Stability analysis and G*E interactions in plantsRachana Bagudam
Gene–environment interaction is when two different genotypes respond to environmental variation in different ways. Stability refers to the performance with respective to environmental factors overtime within given location. Selection for stability is not possible until a biometrical model with suitable parameters is available to provide criteria necessary to rank varieties / breeds for stability. Different models of stability are discussed.
GGEBiplot analysis of genotype × environment interaction in Agropyron interme...Innspub Net
In order to identify genotypes of Agropyron intermedium with high forage yield and stability an experiment was carried out in the Research station of Kermanshah Iran.The 11 accessions were sown in a randomized complete block design with three replications under rainfed and irrigated conditions during 2013-21-014 cropping deasons. Combined analysis of variance indicated high significant differences for location, genotype and G × E interaction (GEI) at 1% level of probability. Mean comparisons over environments introduced G4, G3 and G5 with maximum forage yield over rainfed and irrigated conditions. Minimum forage yield was attributed to genotype G1. GGEbiplot analysis exhibited that the first two principal components (PCA) resulted from GEI and genotype effect justified 99.37% of total variance in the data set. The four environments under investigation fell into two apparent groups: irrigated and rainfed. The presence of close associations among irrigated (E1 and E3) and rainfed (E2 and E4) conditions suggests that the same information about the genotypes could be obtained from fewer test environments, and hence the potential to reduce testing cost.The which-won-where pattern of GGEbiplot introduced genotypes G3 and G4 as stable with high forage yield for rainfed condition, while G5 was stable with high yield for irrigated condition. According to the comparison of the genotypes with the Ideal genotype accessions G4, G3 and G9 were more favorable than all the other genotypes. Get more articles at: http://www.innspub.net/volume-6-number-4-april-2015-jbes/
Stability analysis and G*E interactions in plantsRachana Bagudam
Gene–environment interaction is when two different genotypes respond to environmental variation in different ways. Stability refers to the performance with respective to environmental factors overtime within given location. Selection for stability is not possible until a biometrical model with suitable parameters is available to provide criteria necessary to rank varieties / breeds for stability. Different models of stability are discussed.
GGEBiplot analysis of genotype × environment interaction in Agropyron interme...Innspub Net
In order to identify genotypes of Agropyron intermedium with high forage yield and stability an experiment was carried out in the Research station of Kermanshah Iran.The 11 accessions were sown in a randomized complete block design with three replications under rainfed and irrigated conditions during 2013-21-014 cropping deasons. Combined analysis of variance indicated high significant differences for location, genotype and G × E interaction (GEI) at 1% level of probability. Mean comparisons over environments introduced G4, G3 and G5 with maximum forage yield over rainfed and irrigated conditions. Minimum forage yield was attributed to genotype G1. GGEbiplot analysis exhibited that the first two principal components (PCA) resulted from GEI and genotype effect justified 99.37% of total variance in the data set. The four environments under investigation fell into two apparent groups: irrigated and rainfed. The presence of close associations among irrigated (E1 and E3) and rainfed (E2 and E4) conditions suggests that the same information about the genotypes could be obtained from fewer test environments, and hence the potential to reduce testing cost.The which-won-where pattern of GGEbiplot introduced genotypes G3 and G4 as stable with high forage yield for rainfed condition, while G5 was stable with high yield for irrigated condition. According to the comparison of the genotypes with the Ideal genotype accessions G4, G3 and G9 were more favorable than all the other genotypes. Get more articles at: http://www.innspub.net/volume-6-number-4-april-2015-jbes/
Stability refers to the performance with respective changing environmental factors overtime within given location.
Selection for stability is not possible until a biometrical model with suitable parameters is available to provide criteria necessary to rank varieties / breeds for stability.
The shifted multiplicative model was developed by Cornelius and Seyedsadr in 1992.
SHMM is used to analyze the complete separability, genotypic separability, environmental separability, and inseparability of environment effects and genotypic effects.
Gregorius and Namkoong (1986) defined Separability as the property which is that cultivar effect is separable from environmental effect so that there is no rank.
The shifted multiplicative model (SHMM) is used in an exploratory step-down method for identifying subsets of environments in which genotypic effects are "separable" from environmental effects. Subsets of environments are chosen on the basis of a SHMM analysis of the entire data set. SHMM analyses of the subsets
may indicate a need for further subdivision and/or suggest that a different subdivision at the previous stage should be tried. The process continues until SHMM analysis indicates that a SHMM with only one multiplicative term and its "point of concurrence" outside (left or right) of the cluster of data points adequately fits the data in all subsets.
Genotype-By-Environment Interaction (VG X E) wth ExamplesZohaib HUSSAIN
Introduction
Phenotypic variation can be caused by the combination of genotypes and environments in a population. Genotypes are all equally sensitive to their environments, meaning that a change of environment would impact the phenotype of all genotypes to the same extent. In fact, genotypes very often have different degrees of sensitivity to environmental conditions. This cause of phenotypic variance is called genotype by- environment interaction and is symbolized by VG x E. This adds another term to the expression for the independent causes of total phenotypic variation in a population
Ve = VG + VE + VG xE
Since the modern evolutionary synthesis, it has been a major goal of evolutionary biology to uncover the genetic basis of adaptive evolution. The rapid evolutionary responses of at least some organisms to current climate change indicates that studying contemporary evolutionary changes that result from climate change may provide opportunities to advance this goal. To do so, it is important to combine ecological approaches to studying responses to climate change with a variety of techniques in molecular and quantitative genetics and genomics. Recent advances, including genome sequencing and transcription profiling, help to uncover the genetic basis of climate change adaptation.
This is the presentation given during Diego Sotomayor's PhD defence at the Department of Geography at York University.
