印度喀拉拉邦慕那尔森林区土著社区药用植物的数量民族植物学

Venkatesan K., Murugeswaran R., Mokhtar Alam, Ram Pradap Meena, Pawan Kumar, Mohamed Aslam, Zaheer Ahmed N.
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摘要

定量民族植物学研究的是计算植物和植被对人类的重要性。它有助于生物科学和社会科学中定性数据的量化。民族植物学中的各种定量工具有助于植物分类群的计算。民族植物学方法提供了符合假设检验、后续统计验证和比较分析的数据。药用植物的传统来源是喀拉拉邦农村地区日常治疗的重要途径。其中,草本植物12种,灌木13种,乔木12种,攀缘灌木、缠绕灌木、匍匐灌木、攀缘灌木各1种。对植物的分布状况进行了分析和记录,其中常见植物26种,稀有植物14种,常见栽培植物5种,散发植物2种。在这次交流中,从农村居民那里获得的信息与已有的文献进行了比较。按植物名、族名、凭证标本号、地名、乌拉尼名、使用部位、制备方式、治疗疾病等顺序排列。随机从85名被调查者的部落和传统治疗师中收集数据,采用使用价值(UV)、被调查者共识因子(ICF)、忠诚值(FL)等合适的统计工具和各种排序方法对数据进行统计分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantitative Ethnobotany of Medicinal Plants Used by Indigenous Community in the Munnar Forest Division, Kerala, India
The quantitative ethnobotany deals with the computation of the importance of the plants and vegetation to people. It helps in quantification of qualitative data in the biological and social science. The various quantitative tools in ethnobotany help in the calculation of plant taxon. Ethnobotanical approach provide data which is agreeable to the hypothesis-testing, subsequent statistical validation and comparative analysis. The traditional source of medicinal plants is an important way for daily curative uses in the rural area throughout Kerala. Moreover, among the plant studied habit wise analyzed they are 12 herbs, 13 shrubs, 12 trees and each one of climbing shrub, twining shrub, prostrate shrub, climber. The status of plants are also analyzed and recorded as 26 are common, 14 are rare, 5 are common & Cultivated and 2 are sporadic are described under this study. In this communication, the informations got from the rural inhabitant were compared with the already existing literature. The medicinal plants used by people are arranged alphabetically followed by Botanical name, Family name, Voucher specimen number, Local name, Unani name, Part used, Mode of preparation and Disease cured are discussed. The data were collected randomly from tribal and traditional healers of 85 informants the data were statistically analyzed by using suitable statistical tools such as Use Value (UV), Informant Consensus Factor (ICF), Fidelity Value (FL) and various ranking methods.
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