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1.
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   

2.
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)]ii￠ \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-σi2 (stability variance derived from total GEI) and Sh-Si2 (adjusted stability variance derived from residual GEI) assignable to each genotype for oil content and seed and oil yield. Based on Sh-σi2, 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)]ii￠ \bar{r}_{{ii^{\prime } }}  ≤ 0.94 were considered unstable for seed yield. The relationships between [`(r)]ii￠ \bar{r}_{{ii^{\prime } }} and Sh-σi2 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 (YSi) 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.  相似文献   

3.
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   

4.
The objective of this study was to compare nonparametric stability procedures and apply different nonparametric tests for genotype × environment (G × E) interactions on grain yields of 15 durum wheat genotypes selected from Iran/ICARDA joint project grown in 12 environments during 2004–2006 in Iran. Results of nonparametric tests of G × E interaction and a combined ANOVA across environments indicated the presence of both crossover and noncrossover interactions, and genotypes varied significantly for grain yield. In this study, high values of TOP (proportion of environments in which a genotype ranked in the top third) and low values of sum of ranks of mean grain yield and Shukla’s stability variance (rank-sum) were associated with high mean yield. The other nonparametric stability methods were not positively correlated with mean yield but they characterized a static concept of stability. The results of correlation analysis indicated that only TOP and rank-sum methods would be useful for simultaneous selection for high yield and stability. These two methods identified lines Mrb3/Mna-1, Syrian-4 and Mna-1/Rfm-7 as genotypes with dynamic stability and wide adaptation. According to static stability parameters, the genotypes 12A-Mar8081 and 19A-Mar8081 with lowest grain yield were selected as genotypes with the highest stability.  相似文献   

5.
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   

6.
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 Wi, deviation mean square s2 di, the nonparametric measures Si (1), Si (2), and two new measures Ri and Li as well as between environmental variance S2 xi and regression coefficient bi. The results suggest that Si (1), Si (2), Li, and Ri can be used as alternatives to Wi and the stability variance 2 i. This may be worthwhile, if certain statistical assumptions do not hold, particularly if significance testing is needed.  相似文献   

7.
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   

8.
Reza Mohammadi  Ahmed Amri 《Euphytica》2013,192(2):227-249
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   

9.
Six stability statistics: (bi, s2di, , , 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 (bi) were non-significant, s2di, became an important statistic in estimating stability. It appears that the regression coefficient, bi, was best used to estimate genotypic adaptability, whereas s2di, for stability. Maize and sorghum had negative correlations between the mean yield and stability statistics, s2di, , 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 bi, 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.  相似文献   

10.
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   

11.
Repeatability of different stability parameters for grain yield in chickpea   总被引：1，自引：0，他引：1  
S. Kumar    O. Singh    H. A. Van  Rheenen  K. V. S. Rao 《Plant Breeding》1998,117(2):143-146
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   

12.
Soybean lodging can result in serious yield reduction. Detecting the quantitative trait loci (QTL) associated with lodging tolerance for their further application in marker‐assisted selection (MAS) has the potential to enhance soybean breeding efficiency. In this study, a genome‐wide association analysis (GWAS) was performed to identify soybean accessions that could potentially be used to produce lodging‐tolerant varieties, based on the comprehensive evaluation of lodging scores (LS) obtained for the parental cultivar “Tokachi nagaha” and its 137 derived cultivars. Results showed that genotype, environment and genotype × environment interaction significantly influenced LS. Of the 31 significant SNPs identified, 22 were consistently detected in two or more environments and 27 SNPs were located in or close to agronomically important QTL mapped by linkage analysis. Best linear unbiased predictors (BLUPs) of LS tend to decrease with the elite alleles contained by accessions increasing. Some excellent accessions, with lower BLUPs and Di (stability coefficients) values and more elite alleles, were selected. This study contributed to understand the genetic mechanism of lodging, providing genetic and phenotypic information for MAS.  相似文献   

13.
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 YSi 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 YSi. Combined analysis across two consecutive years (dataset C) was more effective as compared to single year evaluation. GGED was relatively more repeatable than YSi 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, YSi and mean yield.  相似文献   

