate prostrate) were evaluated in replicated trials at three locations for three years under rain fed conditions in Ethiopia. A combined analysis of variance, stability statistics and rank correlations among stability statistics and yield-stability statistic were determined. The genotypes differed significantly for seed yield and genotype × environment (year by location) interaction (GE). The different stability statistics namely Type1, Type 2 and Type 3 measured the different aspects of stability. This was substantiated by rank correlation coefficient. There were strong rank correlations among S_i ²d, W_i ²,σ_i ² and S_i ², where as there was weak correlation between b_iand R_i ², S_i ²d, W_i ², σ_i ² and S_i ². R² was significantly and negatively correlated with W_i ², σ_i ² and S_i ². σ_i ² is significantly correlated with W_i ².Yield is significantly correlated with b_i and R_i ².None of the statistics per se was useful for selecting high yielding and stable genotypes except the YS(yield-stability statistic). Most of the high yielding genotypes were relatively stable. Of the 21 genotypes, only 11genotypes were selected for their high yielding and stable performance. Genotypes with growth habit III and I (in determinate prostrate and determinate bush) were generally more stable than in determinate bush. This revised version was published online in July 2006 with corrections to the Cover Date.     

Rank correlation among parametric and nonparametric measures of phenotypic stability

Summary There is an increasing number of stability parameters for genotypes grown in different environments. It is therefore useful to study the statistical relations between these parameters. One approach is the calculation of rank correlations between different stability parameters in empirical data sets. In the data analysed there are high rank correlations between ecovalence W_i, deviation mean square s² _di, the nonparametric measures S_i ⁽¹⁾, S_i ⁽²⁾, and two new measures R_i and L_i as well as between environmental variance S² _xi and regression coefficient b_i. The results suggest that S_i ⁽¹⁾, S_i ⁽²⁾, L_i, and R_i can be used as alternatives to W_i and the stability variance ² _i. This may be worthwhile, if certain statistical assumptions do not hold, particularly if significance testing is needed.     

Repeatability of different stability parameters for grain yield in chickpea

The presence of genotype × environment (GE) interactions in plant breeding experiments has led to the development of several stability parameters in the past few decades. The present study investigated the repeatability of these parameters for 16 chickpea (Cicer arietinum L.) genotypes by correlating their estimates obtained from extreme subsets of environments within a year and also over years. Based on the estimates of response and stability parameters within each trial, the ranking of genotypes in the low-yielding subset differed from that in the high-yielding subset. This indicates poor repeatability for response and stability parameters over the extreme environmental subsets. The estimates of mean yield and stability parameters represented by ecovalence, W²_i, were consistent over years, whereas those of response parameters (b_i, and S²_i) showed poor repeatability. Our results suggest that single-year results for yield and stability can be used effectively for selecting cultivars with stable grain yield if tested in a wider range of environments.     