This is the abstract of the dissertation:
In arid environments, dominant woody plants such as shrubs or trees, usually facilitate a high density of species in their understories. This phenomemon is composed by a series of direct and indirect effects from the dominant plant to the understory species, and among understory species. The aim of this project was to determine these direct and indirect consequences of dominant plant-plant facilitation in a collection of field sites along the coastal Atacama Desert. The following objectives and hypotheses were examined in this project: (1) to summarize and contextualize the breadth of research on indirect interactions in terrestrial plant communities; (2) that the positive effects of dominant plants on understory communities are spatiotemporally scale dependent, from micro- to broad-scale spatial effects, and from within-seasonal to among-year temporal effects; (3) that dominant plants via their different traits determine the outcome of plant-plant interactions; (4) that dominant plants determine the outcome of interactions amongst understory species and that their responses are species-specific; and (5) that facilitation by dominant plants generates sufficiently different micro-environmental conditions that lead to consistent differences in seeds traits of understory plants. Overall, we found that multiple factors determine the outcome of plant-plant interactions along the field sites studied in this project. These factors impact both the direct and indirect effects of dominant woody plants on their understory communities and include species-specific traits of both the dominant and understory species, and the spatial and temporal environmental gradients that manifest their effects at different scales. Dominant plants usually facilitate increased species richness and density of plants in their understory, that in turn mediates effects amongst these species. However, these direct effects seem to have a limit given that at extremely stressful environmental conditions they tend to change to neutral and even competitive effects of canopies on their understories. This provides evidence that positive effects of dominant plants collapse under extreme spatiotemporal stress. Although we did not find evidence of evolutionary effects of top-down facilitation, the methodology proposed here represents a contribution to test the conditions under which these results hold. Overall, this project illustrates the importance of understanding the multiple drivers that determine the outcome of biotic interactions.
Germination percentage and growing behavior of Salix tetrasperma (Willow) as ...Innspub Net
Propagation through branch cuttings is one of the best methods to produced tree nursery the yield of the tree produced from a cutting can be higher then a tree produced from seedlings, the conditions provided to them are important factor for getting good results. The aim of this study was to find the best size of cuttings for establishment of nursery and also to compare their performance in open air or in artificial conditions under plastic sheets cuttings of 2 inches, 4 and 6 of Salix tetrasperma were raised in plastic bags of size 3x7 their were three treatments with 25 bags in each and replicated 4 times. The data on sprouting percentage, plant height, root development etc was recorded after every two weeks. The data thus collected was analyzed statically using randomized complete block design. Result showed that cutting of 2 inches has high sprouting percentage and growth behavior as compared to other cuttings. Get more articles at: http://www.innspub.net/volume-6-number-4-april-2015-jbes/
Nine groundnut varieties were tested across six environments in western Oromia, Ethiopia during 2013 main cropping season to evaluate the performance of groundnut varieties for kernel yield and their stability across environments. The varieties were arranged in randomized complete block design (RCBD) with three replications. Pooled analysis of variance for kernel yield showed significant (p≤0.01) differences among the varieties, environments and the genotype by environment interaction (GxE). Additive main effect and multiplicative interactions (AMMI) analysis showed highly significant (p≤0.01) differences for varieties, environments and their interaction on kernel yield. Similarly, the first and the second interaction principal component axis (IPCA1 and IPCA 2) were highly significant (p≤0.01) and explained 41.32 and 7.2% of the total GxE sum of squares, respectively. The environment, genotype and genotype by environment interaction accounted 14.7, 24.1 and 53.3% variations, respectively. This indicated the existence of considerable amounts of deferential response among the varieties to changes in growing environments and the deferential discriminating ability of the test environments. Shulamith and Bulki varieties showed the smallest genotype selection index (GSI) values and had the highest kernel yield and stability showing that these varieties had general adaptation in the tested environments. In the genotype and genotype by environment (GGE) biplot analysis, IPCA1 and IPCA 2 explained 63.5% and 22.4%, respectively, of genotype by environment interaction and made a total of 85.9%. GGE biplot analysis also confirmed Bulki and Shulamith varieties showed better stability and thus ideal varieties recommended for production in the test environments and similar agro-ecologies.
Seed Yield Stability and Genotype x Environment Interaction of Common Bean (P...Premier Publishers
When genotypes are introduced into a new and diverse production environments, occurrence of significant genotype by environment interaction (GEI) complicates selection of stable genotypes. Therefore, fifteen introduced and one check small red common bean lines were evaluated at five representative dry bean growing locations of Ethiopia for seed yield performance using a 4x4 triple lattice design in the 2013 and 2014 main cropping seasons to estimate the magnitude of GEI effects and to identify broadly or specifically adapted lines. Combined analysis of variance, Additive Main effects and Multiplicative Interaction (AMMI) and Genotype plus Genotype x Environment interaction (GGE) biplot models were used to interpret the data. Both the main and interaction effects were highly significant (p< 0.01) and environment, line, and GEI explained 81.06%, 3.21% and 15.73% of variations, respectively, indicating greater influence of environments and importance of simultaneous consideration of mean performance and stability. PC1 and PC2 were highly significant (p < 0.01) and together contributed nearly 60% variation in the GEI sum of squares. AMMI 1, GGE ranking, and GGE comparison biplots enabled identification of both high seed yielding and broadly adapted lines, KG-71-1, KG-71-23, and KG-71-44. Polygonal GGE biplot analysis enabled identification of four mega-environments and specifically adapted lines. However, the specific adaptability of lines was not repeated over years and thus, GEI couldn't be exploited and therefore, broadly adapted lines were recommended for verification and release.
Stability refers to the performance with respective changing environmental factors overtime within given location.
Selection for stability is not possible until a biometrical model with suitable parameters is available to provide criteria necessary to rank varieties / breeds for stability.
The shifted multiplicative model was developed by Cornelius and Seyedsadr in 1992.
SHMM is used to analyze the complete separability, genotypic separability, environmental separability, and inseparability of environment effects and genotypic effects.
Gregorius and Namkoong (1986) defined Separability as the property which is that cultivar effect is separable from environmental effect so that there is no rank.
The shifted multiplicative model (SHMM) is used in an exploratory step-down method for identifying subsets of environments in which genotypic effects are "separable" from environmental effects. Subsets of environments are chosen on the basis of a SHMM analysis of the entire data set. SHMM analyses of the subsets
may indicate a need for further subdivision and/or suggest that a different subdivision at the previous stage should be tried. The process continues until SHMM analysis indicates that a SHMM with only one multiplicative term and its "point of concurrence" outside (left or right) of the cluster of data points adequately fits the data in all subsets.
