Investigating the Morphological Characteristics of the Medicinal Plant of Peganum harmala L. in Western Iran

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 harmalahabitats 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 harmalaL., 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 analy