Nonparametric measures of phenotypic stability. Part 2: Applications

Summary The three nonparametric measures of phenotypic stability S_i ⁽¹⁾, S_i ⁽²⁾ and S_i ⁽³⁾ introduced and discussed in Huehn (1990) and the classical parameters: environmental variance, ecovalence, regression coefficient, and sum of squared deviations from regression were computed for winter wheat grain yield data from the official registration trials (1974, 1975 and 1976) in the Federal Republic of Germany.The similarity of the resulting stability rank orders of the genotypes which are obtained by applying different stability parameters were compared using rank correlation coefficients. The correlations between each of S_i ⁽¹⁾, S_i ⁽²⁾ and S_i ⁽³⁾ and the classical stability parameters were different in sign and very low for regression coefficient and environmental variance, but positive and medium for ecovalence and sum of squared deviations from regression (except S_i ⁽³⁾ in 1976). The differences between the correlations for the 3 years were considerable.The parameters S_i ⁽¹⁾ and S_i ⁽²⁾ were very strong intercorrelated with each other with a good agreement of the correlations for the different years. The divergent property of S_i ⁽³⁾ can be explained by its modified definition (confounding of stability and yield level).The previous results and conclusions obtained from the stability analysis of the original uncorrected data x_ij are further strengthened if one uses corrected values % MathType!MTEF!2!1!+-% feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGak0Jf9crFfpeea0xh9v8qiW7rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaaeiwamaaDa% aaleaacaqGPbGaaeOAaaqaaiaabQcaaaGccqGH9aqpcaqGybWaaSba% aSqaaiaabMgacaqGQbaabeaakiabgkHiTiaacIcaceqGybGbaebada% WgaaWcbaGaaeyAaaqabaGccqGHsislceqGybGbaebacaqGUaGaaeOl% aiaacMcaaaa!4724!\[{\text{X}}_{{\text{ij}}}^{\text{*}} = {\text{X}}_{{\text{ij}}} - ({\text{\bar X}}_{\text{i}} - {\text{\bar X}}..)\]: The nonparametric stability measures were nearly perfectly associated (even with S_i ⁽³⁾ included) which, of course, implies no significant differences between the correlations of the different years.For the correlations between each of the S_i ⁽¹⁾, S_i ⁽²⁾ and S_i ⁽³⁾ and the classical parameters, very low values were obtained for regression coefficient and environmental variance, but relatively large values for ecovalence and sum of squared deviations from regression.The differences between the correlations for the different years are low for ecovalence and sum of squared deviations from regression with each of S_i ⁽¹⁾, S_i ⁽²⁾ and S_i ⁽³⁾, but these differences are large for regression coefficient and environmental variance. This transformation x_ijx_ij ^* reduced individual and global significances (stability of single genotypes and stability differences between all the tested genotypes) drastically. The significant results for the transformed data indicate a very reliable quantitative characterization of the stability of the genotypes independent from the yield level.     

Correlations between the stability and adaptability statistics of popcorn cultivars

In plant breeding, correlations between the statistics of stability and adaptability of popcorn cultivars are not yet well understood. Therefore, the objectives of the present experiment was to investigate the correlations between s_di² \sigma_{\rm di}^{2} and b_i \beta_{\rm i} from Eberhart and Russell, ω_i from Wricke, \textS_\texti⁽¹⁾ {\text{S}}_{\text{i}}^{(1)} , \textS_\texti⁽²⁾ {\text{S}}_{\text{i}}^{(2)} and \textS_\texti⁽³⁾ {\text{S}}_{\text{i}}^{(3)} from Huehn, P_i from Lin and Binns and the rank-sum from Kang, and indicate the most reliable method for selecting popcorn cultivars. These statistics were estimated by data of crop yield from 19 Brazilian genotypes under 21 environments and popping expansion under 16 environments. The ω_i, \textS_\texti⁽¹⁾ {\text{S}}_{\text{i}}^{(1)} , \textS_\texti⁽²⁾ {\text{S}}_{\text{i}}^{(2)} , \textS_\texti⁽³⁾ {\text{S}}_{\text{i}}^{(3)} and s_di² \sigma_{\rm di}^{2} were positively and significantly correlated indicating that just one in these five statistics is sufficient for selecting stable genotypes although they were not correlated with the means of crop yield and popping expansion. The b_i \beta_{\rm i} was negatively and significantly correlated with P_i for crop yield indicating that the most adaptable genotypes tend to have the lowest estimates of P_i. Although P_i was not correlated with ω_i, \textS_\texti⁽¹⁾ {\text{S}}_{\text{i}}^{(1)} , \textS_\texti⁽²⁾ {\text{S}}_{\text{i}}^{(2)} , \textS_\texti⁽³⁾ {\text{S}}_{\text{i}}^{(3)} , or s_di² \sigma_{\rm di}^{2} statistics, it displayed positive correlation with the Index 1 (crop yield and popping expansion +  \textS_\texti⁽¹⁾ {\text{S}}_{\text{i}}^{(1)} rank) and Index 2 (crop yield and popping expansion + W_i) indicating that superior popcorn genotypes are also stable. Finally, both P_i and the rank-sum are useful statistics in breeding programmes where crop yield, popping expansion and stability are essential traits for selecting genotypes.     