Genotype-By-Environment Interaction (VG X E) wth ExamplesZohaib HUSSAIN
Introduction
Phenotypic variation can be caused by the combination of genotypes and environments in a population. Genotypes are all equally sensitive to their environments, meaning that a change of environment would impact the phenotype of all genotypes to the same extent. In fact, genotypes very often have different degrees of sensitivity to environmental conditions. This cause of phenotypic variance is called genotype by- environment interaction and is symbolized by VG x E. This adds another term to the expression for the independent causes of total phenotypic variation in a population
Ve = VG + VE + VG xE
Since the modern evolutionary synthesis, it has been a major goal of evolutionary biology to uncover the genetic basis of adaptive evolution. The rapid evolutionary responses of at least some organisms to current climate change indicates that studying contemporary evolutionary changes that result from climate change may provide opportunities to advance this goal. To do so, it is important to combine ecological approaches to studying responses to climate change with a variety of techniques in molecular and quantitative genetics and genomics. Recent advances, including genome sequencing and transcription profiling, help to uncover the genetic basis of climate change adaptation.
This is the presentation given during Diego Sotomayor's PhD defence at the Department of Geography at York University.
This is the abstract of the dissertation:
In arid environments, dominant woody plants such as shrubs or trees, usually facilitate a high density of species in their understories. This phenomemon is composed by a series of direct and indirect effects from the dominant plant to the understory species, and among understory species. The aim of this project was to determine these direct and indirect consequences of dominant plant-plant facilitation in a collection of field sites along the coastal Atacama Desert. The following objectives and hypotheses were examined in this project: (1) to summarize and contextualize the breadth of research on indirect interactions in terrestrial plant communities; (2) that the positive effects of dominant plants on understory communities are spatiotemporally scale dependent, from micro- to broad-scale spatial effects, and from within-seasonal to among-year temporal effects; (3) that dominant plants via their different traits determine the outcome of plant-plant interactions; (4) that dominant plants determine the outcome of interactions amongst understory species and that their responses are species-specific; and (5) that facilitation by dominant plants generates sufficiently different micro-environmental conditions that lead to consistent differences in seeds traits of understory plants. Overall, we found that multiple factors determine the outcome of plant-plant interactions along the field sites studied in this project. These factors impact both the direct and indirect effects of dominant woody plants on their understory communities and include species-specific traits of both the dominant and understory species, and the spatial and temporal environmental gradients that manifest their effects at different scales. Dominant plants usually facilitate increased species richness and density of plants in their understory, that in turn mediates effects amongst these species. However, these direct effects seem to have a limit given that at extremely stressful environmental conditions they tend to change to neutral and even competitive effects of canopies on their understories. This provides evidence that positive effects of dominant plants collapse under extreme spatiotemporal stress. Although we did not find evidence of evolutionary effects of top-down facilitation, the methodology proposed here represents a contribution to test the conditions under which these results hold. Overall, this project illustrates the importance of understanding the multiple drivers that determine the outcome of biotic interactions.
Germination percentage and growing behavior of Salix tetrasperma (Willow) as ...Innspub Net
Propagation through branch cuttings is one of the best methods to produced tree nursery the yield of the tree produced from a cutting can be higher then a tree produced from seedlings, the conditions provided to them are important factor for getting good results. The aim of this study was to find the best size of cuttings for establishment of nursery and also to compare their performance in open air or in artificial conditions under plastic sheets cuttings of 2 inches, 4 and 6 of Salix tetrasperma were raised in plastic bags of size 3x7 their were three treatments with 25 bags in each and replicated 4 times. The data on sprouting percentage, plant height, root development etc was recorded after every two weeks. The data thus collected was analyzed statically using randomized complete block design. Result showed that cutting of 2 inches has high sprouting percentage and growth behavior as compared to other cuttings. Get more articles at: http://www.innspub.net/volume-6-number-4-april-2015-jbes/
Similar to Genotype x environment interaction analysis of tef grown in southern ethiopia using additive main effects and multiplicative interaction model
Nine groundnut varieties were tested across six environments in western Oromia, Ethiopia during 2013 main cropping season to evaluate the performance of groundnut varieties for kernel yield and their stability across environments. The varieties were arranged in randomized complete block design (RCBD) with three replications. Pooled analysis of variance for kernel yield showed significant (p≤0.01) differences among the varieties, environments and the genotype by environment interaction (GxE). Additive main effect and multiplicative interactions (AMMI) analysis showed highly significant (p≤0.01) differences for varieties, environments and their interaction on kernel yield. Similarly, the first and the second interaction principal component axis (IPCA1 and IPCA 2) were highly significant (p≤0.01) and explained 41.32 and 7.2% of the total GxE sum of squares, respectively. The environment, genotype and genotype by environment interaction accounted 14.7, 24.1 and 53.3% variations, respectively. This indicated the existence of considerable amounts of deferential response among the varieties to changes in growing environments and the deferential discriminating ability of the test environments. Shulamith and Bulki varieties showed the smallest genotype selection index (GSI) values and had the highest kernel yield and stability showing that these varieties had general adaptation in the tested environments. In the genotype and genotype by environment (GGE) biplot analysis, IPCA1 and IPCA 2 explained 63.5% and 22.4%, respectively, of genotype by environment interaction and made a total of 85.9%. GGE biplot analysis also confirmed Bulki and Shulamith varieties showed better stability and thus ideal varieties recommended for production in the test environments and similar agro-ecologies.
Seed Yield Stability and Genotype x Environment Interaction of Common Bean (P...Premier Publishers
When genotypes are introduced into a new and diverse production environments, occurrence of significant genotype by environment interaction (GEI) complicates selection of stable genotypes. Therefore, fifteen introduced and one check small red common bean lines were evaluated at five representative dry bean growing locations of Ethiopia for seed yield performance using a 4x4 triple lattice design in the 2013 and 2014 main cropping seasons to estimate the magnitude of GEI effects and to identify broadly or specifically adapted lines. Combined analysis of variance, Additive Main effects and Multiplicative Interaction (AMMI) and Genotype plus Genotype x Environment interaction (GGE) biplot models were used to interpret the data. Both the main and interaction effects were highly significant (p< 0.01) and environment, line, and GEI explained 81.06%, 3.21% and 15.73% of variations, respectively, indicating greater influence of environments and importance of simultaneous consideration of mean performance and stability. PC1 and PC2 were highly significant (p < 0.01) and together contributed nearly 60% variation in the GEI sum of squares. AMMI 1, GGE ranking, and GGE comparison biplots enabled identification of both high seed yielding and broadly adapted lines, KG-71-1, KG-71-23, and KG-71-44. Polygonal GGE biplot analysis enabled identification of four mega-environments and specifically adapted lines. However, the specific adaptability of lines was not repeated over years and thus, GEI couldn't be exploited and therefore, broadly adapted lines were recommended for verification and release.
