Bread wheat (Triticum aestivum L.) is most important cereal crop in Ethiopia. Lack of genotypes with wide stability across environments has been one of the most important constraints of wheat production in the country...Bread wheat (Triticum aestivum L.) is most important cereal crop in Ethiopia. Lack of genotypes with wide stability across environments has been one of the most important constraints of wheat production in the country. Field experiments were conducted in Halaba and Bule, South Ethiopia, in 2016 and 2017, in order to estimate grain yield stability and association among stability parameters. Fifteen improved bread wheat genotypes were grown under randomized complete block design with three replications. Mean yield for Halaba 2016, Halaba 2017, Bule 2016 and Bule 2017 was 3.83, 1.89, 2.90 and 3.59 tons/ha, respectively. Genotypes Lemu (3.25 tons/ha) and Mandoyu (3.18 tons/ha) had high mean yield, and low values of environmental variance (S2i), coefficient of variation (CVi), stability variance (δ2i), ecovalence (Wi) and deviation from regression (S2di). Genotypes Biqa (3.69 tons/ha) and Shorima (3.66 tons/ha) had high mean yield, coefficient of regression (bi) and coefficient of determination (R2i ≥ 0.94) as well as low values of δ2i, Wi and S2di. Grain yield had positive rank correlation with bi (r = 0.75, p 2i (r = 0.70, p δ2i, Wi and S2di was high (r ≥ 0.98, p , Mandoyu and Hidase, and Biqa and Shorima would be recommended for wide adaption, and for more favorable environments, respectively. It could also be suggested that one of Wi, δ2i, S2di and rank sum would be used for ranking of genotypes.展开更多
Quantitative trait loci (QTL) analysis was conducted in bread wheat for 14 important traits utilizing data from four different mapping populations involving different approaches of QTL analysis. Analysis for grain pro...Quantitative trait loci (QTL) analysis was conducted in bread wheat for 14 important traits utilizing data from four different mapping populations involving different approaches of QTL analysis. Analysis for grain protein content (GPC) sug- gested that the major part of genetic variation for this trait is due to environmental interactions. In contrast, pre-harvest sprouting tolerance (PHST) was controlled mainly by main effect QTL (M-QTL) with very little genetic variation due to environmental interactions; a major QTL for PHST was detected on chromosome arm 3AL. For grain weight, one QTL each was detected on chromosome arms 1AS, 2BS and 7AS. QTL for 4 growth related traits taken together detected by different methods ranged from 37 to 40; nine QTL that were detected by single-locus as well as two-locus analyses were all M-QTL. Similarly, single-locus and two-locus QTL analyses for seven yield and yield contributing traits in two populations respectively allowed detection of 25 and 50 QTL by composite interval mapping (CIM), 16 and 25 QTL by multiple-trait composite interval mapping (MCIM) and 38 and 37 QTL by two-locus analyses. These studies should prove useful in QTL cloning and wheat improvement through marker aided selection.展开更多
Investigation of genetic diversity of geographically distant wheat genotypes is </span><span style="font-family:Verdana;">a </span><span style="font-family:Verdana;">useful ...Investigation of genetic diversity of geographically distant wheat genotypes is </span><span style="font-family:Verdana;">a </span><span style="font-family:Verdana;">useful approach in wheat breeding providing efficient crop varieties. This article presents multivariate cluster and principal component analyses (PCA) of some yield traits of wheat, such as thousand-kernel weight (TKW), grain number, grain yield and plant height. Based on the results, an evaluation of economically valuable attributes by eigenvalues made it possible to determine the components that significantly contribute to the yield of common wheat genotypes. Twenty-five genotypes were grouped into four clusters on the basis of average linkage. The PCA showed four principal components (PC) with eigenvalues ></span><span style="font-family:""> </span><span style="font-family:Verdana;">1, explaining approximately 90.8% of the total variability. According to PC analysis, the variance in the eigenvalues was </span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">greatest (4.33) for PC-1, PC-2 (1.86) and PC-3 (1.01). The cluster analysis revealed the classification of 25 accessions into four diverse groups. Averages, standard deviations and variances for clusters based on morpho-physiological traits showed that the maximum average values for grain yield (742.2), biomass (1756.7), grains square meter (18</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;">373.7), and grains per spike (45.3) were higher in cluster C compared to other clusters. Cluster D exhibited the maximum thousand-kernel weight (TKW) (46.6).展开更多
