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1.
Genotype × environment interaction and genetic improvement for yield and yield stability of rainfed durum wheat in Iran 总被引：1，自引：1，他引：0
The genotype × environment (GE) interaction influences genotype selection and recommendations. Consequently, the objectives of genetic improvement should include obtaining genotypes with high potential yield and stability in unpredictable conditions. The GE interaction and genetic improvement for grain yield and yield stability was studied for 11 durum breeding lines, selected from Iran/ICARDA joint program, and compared to current checks (i.e., one durum modern cultivar and two durum and bread wheat landraces). The genotypes were grown in three rainfed research stations, representative of major rainfed durum wheat-growing areas, during 2005–09 cropping seasons in Iran. The additive main effect and multiplicative interaction (AMMI) analysis, genotype plus GE (GGE) biplot analysis, joint regression analysis (JRA) (b and S2di), six stability parameters derived from AMMI model, two Kang’s parameters [i.e., yield-stability (YSi) statistic and rank-sum], GGE distance (mean performance + stability evaluation), and two adaptability parameters [i.e., TOP (proportion of environments in which a genotype ranked in the top third) and percentage of adaptability (Ad)] were used to analyze GE interaction in rainfed durum multi-environment trials data. The main objectives were to (i) evaluate changes in adaptation and yield stability of the durum breeding lines compared to modern cultivar and landraces (ii) document genetic improvement in grain yield and analyze associated changes in yield stability of breeding lines compared to checks and (iii) to analyze rank correlation among GGE biplot, AMMI analysis and JRA in ranking of genotypes for yield, stability and yield-stability. The results showed that the effects due to environments, genotypes and GE interaction were significant (P < 0.01), suggesting differential responses of the genotypes and the need for stability analysis. The overall yield was 2,270 kg ha?1 for breeding lines and modern cultivar versus 2,041 kg ha?1 for landraces representing 11.2 % increase in yield. Positive genetic gains for grain yield in warm and moderate locations compared to cold location suggests continuing the evaluation of the breeding material in warm and moderate conditions. According to Spearman’s rank correlation analysis, two types of associations were found between the stability parameters: the first type included the AMMI stability parameters and joint regression parameters which were related to static stability and ranked the genotypes in similar fashion, whereas the second type consisted of the rank-sum, YSi, TOP, Ad and GGED which are related to dynamic concept of stability. Rank correlations among statistical methods for: (i) stability ranged between 0.27 and 0.97 (P < 0.01), was the least between AMMI and GGE biplot, and highest for AMMI and JRA and (ii) yield-stability varied from 0.22 (between GGE and JRA) to 0.44 (between JRA and AMMI). Breeding lines G8 (Stj3//Bcr/Lks4), G10 (Ossl-1/Stj-5) and G12 (modern cultivar) were the best genotypes in terms of both nominal yield and stability, indicating that selecting for improved yield potential may increase yield in a wide range of environments. The increase in adaptation, yield potential and stability of breeding lines has been reached due to gradual accumulation of favorable genes through targeted crosses, robust shuttle breeding and multi-locational testing. 相似文献
2.
Frew Mekbib 《Euphytica》2003,130(2):147-153
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 Si
2d, Wi
2,σi
2 and Si
2, where as there was weak correlation between biand Ri
2, Si
2d, Wi
2, σi
2 and Si
2. R2 was significantly and negatively correlated with Wi
2, σi
2 and Si
2. σi
2 is significantly correlated with Wi
2.Yield is significantly correlated with bi and Ri
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. 相似文献
3.
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 [NPi(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), NPi(2), NPi(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. 相似文献
4.
Asghar Ebadi Segherloo Sayyed Hossain Sabaghpour Hamid Dehghani Morteza Kamrani 《Euphytica》2008,162(2):221-229
Multi-environment trials (MET) play an important role in selecting the best cultivars and/or agronomic practices to be used
in future years at different locations by assessing a cultivar's stability across environments before its commercial release.
Objective of this study is to identify chickpea (Cicer arietinum L.) genotypes that have high yield and stable performance across different locations. The genotypes were developed by various
breeders at different research institutes/stations of Iran and the International Center for Agricultural Research in Dray
Areas (ICARDA), Syria. Several statistical methods were used to evaluate phenotypic stability of these test chickpea genotypes.