Electrofusion of Protoplasts and Heterokaryon Survival in the Genus Medicago

The objective of this study- was to evaluate whether different statistical measures of phenotypic stability vary in their repeatability. Eight multi year and multi location variety tests of wheat, barley and oat were analysed separately for each year. Single year data of yield, of response parameters: environmental variance (S²₃ and coefficient of regression (b), and of stability parameters: deviation mean squares (S²₃), coefficient of determination (r²), ecovalence (W), and the nor, parametric measure variance of ranks (S_i4), were correlated with multi year, multi location results. Repeatability of single year results was highest for yield, where rank correlation coefficients amounted to about 0.80. s²_x and b showed medium values of nearly 0.55 The stability parameters s²_d, r², W and S_i4 did not differ in their repeatability. Respective correlation coefficients possessed values of approximately 0.40 and were very variable from experiment to experiment. Reliability of single year results was especially low for experiments with high varieties × years interactions. Single year results of the examined variety tests could not serve as a basis to quantify phenotypic stability even if more than ten locations were involved.     

Contribution of Weather Variables to Genotype X Environment Interaction in Grain Sorghum1

Genotype × environment (GE) interactions are a major problem in plant breeding programs that involve testing in diverse environments. These interactions can reduce progress from selection. Few studies have characterized the effects of weather variables on GE interactions in sorghum (Sorghum bicolor [L.] Moench). The present investigation estimated the contribution of environmental index, (?, or mean yield of all cultivars in jth environment minus ?. xor overall mean yield for all cultivars and all environments), rainfall, minimum and maximum temperature, and relative humidity, to GE interaction. Yield means of 5 full-season and 10 medium-season grain sorghum hybrids grown during 1986—1988 at four locations were used in the study. The GE interaction was significant and partitioned into σ²_i, components assignable to each genotype. Weather variables (covariates) were used to remove heterogeneity from the GE interaction. The remainder of the GE interaction variance was partitioned into variance components (s²_i) assignable to each genotype. In both maturity groups, the environmental index removed most, although non-significant, heterogeneity from the GE interaction sums of squares. Of all weather variables, preseason and seasonal rainfall contributed most to the GE interaction sums of squares.     

Trends in genetic variance components during 30 years of hybrid maize breeding at the University of Hohenheim

The ratio of variance due to specific vs. general combining ability (GCA) (σ²_SCA:σ²_GCA) is of central importance for predicting hybrid performance from GCA effects. The objectives of our study were to (1) analyse the changes in estimates of σ²_GCA, σ²_SCA and their ratio during 30 years of hybrid maize breeding and (2) compare the observed trends in genetic variances with those expected under a simple genetic model. We analysed multilocation yield trials based on the North Carolina Design II conducted in the maize breeding programme of the University of Hohenheim from 1975 to 2004 for grain yield (GY) and dry matter content (DMC). GY showed a significant (P < 0.05) annual increase of 0.17 Mg/ha, but no linear trend was found for DMC. Since the beginning of hybrid breeding at the University of Hohenheim, the sum of estimates of σ²_GCA of the flint and dent heterotic groups were higher than the estimates of their σ²_SCA. This predominance did not change with ongoing inter‐population improvement. Consequently, superior hybrids can be identified and selected mainly based on their prediction from GCA effects.     

Stability of performance in lentil (Lens culinaris Medik) genotypes in Iran

The development of genotypes, which can be adapted to a wide range of diversified environment, is the ultimate goal of plant breeders in a crop improvement program. In this study, several stability methods were used to evaluate the genotype by environment interaction (GE) in 11 lentil (Lens culinaris Medik) genotypes. The genotypes were evaluated for grain yield at 4 different locations for 3 years in semi arid areas of Iran. The testing locations have different climatic and edaphic conditions providing the conditions necessary for the assessment of stability. A combined analysis of variance, stability statistics, rank correlations among stability statistics and yield stability statistic were determined. Significant differences were detected between genotypes and their GE interactions. Different univariate stability parameters were used to determine stability of the studied genotypes. The level of association among the parameters was assessed using Spearman's rank correlation. The different stability statistics which measured the different aspects of stability was substantiated by rank correlation coefficient. Rank-correlation coefficients between yield and some stability parameters were highly significant. Genotypes mean yield (Mean) was significantly correlated to the Lin and Binns stability parameter PI (r = 0.93^{* *}í) and desirability index Di (r = 0.89^{* *}í). A principal component analysis based on rank correlation matrix was performed for grouping the different stability parameters studied. In conclusion, based on most stability parameters, the genotypes G2, G5 and G9 were found to be the most stable. Results from rank correlation and principal component analysis showed that the stability variance (σ_i ²) was strongly correlated with Wricke's ecovalance, stability parameters of Plaisted and Peterson, and Plaisted.     

