Prediction of barley progeny performance in the presence of genotype environment interaction

Twenty recombinant inbred line (RIL) populations of European two‐row spring barley and their parents were tested in six environments in the Netherlands to investigate the prediction of progeny yield level, yield variance, stability level and stability variance, based on parent information. Progeny yield level is positively correlated with midparent value for average yield. Progeny yield variance is more difficult to predict, but there does appear to be a promising negative correlation between progeny yield variance and Habgood's (1977) parental similarity measure. To quantify yield stability, three statistics were calculated: Finlay and Wilkinson's (1963) regression coefficient b_i, Shukla's (1972) stability variance σs_i² and Eberhart and Russell's (1966) mean squared deviation d_i². The first stability statistic describes a different aspect of the response pattern to change in environment from the last two. Parents with high b_i values appear to have a better average yield, i.e. they react more positively to an improvement in the environment than the other genotypes. The average b_i value of the progeny is positively correlated with the midparent value, indicating its heritable nature. There are also indications that d_i² and σ_i² are heritable but their repeatability is poor. Therefore, it is concluded that only prediction of bi is useful in practical plant breeding. There is a positive correlation between progeny yield variance and progeny variance for b_i but we conclude that the inaccuracy of the stability variance estimates is too high for good predictors for progeny stability variance to be found.     

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.     

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.     

Estimation,interrelationships and repeatability of genetic variability parameters in spring safflower using multi-environment trial data

The present investigation was carried out to estimate the interrelationships and repeatabilities of various variability parameters, i.e., genotypic (σ²g) and phenotypic (σ²p) variances, genotypic (GCV) and phenotypic (PCV) coefficients of variation, broad-sense heritability (h_b²), genetic advance (GA), and genetic gain (GG), using data derived from a number of plant characters [seed yield (SY), days to heading (DH) and maturity (DM), plant height (PH), and thousand kernel weight (TKW)] of 17 spring safflower genotypes grown in 27 environments in Iran during 2003–2005. The analysis of variance for the five characters revealed significant differences among the genotypes in most of the environments, indicating a very high variability within the genotype. High genotypic and phenotypic variances in the quantitative traits, particularly for SY, were markers of increased success. A close correlation between GCV and PCV was observed for all the traits, indicating that all of the characters were less influenced by the environment and that the variability which exists in these characters is under genetic influence; however, the relatively higher values of PCV indicate the predominant role of environment. High expected genetic advances were observed for SY, but this trait showed large fluctuations in different environments (97.4%). High and low mean values of heritability were observed for DH (74.3%) and SY (59.7%), respectively, whereas high and low mean values of genetic gain were found for SY (60.3%) and DM (4.2%), respectively. High heritability coupled with low GA as a percentage of the mean was observed for DH and MD, whereas moderate heritability with moderate GA as a percentage of the mean was exhibited for SY. Analysis of correlation among the parameters showed that they were strongly and constantly correlated with each other, but none of them were consistently well correlated with the heritability parameter (h_b²). Based on estimates of the genetic variability parameters within each trial, the ranking of genotypes in the low-performance subset for all traits differed from that in the high-performance subset. This result indicates poor repeatability for the genetic variability parameters. The estimates of the parameters in the multi-year trials were almost constant and repeatable, whereas the responses over years showed poor repeatability.     

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.     

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.     

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.     

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.     

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.     

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.     

Evaluation of Genotype × Environment Interactions in Chinese Spring Wheat by the AMMI Model, Correlation and Path Analysis

An understanding of the characteristics of crop varieties and advanced lines could help improve their cultivation and to further enhance their potential. The objectives of this study were to estimate the genotype (G), environment (E) and genotype × environment (GE) interactions on the grain yield of Chinese spring wheat genotypes in 2000 and 2001 by the additive main effects and multiplicative interaction (AMMI) model, and to evaluate the relationships between yield and its components by correlation and path analysis. Grain yield varied from 3.9 to 5.2 t ha^?1, among which SW8188 had the highest yield performance, followed by 58769‐6 and Chuannong 16. Three interaction principal components (IPC) accounted for a total of 79.99 % and 72.96 % of the interactions with 41.05 % and 52.08 % for the corresponding degrees of freedom in 2000 and 2001, respectively. When IPC3 was significant, the stability coefficient D_i was more useful in the evaluation of the stability of each genotype. The estimates of D_i in the 2 years indicated that the D_i values varied between genotypes and years. The D_i values ranged from 1.804 to 14.665 and 2.497 to 12.481 in 2000 and 2001 respectively. The suitable locations (environments) for all genotypes were characterized. These results would be useful for improving the Chinese spring wheat cultivation and improvement.     

