University of Isfahan Taxonomy and Biosystematics 3115-9001 15 54 2023 05 22 Investigating the Morphological Characteristics of the Medicinal Plant of Peganum harmala L. in Western Iran بررسی اولیه صفات مورفولوژیکی گیاه دارویی Peganum harmala L. در غرب ایران 121 136 28044 10.22108/tbj.2023.139760.1245 EN Mehdi Kakaei Associate Professor of Plant Breeding, Department of Agricultural Sciences, Faculty of Engineering, Payame Noor University, Tehran, Iran Fatemeh Hajmoradi Assistant Professor of Plant Systematics, Department of Biology, Faculty of Basic Sciences, Payame Noor University, Tehran, Iran Journal Article 2023 11 13 Genetic diversity, correlation analysis, path analysis, and regression analysis are fundamental tools for developing innovative breeding programs aimed at improving desirable varieties and traits. To evaluate the relationships between traits affecting economic traits and identify habitats as natural potentials in western Iran, we conducted tests on samples from 4 Peganum harmala habitats using a randomized complete-block design. Some of the correlation results indicated a strong correlation between the fresh weight and dry weight of a single stem (0.986**) and between the fresh weight and dry weight of the whole plant (0.856**). Step-by-step regression analysis for the dry yield of the whole plant as the dependent variable revealed that stem height, number of leaves, and number of flowers per plant were successively included in the model. With a cumulative coefficient of determination of 93.6%, these traits explained most of the changes in the dry yield of the whole plant. In the path analysis, stem height was found to have the most direct effect on the dry yield of the whole plant (0.747). Therefore, these traits can be effectively utilized to increase the dry yield of the whole plant. Introduction Peganum harmala L., commonly known as pecan, is a perennial herbaceous plant belonging to the Nitrariaceae family and is renowned for its medicinal properties (Aslam et al., 2014). This plant yields valuable alkaloids, such as harmaline, harmine, and harman, as well as phenolic compounds, which have been utilized in the treatment of various ailments, including cancer, epilepsy, heart disease, mental illnesses, chronic headaches, kidney stones, and memory loss (Roostaei, 2018; Li et al., 2017). Researchers, such as Kakaei and Mazahery-Laghab (2014), have emphasized the significance of studying individual traits and their effects on genetic diversity since a thorough understanding of germplasm diversity is crucial for successful breeding programs. Identifying correlations between influential and less important traits facilitates the analysis of previous results and lays the groundwork for effective future projects. Consequently, the correlation between important and less important traits aids scientists and researchers in indirect selection for desirable traits through less important ones (Kakaei et al., 2015; Saki Nejad & Seyedmohammadi, 2011). In their research, Kakaei & Mazaheri Laghab (2023) highlighted the value of statistical methods, such as regression analysis, correlation analysis, and path analysis, in elucidating the nature of traits when studying different alfalfa ecotypes. Given the agricultural significance of the medicinal plant of pecan, future studies evaluating these traits using statistical methods, including correlation analysis, regression analysis, and path analysis, will be warranted. Materials & Methods To investigate the morphological characteristics of pecan plants from 4 different locations in the western provinces of Iran, including Hamadan and Kermanshah, an experiment was conducted by using a randomized complete block design with 3 replications in 2023. Initially, pecan plant samples were collected simultaneously in July 2023 and then identified, prepared as herbarium sheets, and subjected to morphological examination by consulting floristic sources (El-Hadidi, 1972; Akhyani, 1993). The study focused on the ecotypes of pecan found in these locations, examining traits, such as pecan chlorophyll index (measured using a Chlorophyll Meter Model SPAD-502 Plus), stem height, number of flowers per plant, number of leaves, number of branches per stem, number of nodes, number of branches per plant, fresh weight of the whole plant, dry weight of a single stem, fresh weight of a single stem, dry weight of the whole plant, and stem diameter. Statistical analyses, including simple phenotypic correlation using pearson's coefficient method, regression analysis employing a step-by-step method to identify important traits, and path analysis to determine direct and indirect effects of traits, were conducted. The values entered in the regression model as the independent variables on the attributes of economic yield as the dependent variables were analyzed using statistical software SPSS, version 21 and SAS, version 9 (Rahnamaie Tak et al., 2007). Research Findings Phenotypic Correlation Results In pearson's correlation analysis, plant height exhibited a positive and significant correlation with the number of branches (0.781**), fresh weight of the whole plant (0.849**), and dry weight of the whole plant (0.924**). This indicated that plant height could influence the number of branches, as well as the fresh and dry weights of the whole plant. Furthermore, the number of flowers per plant showed a positive and significant correlation with the number of leaves (0.735**), the fresh weight of a single stem (0.95**), and the dry weight of a single stem (0.901**). Additionally, the number of branches was found to significantly affect the fresh weight of the whole plant (0.724**) and the dry weight of the whole plant (0.697**). Moreover, the fresh weight of a single stem was strongly correlated with the dry weight of a single stem (0.986**) and the fresh weight of the whole plant was similarly correlated with the dry weight of the whole plant (0.856**). This suggested that an increase in the fresh weight of a single stem could lead to a corresponding increase in the dry weight of a single stem. Stepwise Regression Analysis The stepwise regression analysis for the dry yield of the whole plant as the dependent variable revealed that the traits of stem height, number of leaves, and number of flowers per plant were successively included in the model. With a cumulative coefficient of determination of 93.6%, these traits accounted for the majority of changes in the dry yield of the whole plant. The other evaluated traits did not exhibit a significant effect on the model at the 5% probability level. Discussion of Results & Conclusion In the realm of plant biology, understanding the various traits, as well as their functions and interplay, is crucial for advancing research initiatives. This study revealed that an increased number of flowers per plant would lead to the development of more stems and leaves, resulting in higher dry and wet weights for the plant. Notably, the number of leaves and nodes directly impacted the fresh and dry weights of individual stems. This relationship was attributed to the correlation between stem count (side branches) and leaf production. Investigating trait correlations is pivotal for evaluating improvement programs as it sheds light on the influence of one trait on others of interest. Recognizing these correlations facilitates the analysis of previous studies and informs the design of future interdisciplinary projects with specific objectives (Kakaei & Mazaheri Laghab, 2015). In alignment with the findings of this research, Kakaei & Mazaheri Laghab (2015) demonstrated that alfalfa forage yield as a functional variable encompassed 4 key variables: dry forage yield, dry matter percentage, plant height, and the number of alfalfa weevil larvae.

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