Genotype by environment interaction and stability of extra-early maize hybrid...IJEAB
Maize (Zea mays L.) is the most important cereal crop produced in Ghana. However the change in environmental conditions, the expansion of maize to new agro-ecologies coupled with inadequate maize varieties available for the different environments affects yield improvement programmes in Ghana. Hence, the study is to investigate the influence of genotype by environment interaction on the maize hybrids and to identify stable and high yielding hybrids with superior agronomic for famers use in the country. The objectives of the study was to investigate the influence of genotype by environment interaction on the maize hybrids and to identify stable and high yielding hybrids with superior agronomic performance for famers use in Ghana. Thus, fifteen extra-early maize hybrids and three locally released checks were evaluated in a randomized complete block design with three replications in two locations in Ghana. The experiment was carried out at KNUST and Akomadan which represent the forest and forest transition zones of Ghana. Nine of the hybrids out of the fifteen hybrids evaluated produce above the average yield and the effect of genotype, location and genotype by location interaction was significant for grain yield. The GGE biplot used in this study revealed that TZEEI-1 x TZEEI-21, TZEEI-6 x TZEEI-21, TZEEI-15 x TZEEI-1 and TZEEI-29 x TZEEI-21 were high yielding and stable hybrids because they were closer to the ideal. The GGE biplot also identified Akomadan as the most ideal testing environment for these hybrids under irrigation.
Genetic Progress for Yield, Yield Components and Other Agronomic Characters o...Premier Publishers
Genetic progress has brought about increase in yield potential per se for almost all production areas around the world. The present study examines the relationship of groundnut yield with year of release, yield components and other agro-morphological traits using aggregative data from 1976 to 2012 to calculate genetic gain in groundnut grain yield across four locations in Eastern Ethiopia. The direct method, that compares cultivars with their year of release, was used. The relative gain for groundnut grain yield was 1.08% since 1976. A deeper understanding of these issues facilitates the identification of specific yield-limiting factors that can be used for future breeding strategies. Grain yield, 100 seed weight, plant height and harvest index were significantly correlated with year of release for tested locations, emphasizing the most promising traits for groundnut breeders in the past. These traits were also responsible for the significant genetic progress in groundnut yield in Ethiopia since 1976. Further improvement in the yield potential of groundnut will have to involve increase in other traits, like pod weight per plant, seed weight per plant, that have shown significant positive correlation with grain yield.
Yield Stability and Genotype × Environment Interaction of Faba Bean (Vicia fa...Premier Publishers
The present research was conducted to assess the effect of genotype × environment interaction (GEI) on grain yield and determine yield stability of faba bean genotypes using 50 genotypes in randomized complete block design with three replications tested at Holetta, Watebecha Minjaro and Jeldu with and without lime application in 2017. The grain yield performances of genotypes were varied across environments which indicate the existence of GEI. The mean grain yields of genotypes were ranged between 51.16g (Wayu) and 96.40g (CS20DK) with an overall mean value of 78.02g/5plants. The AMMI ANOVA showed that environment, genotype and GEI contributed 58.05, 16.08 and 14.28% of total variation in grain yield, respectively. The significant differences among genotypes, environments and interaction effect of the two way interactions on grain yield showed the differential response of genotypes over locations and managements and the test environments were different each other. Based on mean grain yield, stability parameters from AMMI and GGE-biplot, Tumsa, Cool-0034, EH07015-7 and EKLS/CSR02019-2-4 were identified as the four most stable/relatively stable and productive genotypes whereas Wolki, Numan, EH09004-2 and CS20DK had high grain yield and dynamic response to environments. Therefore, this experiment has to be repeated for one more season for reliable recommendation.
Genetic variability, heritability, genetic advance, genetic advance as percen...Premier Publishers
Field experiment was conducted to estimate genetic variability, heritability, genetic advance, genetic advance as a percent mean and character association for forty nine genotypes of Ethiopian mustards collected from different agro ecologies. The experiment was carried out in a simple lattice design. The analysis of variance showed that there were significant differences among genotypes for all traits compared. The significant difference indicates the existence of genetic variability among the accessions which is important for improvement. High genotypic and phenotypic coefficients of variations were observed in seed yield per plot, oil yield per plot, and plant height. This shows that selection of these traits based on phenotype may be useful for yield improvement. The highest heritability in broad sense was recorded for thousand seed weight (68.80%) followed by days to flowering (65.91%), stand percent (63.14%), linolenic acid(62.58%), days to maturity (60.43%), plant height (59.63%), palmitic acid (58.19%), linoleic acid (57.46%),oil content (50.33%), oil yield (44.84%), seed yield per plot(42.99%),and primary branches(34.20%). This suggests that large proportion of the total variance was due to the high genotypic and less environmental variance. In the correlation coefficient analysis, seed yield per plot showed positive correlation with oil content, oil yield, plant height and seed yield per plant. In the path analysis, number of primary branches and oil yield showed positive direct effect on seed yield per plot. In this study, seed yield per plot, oil content, oil yield and primary branches were found to be the most important components for the improvement of seed and oil. Therefore more emphasis should be given for highest heritable traits of mustard and to those positively correlated traits to improve these characters using the tested genotypes.
Correlation and Path Analysis of Groundnut (Arachis hypogaea L.) Genotypes in...Premier Publishers
Sixteen groundnut genotypes (including local check) were evaluated for quantitative parameters. The crop was sown during 2015 Ethiopian wet season in four locations. The experiment was laid out in Randomized Complete Block Design (RCBD) with three replications. Twelve agromorphological characters were evaluated and the covariance, coheritability, phenotypic, genotypic and environmental correlations and also the direct and indirect effects of the component variables on grain yield/ha were estimated. The results indicated that genotypic correlations were higher than the phenotypic and environmental ones. The grain yield/ha presented positive and significant genetic correlation with PWP, SWP and 100SW. Path analysis based on genotypic and phenotypic correlations showed that direct effects were generally lower than indirect effects on the grain yield showing that no best character contributes to GY/ha. Instead, characters like NMP, PWP, SWP, 100SW, NSPOD and AGBP should be recommended for groundnut breeding for increasing GY/ha.