Repeatability of regression stability indexes for grain yield of oats (Avena sativa L.)

Summary Two lines of descent were established from an F₃ bulk lot of oats (Avena sativa L.) initiated by mixing seeds from approximately 250 crosses. For one line of descent, seeds were radiated with thermal neutrons or X-rays from F₃ through F₆, followed by five generations of bulk propagation. The second was propagated for 10 generations. No artificial selection was practiced in either line of descent. Grain yield data from 20 random strains from each of four generations from the radiated (F₇, F₈, F₉, and F₁₁) and five from the nonradiated (F₃, F₆, F₇, F₈, and F₁₂) line of descent and 20 check cultivars tested in 14 environments were used for estimating regression stability indexes of oat strains.The 14 environments were assigned randomly to two sets of seven, and regression stability indexes were computed for the 180 experimental oat strains for both sets. Intrageneration correlations between regression stability indexes from the two sets of environments ranged from –0.35 to 0.64 (18 d.f.), and only one of nine was significant, indicating poor repeatability for estimates of this statistic computed from different sets of environments.Correlations between regression stability indexes from two sets of environments, one in which the environments varied by soil nitrogen levels and a second in which they varied by soil phosphorus levels, ranged from –0.01 to 0.28, none of which was significant.The relative magnitudes and ranking of the regression stability index values for the oat genotypes were nearly identical when environmental productivity indexes were assessed with any number of check cultivars from 2 to 20.Journal paper No. J-8080 of the Iowa Agriculture and Home Economics Experiment Station. Ames. Iowa. USA 50010. Project 1752.     

Interrelationship among and repeatability of seven stability indices estimated from commercial cotton (<Emphasis Type="Italic">Gossypium hirsutum</Emphasis> L.) variety evaluation trials in three Mediterranean countries

Multi-environment trial data are required, to obtain variety stability performance parameters as selection tools for effective cultivar evaluation. The interrelationship among seven stability parameters and their association with mean yield, along with the repeatability of these parameters across consecutive years was the objective of this study. Cottonseed yield data of 31 cotton cultivars, proprietary of Delta and Pine Land Co and other companies, evaluated in 20 locations over the 1999–2005 year period in Greece, Spain and Turkey were used for combined analysis of variance in four datasets. Across locations in a single evaluation year (dataset A), across locations in each of two single consecutive evaluation year (dataset B), across locations and two consecutive years (dataset C) and across locations and three consecutive years (dataset D). For each dataset, cultivar phenotypic variance was appropriately partitioned in its components and the h² and component estimated. Furthermore, following the appropriate stability analysis and AMMI1 along with the GGE Biplot distance (GGED) and instability (GGEIN) parameters were obtained. The interrelationship among the parameters and their association with mean yield based on Spearman rank correlation was studied in each of the seven single evaluation years (dataset A). Rank correlation coefficients were also used as estimates of the repeatability of these stability parameters across consecutive year combinations (dataset B, C and D). The parameters GGED and YS_i were consistently highly correlated with each other and mean yield in five out of seven single evaluation years. The data provided evidence that single year evaluation across locations might be sufficient to reliably rank cotton cultivars, based on mean yield along with GGED and YS_i. Combined analysis across two consecutive years (dataset C) was more effective as compared to single year evaluation. GGED was relatively more repeatable than YS_i and mean yield in single (dataset B) and 2-year comparisons (dataset C). Although GGED is an index depended and proportional to yield, provides a superior way to integrate mean performance and stability into a single measure, which can be assessed visually on biplots. Regarding the other stability parameters, the results were contradicting and of low repeatability across single years and two consecutive years. Cultivar evaluation combined across locations in 3 years did not improve the repeatability of cultivar variance effects but resulted in very high repeatability of GGED, YS_i and mean yield.     

