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COMPARATIVE STUDY OF SEVERAL MORPHOLOGICAL AND REPRODUCTIONAL ASPECTS FOR SOME SPECIES OF THE BELLEVALIA LAPEYROUSE, 1808 AND ORNITHOGALUM LINNAEUS, 1753 (ASPARAGALES, ASPARAGACEAE) IN CENTRAL AND NORTH OF IRAQ
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This study aims to study some morphological and reproductional characteristics in eleven species of two genera belonging to the family of Asparagaceae, which are Bellevalia Lapeyrouse, 1808 and Ornithogalum Linnaeus, 1753 and the species are: Bellevalia chrisii Yildirim and Sahin, 2014; Bellevalia flexuosa Boissier, 1854; Bellevalia kurdistanica Feinbrun, 1940; Bellevalia longipes Post, 1895; Bellevalia macrobotrys Boissier, 1853; Bellevalia paradoxa Boissier, 1882; Bellevalia parva Wendelbo, 1973; Bellevalia saviczii Woronow, 1927; Ornithogalum brachystachys C. Koch, 1849; Ornithogalum neurostegium Boissier, 1882 and Ornithogalum pyrenaicum Linnaeus, 1753. These species were identified and compared with each other; the results showed that there were differences between the two genera in studying the shape of bulbs, length of leaves and color of the flowers. Each of them showed an important taxonomic mark to be distinguished from the studied species. Morphological studies used with some characters of the reproductive, including the shape and color of the fruits, seeds and embryos were used to diagnoses the species. Also, the current investigation was found that the characteristics of the fruit and how it was connected to the pedicels was very important in the field of taxonomic and identification the species.
Two new record species were studied within the country, namely; Bellevalia chrisii and Bellevalia flexuosa; it should be noted here that the study of embryos is also one of the first studies conducted on these plants.

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Publication Date
Thu Oct 09 2025
Journal Name
Iraqi Journal Of Science
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Wed Feb 01 2017
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Sat Nov 27 2021
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International Conference On Fibre-reinforced Polymer (frp) Composites In Civil Engineering
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Sun Feb 28 2021
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Fri May 01 2020
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Sat Jul 22 2023
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Publication Date
Wed Mar 30 2022
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Publication Date
Thu Jun 01 2023
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Rapid Thrombogenesis Prediction in Covid-19 Patients Using Machine Learning
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