Genetic Variability, Heritability and Genetic Advance Analysis in Upland Rice...Premier Publishers
The experiment was conducted to assess genetic variability, heritability and genetic advance for yield and yield related traits in some upland rice genotypes. A total of 23 rice genotypes were evaluated in a randomized complete block design with three replications in 2017 at Pawe and Assosa. Analysis of variance revealed significant difference among the genotypes for most of the traits at individual and across locations, and error variances of the two locations were homogenous for most of the traits including grain yield. Moreover, the genotypes showed wider variability for grain yield in the range between 3707-6241kg/ha, 4853-7282kg/ha and 4280-6761kg/ha at Pawe, Assosa and over locations, respectively. A relatively high (>20%) phenotypic and genotypic coefficient of variations were estimated merely for number of unfilled grains per panicle. High heritability estimates (> 60%) were obtained for all of the traits, except plant height and Protein content. A relatively high genetic advance was obtained for traits like unfilled grains per panicle and fertile tiller per plant. Thus, this study revealed that there was higher genetic variability among the tested genotypes, which could be potentially exploited in future breeding programs.
Genetic Variability and Multivariate Analysis in Indigenous and Exotic Sesame...Premier Publishers
The productivity of sesame in Ethiopia is below the world average due to lack of high yielding improved varieties. Understanding of genetic variability of characters becomes essential. Therefore, this study was conducted to estimate the
extent of genetic variation among yield and 19 yield components. One hundred sesame genotypes were evaluated in 10x10 triple lattice design at Werer during 2017 and 2018. The combined analysis of variance showed that the genotypes differed significantly. Higher phenotypic and genotypic coefficients of variation were observed for shattering resistance, whereas plant height, number of capsules per plant, harvest index and seed yield showed medium values. High heritability coupled with moderate to high genetic advance were observed for shattering resistance, plant height, capsule per plant, harvest
index and seed yield. The present study revealed that to increase sesame seed yield, the genotypes should possess a
greater number of capsules, shattering resistance and high harvest index, which known to be important yield contributing
characters and selection based on these characters would be most effective. The D2 analysis exhibited the group of
genotypes into seven clusters. Assessment of sesame genetic resources with molecular markers assisted breeding should be
considered in the future.
Genetic Diversity of Sorghum (Sorghum bicolor L. Moench) from East and West H...Premier Publishers
Genetic diversity within local landraces is important input for crop breeding programs and in the preservation of their genetic potential. The objective of this study was to assess the genetic diversity and analyze population structure of sorghum landraces grown in East and west Hararghe Zones of Oromia Regional state, Ethiopia based on SSR markers. A total of 10 accessions of sorghum landraces were estimated using 10 SSR markers. For all the loci analyzed, 70 polymorphic alleles were detected with the number of alleles per locus range from 2 to 18 with an average of seven alleles. Polymorphism information content of each marker was variable and showed a significant correlation with total number of alleles (r = 0.75). The higher the number of alleles per marker, the greater is PIC value. Dendrogram obtained according to UPGMA hierarchical classification model using DICE coefficient of similarity allowed the classification of sorghum accessions into four main groups. It was recommended that a further research on genetic diversity of sorghum should integrate botanical races, agro-morphological traits in addition to molecular markers for a better preservation of the genetic resources of sorghum landraces in Eastern Ethiopia.
Genotype by environment interactions and effects on growth and yield of cowpe...Premier Publishers
Cowpea is widely grown in the humid tropics as staple and is largely affected by genotype by environment interaction (GEI). Data obtained from field trials were subjected to genotype (G) by environment (E) interaction (GEI Biplot) analysis and was applied to examine the nature and magnitude of GEI and quantify their effects on cowpea performance in seven experimental trials in a rainforest and derived savanna agroecologies of south-west Nigeria. Results showed that genotype x environment interactions effects were significant on cowpea growth and yield characters. The differential performance of cowpea varieties as early- and late- rainy season crops at both locations were attributable to variability in the soil, weather and biotic factors of the test environments. Determination of winning genotype(s) and yield ranking across environments showed that cowpea varieties depicted differential performance for the test environments and hence the interaction was crossover type. Varieties IT97K-568-18, IT97K-568-18 and Oloyin Brown are high yielding while IT96D-610 and IT98K-205-8 are poor. Oloyin Brown and IT98K-573-2-1 won in Akure 1, 2, 3 and 4 and Ado 1 while IT97K-568-18 won in Ado 2 and Akure 5. IT96D-610 and IT98K-205-8 did not win in any environment. The best performing varieties, Oloyin Brown, IT97K-568-18 and IT98K-573-2-1 combined both high yield and stable performance across test environments and were characterized as ideal genotypes while most unstable variety, IT96D-610, performed poorly in test environments. It is concluded that Ado-Ekiti was best for the late rainy while Akure location was best for early rainy season cropping.
Effect of nitrogen fertilizer rates and intra-row spacing on yield and yield ...Premier Publishers
A field experiment was conducted at Gode Polytechnic College demonstration farm in 2013 under irrigation to observe the effect of six N rates (0, 46, 69, 92, 115 and 138 kg ha-1) and four intra-row spacing levels (7.5, 10 12.5 and 15 cm) on yield and yield components of onion (Allium cepa L.). The experiment was laid out according to randomized complete block design in factorial arrangement with three replications. Results of the analysis revealed that the interaction effects of N rates and intra-row spacing showed highly significant (P<0.01) effect on harvest index, fresh biomass yield, dry biomass yield, total bulb yield and marketable bulb yield. Thus, according to the result of partial Budget analysis application of 138kg N ha-1 planted at 7.5cm plant to plant distance was found the best treatment than others in relation to yield and yield components of onion under Gode condition.