The 17 genotypes were tested at six different research stations for two years in Iran. Three non-parametric statistical test
of significance for genotype × environment (GE) interaction and ten nonparametric measures of stability analyses were used
to identify stable genotypes across the 16 environments. The non-parametric measures (Kubinger, Hildebrand and Kroon/Van der)
for G × E interaction were highly significant (P < 0.01), suggesting differential responses of the genotypes to the test environments. Based on high values of nonparametric
superiority measure (Fox et al. 1990) and low values of Kang's (1988) rank-sum stability parameters, Flip 94-123C was identified as the most stable genotype. These non parametric parameters
were observed to be associated with high mean yield. However, the other nonparametric methods were not positively correlated
with mean yield and were therefore not used in classifying the genotypes. Simple correlation coefficients using Spearman’s
rank correlation, calculated using ranks was used to measure the relationship between the stability parameters. To understand
the nature of relationships among the nonparametric methods, a hierarchical cluster analysis based on non weighted values
of genotypes, was performed. The 10 stability parameters fell into three groups. 相似文献
5.
Moslem Abdipour Behrouz Vaezi Mehdi Younessi-Hamzekhanlu Seyyed Hamid Reza Ramazani 《Journal of Crop Science and Biotechnology》2017,20(4):305-314
The development of genotypes with adaptation to a wide range of environments is one of the most important goals of plant breeding programs. In order to compare nonparametric stability measures and to identify promising high-yield and stable barley (Hordeum vulgare L.), 20 barley genotypes selected from the Iran/ICARDA joint project and grown in nine environments during 2009-11 in Iran. Four nonparametric statistical tests of significance for genotype × environment (GE) interaction and 10 nonparametric measures of stability were used to identify stable genotypes in nine environments. Results of nonparametric tests of G×E interaction (Kubinger, Hildebrand, and Kroon/ Laan) and a combined ANOVA across environments, indicated the presence of both crossover and non-crossover interactions. Also, only TOP and rank-sum values were positively associated with high yield. Thus, in the simultaneous selection for high yield and stability, only the rank-sum and TOP methods were useful in terms of the principal component analysis results, and correlation analysis of nonparametric stability statistics and yield. According to these stability parameters (TOP and rank-sum), three genotypes (G13, G12, and G17) were the most stable for grain yield. The results also revealed that based on nonparametric test results, stability could be classified into three groups, according to agronomic and biological concepts of stability. 相似文献
6.
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, W2i, were consistent over years, whereas those of response parameters (bi, and S2i) 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. 相似文献
7.
Evaluation of genotype × environment interaction (GEI) is an important component of the variety selection process in multi-environment trials. The objectives of this study were first to analyze GEI on seed yield of 18 spine safflower genotypes grown for three consecutive seasons (2008–2011) at three locations, representative of rainfed winter safflower growing areas of Iran, by the additive main effects and multiplicative interaction (AMMI) model, and second to compare AMMI-derived stability statistics with several stability different methods, and two stability analysis approaches the yield-stability (Ysi) and the GGE (genotype + genotype × environment) biplot that are widely used to identify high-yielding and stable genotypes. The results of the AMMI analysis showed that main effects due to genotype, environment, and GEI as well as first six interaction principle component axes (IPCA1 to 6) were significant (P < 0.01). According to most stability statistics of AMMI analyses, genotypes G5 and G14 were the most stable genotypes across environments. According to the adjusted stability variance (s2), the high-yielding genotype, G2, was unstable due to the heterogeneity caused by environmental index. Based on the definition of stable genotypes by regression method (b = 1, S d 2 = 0), genotypes G11, G9, G14, G3, G12 and G13 had average stability for seed yield. Stability parameters of Tai indicated that genotype G5 had specific adaptability to unfavorable environments. The GGE biplot and the Ysi statistic gave similar results in identifying genotype G2 (PI-209295) as the best one to release for rainfed conditions of Iran. The factor analysis was used for grouping all stability parameters. The first factor separated static and dynamic concepts of stability, in which the Ysi and GGED (i.e., the distance from the markers of individual genotypes to the ideal genotype) parameters had a dynamic concept of stability, and the other remaining parameters had static concept of stability. 相似文献
8.
W. Li Z.-H. Yan Y.-M. Wei X.-J. Lan Y.-L. Zheng 《Journal of Agronomy and Crop Science》2006,192(3):221-227
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 Di was more useful in the evaluation of the stability of each genotype. The estimates of Di in the 2 years indicated that the Di values varied between genotypes and years. The Di 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. 相似文献
9.