Yield Stability in Faba Bean,Vicia faba L. 1. Variation Among Inbred Lines*

Cultivation of faba beans (Vicia faba L.) is hampered by poor yield stability. The genetic variation at the homozygous level for yield stability in the gene pool of the small-seeded and indeterminate European faba beans and the usefulness of auxiliary traits for the improvement of yield stability were investigated. The concept of stability, based on the regression technique, was applied. A sample of 36 faba bean lines was tested in 16 environments and a subsample of eight lines was tested in 11 additional environments. Significant differences were found between lines for yield stability parameters, but the repeatability of the results was limited. Early maturity correlated markedly (r = 0.51^**) with one of the yield stability parameters (deviation from regression). Although lodging resistance was not correlated with the stability parameters, it proved to be a safety factor for performance.     

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.     

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.     

Hybrid performance of sorghum and its relationship to morphological and physiological traits under variable drought stress in Kenya

Sorghum, Sorghum bicolor L. Moench, is grown mostly in semi-arid climates where unpredictable drought stress constitutes a major production constraint. To investigate hybrid performance at different levels of drought stress, 12 single-cross hybrids of grain sorghum and their 24 parent lines were grown in eight site-season combinations in a semi-arid area of Kenya. In addition, a subset of 20 genotypes was evaluated at the seedling stage under polyethylene glycol (PEG)-induced drought stress. Environmental means for grain yield ranged from 47 to 584 g/m²reflecting the following situations: two non-stress, one moderate pre-flowering, four moderate terminal and one extreme drought stress. Mean hybrid superiority over mid-parent values was 54% for grain yield and 35% for above-ground biomass. Across environments, hybrids out-yielded two local varieties by 12%. Differences in yield potential contributed to grain yield differences in all stress environments. Early anthesis was most important for specific adaptation to extreme drought. Field performance was not related to growth reduction and osmotic adjustment under PEG-induced drought stress. In conclusion, exploitation of hybrid vigour could improve the productivity of sorghum in semi-arid areas.     

Genotypic Variation in the Response of Rice (Oryza sativa L.) to Increased Atmospheric Carbon Dioxide and its Physiological Basis

Increasing atmospheric carbon dioxide concentration (C_a) is one aspect of global change that will have a significant impact on the productivity of agricultural crops. Crop yields have been shown to increase with increasing C_a. The magnitude of yield response to increased C_a could vary depending on genotypic and environmental factors. The objective of this study was to determine the genotypic variation of yield response and its physiological basis in rice (Oryza sativa L.) through an initial varietal screening using 16 genotypes. The genotypes were grown under two concentrations of C_a, i.e. 370 ± 28 (ambient) and 570 ± 42 (elevated) μmol mol⁻¹, in open top chambers under lowland field conditions at the Rice Research and Development Institute in Sri Lanka (7°50′N, 80°50′E) from May to August 2001 (yala season) and from November 2001 to March 2002 (maha). C_a within chambers was maintained around target concentrations by a computer‐based real‐time data acquisition and control system. There was significant variation between genotypes in the response of yield to elevated C_a, with absolute increases up to 530 g m⁻² in yala and 347 g m⁻² in maha. In relative terms, percentage yield increases from ambient to elevated C_a ranged from 4 % to 175 % in yala and from 3 % to 64 % in maha. Genotypic variation in yield showed significant positive correlations with light‐saturated net photosynthetic rate of the flag leaf during the grain‐filling stage. This indicated that increased assimilate supply and its genotypic variation contributed to the observed genotypic variation in yield response to elevated C_a. Furthermore, the capacity to develop a larger reproductive sink through increased panicle number per m² and increased number of grains per panicle contributed to greater yield at elevated C_a. There was a significant genotype × season interaction with genotypes responding differentially to increased C_a in the two seasons. This was mainly due to inter‐seasonal variation in incident radiation during the grain‐filling stage. Our results demonstrate the significant genotypic variation that exists within the rice germplasm, in the response to increased C_a of yield and its correlated physiological parameters. A subset of genotypes from screening trials such as the present study can be used for more in‐depth analysis of the influence of elevated C_a on processes responsible for yield determination in rice and for molecular studies to elucidate the genetic basis of the response to increased C_a. This could pave the way for breeding genotypes which are more productive in a future high CO₂ environment, provided that genotypes with greater flexibility in their physiology are selected to counter the genotype × environmental interaction.     

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.