Correlations and Path Analysis of Some Quantitative and Qualitative Character...ijtsrd
Durum wheat is the second most important triticum species next to bread wheat. Ethiopia is one of the centers of diversity for durum wheat. The present study was to determine the interrelationship and direct and indirect effects of yield component traits on grain yield of Ethiopian landraces durum wheat for further breeding activities of yield improvement. Out, 97 durum wheat accessions along with 3 improved varieties were evaluated in 10 x 10 simple lattice design during 2018 main cropping season at Mata Sub site of Haro Sabu Agricultural Research Center. Analysis of variance revealed highly significant differences among accessions for all traits. More than 36 of accessions were superior in mean grain yield than the standard checks. Grain yield exhibited positive and significant correlation both at genotypic and phenotypic level with most of the characters such as plant height rp = 0.22, rg = 0.25 , harvest index rp=0.79, rg = 0.78 , biological yield rp = 0.31, rg = 0.30 , number of kernels per spike rp = 0.17, rg = 0.21 , spike length, rp = 0.36, rg = 0.39 , and hectoliter weight kg hl 1 rp = 0.44, rg = 0.45 . The association between yield, and yield related characters through phenotypic genotypic path coefficients revealed that biological yield, spike length, harvest index and plant height exerted highest positive direct effect on grain yield. This suggests that simultaneous improvement in these characters might be possible Zewdu Tegenu | Dagnachew Lule | Gudeta Nepir "Correlations and Path Analysis of Some Quantitative and Qualitative Characters in Durum Wheat (Triticum Turgidum L.) Accessions in Western Oromia, Ethiopia" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd28112.pdf Paper URL: https://www.ijtsrd.com/engineering/agricultural-engineering/28112/correlations-and-path-analysis-of-some-quantitative-and-qualitative-characters-in-durum-wheat-triticum-turgidum-l-accessions-in-western-oromia-ethiopia/zewdu-tegenu
Barley is one of the most important traditional crops in Ethiopia which is a major center of genetic diversity for barley along with other crop plants species. Two hundred seven accessions and 18 released varieties were laid down in 15*15 simple lattice design and planted in 2008 main cropping season (June to Nov) at Kokate. The objective of the study was to conduct the morphological characterization and to determine the nature and degree of variability in morpho- agronomic traits of landrace of barley in southern Ethiopia collections. The proportion of genotypes in kernel row number were 26.6, 15.3, 16.6, 41.5 and 0.4% for two rowed with lateral floret, two rowed deficient, irregular, six rowed with awns on lateral floret and branched heads, respectively. Genotypes with white kernel color (57.5%) and amber (normal) lemma color (50%) were dominant. The highest diversity indices pooled over the characters within zones/ special woredas were recorded for accessions sampled from Dawro (H’= 0.75 ± 0.05) followed by Sheka (H’=0.74 ± 0.07), Gamgofa (H’ =0.70 ± 0.05) and Keffa (H’= 0.70 ± 0.08). These zones can be used for in situ conservation for barley landraces as representatives of southern Ethiopian high lands. The barley genotypes were clustered into five distinct groups of various sizes based on 8 qualitative traits. The estimates of diversity index (H’) for each trait in each of the three altitudinal class has shown that polymorphism was common in varying degrees for most traits, implying the existence of a wide range of variation in the materials.
Estimates of gene action for yield and its components in bread wheat Triticum...Innspub Net
In order to study gene action for yield and its components using 8 × 8 diallel crosses excluding reciprocals during 2013/2014 and 2014/2015 growing seasons at Tag El-Ezz Research Station, Dakahlia Governorate, the genotypes were Sides 12, Gemmiza 11, Maser 1, Maser 2, Shandaweel 1, Giza 168, Sakha 93, and Sakha 94. Results revealed that both additive (D) and dominance (H1 and H2) genetic variance were significant for the all studied characters, indicating the importance of additive and dominance gene effects in controlling these characters. The dominance genetic variance was higher in the magnitude as compared to additive one, resulting in (H1/D)0.5 exceeding than more unity for all studied characters except spike density and number of tillers/plant. The “F” values which refer to the covariance of additive and dominance gene effects in the parents revealed positive and significant for flag leaf length and flag leaf area, extrusion length, number of tillers/plant number of spikes/plant, number of grains/spike and 1000- grain weight, indicating that dominant alleles were more frequent than the recessive ones in the parents for this character, while negative “F’ value for remaining characters indicated excess of recessive alleles among parents. The overall dominance effects of heterozygous loci h2, indicated directional dominance for heading date, flag leaf length, flag leaf area, spike length, extrusion length, spike density, grain yield/spike, number of tillers/plant number of spikes/plant, number of grains/ spike and grain yield/plant. Proportion of genes with positive and negative effects in the parent (H2/4H1) was deviated from 0.25 for all studied characters Heritability in narrow sense was moderate (0.369) for grain yield/plant.
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Genotype x environment interaction analysis of tef grown in southern ethiopia using additive main effects and multiplicative interaction model
1. Journal of Biology, Agriculture and Healthcare www.iiste.org
ISSN 2224-3208 (Paper) ISSN 2225-093X (Online)
Vol 2, No.1, 2012
Genotype x Environment Interaction Analysis of Tef Grown in
Southern Ethiopia Using Additive Main Effects and
Multiplicative Interaction Model
Mathewos Ashamo1* Getachew Belay2
1. Areka Agricultural Research Center, PO box 79, Areka, Ethiopia
2. Debre Zeit Agricultural Research Center, PO box 32, Debre Zeit, Ethiopia
* E-mail of the corresponding author: ashamom2003@yahoo.com
Abstract
Twenty-two tef [Eragrostis tef (Zucc.) Trotter] genotypes were evaluated for their grain yield performances
at four locations namely Areka, Humbo, Hossana and Alaba in 2002/03. The objectives were to estimate
genotype x environment interaction, to identify stable tef genotypes, and to assess the interaction patterns of
the testing locations. Significant (p<0.05) differences for grain yield among genotypes were observed at
each location; across locations, the effects of location, genotype and G x E were significant (p<0.05).
AMMI partitioned genotype x environment variance into four Interaction Principal Component Axes
(IPCAs), but significant was (p<0.05) only the first IPCA that captured 49% of the total G x E variance.
The study revealed that the released variety DZ-Cr-255 was highly stable and better yielding variety across
the locations. Areka and Hossana showed close IPCA1 scores of similar sign, and coupled with their higher
location mean yields, may represent relatively better testing environments.
Keywords: genotype x environment interaction, AMMI model, stability, variety
1. Introduction
Tef [Eragrostis tef (Zucc.) Trotter] is the most important cereal grown in Ethiopia. In terms of acreage it
occupied about 2.12 million hectares (Central Statistical Authority, 2000) in the production year 1999/200.