Genotype × environment interactions for tea yields 总被引：1，自引：0，他引：1
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 (bi) 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 (bi) 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 bi (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 bi 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. 相似文献
10.
Manfred Huehn 《Euphytica》1990,47(3):195-201
Summary The three nonparametric measures of phenotypic stability Si
(1), Si
(2) and Si
(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 Si
(1), Si
(2) and Si
(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 Si
(3) in 1976). The differences between the correlations for the 3 years were considerable.The parameters Si
(1) and Si
(2) were very strong intercorrelated with each other with a good agreement of the correlations for the different years. The divergent property of Si
(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 xij 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 Si
(3) included) which, of course, implies no significant differences between the correlations of the different years.For the correlations between each of the Si
(1), Si
(2) and Si
(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 Si
(1), Si
(2) and Si
(3), but these differences are large for regression coefficient and environmental variance. This transformation xijxij
* 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. 相似文献
11.
Reza Mohammadi Mohtasham Mohammadi Rahmatollah Karimizadeh Ahmed Amri 《Journal of Crop Science and Biotechnology》2010,13(4):267-274
This study was performed for pattern analysis of genotype-by-environment (GE) interaction on 20 durum wheat genotypes grown
in 15 testing environments during 2004–06 in Iran. Combined analysis of variance showed significant genotypes (G), environments
(E), and GE interactions (P < 0.01), with environmental main effects being the predominant source of variation, followed by GE interaction. The results
showed various patterns of genotype responses to different environment groups and assisted in structuring the durum wheat
testing locations with identification of two major-environment groups with high genotype discrimination ability. The locations
(Gachsaran and Ilam) corresponding to warm and semi-arid aresa were similar in genotype discrimination and showed no association
with the other testing locations (Gonbad, Moghan, and Khoramabad) representing the Mediterranean area, indicating they differ
in rankings of genotypes. The top-yielding genotypes, G13, G14 and G9, were highly adapted to warm and semi-arid environments,
but those corresponding to the Mediterranean area had a high ability to discriminate the genotypes G16, G11, and Saimareh.
The stability and adaptability of specific genotypes were assessed by plotting their nominal grain yields at specific environments
in an ordination biplot, which aided in the identification of environment groups. Appropriate check genotypes for all environments
or for specific environments were also identified. Pattern analysis allowed a sensible and useful summarization of GE interaction
data set and helped to facilitate selecting superior genotypes for target-growing sites. 相似文献
12.
Carlos Alberto Scapim Cleso Antônio Patto Pacheco Antônio Teixeira do Amaral Júnior Rafael Augusto Vieira Ronald José Barth Pinto Thiago Vincenzi Conrado 《Euphytica》2010,174(2):209-218
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 sdi2 \sigma_{\rm di}^{2} and bi \beta_{\rm i} from Eberhart and Russell, ωi 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, Pi 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 ωi,
\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 sdi2 \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 bi \beta_{\rm i} was negatively and significantly correlated with Pi for crop yield indicating that the most adaptable genotypes tend to have the lowest estimates of Pi. Although Pi was not correlated with ωi,
\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 sdi2 \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 + Wi) indicating that superior popcorn genotypes are also stable. Finally, both Pi and the rank-sum are useful statistics in breeding programmes where crop yield, popping expansion and stability are essential
traits for selecting genotypes. 相似文献
13.
Tomislav Živanović Gordana Branković Miroslav Zorić Gordana Šurlan Momirović Snežana Janković Sanja Vasiljević Jovan Pavlov 《Euphytica》2012,185(3):407-417
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 Si(3) S_{i}^{(3)} , R3-set 1 and R5-set 1 determined by Si(3) S_{i}^{(3)} (P < 0.05), and R0-set 3 and R5-set 3 determined by Si(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. 相似文献
14.
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 (Si4), 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. s2x and b showed medium values of nearly 0.55 The stability parameters s2d, r2, W and Si4 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. 相似文献
15.