Genotype × environment interactions and simultaneous selection for high oil yield and stability in rainfed warm areas rapeseed (<Emphasis Type="Italic">Brassica napus</Emphasis> L.) from Iran

Partitioning of the genotypes by environment interaction (GEI) is important in order to determine the nature of the GEI. The objectives of this study were to assess the presence and nature of GEI for nine agronomic traits of rapeseed cultivars, and to identify cultivars with favorable levels of stable oil production. Nine rapeseed cultivars, including seven open pollinated and two hybrids, Hyola308 and Hyola401, were grown in ten environments under rain-fed warm areas of Iran. The GEI was significant for all traits and was partitioned into components representing heterogeneity due to environmental index and the remainder of the GEI. Among the all traits with a highly significant heterogeneity, the largest amount of heterogeneity removed from the GEI was for seeds per pod and seed weight. We found GEIs for both oil content and seed yield were largely influenced by differences in correlations among pairs of cultivars (86.8 and 71.4% of the GEI sum of squares, respectively), suggesting that crossover GEIs (i.e., change in genotype rankings among environments) are present. The mean correlation of each cultivar with all other cultivars ([`(r)]<sub>ii<sup>￠</sup></sub> \bar{r}_{{ii^{\prime}}} ) ranged from 0.53 to 0.83 for oil content and 0.86 to 0.96 for seed yield. A comparison was done of the significance of Sh-σ<sub>i</sub><sup>2</sup> (stability variance derived from total GEI) and Sh-S<sub>i</sub><sup>2</sup> (adjusted stability variance derived from residual GEI) assignable to each genotype for oil content and seed and oil yield. Based on Sh-σ<sub>i</sub><sup>2</sup>, three cultivars were unstable for oil content, whereas six cultivars were unstable for seed and oil yield. The removal of heterogeneity revealed that one unstable cultivar for oil content and three unstable cultivars for oil yield were judged to be stable. All cultivars with [`(r)]_ii^￠ \bar{r}_{{ii^{\prime } }}  ≤ 0.63 were labeled unstable for oil content, whereas all with [`(r)]<sub>ii<sup>￠</sup></sub> \bar{r}_{{ii^{\prime } }}  ≤ 0.94 were considered unstable for seed yield. The relationships between [`(r)]_ii^￠ \bar{r}_{{ii^{\prime } }} and Sh-σ_i² were significant (P < 0.01) for oil content and seed yield. The results of rank correlation coefficients showed significant positive correlations of Yield-Stability statistic (YS_i) with oil content and oil yield. Cultivars such as Option500 and Hyola401 were identified as having stable, high levels to seed yield and oil content.     

Breeding for wide adaptation in faba bean

Summary Broadening agronomic adaptation will improve yield stability in the grain legume Vicia faba L. We gathered information on the adaptation of European and Mediterranean material to European and Mediterranean environments. The material comprised 20 inbred lines (12 European and 8 Mediterranean lines) and 99 intra- and interpool-crosses in generation F₁. These were evaluated in 9 environments: two spring-sown Southern German environments (SGermE), and seven autumn-sown Mediterranean environments (MedE) in Sicily, Puglia, Andalucia and South Africa. Standard ANOVA, stability analyses and AMMI analysis were performed. Mean yield in F₁ was 257 g/row, the overall parental mean was 144 g/row. The range of environmental means was from 94 g/row to 411 g/row. The average regression coefficient in F₁ was b _i =1.07, being significantly greater than for the parents (b _i =0.68). The opposite was true for the relative magnitude of the deviations from the regressions, which were highly correlated to the AMMI-PC1-results. The AMMI analysis clearly separated the SGermE from the MedE, as well as the germplasm pools. Though the superiority of the F₁-hybrids over their parents was striking, their pattern of interactions with the environments strictly reflected that of their parents. A number of promising crosses was identified as a nucleus of a widely adapted faba bean genepool.     