Tef production has been increasing from year to year and so did the demand for tef as staple food grain in
both urban and rural areas. The national average yield, however, is as low as, 8.09 quintals per hectare.
Although the genus Eragrostis has a wide distribution in Africa, tef is the only cultivated species as a food
crop only in Ethiopia and Eritrea (Seifu, 1986). According to Vavilov (1951), Ethiopia is both the center of
origin and diversity for tef.
Tef is mainly used for making injera (a pancake like bread). It is also used to make porridge and native
alcoholic drinks called tella and katikala (Asrat and Frew, 2001). The straw is high in demand for feed and
when mixed with mud it provides the best plastering medium for walls of houses. Nutritionally, tef is no
lesser competent than the other cereals grown in the country.
Most cereals are grown in areas with unpredictable environments and the staple Ethiopian cereal, tef
[Eragrostis tef (Zucc.) Trotter], is no exception. In these environments, crop yields are dependent upon the
interaction of the genetic potential of cultivars and the growing conditions. Crop breeders have long realized
the importance of genotype-environment interaction (G x E) as it affects the progress from selection and
thereby making variety development and recommendation more difficult (Allard and Bradshaw, 1964).
Analysis of G x E helps to determine an optimum breeding strategy; breeding for wide or specific
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2. Journal of Biology, Agriculture and Healthcare www.iiste.org
ISSN 2224-3208 (Paper) ISSN 2225-093X (Online)
Vol 2, No.1, 2012
adaptations. Moreover, analysis of the G x E variance allows the grouping of similar sites in relation to
genotype performance within which the interaction is minimum (Gauch and Zobel, 1997; Annicciarico,
2002). Several biometrical methods have been developed to analyse G x E, and evaluate genotype stability
over a range of environments (for review see, Ramagosa and Fox, 1993). The Additive Main Effects and
Multiplicative Interaction (AMMI) analysis, which combines analysis of variance and principal component
analysis, is the most widely used in recent times for G x E analysis on different crops (Crossa et al., 1991;
Yau, 1995; Annicciarrico, 2002).
There are few G x E interaction studies in tef (Tiruneh, 1999; Fufa et al., 2000); these were carried out mainly
for the environments prevailing in the central highlands of Ethiopia. But tef in this country is grown on over
two million hactares under high variation in climatic and edaphic factors that lead to G x E even within a
small geographic area (Hailu and Getachew, 2006). In his review on G x E in tef, Tiruneh (2001) has
recommended the need for further G x E interaction studies in the various tef-growing regions of the country
for a better understanding of its magnitude and nature.
In Southern Nations, Nationalities and Peoples Regional State (SNNPR), tef is the second (proceeded only by
maize) most important cereal cultivated by the majority of farmers. Report by Central Statistical Authority
(2000) indicates that the total production area is 165,000 ha with an average regional yield of only 0.635 t ha-1
(78% of the national average). Currently, multi-location performance tests on tef are undergoing in the
Region, but with no quantitative estimation of G x E, which is a prerequisite to formulate sound tef breeding
strategy. The objectives of this study, using the AMMI model, were to estimate the magnitude of G x E, to
identify stable tef genotypes suitable to grow across the diverse tef production areas of SNPPR, and to assess
the interaction patterns of the testing locations.
2. Materials and Methods
Twenty-two tef genotypes (12 released varieties and 10 genotypes in advanced stage of yield trials) were
used in this study (Table 2). The plant materials, which were obtained from Debre Zeit Agricultural
Research Center, courtesy of Dr. Hailu Tefera, differ in grain color and other agronomic characteristics
(Hailu et al., 1995). A local check was included in the test genotypes; however, its performance at all the
locations was very poor, and therefore, was excluded from the analysis for reasons of fulfilling statistical
assumptions.
The test varieties and genotypes were planted at four locations (Alaba, 1700 m asl, Andosol; Alaba, 1830 m
asl, Alfisol; Hossana, 2400 m asl, Nitosol; Humbo, 1400 m asl, Nitosol) that represent the major tef growing
areas of SNNPR in a randomized complete block design with three replications in the Meher season of
2002/03. Each experimental plot was 2 m long and consisted of six rows spaced 20 cm apart. Distances
between plots and blocks were 1 m and 1.5 m, respectively. Sowing at all locations was made starting from
end of July to the first week of August based on availability of rainfall and soil moisture. Seed rate of 25 kg
ha-1 was used. The seeds of tef were mixed with sand (1:4) for uniform distribution in a plot. Fertilizers (DAP
and Urea) were applied with the rate of 60 kg ha-1 P2O5 and 60 kg ha-1 N for Nitisol (Hossana) and 40 kg ha-1
N and 60 kg/ha P2O5 at all the other locations. DAP and half of the Urea were incorporated into the soil before
planting and the remaining Urea was applied at early tillering stage. Weeds were controlled manually. Data
on grain yield were recorded on plot basis of the four central rows.
Analysis of variance (ANOVA) for grain yield was carried out at each location. Combined ANOVA over
locations was carried out after testing the homogeneity of error variances (Gomez and Gomez, 1984). The
Additive Main Effects and Multiplicative Interaction (AMMI) analysis was carried out according Gauch and
Zobel (1997) using AgrobaseTM software (Agronomics Software Inc., 1988). AMMI analysis partitions the G
x E sum of squares into interaction principal component axis (IPCAs) and generates scores for the first IPCA,
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3. Journal of Biology, Agriculture and Healthcare www.iiste.org
ISSN 2224-3208 (Paper) ISSN 2225-093X (Online)
Vol 2, No.1, 2012
which are helpful to estimate stability (Gauch and Zobel, 1997). Bi-plot, which provides a graphical view of
G x E was constructed (Kempton, 1984). Interpretation in a bi-plot representation is that genotypes or
environments that occur almost on perpendicular line have similar interaction patterns. Genotypes and
environments with large IPCA1 scores, positive or negative, have high interactions whereas genotypes or
environments with IPCA1 score of zero (or nearly zero) have small interactions (Zobel et al. 1988; Crossa,
1990).
3. Results and Discussions
The combined analysis of variance for the 22 tef varieties and genotypes grown at four locations is given in
Table 1. Genotype, location and G x E variances were significant (p<0.01), indicating that genotypes
performed differently at the different locations. Location accounted the largest (53%) percentage sums of
squares (% SS) remaining among location, genotype and G x E. G x E accounted relatively small (27%) but
larger percentage of the remaining SS than genotype (20%), thus allowing further variance analysis using the
AMMI model.
