Principal components analysis of some iranian and foreign safflower genotypes using morphological and agronomic traits
In this study, the genetic diversity of 122 safflower genotypes from the institute of plant genetics and crop plant research (IPK) and International Maize and Wheat Improvement Center (CIMMYT) were evaluated and their agronomic characteristics were compared with five Iranian Safflower cultivars. This experiment was conducted at research field of Seed and Plant Improvement Research Institute using a Augmented with randomized complete block design in Karaj during 2017-2018 year. The results indicated high genetic variation in the germplasm. Among safflower genotypes, 36 genotypes without thistle, 81 genotypes with thorns and 10 genotypes with few thistle were observed. Principal component analysis led to the identification of three main components that accounted for 56.5% of the total varition. The first and second components accounted for 29.5% and 15.9% of the total variation, respectively. The first and second components were named as a yield components and phenology and plant architecture, respectively. Safflower genotypes divided into four groups by principal components analysis (PCA). Genotypes in the first groups had the higher grain yield than others. Genotype No. 70 with the highest grain yield (5667 kg.ha-1) was placed in this group. Numerical values of yield components such as 1000-seed weight, number of heads and number of seeds per plant in the third group were higher than other groups. Generally, german genotype No. 70 with high yield and genotype No. 45 with early flowering can be used in safflower breeding programs.
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Determining suitable rapeseed cultivars using phenologic, physiologic and agronomic characteristics in Alborz and Golestan provinces
*, Abolfazl Faraji, Abbas Falah Tosi, Amirhossein Shirani Rad, Nadia Safavi Fard, Mohammadbagher Valipour, Ali Ebadi
Journal of Agricultural Science and Sustainable Production, -
Comparison of genetic diversity of Iranian and foreign safflower genotypes using multivariate statistical methods
*, HamidReza Fanaei, Farnaz Shariati, Hamid Sadeghi Garmarodi, Mohamad Abasali, AmirHasan Omidi
Journal of Agricultural Science and Sustainable Production,