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Showing 2 results for Path Analysis
Abdul Karim Tahmasebi, Reza Darvishzadeh, Amir Fayaz Moghaddam, Esmaeil Gholinezhad, Hossein Abdi,
Volume 8, Issue 2 (3-2022)
Abstract
The selection of genotypes based on multiple traits is a fundamental issue and an important part of the process of plant breeding. In the present study, the efficiency of selection indices based on phenological, morphological and physiological traits was studied to improve sesame grain yield. The evaluation of 25 sesame populations was realized in a completely randomized design with 10 replications under Urmia conditions in 2017.The results showed that phenotypic and genotypic correlations between grain yield and No. of capsules per plant, No. of grains per capsule, No. of branches, leaf temperature, leaf index and biological weight were positive and significant. By regression and path analysis, the No. of capsules and No. of branches were identified as the variables of the first-order cause and biological weight, harvest index, leaf index, plant height and chlorophyll as the second-order cause variables, among which only plant height had a direct negative effect. In order to obtain selection indices, two optimal and basic methods and ten different vectors of economic values of traits were used. The vectors were based on the analysis of correlation, regression, path and broad sense heritability. The third and fourth indices, in which the first-order cause entered the model, showed high relative efficiency and in terms of these two indices, and the sesame populations with code number of 12, 17, 18 and 19 populations were identified as the most desirable populations. Finally, it is suggested that the efficiency of these selection indices be evaluated in the field
Kaveh Sadeghi, Mohammadhadi Pahlevani, Mohsen Esmeilzadeh Moghaddam, Khalil Zaynali Nezhad,
Volume 8, Issue 2 (3-2022)
Abstract
Identifying selection indices is the most important step of a breeding project that aims to improve grain yield. The definition of the selection index is usually done by evaluating the variables in multivariate statistical methods. In the present study, the relationship between grain yield and its components in bread wheat genotypes was determined by multivariate statistical methods. The experiment was conducted in a randomized complete block design with 3 replications in the research farm of Gorgan University of Agricultural Sciences and Natural Resources in the 2018-19 crop years. Ten commercial cultivars of bread wheat along with their offspring from direct and inverse crosses in a dialysis arrangement were evaluated for morphological and phenological traits, especially grain yield and its components. The results of genotypic and phenotypic correlation coefficients showed a positive and significant correlation (at 1% probability level) between grain yield and spike length, spike weight, number of fertile tillers, number of seeds per spike, number of spikes per spike, 1000-seed weight, biological yield and harvest index. Based on the results of stepwise regression analysis, biological yield, harvest index, number of grains per main spike and main spike weight were entered into the regression model, respectively, and explained a total of 98% of the variation in grain yield. Based on the results of path analysis, biological yield had the highest direct effect on grain yield. After biological yield, the most direct effect on grain yield was related to the weight of main spike. Also, by considering eigenvalues greater than one in factor analysis, 8 hidden factors were identified that explained a total of 75.18% of the data changes. In general, it can be concluded that biological yield, harvest index, number of seeds per spike and weight of spike compared to other traits can be used as appropriate indicators in breeding programs to select high-yield genotypes in field conditions. Genotypes Alo, Ehsan♂ × Gonbad♀ and Ehsan had the highest value for the studied traits, which can be used in future breeding researches.
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