J. M. Lenis M. Ellersieck D. G. Blevins D. A. Sleper H. T. Nguyen D. Dunn J. D. Lee J. G. Shannon 《Journal of Agronomy and Crop Science》2011,197(4):302-310
Salinity reduces crop yield by limiting water uptake and causing ion‐specific stress. Soybean [Glycine max (L.) Merr.] is sensitive to soil salinity. However, there is variability among soybean genotypes and wild relatives for salt tolerance, suggesting that genetic improvement may be possible. The objective of this study was to identify differences in salt tolerance based on ion accumulation in leaves, stems and roots among accessions of four Glycine species. Four NaCl treatments, 0, 50, 75 and 100 mm , were imposed on G. max, G. soja, G. tomentella and G. argyrea accessions with different levels of salinity tolerance. Tolerant genotypes had less leaf scorch and a greater capacity to prevent Na+ and Cl? transport from soil solution to stems and leaves than sensitive genotypes. Magnitude of leaf injury per unit increase in leaf Na+ or Cl? concentrations was lower in tolerant than in susceptible accessions. Also, plant injury was associated more with Na+ rather than with Cl? concentration in leaves. Salt‐tolerant accessions had greater leaf chlorophyll‐meter readings than sensitive genotypes at all NaCl concentrations. Glycine argyrea and G. tomentella accessions possessed higher salt tolerance than G. soja and G. max genotypes. 相似文献
16.
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 bi, Shukla's (1972) stability variance σsi2 and Eberhart and Russell's (1966) mean squared deviation di2. The first stability statistic describes a different aspect of the response pattern to change in environment from the last two. Parents with high bi 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 bi value of the progeny is positively correlated with the midparent value, indicating its heritable nature. There are also indications that di2 and σi2 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 bi but we conclude that the inaccuracy of the stability variance estimates is too high for good predictors for progeny stability variance to be found. 相似文献
17.
Braulio?J.?Soto-Cerda Fernando?Westermeyer Federico?I?iguez-Luy Gastón?Mu?oz Adolfo?Montenegro Sylvie?Cloutier
High prices of fish oil make linseed attractive for aquaculture and animal feed. To ensure a constant supply of linseed, the development of stable cultivars is of strategic importance. In this study, 35 linseed genotypes were evaluated in five Chilean environments (E) from 2009 to 2012. The additive main effect and multiplicative interaction analysis (AMMI), genotype (G) plus genotype by environment (GE) interaction (GGE) biplot analysis and three stability parameters were tested with the aim of identifying adapted genotypes for the development of linseed cultivars. An association mapping (AM) analysis was also conducted for four agronomic traits and the stability of the associated markers was evaluated using the QQE (QTL main effect and QTL by environment interaction) approach. Combined analysis of variance for yield, seeds per boll (SPB), plant height (PH) and days to flowering (DTF) were significant for G, E and GE (P < 0.001). The combined stability analysis identified some Canadian, Argentinean and Chilean accessions to be the best adapted and highest yielding genotypes. Coancestry analysis indicated that crossing Canadian and Chilean genotypes could maximize transgressive segregation for yield. Significant associations for DTF, PH and SPB explained up to 59 % of the phenotypic variation for these traits. The QQE and AM analyses were consistent in identifying marker LGM27B as the most stable and significant across all environments with the largest effect in reducing DTF. The combined application of the stability, AM and QQE analyses could accelerate the development of marketable linseed cultivars adapted to Southern Chile. 相似文献
18.
19.
Genotype-environment interactions and phenotypic stability analyses of linseed in Ethiopia 总被引：1，自引：0，他引：1
Seed yield of 10 linseed genotypes, tested in a randomized block design with four replications across 18 environments of Ethiopia was analysed using different stability models. The objectives were to assess genotype‐environment (G‐E) interactions, determine stable genotypes, and to compare the stability parameters. Year by location and location variability were the dominant source of interactions. The stability analyses identified ‘R12‐N10D’, ‘Chilalo’ and ‘P13611’ב10314D’ as more stable genotypes, while ‘R11‐N1266’, ‘R10‐N27G’ and ‘R12‐D24C’ were specifically adapted to some environments. The highly significant rank correlations found among the deviations from regression, additive main effects multiplicative interaction, stability values, coefficients of determination, and stability variances indicated their close similarity and effectiveness in detecting stable genotypes over a range of Ethiopian environments. 相似文献
20.
Evaluation of soybean cultivars using parametric and nonparametric stability estimates 总被引：1，自引：0，他引：1
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, Si(1) and Si(2), were significantly and positively correlated with Eberhart and Russell's sdi2 and Wricke's ecovalence (Wi). 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. 相似文献