1- Maragheh University
2- Country Dry Agriculture Research Institute
2- Country Dry Agriculture Research Institute
Abstract: (16 Views)
Background: Today, oilseeds are considered one of the most important agricultural products in the world, and safflower is also one of the most important oilseed plants. Due to the daily increase in population and changes in people's dietary patterns, the consumption of vegetable oils is also increasing. Oilseeds are produced to extract oil from their seeds, but they are also considered a valuable source of protein, and the product residues after oil extraction are used for this purpose. Safflower is one of the most important oilseed plants due to its numerous advantages, including resistance to drought and salinity stresses. Knowledge of the genetic diversity existing between safflower genotypes allows their use in breeding programs with the aim of producing hybrids with desirable quantitative and qualitative yields.The aim of this study was to determine the genetic diversity of the studied safflower genotypes in terms of some morphological and agronomic traits for use in safflower breeding programs, as well as to identify the relationships between morphological and agronomic traits and grouping the genotypes under study.
Methods: For this purpose, 64 safflower samples were obtained from the National Agricultural Research Institute along with five cultivars of Sina, Faraman, Omid, Goldasht and one local cultivar of Islamabad and were studied in an augmented design experiment with four replications at the research farm of the Department of Plant Genetics and Production Engineering, Maragheh University. After land preparation, the seeds were sown in four replications (blocks) of 16 lines with the above cultivars. The seeds of each genotype were sown in rows in plots with a length of 150 cm and a width of 85 cm, each plot having three rows of 150 cm with a distance of 40 cm.At the end of the growth and development period, in addition to the usual agronomic care, some morphological and agronomic traits were measured, including plant height, number of side branches, number of bolls per plant, number of seeds per boll, weight of 1000 seeds, plant type, and yield. Before variance analysis, the normality of the data distribution was examined using the Kolmogorov-Smirnov method. The data related to the cultivars were analyzed by variance analysis and, according to it, the comparison of the line means was performed using the LSD test. In order to examine the relationships between traits, the correlation coefficients between the traits were calculated. The correlation between the yield component trait and its related traits should be calculated and the effect of the yield components on it is determined according to the genotype and environment, which are effective factors in creating diversity.Morphological traits can be measured accurately and easily, and some of them have relatively high heritability, so selection based on these traits may be a suitable way to screen plant communities and improve grain yield. Cluster analysis of genotypes was also performed using the Ward method and the square of Euclidean distance based on the studied traits. In cluster analysis, individuals within a cluster have the greatest similarity and uniformity, and there is maximum difference between clusters. Therefore, if the grouping is successful, individuals within the cluster are genetically closer to each other, and distant clusters will be more different. The cut point of the dendrogram was determined using discriminant function analysis, and the state in which the difference between the grouping levels was maximum was considered as the cut point.To determine the characteristics of each group resulting from cluster analysis in terms of the studied traits, the average of each cluster for each trait and its percentage deviation from the total mean were calculated. Principal component analysis was performed to reduce the volume of data and better interpret them. The data were analyzed using SPSS software.
Results: The results showed that for most of the studied traits, the safflower lines had statistically significant differences with each other and with the control varieties. The correlation results showed that the single plant grain yield had a significant positive correlation with the traits of thousand-grain weight, boll diameter, and number of seeds per boll. Cluster analysis using the Ward method and the Euclidean distance criterion based on the data of 12 traits and the resulting dendrogram cut classified 69 safflower genotypes into four clusters.To determine the cut point of the resulting dendrograms based on morphological and physiological traits, discriminant function analysis was used, and the state in which the difference between the grouping levels was maximum was considered as the cut point. Dendrogram cutting was performed based on multivariate analysis of variance and provided the highest amount of between-group variance into a group with four clusters. In principal component analysis based on the average of 12 traits in 69 safflower genotypes, the first three principal components explained a total of 65.13% of the trait variation. This value for the second and third components was 19.66% and 12.63%, respectively.
Conclusion: The second cluster was identified as the best cluster and the genotypes of this cluster can be used to improve grain yield. According to the principal component analysis, the first component was named the grain yield component. This component can be used in selection for safflower genotypes.Based on the results obtained, the Goldasht variety was considered the superior variety.
Methods: For this purpose, 64 safflower samples were obtained from the National Agricultural Research Institute along with five cultivars of Sina, Faraman, Omid, Goldasht and one local cultivar of Islamabad and were studied in an augmented design experiment with four replications at the research farm of the Department of Plant Genetics and Production Engineering, Maragheh University. After land preparation, the seeds were sown in four replications (blocks) of 16 lines with the above cultivars. The seeds of each genotype were sown in rows in plots with a length of 150 cm and a width of 85 cm, each plot having three rows of 150 cm with a distance of 40 cm.At the end of the growth and development period, in addition to the usual agronomic care, some morphological and agronomic traits were measured, including plant height, number of side branches, number of bolls per plant, number of seeds per boll, weight of 1000 seeds, plant type, and yield. Before variance analysis, the normality of the data distribution was examined using the Kolmogorov-Smirnov method. The data related to the cultivars were analyzed by variance analysis and, according to it, the comparison of the line means was performed using the LSD test. In order to examine the relationships between traits, the correlation coefficients between the traits were calculated. The correlation between the yield component trait and its related traits should be calculated and the effect of the yield components on it is determined according to the genotype and environment, which are effective factors in creating diversity.Morphological traits can be measured accurately and easily, and some of them have relatively high heritability, so selection based on these traits may be a suitable way to screen plant communities and improve grain yield. Cluster analysis of genotypes was also performed using the Ward method and the square of Euclidean distance based on the studied traits. In cluster analysis, individuals within a cluster have the greatest similarity and uniformity, and there is maximum difference between clusters. Therefore, if the grouping is successful, individuals within the cluster are genetically closer to each other, and distant clusters will be more different. The cut point of the dendrogram was determined using discriminant function analysis, and the state in which the difference between the grouping levels was maximum was considered as the cut point.To determine the characteristics of each group resulting from cluster analysis in terms of the studied traits, the average of each cluster for each trait and its percentage deviation from the total mean were calculated. Principal component analysis was performed to reduce the volume of data and better interpret them. The data were analyzed using SPSS software.
Results: The results showed that for most of the studied traits, the safflower lines had statistically significant differences with each other and with the control varieties. The correlation results showed that the single plant grain yield had a significant positive correlation with the traits of thousand-grain weight, boll diameter, and number of seeds per boll. Cluster analysis using the Ward method and the Euclidean distance criterion based on the data of 12 traits and the resulting dendrogram cut classified 69 safflower genotypes into four clusters.To determine the cut point of the resulting dendrograms based on morphological and physiological traits, discriminant function analysis was used, and the state in which the difference between the grouping levels was maximum was considered as the cut point. Dendrogram cutting was performed based on multivariate analysis of variance and provided the highest amount of between-group variance into a group with four clusters. In principal component analysis based on the average of 12 traits in 69 safflower genotypes, the first three principal components explained a total of 65.13% of the trait variation. This value for the second and third components was 19.66% and 12.63%, respectively.
Conclusion: The second cluster was identified as the best cluster and the genotypes of this cluster can be used to improve grain yield. According to the principal component analysis, the first component was named the grain yield component. This component can be used in selection for safflower genotypes.Based on the results obtained, the Goldasht variety was considered the superior variety.
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