Effect of recombination in the maize breeding population with exotic germplasm on the yield stability

A little knowledge exists about the probability that recombination in the parental maize populations will enhance the chances to select more stable genotypes. The synthetic parent maize population ((1601/5 × ZPL913)F₂ = R₀) with 25% of exotic germplasm was used to assess: (i) genotype × environment interaction and estimate stability of genotypes using nonparametric statistics; (ii) the effect of three (R₃) and five (R₅) gene recombination cycles on yield stability of genotypes; (iii) relationship among different nonparametric stability measures. The increase of mean grain yield was significant (P < 0.01) in the R₃ and R₅ in comparison to the R₀, while it was not significant between R₃ and R₅. Analysis of variance showed significant (P < 0.01) effects of environments, families per set, environment × set interaction, family × environment interaction per set on grain yield. The non-significant noncrossover and significant crossover (P < 0.01) G × (E) interactions were found according to Bredenkamp procedures and van der Laan-de Kroon test, respectively. The significant (P < 0.01) differences in stability were observed between R₀-set 3 and R₅-set 3 determined by S_i⁽³⁾ S_{i}^{(3)} , R₃-set 1 and R₅-set 1 determined by S_i⁽³⁾ S_{i}^{(3)} (P < 0.05), and R₀-set 3 and R₅-set 3 determined by S_i⁽⁶⁾ S_{i}^{(6)} (P < 0.05). The significant parameters were those which take into account yield and stability so the differences could be due to differences in yield rather than stability. Findings can help breeders to assume the most optimum number of supplementary gene recombination to achieve satisfactory yield mean and yield stability of maize genotypes originating from breeding populations.     

Yield stability and adaptation of Nordic barleys

Summary Grain yield was studied in a collection of 220 Nordic barley lines at diverse locations in the Nordic countries. Two-row (2r) and six-row (6r) lines differed very significantly in reaction to the growing conditions within and between the two locations, Svalöv (in southern Sweden) and Højbakkegård (in Denmark). This difference was also highly significant at Viikki (in Finland), but not at As (in Norway) or between Viikki and As. Genotype × location (GL) and genotype × year (GY) variance components were used to estimate phenotypic yield stability by Shukla's stability variance (% MathType!MTEF!2!1!+-% feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGak0dh9WrFfpC0xh9vqqj-hEeeu0xXdbba9frFj0-OqFf% ea0dXdd9vqaq-JfrVkFHe9pgea0dXdar-Jb9hs0dXdbPYxe9vr0-vr% 0-vqpWqaaeaabaGaaiaacaqabeaadaqaaqaaaOqaaiaabo8adaahaa% WcbeqaaiaabkdaaaGcdaWgaaqcbaCaaiaabMgaaSqabaaaaa!3B73!\[{\text{\sigma }}^{\text{2}} _{\text{i}} \]). Only 7 lines did not contribute significantly to GL- and GY-interactions, and their yield levels were 7–27% lower than that of the highest yielding line (5057 kg/ha). Estimates of GL- and GY-stability parameters were not significantly correlated. Neither responsiveness, measured by the regression coefficient (b _i ), nor phenotypic yield stability, measured by the deviations from regression (Tai's _i ) were correlated with yield. Pedigree studies showed that both b _i and % MathType!MTEF!2!1!+-% feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGak0dh9WrFfpC0xh9vqqj-hEeeu0xXdbba9frFj0-OqFf% ea0dXdd9vqaq-JfrVkFHe9pgea0dXdar-Jb9hs0dXdbPYxe9vr0-vr% 0-vqpWqaaeaabaGaaiaacaqabeaadaqaaqaaaOqaaiaabo8adaahaa% WcbeqaaiaabkdaaaGcdaWgaaqcbaCaaiaabMgaaSqabaaaaa!3B73!\[{\text{\sigma }}^{\text{2}} _{\text{i}} \] can be changed by recombination and/or induced mutations. Mixing of near isogenic lines with different resistance genes, and selection within a landrace, also resulted in changes in responsiveness. Recently released 2r-cultivars were more unstable than older 2r-cultivars revealed by positive correlation between the year of release and _i . Cultivars originating from southern Scandinavia were higher yielding than cultivars originating from the central or the northern regions of Scandinavia.     