From the AMMI analysis for grain yield (Table 1), four possible interaction principal component axes
(IPCAs) were developed. However, the full model AMMI was retained in the first three IPCAs to capture the
whole pattern, which was contained in the G x E. Among these IPCA axes, the was significant (P < 0.05). The
first IPCA axis (IPCA1) captured 48.70% of the total interaction variance while the second IPCA axis
(IPCA2) captured 36.89%. The total portion of G x E variance captured by the two IPCA axes was 85.59%,
which is congruent with the results (84%) of Tiruneh (1999). In barley, 37-53% of the interaction variance
was explained by the first IPCA alone (Yau, 1995), and in maize, as much as 90% has been reported (Crossa,
1990).
Significant differences (p<0.05) among the test genotypes were observed for grain yield at all the individual
locations (Table 2); grain yield ranged between 1403 kg ha-1 at Alaba and 2493 kg ha-1 at Hossana. The
differential responses of genotypes were also manifested in their ranking orders. Across locations, the
released varieties DZ-Cr-37 (2418 kg ha-1) and DZ-Cr-255 (2309 kg ha-1), and the genotype, DZ-01-1278
(2262 kg ha-1), were the highest yielders.
Tef genotypes under this study showed IPCA scores of different signs and magnitudes (Table 2). Bi-plot
graphical representation for genotypes and locations is shown in Fig. 1. Few genotypes had IPCA score
values of nearly zero, which implies that they are relatively stable (minimum interaction) genotypes across
diverse environmental conditions. Accordingly, the tef variety DZ-Cr-255 was highly stable across the test
environments. This variety gave the second highest mean grain yield (2309 kg ha-1), indicating the possibility
of simultaneous selection for stable and high-yielding genotypes. The bi-plot also showed that genotypes
DZ-Cr-37, DZ-01-1573B, DZ-01-1378B and DZ-01-2507 were relatively stable compared to the rest of the
tef genotypes. IPCA1-score list for genotypes also showed that the scores for these genotypes were small
(near to zero) showing the inclination of the genotypes to be stable across the test environments. High
IPCA-scores for grain yield were obtained for HO-Cr-136, DZ-01-2462 and DZ-01-2457, indicating that
these genotypes were highly unstable; bi-plot indicated that these genotypes were better performing only at
Hossana, where the highest location mean yield was observed.
A bi-plot of AMMI analysis or IPCA-scores for locations express the effect of an environment on different
characters; environments with higher IPCA scores regardless of the sign discriminate among genotypes more
than those with lesser IPCA scores (Kempton 1984). Thus, discrimination among genotypes was high at
Humbo while little discrimination among genotypes was observed at Alaba. IPCA1 list for environments
showed that Hossana also had high genotype discrimination next to Humbo. The IPCA score for Areka and
68
4. Journal of Biology, Agriculture and Healthcare www.iiste.org
ISSN 2224-3208 (Paper) ISSN 2225-093X (Online)
Vol 2, No.1, 2012
Hossana were similar in their sign, and their magnitude is close to each other relative to the remaining test
locations. Therefore, the two environments could belong to the same interaction group. Positive (but low
magnitude) of IPCA-score for Alaba also indicated that there might be few similar agro-climate features of
this test location with Hossana and Areka. Tiruneh (1999) has noted that environments with similar
altitudinal range, rainfall distribution and soil types, exhibit the same sign for IPCA-score and they were put
into same interaction group.
The AMMI analysis was demonstrated to have advantage in partitioning G x E variance over joint regression
analysis (Eberhart and Russel, 1966) under the conditions of small or large data settings (Yau, 1995).
Because the two IPCAs in the present study have cuptured the interaction variances quite substantially, there
are interesting features of practical significance that can be brought to the spot light. First, DZ-Cr-37
(Tsedeay) was previously found to be highly stable for grain yield in different environments from the present
study (Truneh et al., 1999). DZ-Cr-37 was released in 1984, and is currently the most widely grown tef
variety in the relatively low altitude and moisture-stress prone areas. The other variety and which was found
to be highly stable, DZ-Cr-255 (Ghibe), was released in 1993 on the grounds of specific adaptation to the
south and southwest regions of Ethiopia (Hailu Tefera, personal communication). These results are
testimonial to the effectiveness of selection in the national tef project in the development of varieties both for
specific and wide adaptation. For unknown reason, DZ-Cr-255 is not as widely adopted by farmers as
DZ-Cr-37. Tef breeders in the region therefore will have to consider popularization of DZ-Cr-255, and
selecting higher yielding and more stable tef varieties than DZ-Cr-37 as the starting challenges.
Second, DZ-Cr-37 and DZ-Cr-255 are early maturing varieties (Hailu et al., 1995). The negative correlations
between days-to-heading (r= -0.323***) and days-to-mature (r= -0.245**) with grain yield at these locations
(Truneh, 1999) also corroborate the better adaptation of early maturing varieties than the late ones in SNNPR.
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Yau, S.K. (1995). Regression and AMMI analyses of genotype x environment interactions: An empirical
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388-393.
Table 1. Combined and AMMI analyses of variance for grain yield (kg ha-1) of 22 tef genotypes grown at
four locations in SNNPR, 2002/03.
Sum of square Mean square % SS
Source df (SS)
Locations (L) 3 3968.7 1322.9** 53
Genotypes (G) 21 1436.4 68.4** 20
GxE 63 1965.6 31.2** 27
IPCA1 23 349.6 15.2** -
IPCA2 21 241.5 11.5ns -
IPCA3 19 85.5 4.5ns -
Residual 168 2822.4 16.8 (38)*
CV (%) - 21.3 -
Note. The rest 62% of the total variance in the combined analysis is contributed by L, G, and G x L
(remaining variance).
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7. Journal of Biology, Agriculture and Healthcare www.iiste.org
ISSN 2224-3208 (Paper) ISSN 2225-093X (Online)
Vol 2, No.1, 2012
Figure 1. Biplot of genotypes (lower cases) and environments (upper cases) for grain yield using the first
IPCA as ordinate and main effects as abscissa. Note: Environments with similar means are not shown.
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