Quantification of the Ethnomedicine Data Collected from the Kanikkars in Kanyakumari District, Tamil Nadu, India

This study aims to explore the traditional medicinal plants used by the Kanikkar community, which resides in 26 villages within the Petchiparai Panchayat area of Kanyakumari District, Tamil Nadu, India. These plants are crucial in treating various health issues the community faces. To gather ethnomedicinal information, we conducted interviews and group discussions with 252 participants, including 146 men and 106 women aged 20 to 60. Our research identified 215 species of medicinal plants belonging to 179 genera across 75 families, documenting each species and its medicinal uses. We analyzed the ethnomedicinal data using several quantitative metrics, including the Frequency of Citation Percentage (FC%), which tracks how often a plant is referenced for specific ailments; the Relative Frequency of Citation (RFC), which measures a plant's popularity; the Fidelity Level (FL), indicating the consistency of its use for certain conditions; and the Informant Consensus Factor (FIC), which assesses the agreement among informants regarding the uses of specific plants. We employed Direct Matrix Ranking (DMR) and Preference Ranking (PR) techniques to emphasize the importance of various plant species. This comprehensive evaluation highlights the rich ethnomedical knowledge of the Kanikkar community and stresses the significance of protecting their traditional practices along with the biodiversity that supports them.

Authors

Institutions

Publication Details

Journal
International Journal of Ayurvedic Medicine
Published
2025-04-09
DOI
https://doi.org/10.47552/ijam.v16i1.5304
Citations
1
Primary Topic
Traditional Chinese Medicine Studies
Type
article
Field-Weighted Citation Impact
2.38
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Quantification of the Ethnomedicine Data Collected from the Kanikkars in Kanyakumari District, Tamil Nadu, India

A. Döss, A.N. Rai, B. C.
1 citations
International Journal of Ayurvedic Medicine
Traditional Chinese Medicine Studies
2.38
article

Quantification of the Ethnomedicine Data Collected from the Kanikkars in Kanyakumari District, Tamil Nadu, India

A. Döss, A.N. Rai, B. C.
article en
1 citations

Abstract

This study aims to explore the traditional medicinal plants used by the Kanikkar community, which resides in 26 villages within the Petchiparai Panchayat area of Kanyakumari District, Tamil Nadu, India. These plants are crucial in treating various health issues the community faces. To gather ethnomedicinal information, we conducted interviews and group discussions with 252 participants, including 146 men and 106 women aged 20 to 60. Our research identified 215 species of medicinal plants belonging to 179 genera across 75 families, documenting each species and its medicinal uses. We analyzed the ethnomedicinal data using several quantitative metrics, including the Frequency of Citation Percentage (FC%), which tracks how often a plant is referenced for specific ailments; the Relative Frequency of Citation (RFC), which measures a plant's popularity; the Fidelity Level (FL), indicating the consistency of its use for certain conditions; and the Informant Consensus Factor (FIC), which assesses the agreement among informants regarding the uses of specific plants. We employed Direct Matrix Ranking (DMR) and Preference Ranking (PR) techniques to emphasize the importance of various plant species. This comprehensive evaluation highlights the rich ethnomedical knowledge of the Kanikkar community and stresses the significance of protecting their traditional practices along with the biodiversity that supports them.

International Journal of Ayurvedic MedicineVol. 16(1)
Nazareth College (US), Government Medical College Thoothukudi (IN)
Openalex Percentile: Top 14%
Traditional Chinese Medicine Studies
2.38
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.