14.
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   

15.
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   

16.
Soybean accounts for over a quarter of the world's oilseed consumption and over 70% of the world's protein meal consumption. The separate development of high oleic, low linolenic acid (HOLL) soybean and high-protein (HP) soybean means that no soybean cultivar on the market has an optimal fatty acid profile and increased protein. The objective of this study was to develop and evaluate high protein, high oleic acid, and low linolenic acid (HP-HOLL) soybean. A five-gene stack was created using a two-phase forward breeding scheme and marker-assisted selection method. Forty-six HP-HOLL lines from three genetic backgrounds were grown in six environments in the Southeast United States. Although genotype-by-environment interaction was significant for seed composition traits, lines met the >75% and <3% cutoffs for oleic acid and linolenic acid, respectively, and met or exceeded the protein concentration of the HP parent. No negative interaction could be detected between the HP and HOLL traits. Additionally, yield testing in four environments indicated yield parity for some lines, suggesting HP and HOLL soybean cultivars with high yield could be selected.  相似文献   

17.
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   

18.
Genotypic variations in leaf gas exchange and grain yield were analysed in 10 highland‐adapted quinoa cultivars grown in the field under drought conditions. Trials took place in an arid mountain region of the Northwest of Argentina (Encalilla, Amaicha del Valle, 22°31′S, 65°59′W). Significant changes in leaf gas exchange and grain yield among cultivars were observed. Our data demonstrate that leaf stomatal conductance to water vapour (gs) is a major determinant of net CO2 assimilation (An) because quinoa cultivars with inherently higher gs were capable of keeping higher photosynthesis rate. Aboveground dry mass and grain yield significantly varied among cultivars. Significant variations also occurred in chlorophyll, N and P content, photosynthetic nitrogen‐use efficiency (PNUE), specific leaf area (SLA), intrinsic water‐use efficiency (iWUE) and carboxylation capacity (An/Ci). Many cultivars gave promissory grain yields with values higher than 2000 kg ha?1, reaching for Sayaña cultivar 3855 kg ha?1. Overall, these data indicate that cultivars, which showed higher photosynthesis and conductances, were also generally more productive. Carbon isotope discrimination (Δ) was positively correlated with the grain yield and negatively with iWUE, but δ15N did not show significant correlations. This study provides a reliable measure of specific responses of quinoa cultivars to drought and it may be valuable in breeding programmes.  相似文献   

19.
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 σ2i, 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 (s2i) 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.  相似文献   

20.
A seven-year (2009–2015) continuous field experiment was established at the South China Agricultural University in order to identify the effects of sugarcane/soybean intercropping and reduced N rate on ecosystem productivity, yield stability, soil fertility, and N2O emissions. The randomized block experiment was designed with four cropping patterns (sugarcane monocropping (MS), soybean monocropping (MB), sugarcane/soybean (1:1) intercropping (SB1), and sugarcane/soybean (1:2) intercropping (SB2)) and two rates of N fertilization (300 kg hm−2 (N1, reduced rate) and 525 kg hm−2 (N2, conventional rate)). The results showed that the land equivalent ratio (LER) of all intercropping systems was greater than 1 (between 1.10 and 1.84), and the SB2-N1 optimally improved the land utilization rate among all treatments. The cropping patterns and N applied rates had no significant effect on sugarcane yield. The soybean yield was influenced by different cropping patterns because of different planting densities (4, 8 and 16 rows of soybean were plant under SB1, SB2, and MB, respectively) and was adopted in this experiment. In addition, under the SB2 cropping pattern, the soybean yield at the reduced N application rate was higher than that at the conventional N application rate. Wricke’s ecovalence (Wi2), the sustainable yield index (SYI) and the coefficient of variation (CV) were used to evaluate yield stability. Different treatments had no significant effects on sugarcane yield stability, as demonstrated by three indicators (Wi2, SYI and CV), which indicated that intercropping with soybean and reduced N rate had no effect on sugarcane yield. For soybeans, the value of Wi2 demonstrated that the stability of the intercropping system was higher than its counterpart monocropping system, as SYI and CV values indicated that SB2 had higher stability than SB1. During seven years of experiments, there was no significant difference in the soil fertility between MS and SB patterns. The soybean monocropping had a higher available K, pH and lower available P content than sugarcane inter- and mono-cropping. Different cropping patterns had a slight impact on N2O emissions and the greenhouse gas intensity (GHGI) value. Higher N input promoted N2O emissions and increased GHGI values. In conclusion, the present study observed that a 40% reduced nitrogen input combined with intercropping soybeans could maintain sugarcane yield and soil sustainable utilization, and that higher N fertilizer additions induced negative impacts on greenhouse gases emissions. Sugarcane intercropping with soybeans can reduce chemical fertilizer input and simultaneously maintain crop productivity; thus, it can be considered to be a reasonable practice for field management.  相似文献   

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