Comparison of stability statistics for yield and quality traits in bread wheat

The stability and genotypic mean of four traits, grain yield, grain protein content, alveograph W and bread volume, were evaluated in three multi-location trials, each covering two years. The stability of each genotype was evaluated by environmental variance (s² _E), interaction variance (s² _W) and variance of the ranks of the phenotypic values corrected for the genotypic effect (s² _R). The bootstrap method was used to study correlations between the genotypic mean and the three stability statistics and to calculate their accuracy. The repeatability of the stability statistics was measured by correlations between the values obtained in each of the two years. In addition, theoretical smaller trials were generated by random sampling and the stability values calculated were correlated with those of the original trial. Environmental variance appears to be usable both for yield and for quality traits, but there is a risk of counter-selecting a high genotypic mean of W. Whatever the trait and statistic envisaged, stability is poorly repeatable and its evaluation requires several years and a large number of locations per year to minimise sampling and environmental effects. This revised version was published online in August 2006 with corrections to the Cover Date.     

Genetic distance and its association with heterosis in peas

Summary The relationship between the genetic distance of parents and both the heterosis of F₁ hybrids and the variance of F₅ lines was investigated in 72 crosses of pea (Pisum sativum L.). The genetic distance between each pair of parents was estimated, using isozyme (GDi), morphological (GDm) or quantitative (GDq) markers and finally a combination of isozyme and morphological markers (GDi+m). GDm was poorly correlated with the other measures of genetic distance, which in turn were strongly correlated with each other. Genetic distance was moderately correlated with the level of heterosis for yield over midparent in the F₁ generation, with the highest correlation obtained from GDi+m. GD was not significantly correlated with heterosis for yield over the better or best parent but it was significantly correlated with all three measures of heterosis for pods per plant and hundred seed weight. There was no correlation between genetic distance and the level of heterosis for yield and total dry matter in the F₂ generation, but GDi, GDi+m and GDq were predictive for the level of inbreeding depression in grain yield and total dry matter. When parents were high in genetic distance, crosses produced highly transgressive segregants for basal branches per plant, hundred seed weight, harvest index and onset of flowering. Genetic distance between parents was thus a useful measure for predicting a portion of hybrid performance and also of the variance of derived inbred lines. It was concluded that when choosing parents for a cross, consideration should be given to their genetic distance as well as their overall adaptation and their yield. There is considerable potential for optimising choice of parental combinations in the development of improved pea cultivars.     

Heritability estimates of spaced-plant traits in three perennial ryegrass (Lolium perenne L.) cultivars

Summary Seed yield in perennial ryegrass is low and unpredictable. Spaced-plant traits suitable for indirect selection for total seed yield in drilled plots would be very useful. The objectives of this investigation were to evaluate genetic variation for seed yield components and other traits among clones from three perennial ryegrass cultivars differing in seed yield and their open-pollinated progenies. Per cultivar, a random set of 50 genotypes was cloned and on each genotype seed was generated by open pollination. Clonal ramets of the parents were observed for 17 traits in 1986 at two locations. In 1987 and 1988, parents and progenies were observed as single plants in a randomized complete block design with two replications. There was little cultivar-environment interaction for most traits. The parents differed significantly for almost all traits. Half-sib (HS) families differed for only three to five traits. Broad-sense heritabilities (h² _b), based on variance components of the parents, were moderate to high; earliness had the highest h_b ². Narrow-sense heritabilities (h² _n), based on variance components among HS-families, were low to moderate and mostly not significant; for most traits h² _n estimates varied between years and cultivars. Flag leaf width and date of first anthesis showed the highest h² _n. Narrow-sense heritability estimates from parent-offspring regressions (h² _nPO) ranged from non-significant to high, depending on year and cultivar; they were generally higher than the corresponding h² _n estimates. Generally, h² _nPO was highest for earliness, flag leaf width, ear length and the number of spikelets per ear. Breeding methods that capitalize on additive genetic variance, such as mass selection, should result in improvement for these traits.