文摘Bread wheat (Triticum aestivum L.) is most important cereal crop in Ethiopia. Lack of genotypes with wide stability across environments has been one of the most important constraints of wheat production in the country. Field experiments were conducted in Halaba and Bule, South Ethiopia, in 2016 and 2017, in order to estimate grain yield stability and association among stability parameters. Fifteen improved bread wheat genotypes were grown under randomized complete block design with three replications. Mean yield for Halaba 2016, Halaba 2017, Bule 2016 and Bule 2017 was 3.83, 1.89, 2.90 and 3.59 tons/ha, respectively. Genotypes Lemu (3.25 tons/ha) and Mandoyu (3.18 tons/ha) had high mean yield, and low values of environmental variance (S2i), coefficient of variation (CVi), stability variance (δ2i), ecovalence (Wi) and deviation from regression (S2di). Genotypes Biqa (3.69 tons/ha) and Shorima (3.66 tons/ha) had high mean yield, coefficient of regression (bi) and coefficient of determination (R2i ≥ 0.94) as well as low values of δ2i, Wi and S2di. Grain yield had positive rank correlation with bi (r = 0.75, p 2i (r = 0.70, p δ2i, Wi and S2di was high (r ≥ 0.98, p , Mandoyu and Hidase, and Biqa and Shorima would be recommended for wide adaption, and for more favorable environments, respectively. It could also be suggested that one of Wi, δ2i, S2di and rank sum would be used for ranking of genotypes.
基金Project supported by the National Agricultural Technology Projectof Indian Council of Agricultural Research, Department of Biotech-nology of Government of India, Council of Scientific and IndustrialResearch of India and Indian National Science Academy
文摘Quantitative trait loci (QTL) analysis was conducted in bread wheat for 14 important traits utilizing data from four different mapping populations involving different approaches of QTL analysis. Analysis for grain protein content (GPC) sug- gested that the major part of genetic variation for this trait is due to environmental interactions. In contrast, pre-harvest sprouting tolerance (PHST) was controlled mainly by main effect QTL (M-QTL) with very little genetic variation due to environmental interactions; a major QTL for PHST was detected on chromosome arm 3AL. For grain weight, one QTL each was detected on chromosome arms 1AS, 2BS and 7AS. QTL for 4 growth related traits taken together detected by different methods ranged from 37 to 40; nine QTL that were detected by single-locus as well as two-locus analyses were all M-QTL. Similarly, single-locus and two-locus QTL analyses for seven yield and yield contributing traits in two populations respectively allowed detection of 25 and 50 QTL by composite interval mapping (CIM), 16 and 25 QTL by multiple-trait composite interval mapping (MCIM) and 38 and 37 QTL by two-locus analyses. These studies should prove useful in QTL cloning and wheat improvement through marker aided selection.
文摘Investigation of genetic diversity of geographically distant wheat genotypes is </span><span style="font-family:Verdana;">a </span><span style="font-family:Verdana;">useful approach in wheat breeding providing efficient crop varieties. This article presents multivariate cluster and principal component analyses (PCA) of some yield traits of wheat, such as thousand-kernel weight (TKW), grain number, grain yield and plant height. Based on the results, an evaluation of economically valuable attributes by eigenvalues made it possible to determine the components that significantly contribute to the yield of common wheat genotypes. Twenty-five genotypes were grouped into four clusters on the basis of average linkage. The PCA showed four principal components (PC) with eigenvalues ></span><span style="font-family:""> </span><span style="font-family:Verdana;">1, explaining approximately 90.8% of the total variability. According to PC analysis, the variance in the eigenvalues was </span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">greatest (4.33) for PC-1, PC-2 (1.86) and PC-3 (1.01). The cluster analysis revealed the classification of 25 accessions into four diverse groups. Averages, standard deviations and variances for clusters based on morpho-physiological traits showed that the maximum average values for grain yield (742.2), biomass (1756.7), grains square meter (18</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;">373.7), and grains per spike (45.3) were higher in cluster C compared to other clusters. Cluster D exhibited the maximum thousand-kernel weight (TKW) (46.6).