APPLICATION OF MULTIVARIATE STATISTICAL ANALYSIS FOR BREEDING STRATEGIES OF SPRING SAFFLOWER (Carthamus tinctorius L.)

Abstract

This study aimed to assess oil yield components and their interrelationships of spring safflower lines and varieties by using different statistical techniques to increase the oil yield in safflower breeding program. Field experiments were conducted at the Transitional Zone Agricultural Research Institute in Eskisehir, Turkey during 2014, 2015 and 2016. Correlation, simple linear regression, stepwise multiple regression, path, principal component and cluster analyze were used to investigate the relationships between oil yield and some components in spring safflower. The results revealed that characters affecting oil yield, which is important to determine selection criteria in plant breeding, vary according to statistical methods. Therefore, to obtain reliable result, it is essential to use multivariate statistical methods for scanning significant characters in studied material. According to the numbers of common characters determined in different statistical analyzes; oil content, seed weight, seed yield and number of head per plant would be important selection criteria for improved oil yield in the breeding material studied. The lines and varieties may be used in hybridization program and their hybrids may yield more transgressive sergeants for these characters for oil yield improvement.

Selections of donors depending on agronomic traits, seed yield components, and fatty acid profile for genetic improvement of Carthamus using stepwise multiple regression

“…It has been estimated that high heritability values for plant height and 1000-seed weight suggest these traits are under high genetic control and have been least influenced by environmental effect (Adhikari et al, 2018;Kose et al, 2018;Tahernezhad et al, 2018).…”

“…,Erbas and Baydar (2017),Kose et al (2018) illustrated high variations of agronomic traits and seed yield among Carthamus tinctorius lines selected.…”

Multivariate analysis to determine secondary characters in selecting adaptive hybrid corn lines under drought stress

“…The use of principal component analysis in biplots will minimize overlapping of variations so that the group determination can be more objective (Mattjik and Sumertajaya 2011; Leite and Oliveira 2015). Therefore, this analysis can facilitate the determination of characters with the same direction variance to the main characters (Kose et al 2018), especially when using the orthogonal polygonal grouping concept of the outlier object (Leite and Oliveira 2015; Neisse et al 2018). Based on this analysis,these five characters which have the same direction with the productivity can be as the best secondary character candidates in the selection The result of biplot analysis also showed that the maximum temperature of the leaves was the only character with a variance direction in contrast to the productivity group (Figure 1).…”

“…Multivariate analysis can simplify, reduce, and predict the relationship between many variables and objects (Mattjik and Sumertajaya 2011). This approach has been widely used in determining character selection (Hasan et al 2016;Kose et al 2018;Anshori et al 2019;Akbar et al 2019). Multivariate analyses that can be used in identifying the best secondary characters are the biplot analysis based on principal components analysis and the path analysis.…”

Evaluation of yield of safflower (Carthamus tinctoriusL.) genotypes under semi-arid conditions

“…Yield is a complicated quantitative character which is the root of actions and interactions of different traits (Saeed et al ., 2007; Tonk et al ., 2011; Ranawake and Amarasinghe, 2014). In breeding programmes intended to develop high-yielding genotypes, it is essential to know how and to what extent morphological traits affect yield (Kose et al ., 2018). The positive and significant correlation between safflower morphological traits and grain yield in semi-arid areas has been reported (Oarabile et al ., 2016).…”